From 5aacc0652e2caeb668797b7da5c10a179646d585 Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Mon, 25 Feb 2019 15:29:40 -0800 Subject: [PATCH 01/49] module name --- forest_benchmarking/circuit_testing.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 forest_benchmarking/circuit_testing.py diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py new file mode 100644 index 00000000..e69de29b From 0e8786db4234d90b3510598c73e5982fbff272a9 Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Tue, 26 Feb 2019 15:35:46 -0800 Subject: [PATCH 02/49] add random cliffords --- examples/circuit_testing_josh.ipynb | 4252 ++++++++++++++++++++++++ forest_benchmarking/circuit_testing.py | 81 + 2 files changed, 4333 insertions(+) create mode 100644 examples/circuit_testing_josh.ipynb diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb new file mode 100644 index 00000000..24bf90c0 --- /dev/null +++ b/examples/circuit_testing_josh.ipynb @@ -0,0 +1,4252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Circuit testing\n", + "\n", + "\n", + "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", + "\n", + "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", + "\n", + "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import itertools\n", + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time\n", + "from scipy.spatial.distance import hamming\n", + "import scipy.interpolate\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", + "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", + "from pyquil.quilbase import Pragma\n", + "\n", + "from forest_benchmarking.circuit_testing import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def two_q_id(qb1,qb2):\n", + " prog = Program()\n", + " prog +=I(qb1)\n", + " prog +=I(qb2)\n", + " return prog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# if you want to run on a \"real lattice\"\n", + "#from pyquil import *\n", + "#list_quantum_computers()\n", + "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", + "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", + "\n", + "qc_perfect = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", + "qc_noisy = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UYVHh9u7dS3BwMN9//z3vvPMOvr6+Fb5/740bN3BxcaFDhw6EhoZKIRYVRlEUgoKC2LJlCykpKdSvX1/tSEJFUoRFhVAUhcTERIKDg7lw4QLTpk1j+PDhWFlZqZbp+vXrdO/enW7dujFnzhwpxKLCKIrC//3f/xETE0NKSgp2dnZqRxIqkSIsylVRURHr1q1j9uzZaLVapk+fTr9+/ahUST86pv7vf/+jW7dueHh48Mknn0ghFhUqLCyM0NBQUlJSePHFF9WOI1SgH38TCqNz69Ytli9fzpw5c6hbty6ffPIJHh4eelfkatWqRUpKCl27dsXS0pIPPvhA7UjChAQEBFClShUcHR1JSkrilVdeUTuSqGBShEWZysnJYdGiRcybN48WLVoQHR2Ng4OD3hXfu9na2pKSkoKjoyMWFhbSeF9UqDFjxlC5cmWcnJzYunUrbWUNu0mRIizKxNWrV/niiy9YuHAhXbt2ZcuWLbz22mtqxyq1evXqkZqaSpcuXbC0tGTKlClqRxImZODAgdjY2ODu7k5cXBydOnV68KSsLN3s/8xM3Xae1atDy5YwcqS0YDVg8kxY/CPnz59n3rx5xMTE0KdPH6ZOnYq9fbFbJhiE8+fP06VLF/z9/WVPWFHhduzYweDBg1m1ahXOzn92Ps/IgOBgSEzU/frurlvW1rrNSNzcIChI10lOGBQpwuKJHD9+nDlz5hAXF8eIESOYPHkyzz77rNqxysSZM2fo0qUL06ZNw8/PT+04wsTs2bOHvn37smTJEnpfuABTpkBenq7YlkSj0RXkkBBdAxthMOR2tHgs33//PcHBwaSlpTFhwgSOHz9udA0H7OzsSE1NxdHREUtLS0aPHq12JGFCHBwcSEhIYF337rjfukWl27cf/SZF0W1m8tdjFCnEBkOKsHgkRVHYs2cPwcHBZGZmMnnyZKKjo6latara0crNCy+8wM6dO+natSsWFhYMGzZM7UjChLQD2hQUYFaaAny3vwpxu3aySYmBkN7RokSKorB161Y6derEqFGj6NOnDydPniQwMNCoC/BfXnzxRZKTk5k+fTqrV69WO44wJcHBmN26dc9LXwJtgaeAEQ97b16e7hmyMAjyTFg8oLCwkHXr1hEcHIyZmRlBQUH4+Phgbm6udjRV/PDDDzg7OzN//nx8fHzUjiOMXVYW2NndOwELiEM3akoC8oCYh13DygrOnpVZ0wZAbkeLO/Lz81m2bBlz5syhQYMGzJ49Gzc3N71e41sRXnnlFRITE+nRowcWFhZ4enqqHUkYs5iYYl/u8+c/DwDnH3UNjUZ3nXffLbNYonxIERb88ccfREZGEhoaSuvWrVm2bFnx6xRNWKtWrUhISMDd3Z1KlSrh4eGhdiRhrDIzHxgFP7a8PN1uYkLvyTNhE3blyhXee+89nn/+eQ4ePEhiYiIJCQlSgEvQpk0bNm/ezMiRI0lKSlI7jjBWZbXX8LVrZXMdUa6kCJugc+fOERAQgL29PZcuXWL//v2sXr2aV199Ve1oeq99+/Zs3LiRIUOGkJqaqnYcYYyqVy+b69SsWTbXEeVKirAJOXbsGKNGjeLVV1+lUqVK/PDDD0RFRdGkSRO1oxmUjh07sn79evr378/u3bvVjiOMTcuWKP90i09ra2jRomzyiHIlRdgEfPfdd/j4+ODg4ECjRo345ZdfCAkJoUGDBmpHM1hdunRhzZo1+Pj48PXXX6sdRxiJgoICNlavzu37licBFAL5QNGfX/l/vlYsRYERI8oppShLUoSNlKIo7Nq1C1dXVzw9PenYsSMnT57k/fffp1atWmrHMwrdu3dn+fLleHl58e2336odRxiw7OxsQkJCaNy4MWGrVnH19ddR7luV8AlgDcwGVvz5758UdzGNBtzdZXmSgZB1wkZGq9WydetWgoODuXr1KtOmTWPIkCE89dRTakczWlu3bmX06NEkJiYa1M5RQn2nT58mPDycZcuW0aNHDyZPnqzbyjAjAxwddR2wHpeNDaSnS8csAyEjYSNRWFjIypUradmyJR9++CGTJ0/m6NGjjB49WgpwOevZsyeRkZG4u7uTmZmpdhxhAL755hv69etHmzZtqFSpEocPH2bVqlV/7yXcrp1uMwYbm8e6rtbKSvc+KcAGQ9YJG7j8/Hy++uor5s6dS8OGDfn8889xcXEx+QYbFc3b25uCggJcXV1JSUmhefPmakcSeqaoqIhNmzbx+eefc+HCBQICAh7eg/2vTRhKuYtSgYUFH9rY8LanJzLbw3BIETZQN27cICIigrCwMNq2bcuKFSt488031Y5l0vr160dBQQEuLi6kpqby0ksvqR1J6IGcnByWLl1KWFgYdevWJTAwEG9vbypVKsVfv35+ulFxcDBs26Z73puX9/fxv/YTdnfHIiiIqikpuLq6snv3bmrKEiWDIEXYwFy+fJnw8HAiIyNxcXEhKSmJli1bqh1L/Gnw4MEUFhbi5OREWlqaLP8yYefPn2f+/PlER0fj6OjIypUreeONNx7/Qm3bwoYNcPmyrhXlkSO6Rhw1a+qWIY0YcWcS1rQ2bcjKyqJXr17s2LEDm8e8nS0qnkzMMhBnz54lJCSEFStW0K9fP959910aN26sdixRgsWLF/Pxxx+Tnp7O888/r3YcUYEOHjzIvHnz2LZtG8OGDWPSpEm88MILFfb9tVotw4cP59q1a2zcuBELC4sK+97i8cnELD139OhRRowYQevWrbGysuLHH38kMjJSCrCeGzt2LNOnT6dbt26cPXtW7TiinGm1WrZs2ULXrl3x9PTk1Vdf5eTJk4SFhVVoAQYwMzNj6dKlKIrC6NGj0Wq1Ffr9xeORkbCeysjIIDg4mL179zJp0iTefvttecZjgMLCwvjyyy9JT0/nmWeeUTuOKGO5ubksX76c0NBQqlSpQmBgIG+99ZZejD5zc3NxdnamQ4cOhISEyGRNPSXPhPWIoiikpaURHBzMsWPHCAwMJDY2lsqVK6sdTTyhgIAACgoK6NatG7t27aJ+/fpqRxJl4Pfff2fBggUsWrSIDh06EBUVRefOnfWq0NnY2LBlyxY6d+5MvXr1mDp1qtqRRDGkCOsBrVbL5s2bCQ4OJjs7m2nTpjF48GAsLS3VjibKwLvvvsvt27fp3r07u3btom7dumpHEk/oyJEjhIaGsnHjRgYOHMjevXuxt7dXO1aJatWqRVJSEh07dsTW1pZRo0apHUncR4qwigoKCli9ejWfffYZ1tbWBAUF4eXlhbm5udrRRBmbOXMmt2/fxsnJidTUVGxtbdWOJEpJURR27NjBvHnzyMzMZOLEiZw4ccJg/h8+88wzJCUl4ejoSO3atfH09FQ7kriLFGEV5OXlsXTpUubOncvzzz9PWFgYTk5OenUrS5S9Dz/8kNu3b+Ps7MzOnTulh7eey8/PZ9WqVcybNw8zMzMmT57M5s2bDbID3UsvvcSWLVtwd3enZs2adO7cWe1I4k8yMas0srJ06/MyM3UbblevDi1bwsiRj9UkPTs7m4ULFxIeHk779u0JCgqiQ4cO5Zdb6B1FUZgyZQq7d+8mJSWF6mW1d6woM1euXCEiIoKFCxfSqlUrJk+ebDQfknfu3MnAgQNJTk6W/cP1hSJK9u23iuLtrShWVrovXW8a3Ze1te41b2/deQ9x6dIlJSgoSKlVq5YyZMgQ5ciRIxX0Awh9pNVqlX/9619Khw4dlBs3bqgdR/zp6NGjiq+vr1KjRg1l9OjRyg8//KB2pHKxdu1apUGDBsqvv/6qdhShKIqsEy5JRIRuF5P4eMjP133dLS9P91p8vO68iIgHLnH69GkmTpxI06ZNyc7O5sCBA8TGxvLKK69UyI8g9JNGoyE8PJxWrVrh7u5OTk6O2pFMlvLnioRevXrdmUX8888/s2TJEqPt//3WW2/x73//GxcXF37//Xe145g8KcLFiYjQNU3PzX1403TQHc/N1Z3/ZyH+6aefGDZsGG3atKFKlSr89NNPLFiwQDoniTs0Gg0LFizA3t6eXr16kfskW9aJJ3b79m1WrFhBmzZt8PPzo1evXpw5c4ZZs2ZRr149teOVOz8/P4YNG0aPHj3Izs5WO45Jk2fC9ythH8/TwATgv8BTgA8Qxr0z24qsrJj6+uusPHaMSZMmMWHCBGrUqFExuYVBKioqYuTIkVy8eJEtW7ZgZWWldiSjdu3aNaKiopg/fz4vvfQSkydPxs3NDTMz0xuPKIrCpEmTyMzMJCkpSX7vqUSK8P369NHdYr7vP4s7UBeIBK4DzsBYYNJd5xQBp159lQZffy2N00WpFRUVMWTIELKzs9m4caNBzr7Vd7/++ivh4eGsWLGCnj17MnnyZFq1aqV2LNVptVoGDx5Mfn4+69atK93OTqJMmd7Hv4fJyoLExGJvQZ8C+gFWwNNAD+DH+84xB5ocO4bNzZvlnVQYEXNzc5YvX46NjQ1vvfUWt2/fVjuSUVAUhX379tGnTx/at29P5cqVOXLkCMuXL5cC/CczMzOWLVtGbm4u48ePR8ZkFU+K8N1iYko8FACsAXKBC0AiukL8AI3modcRojgWFhasWrUKjUbDwIEDKSgoUDuSwSosLGTt2rV06NCB4cOH061bN06fPk1wcLD07y6GpaUlGzZs4MiRI8ycOVPtOCZHivDdMjMfnAX9p87oRr7VgGeBtoBXcSfm5en2+xTiMVlaWrJ27Vry8vIYNmwYhYWFakcyKDdu3GDevHk0btyY+fPnM336dI4dO8bEiROpUqWK2vH0WpUqVUhISGDjxo2EhoaqHcekSBG+WwmzBLXoRr19gJvAFeAaMK2k61y7Vg7hhCl46qmniIuL4+rVq4waNYqioiK1I+m9s2fPEhgYyPPPP8+3337LunXr2LNnD97e3tIC9jHY2tqSlJREaGgosbGxascxGVKE71ZC96L/AWeBiehmRtcGRgLbSrqObDko/gErKyvi4+M5d+4c48aNk/1gS5CRkcGAAQPuPN89ePAga9as4fXXX1c5meF67rnn2L59O++++y4JCQlqxzEJUoTv1rIlFDNN3xZ4HogACtHNjl4GtCzuGtbW0KJFOYYUpuCvbeiOHz/OhAkTZMLMn4qKiti4cSMODg74+Pjw+uuvc/r0aT7//HPs7OzUjmcUmjVrRnx8PCNGjODrr79WO47RkyVKd8vKAju7Yp8LH0I3OeswulnQ3YD5wAPL+q2s4OzZx+opLURJ/vjjD1xdXWnTpg1ffPGFUfQvfhI5OTnExMQQFhZG7dq1CQwMpE+fPrKkphxt376d4cOHs3PnTunyV45kJHy3unXBzU03w/k+rYBd6J4FXwHWUkwB1mjA3V0KsCgzVatWJTExkf379xMYGGhyI+LffvuNoKAgGjVqRFpaGsuWLWP//v3069dPCnA569GjB6Ghobi5uXH69Gm14xgtKcL3CwrS3VJ+EtbWuvcLUYaqV6/Ojh07SEtLIygoyCQK8aFDhxg2bBjNmzcnJyeHb775hg0bNtCxY0eTvRughkGDBvHuu+/i4uJCVlaW2nGMkhTh+7VrByEh8Jgdr25VqoQSEgJt25ZTMGHKatasSXJyMtu2bePDDz9UO0650Gq1JCQk0L17d3r27Enz5s05efIk8+fPp3HjxmrHM1mTJk2if//+uLu788cff6gdx+jI/Zzi+Pnp/jllim7d78NGHhoNipUVc2vVwuzaNWZUTEJhgmxtbUlJScHR0RELCwv+/e9/qx2pTOTl5REbG0toaChWVlYEBgbSr18/LC0t1Y4m/vTRRx+RlZWFt7c3CQkJ0lq1DMnErIc5cACCg2HbNt3z3ry8v49ZW+uKs7s7BAXxW4MGdOzYkZkzZzJmzBj1Mgujd/HiRRwdHRk9ejRTp05VO84Tu3TpEgsXLiQyMpLXX3+dyZMn4+joKLeb9VRRURH9+/dHo9GwZs0aWYNdRqQIl8bly7pWlEeO6Bpx1KypW4Y0YsQ9k7COHz9Oly5dWLRoEb1791YtrjB+Fy5coEuXLkycOJGAgAC14zyWH3/8kdDQUDZs2ED//v0JCAigadOmascSpXDr1i3c3d2xt7dn4cKF8oGpDEgRLmMZGRm4u7uzceNGOnXqpHYcYcTOnj10xjq3AAAgAElEQVRLly5dmDJlCm+//bbacR5KURRSUlKYN28e33//PW+//Tbjx4+njqwkMDg3btyga9eu9OzZk1mzZqkdx+DJM+Ey1q5dO1auXEnfvn1lfZ0oV8899xypqak4OjpiaWnJ2LFj1Y70gFu3brF69WrmzZuHVqtl8uTJbNy4UfauNWDVqlUjMTGRTp06UadOHSZOnKh2JIMmRbgcuLi43Flft2/fPp577jm1Iwkj9fzzz7Nz5066du2KhYUFI0aMUDsSAFevXiUyMpIFCxbQokUL5s6di4uLi9y+NBJ169YlKSkJBwcHbG1tGTBggNqRDJYU4XIyaNAgLl26hKurK3v37qV27dpqRxJGqkmTJqSkpNCtWzcsLCwYPHiwalmOHz9OWFgYq1evxtvbm6SkJFpIG1ej9Pzzz5OYmIiTkxO1atXCxcVF7UgGSdYJl6N33nmH3r174+Hhwc2bN9WOI4zYSy+9RHJyMlOmTGHt2rUV+r0VRSE9PZ3evXvTqVMnateuzdGjR1m6dKkUYCPXokUL4uLiGDJkCN98843acQySTMwqZ4qiMHLkSC5fvkx8fDwWFhZqRxJGLDMzExcXFyIiIvD29r73YFaWbpZ/ZqZu287q1XWblowc+UStVgsKCli3bh2ff/45OTk5vPPOOwwbNgybx2x0Iwzf1q1bGTNmDGlpabz88stqxzEoUoQrQEFBAV5eXtja2hITEyPPxUS5OnjwIG5ubixZsoRevXpBRoZuvXtiou6Euzco+Wu9u5ubruVqu3aPvP7169eJiopi/vz5NGnShMmTJ+Ph4YGZmdxYM2XLly/nvffeY+/evTRs2FDtOAZDinAFuXnzJk5OTnTu3JnPPvtM7TjCyGVkZODh4UFa//40X7q0VJ3fsLbWtWz9q2PcfU6dOkVYWBixsbG4u7szefJkXnvttXL6CYQh+vzzz4mOjmbPnj0yD6aU5KNrBalcuTJbt25l8+bNzJs3T+04wsi1a9eO/w4bxvNffgm5uQ8vwKA7npura9UaEXHPof/+97/4+PjQtm1brKysyMzMZMWKFVKAxQMCAwPp1auXzIN5DDISrmBnz56lU6dOBAcHqzqLVRi5jAxwdNQV1sdlY0Phzp1sPHeOefPmcenSJQICAhg1ahRVqlQp86jCuCiKwpgxY7hw4QKbN2+WHuCPIEVYBT/++CPdunVj+fLluLq6qh1HGKM+fSA+vsQR8AmgBeADrLjvmFajIcnamk9atSIwMBBPT0/pEyweS2FhIX379qVy5cqsWLFC5gs8hBRhlezbtw8vLy8SEhJ4/fXX1Y4jjElWFtjZ3TsB6z4uQB5gx4NFGEBraYnZ+fNPNGtaCNDtjuXq6kqrVq0IDw+XCaklkI8nKunYsSNLly7F09OTY8eOqR1HGJOYmIceXgPUALo/5Bwzc/NHXkeIh7G2tmbz5s2kp6fzf//3f2rH0VtShFXUq1cvPv30U3r06MFvv/2mdhxhLDIzSxwF3wDeBx45NTAvT7drmBD/QI0aNdi+fTtLly4lKipK7Th6SdpWqmzUqFFcunQJNzc30tPTqVGjhtqRhKHLzi7x0HvAaODZ0lzn2rUyCiRMWf369dmxYwedO3emdu3a9O3bV+1IekWKsB6YPn06Fy9exNPTk6SkJNlhRvwz1asX+/IhIAX4vrTXqVmzjAIJU9e4cWO2bt2Kq6srNWvWpFu3bmpH0htyO1oPaDQawsLCePrppxk0aBBFRUVqRxKGrGVLlGI+yO0CTgPPAU8DIcAGoNjVvtbWIH2fRRlq3bo1a9euZcCAAXz33Xdqx9EbUoT1hJmZGcuXL+fGjRtMmDABmbQunkReXh4rKlXi9q1bDxwbB/yKbkR8CBgPeABJxV1IUUBPtkUUxsPR0ZGoqCh69erFiRMn1I6jF6QI65GnnnqKuLg4Dhw4wKxZs9SOIwzI+fPnmTFjBnZ2dqxJTeX6G2+g3LckxAbdCPivryqAFfDAIiSNBtzdZXmSKBdeXl58/PHHuLi4yIRUpAjrnWrVqrFt2zZWrFhBxH3tA4W43/79+xkwYAAtW7YkJyeHffv2sXXrVuqFhaGxtn7oez+k+DXCWFvrNnMQopyMHj0aX19fXF1duWbiEwClCOuhevXqkZSUxMcff8z69evVjiP0zO3bt1m1ahXt27dn8ODBdOjQgVOnTvHFF1/w4osv6k5q1063GcNjbiuYq9GQNW0atG1bDsmF+Nu0adNwdnamV69e5D5Je1UjIR2z9Nj333+Pq6sra9euxdHRUe04QmWXL19m0aJFRERE8NJLL+Hv70/Pnj0f3lIyIkK3KUMpd1Ha4+XF4D17SEtLo3HjxmX/QwhxF61Wy/Dhw7l27RobN240yf3WZSSsx1q3bs2aNWvo168fhw4dUjuOUMnhw4cZNWoU9vb2nD59msTERFJTU0vX09nPD9LTwdsbrKx0t5rvZm2te93bG9LTcVi5kpkzZ9KtWzdOnjxZfj+UEOgmpC5duvTOpg9arVbtSBVORsIGYN26dQQEBLBnzx5eeOEFteOIClBUVMSWLVsIDw/n+PHjTJgwgXHjxlHnn0yWunxZ14ryyBFdI46aNXXLkEaMeGAS1sKFC5kzZw67du2iUaNG/+RHEeKRcnNzcXZ25s0332Tu3Llqx6lQ0qzDALz11ltcvnwZV1dX9u3bR926ddWOJMpJdnY20dHRfPnll9StWxd/f398fHzK5jZdnTrw7rulOnXChAlotVq6detGWloadnZ2//z7C1ECGxsbtmzZQufOnalTpw5Tp05VO1KFkSJsICZMmMDvv/+Oh4cHqampVK1aVe1IogwdP36c+fPns3LlSlxdXVm1ahUdOnRQNdPEiRPRarV07dqVXbt28dxzz6maRxi3WrVqkZSURMeOHbG1tWXUqFFqR6oQUoQNyKxZs/j999/p27cvW7dulc2yDZyiKCQnJxMeHk5GRgZjx47lyJEjPPPMM2pHu2PSpEkUFRXRrVs3du3axbPPlqrrtBBP5JlnniEpKQlHR0dsbW3p3bu32pHKnTwTNjCFhYX4+PhgY2Mjm2UbqNzcXGJjYwkPD6dSpUr4+/szaNAgrB+xrldNISEhLFq0iF27dunVhwRhnA4cOIC7uzvr16+nc+fOascpV1KEDVBeXh4uLi60adOG0NBQ2SzbQJw9e5YFCxawdOlS3nzzTQICAnB0dDSY/39z5swhOjqatLQ0GjRooHYcYeR27tzJoEGD2LFjB6+++qraccqNFGEDde3aNTp37syQIUOYNm2a2nFECRRFYd++fYSHh5OamsqwYcOYOHGiwa7BDQ4OZtmyZaSlpVG/fn214wgjt27dOt555x12795978qQrCzdTP/MTN3WndWrQ8uWMHKk4bVbVYTBOn/+vGJnZ6csXbpU7SjiPvn5+cry5cuVNm3aKE2aNFG++OIL5caNG2rHKhMff/yx0rRpU+X3339XO4owAQsXLlQaN26sXLx4UVG+/VZRvL0VxcpK96VrQaP7srbWvebtrTvPQMhI2MD9/PPPODo6smTJEnr27Kl2HJN36dIlIiMjiYyM5JVXXsHf3x93d3eje3Y/a9Ys1q5dS1pamiyZE+Xuo48+wnzxYmZcvYomP79U3d8ICdE1q9FzxvU3gwlq2rQpmzZtYuTIkXz99ddqxzFZ33//PSNGjKBp06b89ttvpKSkkJycTM+ePY2uAAN88MEH+Pj40K1bNy5fvqx2HGHk3qtThym//47mUe1XQXc8N1fXrtUANsGRkbCR2L59O8OHDyctLY1mzZqpHcckFBYWsmnTJsLDwzl16hRvv/02Y8eOpXbt2mpHqxCKovDee++xefNmUlNTsbW1VTuSMEYZGeDoqCusd/kfMBrYAdgCwcCg+99rY6Nr26rHG5JIETYisbGxzJw5k3379tGwYUO14xita9eu3elq9cwzz+Dv74+3t7dJNp9XFIUZM2aQmJjIzp07TeYDiKhAffpAfPwDI+CBgBaIBg4BHsDXQPO7T9JodH3RN2yooLCPT4qwkfn888+Jjo5m79691KpVS+04RuXnn3/miy++YPXq1Xh4eODv70+7du3UjqU6RVGYPn06ycnJpKSkyO87UXayssDODvLz73n5JlAT+AGw//O1ocAzwOz7r2FlBWfP6u2saeN7WGXiAgMD8fDwoGfPnia9R2dZ0Wq1JCYm0qNHD7p06YKtrS0//fQTK1askAL8J41Gw+zZs+nWrRvOzs4mv0m7KEMxMcW+fBxdu0f7u157FfixuJM1mhKvow+kCBuhzz77jCZNmtC/f38KCwvVjmOQcnJyWLhwIc2aNWPGjBkMGDCAM2fO8NFHH8n62GJoNBrmzp1L586dcXFx4fr162pHEsYgM/OBUTBADlDtvteqA38Ud428PN3OYXpKirARMjMzIzo6msLCQsaNG4c8cSi906dPM2XKFBo1akRKSgpRUVEcPHiQESNGYGVlpXY8vabRaJg3bx5vvvkmrq6uZGdnqx1JGLoSfg9VAW7c99oNoMRtbfT47owUYSNlYWHBunXr+PHHH5k5c6bacfSaoiikp6fTp08f2rRpA+h618bFxdG5c2eDaSupDzQaDWFhYbRr144ePXpw48b9f1UK8RiqVy/2ZXugEDhx12uHuW9S1t1q1izTWGVJirARq1KlCgkJCcTFxREeHq52HL2Tn59PTEwMr732GuPGjcPJyYkzZ84QEhIiG9n/AxqNhvnz59O6dWvc3Nz4449ibxIK8UhKy5YUFbNbXGWgD/A+ukla+4BN6CZnPcDaGlq0KMeU/4zMjjYBZ86coVOnTsydO5cBAwaoHUd1Fy9eJCIigqioKFq1aoW/vz+urq5G2VRDTVqtFj8/P3766ScSExOpUqWK2pGEgdBqtcTHx7Po44/ZfPgwTxVTpv4HjAKSgdroZkU/sE4YZHa0UJ+dnR3btm3D39+f5ORkteOo5sCBAwwdOpRmzZpx+fJl0tLS2L59O25ublKAy4GZmRkRERE0bdoUd3d3cnJy1I4k9NytW7eIjo6mWbNmzJ49m/Hvv4+lp6duhvN9agHx6EbCZymhAGs04O6utwUYZCRsUvbs2UOfPn1ITEykrR53kClLhYWFd27Hnz9/nokTJzJmzBhq6vEzImOj1WoZO3Ysv/76KwkJCVSuXFntSELP/PHHH0RFRREaGkrz5s2ZPn3639t8ltAxq1QMoGOW7KJkYjZu3Kg8/fTTyvHjx9WOUq6uXLmiBAcHK88++6zi4OCgrF+/XikoKFA7lskqKipShg8frnTt2lW5efOm2nGEnrh06ZIyc+ZMpXbt2kr//v2VgwcPFn/iwoWKYmNz765Jj/qysdG9T8/JPTgT4+XlxUcffYSrqysXL15UO06Z+/HHH/H19aVJkyYcPXqUTZs2sXv3bvr27UulSpXUjmey/lo29+yzz+Lp6UleXp7akYSK/uq13rRpU65evco333zDmjVraN26dfFv8PPT7YpkY1Psrel7aDS682QXJaGvxo4dy6hRo3BzczOKtZxarZatW7fi7OyMk5MTDRo04Oeff2bZsmW89tprascTfzI3N+err76ibt26UohNVGZmJoMHD6Zt27ZUq1aNn376iYiICBo3bvzoN/v56W4te3vrJltZW9973Npa97q3t+48AyjAIM+ETZaiKPzrX//ixx9/JDEx0SAbUfzxxx/ExMQwf/58qlatSkBAAP369eOpp55SO5p4iMLCQoYOHcq1a9eIj483yN97ovQURWHPnj3Mnj2bQ4cOERAQgK+vL9VLWANcKpcv61pRHjmia8RRs6ZuGdKIEXo9Cas4UoRNWFFREQMHDkRRFNasWYO5ubnakUrl5MmTzJ8/n+XLl9OtWzf8/f3p2LGjNNUwIIWFhQwePJicnBzi4uLkg5MR0mq1bNmyhdmzZ3PlyhWmTp3K0KFD5UPXfaQIm7hbt27h5ubGyy+/zJdffqm3hUxRFHbt2kV4eDh79+5l9OjRvP322zz33HNqRxNPqKCggEGDBpGXl8eGDRukEBuJ27dvs2rVKubMmYONjQ3Tpk2jT58+BvMhv6JJERZkZ2fj6OhInz59eO+999SOc4+8vDxWrVrFF198QUFBAZMmTWLo0KGyzMVIFBQUMGDAAAoKCli/fj2WxXRHEoYhJyeHJUuWMG/ePOzt7Zk+fTrdu3fX2w/2+kKKsADg999/p2PHjkybNo1x48apHYcLFy6wcOFCFi9eTLt27fD398fZ2Vn+QBuh27dv069fPzQaDWvXrsXCwkLtSOIxXLlyhfnz57Nw4UIcHR2ZNm2ayfQhKAsyO1oA8PTTT5OUlMSHH37Ixo0bVcvxzTffMGjQIFq0aMGNGzfYu3cvCQkJuLi4SAE2UpaWlqxdu5aioqI7o2Kh/86cOcOkSZOwt7fn4sWL7Nu3j3Xr1kkBfkxShMUdTZo0YcuWLfj6+rJ79+4K+74FBQWsXr2aDh06MGDAANq2bXtn8pW9vf2jLyAMnqWlJevWrePWrVsMGjRICrEe++GHHxg2bBivvfYaVlZW/PDDD0RFRcmf1Sckt6PFA1JSUhg0aBApKSm0bNny7wNZWbplAZmZun0+q1eHli1h5MgnWhZw5coVoqKiWLhwIS+++CL+/v706tVLJnCYsFu3buHt7U3VqlVZuXKlNFjRI/v27WP27NlkZGTg7++Pn58fNWrUUDuWwZMiLIr1n//8h8DAQPbu3Uujy5chOBgSE3UH8/P/PtHaWtckzs0NgoKgXbtHXvvIkSOEh4ezYcMGvL298ff359VXXy2nn0QYmvz8fLy8vKhVqxbLly+XQqwirVbLtm3bmD17NhcvXuTdd99l+PDhWN/fKEM8MSnCokTz58/nyief8OEff6DJz9cV25JoNLqCXEKruKKiIrZu3Up4eDjHjh3Dz88PX19f6hjYwnpRMfLy8vD09KRu3bosW7ZM7o5UsIKCAtasWcOcOXOoVKkS06ZNw8fHRz4QlQMpwqJkERHcnjQJy8LC0r/nvp6tN27cYOnSpcyfP5/atWsTEBCAj4+PLEURj5Sbm0vv3r1p0KABX331lRTiCpCbm0t0dDQhISG88MILTJ8+XSZFljMpwqJ4xWwfdv+W7HnABGD+/e+1seFsbCwhu3axYsUKXFxc8Pf3p0OHDvKHWTyW3NxcevbsiZ2dHdHR0bLvczm5evUqCxYsYMGCBXeWKrZv317tWCZBfkeL4gUHw30N9nPu+vodsAbeKuat2txcDg8cSJUqVcjMzGTNmjW88cYbUoDFY7OxsWHLli2cOnWKsWPHotVq1Y5kVM6dO8c777zDiy++yJkzZ0hPTycuLk4KcAWSkbB4UFYW2NndOwHrPsuAWcCvQHGlVXnqKTTnzhlcM3Whn3JycnB3d+ell15i0aJFMiL+h44ePcqcOXPYtGkTo0aNIiAggGeffVbtWCZJfieLB8XEPPKUZcAwii/AABozs1JdR4jSqFKlCtu2bePo0aNMmDBBRsRPaP/+/Xh5eeHo6Ejjxo355ZdfCAkJkQKsIinC4kGZmQ8dBZ8B0oHhD7tGXp5umzEhykiVKlVITEwkMzOTiRMnIjfxSkdRFBITE+nSpQsDBw7E2dmZU6dO8e9//5tatWqpHc/kSREWD8rOfujhWKAT8PyjrnPtWhkFEkKnatWqbN++nYMHDzJp0iQpxA9RWFjIqlWraNWqFdOmTcPX15cTJ07w9ttvY2Njo3Y88ScpwuJBj9hsezmPGAX/pWbNskgjxD2qVatGUlIS3377LQEBAVKI75OXl8fChQuxt7cnMjKS4OBgDh8+zKBBg2Sdrx6SIiwe1LIllLDx9tfABYqfFX0Pa2to0aKMgwmhU716dZKSkvj6668JDAyUQgxcu3aNTz/9lOeff56kpCRWrFjB7t27cXd3l5UJekyKsHjQiBElHloG9AGqPuoaivLQ6wjxT9WoUYMdO3aQnp7O1KlTTbYQX7hwgSlTptC4cWNOnDhBamoqmzZt4s0331Q7migFKcLiQXXr6npBF/PpeRG6Z8IPpdGAu7ssTxLlrmbNmiQnJ5OSksL06dNNqhAfO3aMMWPG0KJFC4qKijh06BAxMTE0a9ZM7WjiMUgRFsULCtLdUn4CirW17v1CVIBatWqRkpJCUlISM2fONPpC/O2339K3b18cHBxo2LAhJ06cIDQ0lOeee07taOIJSBEWxWvXTtcD+jFnUd4yN2degwZkv/hiOQUT4kG1a9cmJSWFrVu38v777xtdIVYUhR07dtCtWzfeeustunTpwqlTp/jggw+oXbu22vHEPyBT5UTJ/toNacoU3brfUuyiZDF3Lqd++omuXbuSlJQkuySJCmNra8vOnTvp2rUr5ubmfPjhh2pH+seKiopYv349n332Gbdv32batGkMGDAACwsLtaOJMiJtK8WjHTig6yW9bZuu2N7dU/qv/YTd3XW3oNu2RVEU3n//fdatW0dycjINGzZUL7swOVlZWXTt2pX+/fvz/vvvqx3nieTn57Ns2TLmzp1LvXr1mD59Oh4eHtKu0whJERald/myrhXlkSO6Rhw1a+qWIY0YUewkrM8//5z58+ezY8cO7O3tKzyuMF2XLl3C0dGRIUOGMHPmTLXjlFp2djYRERGEh4fTpk0bpk+fTqdOndSOJcqR3I4WpVenDrz7bqlPDwwMpEaNGjg6OpKYmMirr75ajuGE+Fu9evVITU29c2t6+vTpakd6qIsXLxIWFsaSJUtwd3dnx44dtJB19iZBirAoV6NHj6Z69eq4uLgQFxdHx44d1Y4kTET9+vVJTU3F0dERMzMzpk6dqnakB/zyyy/MnTuXdevWMWTIEL777jsaNWqkdixRgaQIi3Ln4+ND1apV8fLyYuXKlbi4uKgdSZiIBg0akJaWhqOjI+bm5gQGBqodCYDvvvuOzz77jLS0NPz8/Dh27JhMYjRR8pRfVAhXV1c2btzIkCFDWL9+vdpxhAl55plnSEtLIyIigtDQUNVyKIrCzp07cXZ2xsvLizfeeINTp07x0UcfSQE2YTISFhWmU6dO7NixA3d3d27cuMGoUaPUjiRMxLPPPnvn1rS5uTmTJk2qsO9dVFREfHw8s2fPJicnh2nTpjFo0CAsLS0rLIPQX1KERYVq1aoVu3btwtnZmezsbN555x21IwkT8dxzz91za/rtt98u1+9369YtYmNjmTNnDrVq1WLmzJn07t1blhmJe0gRFhXO3t6ePXv24OzszLVr15g1a5bs8iIqhJ2d3Z1CbGZmht9fDWn+kpWlW4aXmanbV7t6dd2uYiNHlroX+o0bN1i0aBFhYWG0bNmSxYsX07lzZ/k9Lool64SFarKysnB1dcXBwYGwsDAZIYgKc/LkSbp27crMmTMZN24cZGToGtIkJupOyM//++S/GtK4ueka0rRrV+w1L126RHh4OFFRUbi4uDB16lRatWpVAT+NMGRShIWqrl+/Ts+ePXnhhRdYunSpbDouKsyvv/5K165dWdW5M502bix1a1ZCQv5u6YquoIeEhLBmzRoGDhxIYGAgL7zwQgX8BMIYSBEWqsvNzaVv375YWVmxevVqrKys1I4kTMSlWbOoOmsWNo/z16CNDYSEcOiNN/jss89ITk7G19eXSZMmUa9evfILK4ySFGGhF27fvs2QIUO4evUq8fHxVK1aVe1IwthlZICjI+Tm3vPyEGAncBN4GpgKjLnvrflmZnjXqkX3adMYN24c1apVq4jEwghJERZ6o6ioiPHjx3PkyBG2bdtGrVq11I4kjFmfPhAf/8At6B+BJsBTwM+AI5AAtLnrHK1Gg9K7N+bx8RUUVhgrmQkj9Ia5uTlRUVE4ODjQpUsXLl68qHYkYayysnSTsIoZgzRHV4ABNH9+/XrfOWaKgnlSkm5TEyH+ASnCQq9oNBrmzJnDwIED6dSpE6dOnVI7kjBGMTEPPTwBsAGaAvUB9+JO0mgeeR0hHkWmogq9o9FomDFjBjVq1MDBwYGkpCSaN2+udixhTDIz712GdJ+FwHzgv8Au/h4Z3yMvT7etpxD/gIyEhd6aMGECs2fPpnv37mRkZKgdRxiT7OxHnmIOdALOAxElnXTtWtllEiZJRsJCrw0ZMoRq1arh4eHB2rVrcXR0VDuSMAbVq5f61EIefCZ8R82aZZFGmDAZCQu917t3b/7zn//Qr18/tmzZonYcYcAURSElJYXlhw+TV8zxLGANkAMUAUnAaqB7cReztoYWLcotqzANUoSFQejatSsJCQmMHTuWlStXqh1HGJhr164RFhZG06ZNmTx5MtqhQ7F66sEnvRp0t56fBWoCU4AwoHdxF1UUGDGi/EILkyC3o4XBaNeuHTt37qRHjx5kZ2czYcIEtSMJPXfgwAEiIiLYsGEDHh4eREdH07FjR91mCvv3P7BOuA6QXpoLazTg7l7qTR2EKIkUYWFQmjdvTnp6Os7Ozly/fp2goCDZnUbcIzc3l//85z9ERESQlZWFr68vx48fp27duveeGBQESUkPdMwqFWtr3fuF+IekY5YwSBcvXsTZ2Rl3d3c+++wzKcSC48ePExkZyfLly2nfvj1+fn64ublhbm5e8psiImDKlMcrxH/2jub+bRCFeALyTFgYpPr165Oenk56ejq+vr4UFRWpHUmooLCwkI0bN+Ls7EynTp2wtLQkIyODhIQEevbs+fACDLpCGhKiK6yP+iCn0UgBFmVORsLCoP3xxx94eXlha2tLbGwslpaWakcSFeC3335jyZIlREVFYWdnx4QJE/Dx8eGpYiZblcqBA7r9hLdt0xXbvLvmTv+1n7C7u+4WdNu2ZfNDCIEUYWEE8vPzGTBgALdu3WLDhg3Y2NioHUmUA0VR2LVrFwsXLiQlJYX+/fvj5+fHq6++Wnbf5PJlXSvKI0d0jThq1tQtQ6kV+DcAAAmKSURBVBoxQiZhiXIhRVgYhcLCQkaNGsWpU6fYunUr1R+jGYPQb9evX2f58uVERERgbm6On58fQ4cOle0DhVGQIiyMhlarxd/fn3379rF9+/YHZ8MKg3Lw4EEiIiJYv349rq6uTJgwAQcHB5mEJ4yKTMwSRsPMzIwvvviCnj174uDgwLlz59SOJB5TXl4ey5Yto0OHDnh5edGoUSOOHj3KmjVr6Ny5sxRgYXRknbAwKhqNho8++ujODkw7duzA3t5e7VjiEX755RciIyNZtmwZbdu2ZcaMGXh4eDx6drMQBk6KsDBKkydPpkaNGjg6OrJt2zZatWqldiRxn8LCQhISEli4cCEHDx5k5MiR7N+/n8aNG6sdTYgKI0VYGK1Ro0ZRrVo1XF1diYuLo2PHjmpHEsDvv/9+Z3nRM888w4QJE9i0aRNWVlZqRxOiwskzYWHUfHx8WL58OV5eXiQlJakdx2QpikJ6ejr9+/fn5Zdf5ty5c2zevJn//ve/DB06VAqwMFkyO1qYhH379tGnTx8WLFiAj4+P2nFMRnZ2NrGxsURERKAoCn5+fgwbNkyWkAnxJ7kdLUxCx44dSUpKwt3dnezsbEaPHq12JKN26NAhIiIiWLt2LS4uLixYsIAuXbrI7GYh7iNFWJiMVq1asWvXLlxcXMjOzmby5MlqRzIq+fn5rFu3joiICM6dO4evry8//fQT9evXVzuaEHpLbkcLk3Pu3DmcnZ156623+Oijj2R09g+dPHmSyMhIYmJiaN26NRMmTMDDw4NKleQzvhCPIhOzhMlp2LAhu3fvJiEhgUmTJqHVatWOZHCKiorYsmULbm5utG/fHq1Wy759+0hKSsLT01MKsBClJCNhYbKys7Pp2bMnjRo14quvvpLCUQqXLl0iOjqaRYsWUb9+ffz8/OjXrx/W1tZqRxPCIMlIWJis6tWrk5SUxJUrV/Dx8SE/P1/tSHpJURT27NnDwIEDadq0KSdPniQuLo79+/czfPhwKcBC/AMyEhYm7/bt2wwdOpQrV64QHx9P1apV1Y6kF27cuMGKFStYuHAhhYWF+Pn5MXz4cGrU+P/27iY06vQO4Ph3NtWaCMaE7FKxsig9CMGcdvZQNDGJihubEPdgA76s0uYwY4tQveit0LIoi4UWZoz4djC+xAqCGg1WyBqUQASbRAUPInpwu1E00WgSV00PE0XZ8SVO4qPJ9wNimMyMPw/y9T95nuc/NfRo0phhhCVSP+OMxWK0t7dz4sQJ8vPzQ48UTEdHB8lkkgMHDrBgwQJisRilpaUuYJNGgR9HS0BWVhZ1dXWUlJRQXFzMzZs3Q4/0Xg0MDFBfX8/cuXOpqKhg2rRpXLp0iUOHDlFWVmaApVHiShRpSCQSYfPmzeTl5TFv3jxOnTrFrFmzQo81qq5du0ZdXR27d++mqKiI9evXU1lZ6SI16T3xX5r0gkgkwsaNG8nNzaW4uJimpiYKCwtDjzWinjx5wsmTJ0kmk7S2trJq1SpaWlq85aMUgBGW0ojH4+Tm5lJeXs7Ro0eJRqOhR8pYV1cXu3btoq6ujoKCAuLxOA0NDeTk5IQeTRq3jLD0CsuXL2fKlCksWbKEgwcPUlpaGnqkYRscHOTcuXMkEgkaGxtZunQpDQ0NY+I/FdJY4Opo6Q2am5tZtmwZO3bsoKqqKvQ4b+X+/fvU19eTSCTo7+9/vr1oPK/6lj5EXglLbzB//nyOHz9OZWUl9+7dY8WKFaFHeqWLFy+STCbZv38/paWlbN26lbKyMj75xI0Q0ofICEtvIRqNcvr0aRYvXkxPTw9r164NPdJzjx494vDhwySTSa5evUptbS2dnZ1Mnz499GiS3sAIS2+psLCQM2fOsHDhQrq7u9m0aVPQ/bPXr19n+/bt7Ny5k8LCQtatW0dVVRUTJkwINpOk4THC0jDMnDmTlpYWFi1aRHd3N1u2bHmvIX769ClNTU0kk0nOnj3LypUraW5uZvbs2e9tBkkjx4VZ0ju4c+cOFRUVzJkzh23btpGVlfXyE7q6YM8e6OiAnh7IzYWiIlizBj79dNh/3u3bt59vL5o6dSrxeJyamhomT548Mn8hSUEYYekd9fb2Ul1dTX5+Pnv37mXixInQ1gbffgsnTqSe9OKdmbKzYXAQvvoKNm6EN2wTGhwcpLW1lUQiwbFjx6iuriYWixGNRj1GUhojjLCUgf7+fmpqahgYGODI4sX8ctMm6OtLxfZVIpFUkL/7DmKxn327t7eXffv2kUgkePDgAbFYjNWrV7u9SBqDjLCUocePH7N37lx+39ZG9tOnb//CnJyXQnz58mWSyST19fWUlJQQj8cpLy93e5E0hrkwS8rQLy5c4JvOTiJpAnwA+CtwA/gVsAeY9+ybDx8yuGED/+nu5u9NTVy5coXa2lra29uZMWPGe5peUkheCUuZ+vprOHLkZx9BnwL+CBwEvgR+GHr8xd27T4CzBQX8mEhQXV3t9iJpnDHCUia6uuDzz19egDXkt8Afhn691qRJcOPGO62alvRx84dNUib27En78BPgPHAL+A3wa+BPQF+6J0cir3wfSWObEZYy0dGR9ir4R+An4N9AC/Bf4ALwt3Tv0dcHnZ2jOKSkD5URljLR05P24eyh3/8MTAMKgL8Aja96n7t3R3oySR8BIyxlIjc37cN5pD6CfvFIjdcer5GXN3IzSfpoGGEpE0VFqYVVaawB/gV0AXeBfwC/S/fE7GyYM2e0JpT0AXN1tJSJ16yO/glYB+wDJgHLgC1DX7/E1dHSuOWVsJSJzz5LnQWd5iznCUAC6Ab+B/yTNAGORKCiwgBL45RXwlKm2tpg/nx4+HD4r83Jge+/hy++GPGxJH34vBKWMhWNps6AzskZ3uuenR1tgKVxy7OjpZHw7G5IGzZkfBclSeOHH0dLI+n8+dT9hBsbU7Hte+GMrGf3E66oSN1P2CtgadwzwtJouHUrdRRlZ2fqII68vNQ2pNWrXYQl6TkjLElSIC7MkiQpECMsSVIgRliSpECMsCRJgRhhSZICMcKSJAVihCVJCsQIS5IUiBGWJCkQIyxJUiBGWJKkQIywJEmBGGFJkgIxwpIkBWKEJUkKxAhLkhSIEZYkKRAjLElSIEZYkqRAjLAkSYEYYUmSAjHCkiQFYoQlSQrECEuSFIgRliQpECMsSVIgRliSpECMsCRJgRhhSZICMcKSJAVihCVJCsQIS5IUiBGWJCkQIyxJUiBGWJKkQIywJEmBGGFJkgIxwpIkBfJ/6DcF6dAwEL0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw(qc_perfect.qubit_topology(),with_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "G = qc_perfect.qubit_topology()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# gate sets" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "one_q_gates = [X,Z,I]\n", + "two_q_gates = [two_q_id,CZ]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z 0\n", + "Z 1\n", + "X 2\n", + "I 3\n", + "Z 4\n", + "X 5\n", + "X 6\n", + "I 7\n", + "X 8\n", + "CZ 0 3\n", + "CZ 0 1\n", + "CZ 1 4\n", + "I 1\n", + "I 2\n", + "CZ 2 5\n", + "CZ 3 6\n", + "CZ 3 4\n", + "CZ 4 7\n", + "I 4\n", + "I 5\n", + "I 5\n", + "I 8\n", + "I 6\n", + "I 7\n", + "I 7\n", + "I 8\n", + "\n" + ] + } + ], + "source": [ + "prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "print(prog1+prog2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# random cliffords" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from forest_benchmarking.rb import get_rb_gateset" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# my config has gone all cattywampus so i need to do this\n", + "bm = get_benchmarker(endpoint='tcp://localhost:6000')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'tcp://localhost:6000'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm.client.endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", + "gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RX(pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RZ(pi/2) 2\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RZ(-pi) 5\n", + "RX(-pi) 5\n", + "RZ(-pi) 6\n", + "RX(-pi) 6\n", + "RX(-pi/2) 7\n", + "RZ(-pi) 8\n", + "RZ(-pi) 8\n", + "\n" + ] + } + ], + "source": [ + "progy = random_single_qubit_cliffords(bm,G)\n", + "print(progy)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[, , , , , , , , , , , , , , , , ]\n" + ] + } + ], + "source": [ + "print(gateset_2q)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RX(-pi/2) 0\n", + "CZ 3 0\n", + "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 3\n", + "RZ(pi/2) 0\n", + "RX(-pi) 0\n", + "RZ(-pi) 1\n", + "RX(-pi) 1\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "CZ 1 4\n", + "RX(pi/2) 4\n", + "CZ 1 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi) 4\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 1\n", + "CZ 2 1\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 5\n", + "CZ 3 6\n", + "RX(pi/2) 6\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 3 6\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 7 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 7\n", + "CZ 4 5\n", + "RX(-pi/2) 4\n", + "CZ 4 5\n", + "RX(pi/2) 5\n", + "CZ 4 5\n", + "RZ(pi/2) 5\n", + "RX(-pi/2) 4\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 5\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RX(-pi/2) 5\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RZ(-pi/2) 8\n", + "CZ 6 7\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 6\n", + "CZ 7 8\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 7\n", + "RZ(-pi/2) 7\n", + "\n" + ] + } + ], + "source": [ + "print(random_two_qubit_cliffords(bm,G))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot the distribution of sublattice widths" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[16, 18, 24, 35, 52, 76, 108, 135, 156, 166, 164, 149, 120, 76, 16, 1]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = qc_perfect.qubit_topology()\n", + "len(qc_perfect.qubit_topology())\n", + "# distribution of graph lengths\n", + "disty = []\n", + "for gdx in range(1,len(G.nodes)+1):\n", + " listg = generate_connected_subgraphs(G,gdx)\n", + " disty.append(len(listg))\n", + "\n", + "cir_wid = list(range(1,len(G.nodes)+1))\n", + "plt.bar(cir_wid, disty, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "disty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acquire data in Z basis" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# # with these parameters the cell below takes about 1 hour 40 minutes\n", + "# num_shots_per_circuit = 400\n", + "# num_rand_subgraphs = 16\n", + "# circuit_depth = 18\n", + "# circuit_width = 15 #max = len(G.nodes)\n", + "# x_basis = False\n", + "# active_reset = True\n", + "# total == 6077" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# with these parameters the cell below takes about 5 minutes\n", + "num_shots_per_circuit = 1000\n", + "num_rand_subgraphs = 20\n", + "circuit_depth = 6\n", + "circuit_width = 4 #max = len(G.nodes)\n", + "x_basis = False\n", + "active_reset = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthIn X basisLatticeProgramTrialsWidth
0False1False(13)(I 13)10001
1False1False(1)(I 1, X 1)10001
2False1False(7)(I 7)10001
3False1False(7)(I 7, X 7)10001
4False1False(2)(I 2, X 2)10001
5False1False(10)(I 10, X 10)10001
6False1False(7)(I 7)10001
7False1False(4)(I 4)10001
8False1False(13)(I 13)10001
9False1False(11)(I 11)10001
10False1False(10)(I 10, X 10)10001
11False1False(14)(I 14)10001
12False1False(11)(I 11)10001
13False1False(2)(I 2)10001
14False1False(12)(I 12)10001
15False1False(10)(I 10)10001
16False1False(2)(I 2, X 2)10001
17False1False(16)(I 16)10001
18False1False(15)(I 15, X 15)10001
19False1False(11)(I 11, X 11)10001
20False1False(13, 14)(I 13, I 14, X 13)10002
21False1False(17, 10)(I 17, I 10, X 17)10002
22False1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)10002
23False1False(16, 17)(I 16, I 17, X 16)10002
24False1False(1, 2)(I 1, I 2, CNOT 1 2)10002
25False1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)10002
26False1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)10002
27False1False(17, 10)(I 17, I 10, CNOT 17 10)10002
28False1False(16, 15)(I 16, I 15, X 16)10002
29False1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)10002
........................
450False6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...10003
451False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...10003
452False6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...10003
453False6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...10003
454False6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...10003
455False6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...10003
456False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...10003
457False6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...10003
458False6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...10003
459False6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...10003
460False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...10004
461False6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...10004
462False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...10004
463False6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...10004
464False6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...10004
465False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...10004
466False6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...10004
467False6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...10004
468False6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...10004
469False6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...10004
470False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...10004
471False6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...10004
472False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...10004
473False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...10004
474False6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...10004
475False6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...10004
476False6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...10004
477False6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...10004
478False6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...10004
479False6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...10004
\n", + "

480 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Active Reset Depth In X basis Lattice \\\n", + "0 False 1 False (13) \n", + "1 False 1 False (1) \n", + "2 False 1 False (7) \n", + "3 False 1 False (7) \n", + "4 False 1 False (2) \n", + "5 False 1 False (10) \n", + "6 False 1 False (7) \n", + "7 False 1 False (4) \n", + "8 False 1 False (13) \n", + "9 False 1 False (11) \n", + "10 False 1 False (10) \n", + "11 False 1 False (14) \n", + "12 False 1 False (11) \n", + "13 False 1 False (2) \n", + "14 False 1 False (12) \n", + "15 False 1 False (10) \n", + "16 False 1 False (2) \n", + "17 False 1 False (16) \n", + "18 False 1 False (15) \n", + "19 False 1 False (11) \n", + "20 False 1 False (13, 14) \n", + "21 False 1 False (17, 10) \n", + "22 False 1 False (4, 5) \n", + "23 False 1 False (16, 17) \n", + "24 False 1 False (1, 2) \n", + "25 False 1 False (3, 4) \n", + "26 False 1 False (0, 7) \n", + "27 False 1 False (17, 10) \n", + "28 False 1 False (16, 15) \n", + "29 False 1 False (17, 10) \n", + ".. ... ... ... ... \n", + "450 False 6 False (17, 10, 11) \n", + "451 False 6 False (4, 5, 6) \n", + "452 False 6 False (16, 14, 15) \n", + "453 False 6 False (13, 14, 15) \n", + "454 False 6 False (16, 14, 15) \n", + "455 False 6 False (16, 14, 15) \n", + "456 False 6 False (4, 5, 6) \n", + "457 False 6 False (0, 1, 2) \n", + "458 False 6 False (0, 6, 7) \n", + "459 False 6 False (16, 2, 15) \n", + "460 False 6 False (0, 1, 2, 15) \n", + "461 False 6 False (4, 5, 6, 7) \n", + "462 False 6 False (16, 1, 14, 15) \n", + "463 False 6 False (16, 1, 10, 17) \n", + "464 False 6 False (2, 3, 4, 15) \n", + "465 False 6 False (16, 1, 14, 15) \n", + "466 False 6 False (2, 13, 14, 15) \n", + "467 False 6 False (11, 12, 13, 14) \n", + "468 False 6 False (16, 17, 2, 15) \n", + "469 False 6 False (0, 1, 6, 7) \n", + "470 False 6 False (10, 11, 12, 13) \n", + "471 False 6 False (0, 1, 16, 15) \n", + "472 False 6 False (10, 11, 12, 13) \n", + "473 False 6 False (0, 1, 2, 15) \n", + "474 False 6 False (16, 1, 2, 3) \n", + "475 False 6 False (17, 10, 11, 12) \n", + "476 False 6 False (16, 17, 14, 15) \n", + "477 False 6 False (16, 17, 10, 15) \n", + "478 False 6 False (16, 13, 14, 15) \n", + "479 False 6 False (2, 3, 4, 5) \n", + "\n", + " Program Trials Width \n", + "0 (I 13) 1000 1 \n", + "1 (I 1, X 1) 1000 1 \n", + "2 (I 7) 1000 1 \n", + "3 (I 7, X 7) 1000 1 \n", + "4 (I 2, X 2) 1000 1 \n", + "5 (I 10, X 10) 1000 1 \n", + "6 (I 7) 1000 1 \n", + "7 (I 4) 1000 1 \n", + "8 (I 13) 1000 1 \n", + "9 (I 11) 1000 1 \n", + "10 (I 10, X 10) 1000 1 \n", + "11 (I 14) 1000 1 \n", + "12 (I 11) 1000 1 \n", + "13 (I 2) 1000 1 \n", + "14 (I 12) 1000 1 \n", + "15 (I 10) 1000 1 \n", + "16 (I 2, X 2) 1000 1 \n", + "17 (I 16) 1000 1 \n", + "18 (I 15, X 15) 1000 1 \n", + "19 (I 11, X 11) 1000 1 \n", + "20 (I 13, I 14, X 13) 1000 2 \n", + "21 (I 17, I 10, X 17) 1000 2 \n", + "22 (I 4, I 5, X 4, X 5, CNOT 4 5) 1000 2 \n", + "23 (I 16, I 17, X 16) 1000 2 \n", + "24 (I 1, I 2, CNOT 1 2) 1000 2 \n", + "25 (I 3, I 4, X 3, CNOT 3 4) 1000 2 \n", + "26 (I 0, I 7, X 7, CNOT 0 7) 1000 2 \n", + "27 (I 17, I 10, CNOT 17 10) 1000 2 \n", + "28 (I 16, I 15, X 16) 1000 2 \n", + "29 (I 17, I 10, X 10, CNOT 17 10) 1000 2 \n", + ".. ... ... ... \n", + "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... 1000 3 \n", + "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... 1000 3 \n", + "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... 1000 3 \n", + "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... 1000 3 \n", + "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... 1000 3 \n", + "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... 1000 3 \n", + "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... 1000 3 \n", + "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... 1000 3 \n", + "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... 1000 3 \n", + "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... 1000 3 \n", + "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... 1000 4 \n", + "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... 1000 4 \n", + "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... 1000 4 \n", + "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... 1000 4 \n", + "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... 1000 4 \n", + "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... 1000 4 \n", + "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... 1000 4 \n", + "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... 1000 4 \n", + "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... 1000 4 \n", + "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... 1000 4 \n", + "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... 1000 4 \n", + "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... 1000 4 \n", + "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... 1000 4 \n", + "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... 1000 4 \n", + "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... 1000 4 \n", + "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... 1000 4 \n", + "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... 1000 4 \n", + "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... 1000 4 \n", + "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... 1000 4 \n", + "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... 1000 4 \n", + "\n", + "[480 rows x 7 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp =generate_rand_cir_for_rand_lattices_experiments(qc_noisy, \n", + " circuit_depth, \n", + " circuit_width,\n", + " num_rand_subgraphs, \n", + " num_shots_per_circuit, \n", + " in_x_basis=x_basis, \n", + " use_active_reset=active_reset)\n", + "exp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Collect data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "257.87861728668213\n" + ] + } + ], + "source": [ + "t0 = time.time()\n", + "data_zbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp)\n", + "t1 = time.time()\n", + "total = t1-t0\n", + "print(total)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetAnswerDepthIn X basisLatticeProgramSamplesTrialsWidth
0False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
1False[[1]]1False(1)(I 1, X 1)[[1], [1], [1], [1], [1], [1], [1], [0], [0], ...10001
2False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
3False[[1]]1False(7)(I 7, X 7)[[1], [1], [0], [1], [1], [1], [0], [1], [1], ...10001
4False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
5False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [1], [1], [1], ...10001
6False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
7False[[0]]1False(4)(I 4)[[0], [0], [0], [1], [0], [0], [0], [0], [0], ...10001
8False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
9False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
10False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [0], [1], [1], ...10001
11False[[0]]1False(14)(I 14)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
12False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
13False[[0]]1False(2)(I 2)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
14False[[0]]1False(12)(I 12)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
15False[[0]]1False(10)(I 10)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
16False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [1], [1], [0], [0], [1], ...10001
17False[[0]]1False(16)(I 16)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
18False[[1]]1False(15)(I 15, X 15)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
19False[[1]]1False(11)(I 11, X 11)[[1], [1], [0], [1], [1], [1], [1], [1], [1], ...10001
20False[[1, 0]]1False(13, 14)(I 13, I 14, X 13)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
21False[[1, 0]]1False(17, 10)(I 17, I 10, X 17)[[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0...10002
22False[[1, 0]]1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)[[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0...10002
23False[[1, 0]]1False(16, 17)(I 16, I 17, X 16)[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0...10002
24False[[0, 0]]1False(1, 2)(I 1, I 2, CNOT 1 2)[[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0...10002
25False[[1, 1]]1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)[[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0...10002
26False[[0, 1]]1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
27False[[0, 0]]1False(17, 10)(I 17, I 10, CNOT 17 10)[[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0...10002
28False[[1, 0]]1False(16, 15)(I 16, I 15, X 16)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
29False[[0, 1]]1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)[[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
..............................
450False[[0, 0, 0]]6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [...10003
451False[[1, 1, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...[[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [...10003
452False[[0, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
453False[[0, 1, 0]]6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...[[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [...10003
454False[[1, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...[[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
455False[[1, 0, 0]]6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...[[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [...10003
456False[[0, 0, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...[[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [...10003
457False[[0, 1, 1]]6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...[[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [...10003
458False[[1, 0, 1]]6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...[[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
459False[[0, 0, 1]]6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
460False[[1, 0, 1, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...[[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,...10004
461False[[0, 1, 1, 1]]6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...[[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,...10004
462False[[1, 0, 0, 1]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...[[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,...10004
463False[[1, 0, 0, 0]]6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,...10004
464False[[1, 1, 1, 1]]6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...[[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,...10004
465False[[0, 1, 0, 0]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...[[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,...10004
466False[[1, 1, 1, 0]]6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...[[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,...10004
467False[[0, 0, 1, 0]]6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...[[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,...10004
468False[[0, 1, 0, 0]]6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...[[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
469False[[0, 1, 1, 1]]6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...[[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
470False[[1, 0, 0, 0]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...[[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,...10004
471False[[0, 0, 1, 1]]6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...[[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,...10004
472False[[0, 1, 1, 1]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...[[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,...10004
473False[[1, 1, 0, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...[[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,...10004
474False[[0, 0, 0, 0]]6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
475False[[0, 0, 1, 1]]6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,...10004
476False[[1, 0, 0, 1]]6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...[[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,...10004
477False[[0, 0, 0, 0]]6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
478False[[1, 1, 1, 0]]6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...[[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,...10004
479False[[0, 0, 0, 1]]6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...[[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,...10004
\n", + "

480 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Active Reset Answer Depth In X basis Lattice \\\n", + "0 False [[0]] 1 False (13) \n", + "1 False [[1]] 1 False (1) \n", + "2 False [[0]] 1 False (7) \n", + "3 False [[1]] 1 False (7) \n", + "4 False [[1]] 1 False (2) \n", + "5 False [[1]] 1 False (10) \n", + "6 False [[0]] 1 False (7) \n", + "7 False [[0]] 1 False (4) \n", + "8 False [[0]] 1 False (13) \n", + "9 False [[0]] 1 False (11) \n", + "10 False [[1]] 1 False (10) \n", + "11 False [[0]] 1 False (14) \n", + "12 False [[0]] 1 False (11) \n", + "13 False [[0]] 1 False (2) \n", + "14 False [[0]] 1 False (12) \n", + "15 False [[0]] 1 False (10) \n", + "16 False [[1]] 1 False (2) \n", + "17 False [[0]] 1 False (16) \n", + "18 False [[1]] 1 False (15) \n", + "19 False [[1]] 1 False (11) \n", + "20 False [[1, 0]] 1 False (13, 14) \n", + "21 False [[1, 0]] 1 False (17, 10) \n", + "22 False [[1, 0]] 1 False (4, 5) \n", + "23 False [[1, 0]] 1 False (16, 17) \n", + "24 False [[0, 0]] 1 False (1, 2) \n", + "25 False [[1, 1]] 1 False (3, 4) \n", + "26 False [[0, 1]] 1 False (0, 7) \n", + "27 False [[0, 0]] 1 False (17, 10) \n", + "28 False [[1, 0]] 1 False (16, 15) \n", + "29 False [[0, 1]] 1 False (17, 10) \n", + ".. ... ... ... ... ... \n", + "450 False [[0, 0, 0]] 6 False (17, 10, 11) \n", + "451 False [[1, 1, 1]] 6 False (4, 5, 6) \n", + "452 False [[0, 0, 1]] 6 False (16, 14, 15) \n", + "453 False [[0, 1, 0]] 6 False (13, 14, 15) \n", + "454 False [[1, 0, 1]] 6 False (16, 14, 15) \n", + "455 False [[1, 0, 0]] 6 False (16, 14, 15) \n", + "456 False [[0, 0, 1]] 6 False (4, 5, 6) \n", + "457 False [[0, 1, 1]] 6 False (0, 1, 2) \n", + "458 False [[1, 0, 1]] 6 False (0, 6, 7) \n", + "459 False [[0, 0, 1]] 6 False (16, 2, 15) \n", + "460 False [[1, 0, 1, 1]] 6 False (0, 1, 2, 15) \n", + "461 False [[0, 1, 1, 1]] 6 False (4, 5, 6, 7) \n", + "462 False [[1, 0, 0, 1]] 6 False (16, 1, 14, 15) \n", + "463 False [[1, 0, 0, 0]] 6 False (16, 1, 10, 17) \n", + "464 False [[1, 1, 1, 1]] 6 False (2, 3, 4, 15) \n", + "465 False [[0, 1, 0, 0]] 6 False (16, 1, 14, 15) \n", + "466 False [[1, 1, 1, 0]] 6 False (2, 13, 14, 15) \n", + "467 False [[0, 0, 1, 0]] 6 False (11, 12, 13, 14) \n", + "468 False [[0, 1, 0, 0]] 6 False (16, 17, 2, 15) \n", + "469 False [[0, 1, 1, 1]] 6 False (0, 1, 6, 7) \n", + "470 False [[1, 0, 0, 0]] 6 False (10, 11, 12, 13) \n", + "471 False [[0, 0, 1, 1]] 6 False (0, 1, 16, 15) \n", + "472 False [[0, 1, 1, 1]] 6 False (10, 11, 12, 13) \n", + "473 False [[1, 1, 0, 1]] 6 False (0, 1, 2, 15) \n", + "474 False [[0, 0, 0, 0]] 6 False (16, 1, 2, 3) \n", + "475 False [[0, 0, 1, 1]] 6 False (17, 10, 11, 12) \n", + "476 False [[1, 0, 0, 1]] 6 False (16, 17, 14, 15) \n", + "477 False [[0, 0, 0, 0]] 6 False (16, 17, 10, 15) \n", + "478 False [[1, 1, 1, 0]] 6 False (16, 13, 14, 15) \n", + "479 False [[0, 0, 0, 1]] 6 False (2, 3, 4, 5) \n", + "\n", + " Program \\\n", + "0 (I 13) \n", + "1 (I 1, X 1) \n", + "2 (I 7) \n", + "3 (I 7, X 7) \n", + "4 (I 2, X 2) \n", + "5 (I 10, X 10) \n", + "6 (I 7) \n", + "7 (I 4) \n", + "8 (I 13) \n", + "9 (I 11) \n", + "10 (I 10, X 10) \n", + "11 (I 14) \n", + "12 (I 11) \n", + "13 (I 2) \n", + "14 (I 12) \n", + "15 (I 10) \n", + "16 (I 2, X 2) \n", + "17 (I 16) \n", + "18 (I 15, X 15) \n", + "19 (I 11, X 11) \n", + "20 (I 13, I 14, X 13) \n", + "21 (I 17, I 10, X 17) \n", + "22 (I 4, I 5, X 4, X 5, CNOT 4 5) \n", + "23 (I 16, I 17, X 16) \n", + "24 (I 1, I 2, CNOT 1 2) \n", + "25 (I 3, I 4, X 3, CNOT 3 4) \n", + "26 (I 0, I 7, X 7, CNOT 0 7) \n", + "27 (I 17, I 10, CNOT 17 10) \n", + "28 (I 16, I 15, X 16) \n", + "29 (I 17, I 10, X 10, CNOT 17 10) \n", + ".. ... \n", + "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... \n", + "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... \n", + "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... \n", + "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... \n", + "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... \n", + "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... \n", + "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... \n", + "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... \n", + "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... \n", + "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... \n", + "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... \n", + "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... \n", + "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... \n", + "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... \n", + "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... \n", + "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... \n", + "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... \n", + "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... \n", + "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... \n", + "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... \n", + "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... \n", + "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... \n", + "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... \n", + "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... \n", + "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... \n", + "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... \n", + "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... \n", + "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... \n", + "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... \n", + "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... \n", + "\n", + " Samples Trials Width \n", + "0 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", + "1 [[1], [1], [1], [1], [1], [1], [1], [0], [0], ... 1000 1 \n", + "2 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "3 [[1], [1], [0], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", + "4 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", + "5 [[0], [1], [1], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", + "6 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "7 [[0], [0], [0], [1], [0], [0], [0], [0], [0], ... 1000 1 \n", + "8 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", + "9 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "10 [[0], [1], [1], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", + "11 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "12 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "13 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "14 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "15 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "16 [[1], [1], [1], [1], [1], [1], [0], [0], [1], ... 1000 1 \n", + "17 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "18 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", + "19 [[1], [1], [0], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", + "20 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "21 [[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0... 1000 2 \n", + "22 [[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0... 1000 2 \n", + "23 [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "24 [[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0... 1000 2 \n", + "25 [[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0... 1000 2 \n", + "26 [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", + "27 [[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0... 1000 2 \n", + "28 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "29 [[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", + ".. ... ... ... \n", + "450 [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [... 1000 3 \n", + "451 [[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [... 1000 3 \n", + "452 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", + "453 [[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [... 1000 3 \n", + "454 [[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", + "455 [[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [... 1000 3 \n", + "456 [[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [... 1000 3 \n", + "457 [[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [... 1000 3 \n", + "458 [[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", + "459 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", + "460 [[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,... 1000 4 \n", + "461 [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,... 1000 4 \n", + "462 [[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,... 1000 4 \n", + "463 [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,... 1000 4 \n", + "464 [[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,... 1000 4 \n", + "465 [[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,... 1000 4 \n", + "466 [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,... 1000 4 \n", + "467 [[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,... 1000 4 \n", + "468 [[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", + "469 [[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", + "470 [[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,... 1000 4 \n", + "471 [[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,... 1000 4 \n", + "472 [[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,... 1000 4 \n", + "473 [[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,... 1000 4 \n", + "474 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", + "475 [[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,... 1000 4 \n", + "476 [[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,... 1000 4 \n", + "477 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", + "478 [[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,... 1000 4 \n", + "479 [[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,... 1000 4 \n", + "\n", + "[480 rows x 9 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_zbasis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#data_zbasis.to_pickle(\"data_z_Aspen-1-16Q-A_2019_02_16.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_zbasis = pd.read_pickle('data_z_Aspen-1-16Q-A_2019_02_16.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# circuit_width = df['Width'].max()\n", + "# circuit_depth = df['Depth'].max()\n", + "# for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", + "# print(depth,subgraph_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfz = pd.DataFrame(data_zbasis)\n", + "dfz.to_pickle(\"data_z_Aspen_1_15Q_A_2019_02_09.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_pickle('data_z_Aspen_1_15Q_A_2019_02_09.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acquire data in X basis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exp_xbasis = exp.copy()\n", + "exp_xbasis['In X basis']=True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t0x = time.time()\n", + "data_xbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp_xbasis)\n", + "t1x = time.time()\n", + "totalx = t1x-t0x\n", + "print(totalx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfx = pd.DataFrame(data_xbasis)\n", + "dfx.to_pickle(\"data_x_Aspen_1_15Q_A_2019_02_09.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now put the data into a dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#data_xbasis.to_pickle(\"data_x_Aspen-1-16Q-A_2019_02_16.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#data_xbasis = pd.read_pickle('data_x_Aspen-1-16Q-A_2019_02_16.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data processing and estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "res_df = estimate_random_classical_circuit_errors(data_zbasis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "circuit_width = res_df['Width'].max()\n", + "\n", + "for subgraph_size in range(1, circuit_width+1):\n", + " wdx = data_zbasis['Width']==subgraph_size\n", + " res_df[wdx]\n", + " \n", + " df.append(df2, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "circuit_width = res_df['Width'].max()\n", + "circuit_depth = res_df['Depth'].max()\n", + "results = []\n", + "for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", + " wdx = data_zbasis['Width']==subgraph_size\n", + " ddx = data_zbasis['Depth']==depth\n", + " ndf= res_df[wdx&ddx].copy()\n", + " results.append({'Depth': depth,\n", + " 'Width': subgraph_size,\n", + " 'In X basis': ndf['In X basis'].iloc[0],\n", + " 'Active Reset': ndf['Active Reset'].iloc[0],\n", + " 'Trials': ndf['Trials'].iloc[0],\n", + " 'Hamming dist. data': ndf['Hamming dist. data'].mean(),\n", + " 'Hamming dist. rand': ndf['Hamming dist. rand'].mean(),\n", + " 'Hamming dist. ideal': ndf['Hamming dist. ideal'].mean(),\n", + " 'TVD(data, ideal)': ndf['TVD(data, ideal)'].mean(),\n", + " 'TVD(data, rand)': ndf['TVD(data, rand)'].mean(),\n", + " 'Pr. success data': ndf['Pr. success data'].mean(),\n", + " 'Pr. success rand': ndf['Pr. success rand'].mean(),\n", + " 'loge = basement[log_2(Width)-1]': ndf['loge = basement[log_2(Width)-1]'].mean(),\n", + " 'Pr. success loge data': ndf['Pr. success loge data'].mean(),\n", + " 'Pr. success loge rand': ndf['Pr. success loge rand'].mean(),\n", + " }) \n", + "munged = pd.DataFrame(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthHamming dist. dataHamming dist. idealHamming dist. randIn X basisPr. success dataPr. success loge dataPr. success loge randPr. success randTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False1[0.9251000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925100.925100.50000.50000.0374500.462550100010.0
1False1[0.8674, 0.12184999999999999, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.867400.867400.25000.25000.1272250.622775100020.0
2False1[0.73105, 0.21615, 0.046950000000000006, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.731050.731050.12500.12500.2660250.608975100030.0
3False1[0.7171500000000001, 0.23810000000000003, 0.03...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.717150.955250.31250.06250.2826750.675675100041.0
4False2[0.9201, 0.0][1.0, 0.0][0.5, 0.5]False0.920100.920100.50000.50000.0399500.460050100010.0
5False2[0.8482000000000003, 0.1441, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.848200.848200.25000.25000.1479500.602050100020.0
6False2[0.7371000000000001, 0.21269999999999997, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.737100.737100.12500.12500.2596750.615325100030.0
7False2[0.67555, 0.24490000000000003, 0.0602499999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.675550.920450.31250.06250.3220000.645700100041.0
8False3[0.9037499999999999, 0.0][1.0, 0.0][0.5, 0.5]False0.903750.903750.50000.50000.0481250.451875100010.0
9False3[0.8446999999999999, 0.14550000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.844700.844700.25000.25000.1504000.599600100020.0
10False3[0.75855, 0.20669999999999997, 0.0327500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.758550.758550.12500.12500.2404500.634550100030.0
11False3[0.6030999999999999, 0.2619, 0.103649999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.603100.865000.31250.06250.3947000.581100100041.0
12False4[0.9255500000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925550.925550.50000.50000.0372250.462775100010.0
13False4[0.8305999999999999, 0.15719999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.830600.830600.25000.25000.1633000.586700100020.0
14False4[0.76205, 0.19485000000000002, 0.0389500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.762050.762050.12500.12500.2358750.639125100030.0
15False4[0.5921999999999998, 0.26195, 0.10720000000000...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.592200.854150.31250.06250.4059000.565650100041.0
16False5[0.9231000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.923100.923100.50000.50000.0384500.461550100010.0
17False5[0.85725, 0.13285000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.857250.857250.25000.25000.1378000.612200100020.0
18False5[0.7151500000000002, 0.23395000000000002, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.715150.715150.12500.12500.2831250.592275100030.0
19False5[0.5072000000000001, 0.29245, 0.14304999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.507200.799650.31250.06250.4882250.505625100041.0
20False6[0.9045, 0.0][1.0, 0.0][0.5, 0.5]False0.904500.904500.50000.50000.0477500.452250100010.0
21False6[0.8439, 0.14684999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.843900.843900.25000.25000.1514750.598525100020.0
22False6[0.7076000000000001, 0.23464999999999997, 0.05...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.707600.707600.12500.12500.2908500.592950100030.0
23False6[0.54185, 0.28845, 0.12315000000000001, 0.0422...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.541850.830300.31250.06250.4560000.537950100041.0
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Hamming dist. data \\\n", + "0 False 1 [0.9251000000000001, 0.0] \n", + "1 False 1 [0.8674, 0.12184999999999999, 0.0] \n", + "2 False 1 [0.73105, 0.21615, 0.046950000000000006, 0.0] \n", + "3 False 1 [0.7171500000000001, 0.23810000000000003, 0.03... \n", + "4 False 2 [0.9201, 0.0] \n", + "5 False 2 [0.8482000000000003, 0.1441, 0.0] \n", + "6 False 2 [0.7371000000000001, 0.21269999999999997, 0.04... \n", + "7 False 2 [0.67555, 0.24490000000000003, 0.0602499999999... \n", + "8 False 3 [0.9037499999999999, 0.0] \n", + "9 False 3 [0.8446999999999999, 0.14550000000000002, 0.0] \n", + "10 False 3 [0.75855, 0.20669999999999997, 0.0327500000000... \n", + "11 False 3 [0.6030999999999999, 0.2619, 0.103649999999999... \n", + "12 False 4 [0.9255500000000001, 0.0] \n", + "13 False 4 [0.8305999999999999, 0.15719999999999998, 0.0] \n", + "14 False 4 [0.76205, 0.19485000000000002, 0.0389500000000... \n", + "15 False 4 [0.5921999999999998, 0.26195, 0.10720000000000... \n", + "16 False 5 [0.9231000000000001, 0.0] \n", + "17 False 5 [0.85725, 0.13285000000000002, 0.0] \n", + "18 False 5 [0.7151500000000002, 0.23395000000000002, 0.04... \n", + "19 False 5 [0.5072000000000001, 0.29245, 0.14304999999999... \n", + "20 False 6 [0.9045, 0.0] \n", + "21 False 6 [0.8439, 0.14684999999999998, 0.0] \n", + "22 False 6 [0.7076000000000001, 0.23464999999999997, 0.05... \n", + "23 False 6 [0.54185, 0.28845, 0.12315000000000001, 0.0422... \n", + "\n", + " Hamming dist. ideal Hamming dist. rand \\\n", + "0 [1.0, 0.0] [0.5, 0.5] \n", + "1 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "2 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "3 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "4 [1.0, 0.0] [0.5, 0.5] \n", + "5 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "6 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "7 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "8 [1.0, 0.0] [0.5, 0.5] \n", + "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "11 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "12 [1.0, 0.0] [0.5, 0.5] \n", + "13 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "14 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "15 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "16 [1.0, 0.0] [0.5, 0.5] \n", + "17 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "18 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "19 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "20 [1.0, 0.0] [0.5, 0.5] \n", + "21 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "22 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "23 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "\n", + " In X basis Pr. success data Pr. success loge data \\\n", + "0 False 0.92510 0.92510 \n", + "1 False 0.86740 0.86740 \n", + "2 False 0.73105 0.73105 \n", + "3 False 0.71715 0.95525 \n", + "4 False 0.92010 0.92010 \n", + "5 False 0.84820 0.84820 \n", + "6 False 0.73710 0.73710 \n", + "7 False 0.67555 0.92045 \n", + "8 False 0.90375 0.90375 \n", + "9 False 0.84470 0.84470 \n", + "10 False 0.75855 0.75855 \n", + "11 False 0.60310 0.86500 \n", + "12 False 0.92555 0.92555 \n", + "13 False 0.83060 0.83060 \n", + "14 False 0.76205 0.76205 \n", + "15 False 0.59220 0.85415 \n", + "16 False 0.92310 0.92310 \n", + "17 False 0.85725 0.85725 \n", + "18 False 0.71515 0.71515 \n", + "19 False 0.50720 0.79965 \n", + "20 False 0.90450 0.90450 \n", + "21 False 0.84390 0.84390 \n", + "22 False 0.70760 0.70760 \n", + "23 False 0.54185 0.83030 \n", + "\n", + " Pr. success loge rand Pr. success rand TVD(data, ideal) \\\n", + "0 0.5000 0.5000 0.037450 \n", + "1 0.2500 0.2500 0.127225 \n", + "2 0.1250 0.1250 0.266025 \n", + "3 0.3125 0.0625 0.282675 \n", + "4 0.5000 0.5000 0.039950 \n", + "5 0.2500 0.2500 0.147950 \n", + "6 0.1250 0.1250 0.259675 \n", + "7 0.3125 0.0625 0.322000 \n", + "8 0.5000 0.5000 0.048125 \n", + "9 0.2500 0.2500 0.150400 \n", + "10 0.1250 0.1250 0.240450 \n", + "11 0.3125 0.0625 0.394700 \n", + "12 0.5000 0.5000 0.037225 \n", + "13 0.2500 0.2500 0.163300 \n", + "14 0.1250 0.1250 0.235875 \n", + "15 0.3125 0.0625 0.405900 \n", + "16 0.5000 0.5000 0.038450 \n", + "17 0.2500 0.2500 0.137800 \n", + "18 0.1250 0.1250 0.283125 \n", + "19 0.3125 0.0625 0.488225 \n", + "20 0.5000 0.5000 0.047750 \n", + "21 0.2500 0.2500 0.151475 \n", + "22 0.1250 0.1250 0.290850 \n", + "23 0.3125 0.0625 0.456000 \n", + "\n", + " TVD(data, rand) Trials Width loge = basement[log_2(Width)-1] \n", + "0 0.462550 1000 1 0.0 \n", + "1 0.622775 1000 2 0.0 \n", + "2 0.608975 1000 3 0.0 \n", + "3 0.675675 1000 4 1.0 \n", + "4 0.460050 1000 1 0.0 \n", + "5 0.602050 1000 2 0.0 \n", + "6 0.615325 1000 3 0.0 \n", + "7 0.645700 1000 4 1.0 \n", + "8 0.451875 1000 1 0.0 \n", + "9 0.599600 1000 2 0.0 \n", + "10 0.634550 1000 3 0.0 \n", + "11 0.581100 1000 4 1.0 \n", + "12 0.462775 1000 1 0.0 \n", + "13 0.586700 1000 2 0.0 \n", + "14 0.639125 1000 3 0.0 \n", + "15 0.565650 1000 4 1.0 \n", + "16 0.461550 1000 1 0.0 \n", + "17 0.612200 1000 2 0.0 \n", + "18 0.592275 1000 3 0.0 \n", + "19 0.505625 1000 4 1.0 \n", + "20 0.452250 1000 1 0.0 \n", + "21 0.598525 1000 2 0.0 \n", + "22 0.592950 1000 3 0.0 \n", + "23 0.537950 1000 4 1.0 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "munged" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.54185, 0.28845, 0.12315, 0.04225, 0. ])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_df[wdx&ddx]['Hamming dist. data'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0625, 0.25 , 0.375 , 0.25 , 0.0625])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_df[wdx&ddx]['Hamming dist. rand'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot a particular depth and width" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dep = 6\n", + "wid = 4\n", + "\n", + "distz = get_hamming_dist(res_df, dep, wid)\n", + "\n", + "\n", + "# combine data from different subgraphs\n", + "avg_dist = distz['Hamming dist. data'].mean()\n", + "\n", + "# rand data\n", + "rand_dist = distz['Hamming dist. rand'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_labels = np.arange(0, len(avg_dist))\n", + "plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + "plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + "plt.xticks(x_labels)\n", + "plt.xlabel('Hamming Weight of Error')\n", + "plt.ylabel('Relative Frequency of Occurence')\n", + "plt.ylim([0,1])\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.legend(['data','random'])\n", + "plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For a particular width plot all depths" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "wid = 4\n", + "df_fn_depth = get_hamming_dists_fn_depth(res_df, wid)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for dep in range(1, df_fn_depth.Depth.max()+1):\n", + " idx = df_fn_depth['Depth']== dep\n", + " avg_dist = df_fn_depth[idx]['Hamming dist. data'].mean() \n", + " rand_dist = df_fn_depth[idx]['Hamming dist. rand'].mean() \n", + " x_labels = np.arange(0, len(avg_dist))\n", + " plt.subplot(1,df_fn_depth.Depth.max(),dep)\n", + " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + " plt.xticks(x_labels)\n", + " plt.xlabel('Hamming Weight of Error')\n", + " plt.ylabel('Relative Frequency of Occurence')\n", + " plt.ylim([0,1])\n", + " plt.grid(axis='y', alpha=0.75)\n", + " plt.legend(['data','random'])\n", + " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data fron the data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "depth_vec = []\n", + "pcheck = []\n", + "pcheck_rand = []\n", + "pcheck_log_errors = []\n", + "pcheck_log_errors_rand = []\n", + "tvd_rand = []\n", + "tvd_ideal = []\n", + "\n", + "for dep in range(1, df_fn_depth.Depth.max()+1):\n", + " idx = df_fn_depth['Depth']== dep\n", + " depth_vec.append(dep)\n", + " pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", + " pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", + " pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", + " pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", + " tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", + " tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Success probablity and success probablity including a small number of errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will plot the success probablity of a circuit with a certain width as a function of depth. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", + "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Pr(success)')\n", + "plt.title('Pr(success) vs Depth for Width = {}'.format(wid))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sucess if we allow for a small number of errors**\n", + "\n", + "Some near term algorithms have robustness to noise. In light of that we might want to consider answers that are only a little wrong successes.\n", + "\n", + "To make this notion formal we allow a logarithmic number of bits to flip from the correct answer and call all such instances \"success\".\n", + "\n", + "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", + "\n", + "$\\log_2 ({\\rm number\\ of\\ bits}) -1$\n", + "\n", + "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", + "\n", + "\n", + "Supose we have a circuit of width 4, this means correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4)-1 = 1$.\n", + "\n", + "So any string with hamming weight zero or one counts as a success.\n", + "\n", + "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UlPX8+fMuR48ebZGQkNAtMjLSGhEREf3ll1+2cs4dp/LUZzXlOgAzRGQNgD4AclU1ux7jISKiapjz8e6O+0/l35A43AxLe+9LL4+NO17ZPseOHWv5zjvv/DBkyJAjAPCHP/zhRLt27UqKi4vRv3//btu3b/fs06dPIQAEBAQUZ2Zm7l24cGHgwoUL23300UdH586dG9yvX7+CV155JXvNmjWt165dGwAA33zzjdfq1av909LS9qoq4uPjo4YMGZIfEBBQcvz4cY+PPvrocHx8/JHY2NioDz74wD81NTVr9erVbebPnx80aNCgQ/Yxvvzyy4Ft2rQpOXToUMa3337r0a9fv+iqrv3Xv/51x1mzZv00bNiwggMHDrgPGzYs4vDhwxmLFy9uv2TJkqNDhw69mJub6+Ll5VX62muvBQ4ZMiR30aJFp4qLi5Gfn98oWpKaCmcOVfAhgH8D6CYiP4rIwyLyGxH5jblLMoDDAA4C+AuA3zorFiIiajqCgoKuDhky5KJt+d133/WzWq1RVqvVeuDAAY/du3d72LZNmjTpPAAkJiZeOn78eEsA2LZtm/dDDz10FgAmTJiQ6+PjUwIAmzdvbjVixIgLPj4+pa1bty4dOXLk+U2bNnkDQEhIyJXExMRCV1dXWCyWwsGDB+e5uLigV69el3788ceWZWPcunVrq4kTJ54DgN69e1+2WCyXqrquLVu2+Pzud7/rFBkZab3nnnvCCwoKXHNzc1369u1bMHv27I4vvPBC2zNnzri2aNECffv2vfjhhx8GzJo1K3jHjh2evr6+pTd3V6k6nFbzpKoTq9iuAP7TWecnIiLnqqqGyFm8vLyuJQpZWVnub7zxRru0tLS9gYGBJWPGjAm9fPnytYoBDw8PBQA3NzctLi6Wmp7T3d39Wtuei4vLtXJdXV1RUlJSrXJFft69sLDw2oKqYufOnXu9vLyuGwpiwYIFp0aPHp372Weftb711lsj169ff2D48OEFKSkp+/7617+2fuihh7rMmDHjpxkzZpyt6fVR9TS/ar7v1wKvxgDPtjH+/X5tfUdEREQ1dP78eVdPT89SPz+/kuPHj7tt3ry5dVXH9O3bN3/lypX+ALB27VqfvLw8VwAYNGhQQXJycpv8/HyXvLw8l+TkZN9Bgwbl1ySufv36FaxZs8YXANLS0jz279/vadvm7+9ftHPnTo+SkhJ89tlnvrb1AwcOzHvxxRfb2pa3bt3qCQAZGRktExMTC+fPn38qNjb2Ynp6usf+/fvdO3ToUPT444+fSUpKytm5c2etNp9S5ZrXo5nfrwU+fwwoKjSWc48bywAQO67+4iIiohrp169fYUxMzKWwsLCYoKCgq/Hx8QVVHbNw4cKTY8aM6RoeHh6dkJBQEBQUdBUABg4ceGnSpElne/XqFQUAU6ZMyRkwYEDhvn373Ksb15w5c3LGjRsXGhYWFh0WFnY5PDz8sq+vbwkAPPfccyfuvffecD8/v+K4uLhLFy9edAGAt99++/gjjzzSyWKxWEtKSqRPnz75/fv3P/bSSy+13bp1q4+IaLdu3QrHjh2bu2zZMr8lS5a0d3NzUy8vr5IPPvjgh+rGSDUn9k8ZNAYJCQmamppas4NfjTESprJadwRmljuLDBFRkyAiaaqacLPl7N69+0hcXNyZ2oipKSsuLsbVq1fFy8tLMzIyWg4dOtRy6NChdFtzHzV8u3fvDoiLiwstb1vzqnnK/bF664mIiGogPz/f5dZbb+1WVFQkqopXX331KBOnpqN5JU+tO1RQ89Sh7mMhIqImy9fXtzQ9PX1vfcdBztG8OowPeRpo4Xn9uhaexnoiIiIiBzSv5Cl2HHDPEqOPE8T4954l7CxOREREDmtezXaAkSg1p2Tp+7XAP+YZ/bpadzBq2ZrT9RMREdWy5pc8NSccmoGIiKjWNa9mu+bmH/N+TpxsigqN9UREBMCYQDg7O7tZVyZ88cUX3oMGDQqv7zgaCyZPTRmHZiCiJqy0tBQlJSX1HUajEBIS0r2+Y7BXXFx83XJRUZFDxzm6n7MxeWrKKhqCgUMzEFEjtW/fPvfQ0NCYX/7yl6EWiyX60KFD7pMnT+4UExMTFR4eHj1z5sxg274hISHdZ86cGWy1WqMsFot1165dHgBw6tQp1wEDBkSEh4dHjx8/vrP9YNHPPvtsu4iIiOiIiIjoefPmtbWds0uXLtFjxowJDQ0NjRk1alSXTz/91LtXr16RnTt3jtm0adMNU6Pk5+e7jBgxomtYWFj0nXfeGRYbGxuZkpLiBQBeXl49bfutWLHCd8yYMaEAcPLkSbdhw4aFxcTERMXExER99dVXtwDA+vXrW0VGRlojIyOtUVFR1vPnz7scPXq0RUJCQrfIyEhrRERE9Jdfftmqtu7xTz/95HrHHXeEWSwWa1xcXOT27ds9bfH179//2n0LDg4ut8buk08+8enRo0ek1WqNGj58eNfc3FwX2+cxffr0EKvVGrV8+XLfxMTEbg899FDHmJiYqBdeeKHdvn373Pv27WuxWCzWfv36WQ4cOOAOAGPGjAmdNGlSp9jY2Mjp06d3KO9+1Na1O6pZV1M2eUOevr7PE8ChGYio9nz6nx1xOrN251Rra72E0X+qdMLhY8eOtXznnXd+GDJkyBEA+MMf/nCiXbt2JcXFxejfv3+37du3e/bp06cQAAICAoozMzP3Lly4MHDhwoXtPvroo6Nz584N7tevX8Err7ySvWbNmtZr164NAIBvvvnGa/Xq1f5paWl7VRXx8fFRQ4YMyQ8ICCg5fvy4x0cffXQ4Pj7+SGxsbNQHH3zgn5qamrV69eo28+fPDxo0aNAh+xhffvnlwDZt2pQcOnQo49tvv/Xo169fdFWX/utf/7rjrFmzfho2bFjBgQMH3IcNGxZx+PDhjMWLF7dfsmTJ0aFDh17Mzc118fLyKn3ttdcChwwZkrto0aJTxcXFyM/Pr7UE4oknngiOi4u7tHHjxkPr1q3znjp1apesrKzMuXPnBt922235L7744qmPP/7Yx3bf7GVnZ7stWLAgKCUlZb+Pj0/pU0891f75559v98orr2QDgL+/f3FmZuZeAFi2bFnbq1evim08rMGDB4dPnjz57KOPPnr2tdde858+fXrHjRs3HjLLdd+5c2eWm5sbBg8eHF72ftTWtTuKNU9NGYdmIKImKCgo6OqQIUMu2pbfffddP6vVGmW1Wq0HDhzw2L17t4dt26RJk84DQGJi4qXjx4+3BIBt27Z5P/TQQ2cBYMKECbk+Pj4lALB58+ZWI0aMuODj41PaunXr0pEjR57ftGmTNwCEhIRcSUxMLHR1dYXFYikcPHhwnouLC3r16nXpxx9/bFk2xq1bt7aaOHHiOQDo3bv3ZYvFcqmq69qyZYvP7373u06RkZHWe+65J7ygoMA1NzfXpW/fvgWzZ8/u+MILL7Q9c+aMa4sWLdC3b9+LH374YcCsWbOCd+zY4enr63tDAjFlypROthqa06dPt7C9f/LJJ9tXFseOHTu8H3744bMAMGrUqPwLFy64nTt3zmXHjh2tpk6deg4Axo4dm2e7b/Y2b958y6FDhzwSExMjIyMjrWvWrPE/duzYtbkBk5KSztvvb7tHALBr165bpk2bdg4Apk+ffi4tLe1abdp999133s3NqO8p737UNdY8NXXNbWgGIqo7VdQQOYt9TUNWVpb7G2+80S4tLW1vYGBgyZgxY0IvX758rWLANiWKm5ubFhcXS03P6e7ufq1tz8XF5Vq5rq6uKCkpqVa5Ij/vXlhYeG1BVbFz5869Xl5e103jsmDBglOjR4/O/eyzz1rfeuutkevXrz8wfPjwgpSUlH1//etfWz/00ENdZsyY8dOMGTPO2h+3atWqY7b3ISEh3bOysjKrE2dNqCoGDhyY9/nnn5c7UbG3t3dpZcsVadWq1bX9yrsfPXv2vHxzkVcPa56IiKjROn/+vKunp2epn59fyfHjx902b97cuqpj+vbtm79y5Up/AFi7dq1PXl6eKwAMGjSoIDk5uU1+fr5LXl6eS3Jysu+gQYPyaxJXv379CtasWeMLAGlpaR779++/Nr2Fv79/0c6dOz1KSkrw2Wef+drWDxw4MO/FF19sa1veunWrJwBkZGS0TExMLJw/f/6p2NjYi+np6R779+9379ChQ9Hjjz9+JikpKWfnzp211nzap0+f/BUrVvgDxlN4vr6+xX5+fqW9e/cuWLVqlR9g9Guy3Td7t99++8XU1NRW6enpLQEgLy/P5fvvv7+hZq48PXv2vLhs2TJfAFi6dKlfQkJCQXn7lXc/anqtNcWaJyIiarT69etXGBMTcyksLCwmKCjoanx8fLlfuPYWLlx4csyYMV3Dw8OjExISCoKCgq4CwMCBAy9NmjTpbK9evaIAYMqUKTkDBgwo3Ldvn3vlJd5ozpw5OePGjQsNCwuLDgsLuxweHn7Z19e3BACee+65E/fee2+4n59fcVxc3KWLFy+6AMDbb799/JFHHulksVisJSUl0qdPn/z+/fsfe+mll9pu3brVR0S0W7duhWPHjs1dtmyZ35IlS9q7ubmpl5dXyQcffFBuTU9NLFq06OTkyZNDLRaL1dPTs3TlypU/2O7b2LFju0ZERPjHx8cXBAQEFLVp0+a6prvg4ODipUuXHpkwYULXq1evCgA888wzJ2JjY69Udd633nrrWFJSUugf//jH9v7+/sXvvffekfL2K+9+1MJlV4vYP2XQGCQkJGhqamp9h0FE1KiISJqqJtxsObt37z4SFxd3pjZiasqKi4tx9epV8fLy0oyMjJZDhw61HDp0KN3W3NcYFRYWipubm7Zo0QIbN268ZcaMGZ3roimwvuzevTsgLi4utLxtrHmipoXT0RBRA5Cfn+9y6623disqKhJVxauvvnq0MSdOAHDw4EH3cePGhZWWlqJFixa6dOnSI/UdU31h8kRNB6ejIaIGwtfXt9T2CH5T0b179yt79+5tsjVN1cEO49R0NLfpaL5fC7waAzzbxvj3+7X1HRERUbPAmidqOprTdDSsZSMiqjeseaKmozlNR9PcatmIiBoQJk/UdAx52ph+xl5TnY6mOdWyERE1MEyeqOloTtPRNKdaNqIynnzyyfbh4eHRFovFGhkZaf36669vqe+Y9u3b5+7h4dErMjLSGhYWFj1p0qROJSU3zF5S6fERERFVzn9nLzExsZttsmF7S5Ys8U9KSuoEAC+99FLgG2+84W9bf+TIkbqfy6QJYp8nalqay3Q0nPS56eOwG+XauHHjLRs2bGizZ8+eTE9PT83Ozna7cuVKjaddqU0dO3a8kpWVlVlUVIR+/fp1e//999tMnTr1gm17UVER6noetieeeCLH9v79998P6NGjR2FoaGhRnQbRBLHmiagxak61bM2R7YGA3OMA9OcHAhrhE5Xvbzvqlzh/Y/cuc9fHJ87f2P39bUf9bqa8EydOtPDz8yv29PRUAAgKCiq2JQMhISHds7Oz3QAgJSXFKzExsRsA5ObmuowdOzbUYrFYLRaLdeXKlW0AY4qRHj16RFqt1qjhw4d3zc3NdQGA3/72tyFhYWHRFovFOm3atA4AsHz5ct+IiIjobt26WRMSErpVFmOLFi2QmJhYcODAgZZffPGFd3x8fLfBgweHR0RExADAs88+2y4iIiI6IiIiet68edemYykuLsaoUaO6dO3aNfquu+7qmp+f7wIAs2fPDoqJiYmKiIiInjhxYufS0p+ng1uxYoV/ZGSkNSIiInrTpk031ELNmjUr+Omnn263YsUK3/T0dK+kpKSu5oS9re+4444w235/+9vffO68886wssdT+Zg8ETVWseOAmenAsxeMf5k4NR1N5IGA97cd9Xv+i8zOp/OvuCuA0/lX3J//IrPzzSRQo0ePzjt58qR7aGhozAMPPNBp/fr1rao6Zu7cuUE+Pj4l+/fvz9y/f3/myJEj87Ozs90WLFgQlJKSsj8zM3Nvr169Lj3//PPtTp065ZqcnOx74MCBjP3792cuWLAgGwAWLlwY9NVXX+3ft29f5pdffnmwsvPl5+e7pKSk+MTGxhYCQGZmptebb7557MiRI+nffPON1+rVq/3T0tL2pqam7n3vvffBUVJPAAAgAElEQVQCt2zZ4gkAR44c8ZgxY8bpw4cPZ3h7e5e+/PLLgQAwZ86c0+np6XsPHDiQUVhY6LJmzZpr8/cVFha6ZGVlZS5ZsuTotGnTulQU04MPPng+Jibm0nvvvXc4Kysrc9y4cbmHDh3yOHnypBsALF++3P/BBx/kyPEOYvJERNTQNJEHApb840DIleLS675nrhSXuiz5x4GQmpbZunXr0vT09Mw33njjaGBgYPHUqVPDlixZ4l/ZMSkpKT4zZ848bVsODAws2bx58y2HDh3ySExMjDRrYvyPHTvm7u/vX9KyZcvS8ePHh7777rttWrVqVQoACQkJBZMnTw5dvHhxQHFxcbnnOX78eMvIyEhrYmJi5NChQ3PHjRuXBwCxsbEXIyMjrwLA5s2bW40YMeKCj49PaevWrUtHjhx5ftOmTd4A0L59+6tDhw69CABTpkw5u3Xr1lYA8Pe//907NjY20mKxWLdu3eqdnp5+7cmYSZMmnQOA4cOHFxQUFLicOXPmhsl6y+Pi4oJx48ad/ctf/uJ35swZ1507d7a6//7763yOuMaKfZ6IiBqa1h3MJrty1jciOflXyp1Qt6L1jnJzc8Pdd9+df/fdd+fHxsYWrlq1yv+xxx476+rqqrYmrcLCwkorB1QVAwcOzPv8889vmFD3u+++27tu3Tqfjz/+2PfPf/5z223btu1fvXr1sa+//vqWdevWtY6Pj7empaVltm/f/roe4bY+T2XL8/LyKi27rjwicsPypUuX5PHHH++8ffv2zPDw8KJZs2YFX7582aWyYxw1ffr0syNHjgz38PDQe+6553xd98dqzFjzRETU0DSRYTcCvVterc56R+zevbvlnj17WtqWd+3a5dmhQ4erANChQ4erW7Zs8QKAtWvX+tr2ue222/JeffXVa32LcnJyXG+//faLqamprdLT01sCQF5ensv333/fMjc31+XcuXOu48ePz33rrbeOZ2VleQFARkZGy8GDB1987bXXTvr6+hYfPny4RgngoEGDCpKTk9vk5+e75OXluSQnJ/sOGjQoHwCys7PdN27ceAsAfPDBB379+/cvuHTpkgsAtG/fvjg3N9fl888/97Uv78MPP/QFgA0bNrTy9vYu8ff3r/ARv1atWpXk5uZeq5kKDQ0tateuXdHixYuDpk2bxia7anBq8iQid4nIPhE5KCJzy9neSUQ2icguEfleREY4Mx4iasSa03Q0TeSBgMeGRJxo6eZyXa1LSzeX0seGRJyoaZl5eXmuSUlJXWwdurOysjwXLVp0EgCefvrpk0888USnmJiYKFdX12uT8L744ovZFy5ccLV1+E5OTvYODg4uXrp06ZEJEyZ0tVgs1oSEhMg9e/Z4XLhwwfWuu+6KsFgs1n79+nV7/vnnjwPAzJkzO1gsFmtERER07969C/r27VtYUYyVGThw4KVJkyad7dWrV1R8fHzUlClTcgYMGFAIAKGhoZdff/31tl27do2+cOGC2+zZs3MCAgJKJk+enBMVFRU9aNAgS1xc3EX78jw8PDQqKso6Y8aMzlVN1JuUlHTm0Ucf7RwZGWktKCgQAJgwYcLZoKCgq7169bpck+tprkTVOZM8i4grgP0A7gTwI4BvAUxU1Uy7fd4GsEtV/ywiVgDJqhpaWbkJCQmamprqlJiJqIEqOx0NYNTENMKEor6ISJqqJtxsObt37z4SFxfncC3F+9uO+i35x4GQnPwr7oHeLa8+NiTixAN9O5+72TiodiQlJXXq2bPnpZkzZ7LmqYzdu3cHxMXFhZa3zZl9nhIBHFTVwwAgImsA3AvAvj1YAfiY71sDOOnEeIiosars6TMmTw3aA307n2Oy1DBFR0dHeXp6li5durScDnZUGWcmTyEA7D+QHwH0KbPPswC+EpFHAdwC4I7yChKRaQCmAUCnTp1qPVAiauCayNNnRA1JRkbG3vqOobGq7w7jEwGsVNUOAEYAWCUiN8Skqm+raoKqJgQGBtZ5kERUzzgdDRE1IM5Mnk4A6Gi33MFcZ+9hAGsBQFX/DcADQIATYyKixqiJPH1GRE2DM5OnbwFEiEgXEXEHMAHAujL7HAMwBABEJApG8pQDIiJ7TeTpMyJqGpzW50lVi0VkBoANAFwBLFfVDBGZByBVVdcBeBzAX0RkJozO479SZz3+R0SNW3OZ9JmIGrxKa55EpIOIzBaRz0TkWxFJEZE3RWRkeX2TylLVZFW1qGqYqs431z1tJk5Q1UxVHaCqcaraQ1W/qp3LIiKipurJJ59sHx4eHm2xWKyRkZHWr7/++pb6jmnfvn3uHh4evSIjI61hYWHRkyZN6lRSUuF4leUeHxEREV2dcyYmJnZLSUm5YTLgJUuW+CclJXUCgJdeeinwjTfe8LetP3LkiNOGEf/iiy+8Bw0aFO6s8huSCmueRGQFjCfmvgCwCMBpGM1qFgB3AXhKROaqakpdBEpERLRx48ZbNmzY0GbPnj2Znp6emp2d7XblyhXH5yRxItv0LEVFRejXr1+3999/v83UqVMv2LYXFRWhrqdAeeKJJ651hXn//fcDevToURgaGlpUk7JCQkK6nzhxYk/tRXdziouL4eb2cxrj6P2tjc+hstqjxao6VFWXqOpWVT2oqumq+omqPgrgdnBcJiIiqsy37/jhFUt3PNsmHq9YuuPbd/xuprgTJ0608PPzK/b09FQACAoKKrYlAyEhId2zs7PdACAlJcUrMTGxGwDk5ua6jB07NtRisVgtFot15cqVbQDgk08+8enRo0ek1WqNGj58eNfc3FwXAPjtb38bYhvBfNq0aR0AYPny5b62EcoTEhK6VRZjixYtkJiYWHDgwIGWX3zxhXd8fHy3wYMHh0dERMQAwLPPPtsuIiIiOiIiInrevHnXpo0pLi7GqFGjunTt2jX6rrvu6pqfn+8CALNnzw6KiYmJioiIiJ44cWJn2/x9ALBixQr/yMhIa0RERPSmTZtuqIWaNWtW8NNPP91uxYoVvunp6V5JSUldzYmQW99xxx1htv3+9re/+dx5551hZY+vqZ9++sn1jjvuCLNYLNa4uLjI7du3ewLAyZMn3fr37x8RHh4ePX78+M7BwcHXPjN7FX02ISEh3adPnx5itVqjli9f7puYmNjtoYce6hgTExP1wgsvtNu3b5973759LeYI8ZYDBw64A8CYMWNCJ02a1Ck2NjZy+vTpHdavX98qMjLSGhkZaY2KirKeP3++Wn3AK9xZVdPLrhMRXxGJNbdfVdWD1TkZERE1I9++44cNv++Mgp/cAQUKfnLHht93vpkEavTo0XknT550Dw0NjXnggQc6rV+/vlVVx8ydOzfIx8enZP/+/Zn79+/PHDlyZH52drbbggULglJSUvZnZmbu7dWr16Xnn3++3alTp1yTk5N9Dxw4kLF///7MBQsWZAPAwoULg7766qv9+/bty/zyyy8r/e7Lz893SUlJ8YmNjS0EgMzMTK8333zz2JEjR9K/+eYbr9WrV/unpaXtTU1N3fvee+8FbtmyxRMAjhw54jFjxozThw8fzvD29i59+eWXAwFgzpw5p9PT0/ceOHAgo7Cw0GXNmjWtbecqLCx0ycrKylyyZMnRadOmdakopgcffPB8TEzMpffee+9wVlZW5rhx43IPHTrkcfLkSTcAWL58uf+DDz5Ya6OMP/HEE8FxcXGX9u/fn/n888+fmDp1ahcAmDt3bvBtt92Wf/DgwYz777//fHZ29g1zBFb02di2+/v7F2dmZu6dNm3aeQC4evWqpKen733uued+mj59eqfJkyef3b9/f+b48ePPTp8+vaNdue47d+7MWrZs2Y+LFy9uv2TJkqNZWVmZ27Zty2rVqpVDkzfbVJlpichmEfERET8AO2F08H61OichIqJm6J+LQlB85frvmeIrLvjnopCaFtm6devS9PT0zDfeeONoYGBg8dSpU8OWLFniX9kxKSkpPjNnzjxtWw4MDCzZvHnzLYcOHfJITEyMNGti/I8dO+bu7+9f0rJly9Lx48eHvvvuu21sX6oJCQkFkydPDl28eHFAcXFxuec5fvx4y8jISGtiYmLk0KFDc8eNG5cHALGxsRcjIyOvAsDmzZtbjRgx4oKPj09p69atS0eOHHl+06ZN3gDQvn37q0OHDr0IAFOmTDm7devWVgDw97//3Ts2NjbSYrFYt27d6p2enn5t3I5JkyadA4Dhw4cXFBQUuJw5c8a1bFzlcXFxwbhx487+5S9/8Ttz5ozrzp07W91///25ZfebMmVKJ1sNzenTp1vY3j/55JPtKyt/x44d3g8//PBZABg1alT+hQsX3M6dO+eyY8eOVlOnTj0HAGPHjs3z8fG5oWNYRZ+NbXtSUtJ5+/0nTpx4bQT7Xbt23TJt2rRzADB9+vRzaWlp15Lr++6777ytma9v374Fs2fP7vjCCy+0PXPmjGt1m/EcedqutarmicgjAN5T1WdE5PtqnYWIiJqfgtM31CpUut5Bbm5uuPvuu/Pvvvvu/NjY2MJVq1b5P/bYY2ddXV3V1qRVWFhYaeWAqmLgwIF5n3/++Q9lt3333Xd7161b5/Pxxx/7/vnPf267bdu2/atXrz729ddf37Ju3brW8fHx1rS0tMz27dtf98Vv6/NUtjwvLy+HajVE5IblS5cuyeOPP955+/btmeHh4UWzZs0Kvnz5sktlxzhq+vTpZ0eOHBnu4eGh99xzz/nyEohVq1Yds70PCQnpXt711bbKPhsA8Pb2Lq1suSL2tUsLFiw4NXr06NzPPvus9a233hq5fv36Az179nR4cmRH2vjcRCQIwDgYnceJiIiq1qrt1Wqtd8Du3btb7tmzp6VtedeuXZ4dOnS4CgAdOnS4umXLFi8AWLt2ra9tn9tuuy3v1Vdfvda3KCcnx/X222+/mJqa2io9Pb0lAOTl5bl8//33LXNzc13OnTvnOn78+Ny33nrreFZWlhcAZGRktBw8ePDF11577aSvr2/x4cOHa5QADho0qCA5OblNfn6+S15enktycrLvoEGD8gGjWWnjxo23AMAHH3zg179//4JLly65AED79u2Lc3NzXT7//HNf+/I+/PBDXwDYsGFDK29v7xJ/f/8KH/Fr1apVSW5u7rWaqdDQ0KJ27doVLV68OGjatGm1OjFwnz598lesWOEPGE/h+fr6Fvv5+ZX27t27YNWqVX6A0a8pLy/vhpqyij4bR87bs2fPi8uWLfMFgKVLl/olJCQUlLdfRkZGy8TExML58+efio2NvZienu5RnetzJHl6DsZYTQdV9VsR6QrgQHVOQkREzdBtT56AW8vrawXcWpbitifLzjbhsLy8PNekpKQutg7dWVlZnosWLToJAE8//fTJJ554olNMTEyUq6vrtTEDX3zxxewLFy642jp8JycnewcHBxcvXbr0yIQJE7paLBZrQkJC5J49ezwuXLjgetddd0WYHY67Pf/888cBYObMmR0sFos1IiIiunfv3gV9+/YtrCjGygwcOPDSpEmTzvbq1SsqPj4+asqUKTkDBgwoBIDQ0NDLr7/+etuuXbtGX7hwwW327Nk5AQEBJZMnT86JioqKHjRokCUuLu6ifXkeHh4aFRVlnTFjRuelS5ceqezcSUlJZx599NHOkZGR1oKCAgGACRMmnA0KCrraq1cvh2tdHLFo0aKTu3bt8rJYLNannnoqZOXKlT8AwMKFC09+/fXXPhEREdFr1671DQgIKGrTps11CV9Fn40j533rrbeOrVq1KsBisVg//PBD/zfffLPcSY9feumlthEREdEWi8XaokULHTt27A1NlpWRysakFBFXAI+paoPp45SQkKCpqak1Pv7TXSfw8oZ9OHmhEMFtPDFnWDeM7lnj5nciokZBRNJUNeFmy9m9e/eRuLg4x2spvn3HD/9cFIKC0+5o1fYqbnvyBHo/fK7qA6kuJCUlderZs+elmTNn1mrNU0UKCwvFzc1NW7RogY0bN94yY8aMznXRFFgTu3fvDoiLiwstb1ulfZ5UtUREJgJoMMnTzfh01wn8/pM9KCwyktwTFwrx+0+MISuYQBEROUHvh88xWWqYoqOjozw9PUuXLl1abu2MMxw8eNB93LhxYaWlpWjRooVWVVvWUDnSYXyLiLwB4CMA16oLVXWn06Jykpc37LuWONkUFpXg5Q37mDwREVGzkpGRsbeuz9m9e/cre/fubZA1TdXhSPLUw/x3nt06BTC49sNxrpMXym+irmg9ERHdoLS0tFRcXFw4Dyk1WaWlpQKgwqf4qkyeVHVQrUZUj4LbeOJEOYlScBvPcvYmIqJypOfk5FgDAwNzmUBRU1RaWio5OTmtAdwwWLhNlcmTiLQG8AyAX5ir/glgnqpWq2d6QzBnWLfr+jwBgGcLV8wZVulI+0REZCouLn7k1KlTy06dOhUDx57YJmpsSgGkFxcXP1LRDo402y2HkX2NM5enAFgB4L6bDq+O2fo18Wk7IqKaiY+PPw1gVH3HQVSfHEmewlR1jN3ycyLynbMCcrbRPUOYLBEREVGNOZI8FYrIQFX9FwCIyAAA7GHdSHBcKyIiotrlSPL0GwDvmX2fAOA8gKnOC4lqC8e1IiIiqn2VJk8i4gKgm6rGiYgPAKhqXp1ERjeN41oRERHVvqpmnS4F8IT5Po+JU+PCca2IiIhqnyOPmW4Ukdki0lFE/Gwvp0dGN62i8as4rhUREVHNOZI8jQfwnwBSAKSZr5rPzEt1Zs6wbvBs4XrdOo5rRUREdHMc6fP0gKpuqaN4qBZxXCsiIqLaV2nypKql5qTAPesoHqplHNeKiIiodjnSbPcPERkjIuL0aIiIiIgaOEeSp18D+F8AV0UkT0TyRYRP3REREVGzVOUgmarqXReBEBERETUGVdY8ieEBEfkfc7mjiCQ6PzQiIiKihseRZrs3AfQDMMlcLgDwJ6dFRERERNSAOTK3XR9V7SUiuwBAVc+LiLuT4yIiIiJqkBypeSoSEVcACgAiEgig1KlRERERETVQjiRPSwD8DUBbEZkP4F8AFjhSuIjcJSL7ROSgiMytYJ9xIpIpIhkistrhyInK8emuExiw8Gt0mbseAxZ+jU93najvkIiIqIlx5Gm7D0QkDcAQAAJgtKrureo4s7bqTwDuBPAjgG9FZJ2qZtrtEwHg9wAGmM2BbWt4HUT4dNcJ/P6TPSgsKgEAnLhQiN9/sgcAOFAoERHVGkf6PEFVswBkVbPsRAAHVfUwAIjIGgD3Asi02+c/APxJVc+b5zldzXMQXfPyhn3XEiebwqISvLxhX5NMnj7ddYJT7xAR1QNHmu1qKgTAcbvlH8119iwALCKyRUS2ichd5RUkItNEJFVEUnNycpwULjV2Jy8UVmt9Y2arZTtxoRCKn2vZ2ExJROR8zkyeHOEGIALA7QAmAviLiLQpu5Oqvq2qCaqaEBgYWMchUmMR3MazWusbs8pq2YiIyLmcmTydANDRbrmDuc7ejwDWqWqRqv4AYD+MZIqo2uYM6wbPFq7XrfNs4Yo5w7rVU0TO05xq2YiIGhpHRhjPN+e0s38dF5G/iUjXSg79FkCEiHQxx4WaAGBdmX0+hVHrBBEJgNGMd7hGV0LN3uieIXjxvu4IaeMJARDSxhMv3te9SfYDak61bEREDY0jHcZfg1FDtBrG03YTAIQB2AlgOczkpyxVLRaRGQA2AHAFsFxVM0RkHoBUVV1nbhsqIpkASgDMUdWzN3dJ1JyN7hnSJJOlsuYM63bdk4VA061lIyJqaERVK99BZLeqxpVZ952q9ihvm7MlJCRoampqXZ6SqEHi03ZUHSKSpqoJ9R0HUVPgSM3TJREZB+Bjc3ksgMvm+8ozLyJymuZSy0ZE1NA40mF8MoApAE6brykAHhARTwAznBgbERERUYPjyAjjhwHcU8Hmf9VuOEREREQNW5XJk4h0APA6gAHmqm8A/E5Vf3RmYEREzRn7tBE1XI40262AMcRAsPn63FxHREROwBHkiRo2R5KnQFVdoarF5mslAA7zTUTkJBxBnqhhcyR5OisiD4iIq/l6AADHYiIichKOIE/UsDmSPD0EYByAUwCyYQxV8KAzgyIias44gjxRw1Zl8qSqR1V1lKoGqmpbVR2tqsfqIjgiouaoOc3TSNQYVfi0nYi8jkoGwVTVx5wSERFRM2d7qo5P2xE1TJUNVcA5UIiowWhuj+5zBHmihqvC5ElV363LQIiIKmJ7dN/2BJrt0X0ATDCIqM450mEcACAiv3dmIEREFeGj+0TUkDicPAG432lREBFVgo/uE1FDUp3kiYioXvDRfSJqSCpNnkTkBxE5LCI/ALDa3ovI4TqKj4iIj+4TUYNS6cTAqtrF9l5EdqlqT+eHRER0PT66T0QNSaXJExFRQ8FH94mooahOn6ctTouCiIiIqJFwOHlS1RnODISIiIioMagyeRKR+0XE23z/3yLyiYj0cn5oRERERA2PIzVP/6Oq+SIyEMAdAN4B8GfnhkVERETUMDmSPNmG9R0J4G1VXQ/A3XkhERERETVcjiRPJ0RkKYDxAJJFpKWDxxERERE1OY4kQeMAbAAwTFUvAPADMMepURERERE1UI6M8xQEYL2qXhGR2wHEAnjPqVERERERNVCO1Dz9FUCJiIQDeBtARwCrnRoVERERUQPlSPJUqqrFAO4D8LqqzoFRG0VERETU7DiSPBWJyEQASQC+MNe1cF5IRERERA2XI8nTgwD6AZivqj+ISBcAq5wbFhEREVHDVGWHcVXNFJEnAXQyl38AsMjZgRERERE1RI5Mz3IPgO8AfGku9xCRdY4ULiJ3icg+ETkoInMr2W+MiKiIJDgaOBEREVF9cKTZ7lkAiQAuAICqfgega1UHiYgrgD8BGA7ACmCiiFjL2c8bwO8AbHc4aiIiIqJ64lCHcVXNLbOu1IHjEgEcVNXDqnoVwBoA95az3/MwmgEvO1AmERERUb1yJHnKEJFJAFxFJEJEXgew1YHjQgAct1v+0Vx3jYj0AtDRnC+vQiIyTURSRSQ1JyfHgVMTEREROYcjydOjAKIBXIExOGYugP+62ROLiAuAPwB4vKp9VfVtVU1Q1YTAwMCbPTURERFRjTnytN0lAE+Zr+o4AWM0cpsO5jobbwAxADaLCAC0B7BOREapamo1z0VERERUJxx52u7/RKSN3bKviGxwoOxvAUSISBcRcQcwAcC1p/RUNVdVA1Q1VFVDAWwDwMSJiIiIGjRHmu0CVPWCbUFVzwNoW9VB5pQuMwBsALAXwFpVzRCReSIyqqYBExEREdWnKpvtAJSKSCdVPQYAItIZgDpSuKomA0gus+7pCva93ZEyiYiIiOqTI8nTUwD+JSL/BCAAbgUwzalRERERETVQjnQY/9IcUqCvueq/VPWMc8MiIiIiapgc6TD+SxgDZX6hql8AKBaR0c4PjYiIiKjhcaTD+DP2I4ybncefcV5IRERERA2XI8lTefs40leKiIiIqMlxJHlKFZE/iEiY+foDgDRnB0ZERETUEDk6PctVAB+ZrysA/tOZQRERERE1VI48bXcRwNw6iIWIiIiowasyeRKRTShnUExVHeyUiIiIiIgaMEc6fs+2e+8BYAyAYueEQ0RERNSwOdJsV7Zz+BYR2eGkeOrG3+cCp/bUdxRERFVr3x0YvrC+oyAiO4402/nZLboAiAfQ2mkRERERETVgjjTbpcHo8yQwmut+APCwM4NyOv4VR0RERDXkSLNdl7oIhIiIiKgxcGRuu/tFxNt8/98i8ok5UTARERFRs+PIIJn/o6r5IjIQwB0A3gHwZ+eGRURERNQwOZI8lZj/jgTwtqquB+DuvJCIiIiIGi5HkqcTIrIUwHgAySLS0sHjiIiIiJocR5KgcQA2ABimqhcA+AGY49SoiIiIiBqoCp+2E5FWqlqgqpcAfGJbr6rZALLt93F+mEREREQNQ2U1T5+JyGIR+YWI3GJbKSJdReRhEdkA4C7nh0hERETUcFRY86SqQ0RkBIBfAxggIr4wBsncB2A9gKmqeqpuwiQiIiJqGKoaJPPvAPao6vG6CIaIiIiooau0w7iqKoDkOoqFiIiIqMFz5Gm7nSLS2+mREBERETUCjkwM3AfAAyJyBMBFGBMEq6rGOjMwIiIioobIkeRpmNOjICIiImokKhvnyQPAbwCEA9gD4B1VLa6rwIiIiIgaosr6PL0LIAFG4jQcwOI6iYiIiIioAaus2c6qqt0BQETeAbCjbkIiIiIiargqq3kqsr2paXOdiNwlIvtE5KCIzC1n+ywRyRSR70XkHyLSuSbnISIiIqorlSVPcSKSZ77yAcTa3otIXlUFi4grgD/BaPKzApgoItYyu+0CkGA+ufcxgJdqdhlEREREdaOy6Vlcb7LsRAAHVfUwAIjIGgD3Asi0O8cmu/23AXjgJs9JRERE5FSODJJZUyEA7Kd1+dFcV5GHYUwHcwMRmSYiqSKSmpOTU4shEhEREVWPM5Mnh4nIAzCe7Hu5vO2q+raqJqhqQmBgYN0GR0RERGTHkUEya+oEgI52yx3MddcRkTsAPAXgNlW94sR4iIiIiG6aM2uevgUQISJdRMQdwAQA6+x3EJGeAJYCGKWqp50YCxEREVGtcFryZA5vMAPABgB7AaxV1QwRmScio8zdXgbQCsD/ish3IrKuguKIiIiIGgRnNttBVZMBJJdZ97Td+zuceX4iIiKi2tYgOowTERERNRZMnoiIiIiqgckTERERUTUweSIiIiKqBiZPRERERNXA5ImIiIioGpg8EREREVUDkyciIiKiamDyRERERFQNTJ6IiIiIqoHJExEREVE1MHkiIiIiqgYmT0RERETVwOSJiIiIqBqYPBERERFVA5MnIiIiompg8kRERERUDUyeiIiIiKqByRMRERFRNTB5IiIiIqoGJk9ERERE1eBW3wHUh+c+z0Dmybz6DoOIqErWYB88c6oClcAAAAbKSURBVE90fYdBRHZY80RERERUDc2y5ol/xREREVFNseaJiIiIqBqYPBERERFVA5MnIiIiompg8kRERERUDUyeiIiIiKqByRMRERFRNTB5IiIiIqoGpyZPInKXiOwTkYMiMrec7S1F5CNz+3YRCXVmPEREREQ3y2nJk4i4AvgTgOEArAAmioi1zG4PAzivquEAXgWwyFnxEBEREdUGZ9Y8JQI4qKqHVfUqgDUA7i2zz70A3jXffwxgiIiIE2MiIiIiuinOTJ5CABy3W/7RXFfuPqpaDCAXgH/ZgkRkmoikikhqTk6Ok8IlIiIiqlqj6DCuqm+raoKqJgQGBtZ3OERERNSMOTN5OgGgo91yB3NdufuIiBuA1gDOOjEmIiIiopvizOTpWwARItJFRNwBTACwrsw+6wBMNd+PBfC1qqoTYyIiIiK6KW7OKlhVi0VkBoANAFwBLFfVDBGZByBVVdcBeAfAKhE5COAcjASLiIiIqMFyWvIEAKqaDCC5zLqn7d5fBnC/M2MgIiIiqk2NosM4ERERUUPB5ImIiIioGpg8EREREVUDkyciIiKiapDGNjKAiOQAOFoLRQUAOFML5TQWvN6mqzldK8DrranOqspRholqQaNLnmqLiKSqakJ9x1FXeL1NV3O6VoDXS0T1j812RERERNXA5ImIiIioGppz8vR2fQdQx3i9TVdzulaA10tE9azZ9nkiIiIiqonmXPNEREREVG1Mnoj+f3v3FmJVFcdx/PtrNDK7CGUhGWhXqB5UVAhFpEiSJKQeCrpAREaUGBVh9iC9BYH0YCQ1FlZmhOZLRRokmA+paV7y0osJGcUIYWVEl+nXw1mSKCon3bOcc34fGGbP3ovNbz8M/Pdae60VERHRhq4rniS9IalP0te1szRN0pWS1knaLWmXpHm1MzVJ0nmSNknaXp73hdqZBoKkHklfSfqwdpamSdovaaekbZK+rJ2nSZJGSFopaa+kPZJurp0pIlq67psnSdOAw8Bbtm+qnadJkkYBo2xvlXQhsAWYbXt35WiNkCRguO3DkoYCG4B5tr+oHK1Rkp4CJgIX2Z5VO0+TJO0HJtru+EUyJS0DPrfdK+lc4Hzbh2rniogu7HmyvR74qXaOgWD7B9tby/GvwB7girqpmuOWw+XPoeWno98OJI0G7gB6a2eJM0fSxcA0YCmA7T9TOEWcPbqueOpWksYA44GNdZM0qwxhbQP6gE9td/TzAi8DzwL/1A4yQAyslbRF0pzaYRo0FjgIvFmGZHslDa8dKiJaUjx1AUkXAKuAJ23/UjtPk2z32x4HjAYmS+rYoVlJs4A+21tqZxlAU21PAGYCj5dh+E40BJgAvGp7PPAbML9upIg4IsVThyvf/qwCltv+oHaegVKGONYBt9fO0qApwJ3lO6D3gFskvVM3UrNsf19+9wGrgcl1EzXmAHDgqJ7TlbSKqYg4C6R46mDlA+qlwB7bi2rnaZqkkZJGlONhwG3A3rqpmmP7OdujbY8B7gU+s31/5ViNkTS8THygDGHNADpy1qztH4HvJF1fTt0KdOREj4jBaEjtAANN0gpgOnCppAPAQttL66ZqzBTgAWBn+Q4IYIHtjytmatIoYJmkHlovBu/b7vjp+13kcmB1652AIcC7tj+pG6lRc4HlZabdPuChynkioui6pQoiIiIiTkeG7SIiIiLakOIpIiIiog0pniIiIiLakOIpIiIiog0pniIiIiLakOIp4hiS+iVtk7RL0nZJT0v63/8rkhYcdTxGUkeuTRQR0S1SPEUc73fb42zfSGuhzZnAwtO434JTN4mIiMEixVPESZRtQOYAT6ilR9JLkjZL2iHpUQBJ0yWtl/SRpG8kLZF0jqQXgWGlJ2t5uW2PpNdLz9bashp6REQMEimeIk7B9j6gB7gMeBj42fYkYBLwiKSxpelkWqtC3wBcDdxlez7/9WTdV9pdC7xSerYOAXcP3NNERMTpSvEU0Z4ZwINlu5uNwCW0iiGATbb32e4HVgBTT3CPb20f2S5nCzCmwbwREXGGdd3edhHtknQV0A/0AQLm2l5zTJvpwLF7HZ1o76M/jjruBzJsFxExiKTnKeIkJI0ElgCL3doIcg3wmKSh5fp1koaX5pMljS0z8+4BNpTzfx1pHxERg196niKON6wMyw0F/gbeBhaVa720htm2ShJwEJhdrm0GFgPXAOuA1eX8a8AOSVuB5wfiASIiojlqvUxHxOkow3bP2J5VO0tERDQrw3YRERERbUjPU0REREQb0vMUERER0YYUTxERERFtSPEUERER0YYUTxERERFtSPEUERER0YZ/AaHcnltEQS58AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", + "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", + "plt.scatter(depth_vec,pcheck_log_errors,label='Sucess Probablity + log errors')\n", + "plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Pr(success+log errors)')\n", + "plt.title('Pr(success+log errors) vs Depth for Width = {}'.format(wid))\n", + "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Total variation distance from ideal answer and random distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(depth_vec,tvd_ideal,label='TVD(data, ideal)')\n", + "plt.scatter(depth_vec,tvd_rand,label='TVD(data, rand)')\n", + "plt.scatter(depth_vec,1-np.asarray(pcheck),label='1-Sucess Probablity',alpha=0.33,marker='^',s=80)\n", + "#plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Total variation distance')\n", + "plt.title('Width = {}'.format(wid))\n", + "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot depth = width" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_idx = min([max(res_df['Depth']),max(res_df['Width'])])\n", + "\n", + "for idx in range(1,max_idx+1):\n", + " distz = get_hamming_dist(res_df, idx, idx)\n", + " # combine data from different subgraphs\n", + " avg_dist = distz['Hamming dist. data'].mean()\n", + " # rand data\n", + " rand_dist = distz['Hamming dist. rand'][0]\n", + " dep = idx\n", + " wid = idx\n", + " x_labels = np.arange(0, len(avg_dist))\n", + " plt.subplot(1,max_idx,idx)\n", + " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + " plt.xticks(x_labels)\n", + " plt.xlabel('Hamming Weight of Error')\n", + " plt.ylabel('Relative Frequency of Occurence')\n", + " plt.ylim([0,1])\n", + " plt.grid(axis='y', alpha=0.75)\n", + " plt.legend(['data','random'])\n", + " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot success probablity landscape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just the success probablity as a function of depth and width." + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.9251 , 0.8674 , 0.73105, 0.71715, 0.9201 , 0.8482 , 0.7371 ,\n", + " 0.67555, 0.90375, 0.8447 , 0.75855, 0.6031 , 0.92555, 0.8306 ,\n", + " 0.76205, 0.5922 , 0.9231 , 0.85725, 0.71515, 0.5072 , 0.9045 ,\n", + " 0.8439 , 0.7076 , 0.54185])" + ] + }, + "execution_count": 326, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values = np.asarray([munged['Pr. success data'][idx] for idx in munged.index])\n", + "values" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", + " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", + " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625])" + ] + }, + "execution_count": 327, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values_rand = np.asarray([munged['Pr. success rand'][idx] for idx in munged.index])\n", + "values_rand" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(min(res_df['Depth']), max(res_df['Depth'])+1)\n", + "\n", + "y = np.arange(min(res_df['Width']), max(res_df['Width'])+1)\n", + "\n", + "X, Y = np.meshgrid(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "(x1,x2) = X.shape\n", + "Zdata = np.reshape(values,(x2,x1)).T\n", + "Zrand = np.reshape(values_rand,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.9251 , 0.9201 , 0.90375, 0.92555, 0.9231 , 0.9045 ],\n", + " [0.8674 , 0.8482 , 0.8447 , 0.8306 , 0.85725, 0.8439 ],\n", + " [0.73105, 0.7371 , 0.75855, 0.76205, 0.71515, 0.7076 ],\n", + " [0.71715, 0.67555, 0.6031 , 0.5922 , 0.5072 , 0.54185]])" + ] + }, + "execution_count": 331, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Zdata" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": {}, + "outputs": [], + "source": [ + "tvd_rand_values = np.asarray([munged['TVD(data, rand)'][idx] for idx in munged.index])\n", + "tvd_ideal_values = np.asarray([munged['TVD(data, ideal)'][idx] for idx in munged.index])\n", + "Ztvd_rand = np.reshape(tvd_rand_values,(x2,x1)).T\n", + "Ztvd_ideal = np.reshape(tvd_ideal_values,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.46255 , 0.622775, 0.608975, 0.675675, 0.46005 , 0.60205 ,\n", + " 0.615325, 0.6457 , 0.451875, 0.5996 , 0.63455 , 0.5811 ,\n", + " 0.462775, 0.5867 , 0.639125, 0.56565 , 0.46155 , 0.6122 ,\n", + " 0.592275, 0.505625, 0.45225 , 0.598525, 0.59295 , 0.53795 ])" + ] + }, + "execution_count": 357, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tvd_ideal_values\n", + "tvd_rand_values" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "loge_rand_values = np.asarray([munged['Pr. success loge rand'][idx] for idx in munged.index])\n", + "loge_data_values = np.asarray([munged['Pr. success loge data'][idx] for idx in munged.index])\n", + "Zlge_rand = np.reshape(loge_rand_values,(x2,x1)).T\n", + "Zlge_data = np.reshape(loge_data_values,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 363, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zlge_data, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zlge_rand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 432, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": {}, + "outputs": [], + "source": [ + "size = Y.shape\n", + "width_1d = Y.reshape((1,np.prod(size)))\n", + "depth_1d = X.reshape((1,np.prod(size)))" + ] + }, + { + "cell_type": "code", + "execution_count": 441, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 24)" + ] + }, + "execution_count": 441, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_1d = Zdata.reshape((1,np.prod(size)))\n", + "data_1d.shape\n", + "width_1d.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 435, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0],\n", + " [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,\n", + " 4, 4],\n", + " [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n", + " 5, 6]])" + ] + }, + "execution_count": 435, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims = np.zeros_like(width_1d)\n", + "dims[0,0] = size[0]\n", + "dims[0,1] = size[1]\n", + "\n", + "xdata = np.vstack((dims,width_1d, depth_1d))\n", + "\n", + "\n", + "\n", + "xdata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two parameter model \n", + "\n", + "\n", + "$f(W,D,p_W,p_D) = (1-p_W)^W * (1-p_D)^D $\n", + "\n", + "The fidelity is proporional to $1 - p$" + ] + }, + { + "cell_type": "code", + "execution_count": 455, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param(x,pw,pd):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = (1-pw)**(width) * (1-pd)**depth\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One parameter model\n", + "\n", + "$f(W,D,p) = (1-p)^{W * D} $" + ] + }, + { + "cell_type": "code", + "execution_count": 447, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param(x,p):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = (1-p)**(width*depth)\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From my prior work a better model to fit to is\n", + "\n", + "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" + ] + }, + { + "cell_type": "code", + "execution_count": 510, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param_exp(x,p,a,b):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = np.exp(-(a*p + b) * width * depth)\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Start with one paramter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 531, + "metadata": {}, + "outputs": [], + "source": [ + "pguess = 0.1\n", + "popt, pcov = curve_fit(one_param, xdata, data_1d.ravel(), p0=pguess, bounds=(0, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 532, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The estimated error is p = 0.0276\n", + "The estimated product of the one and two qubit fidelity is F = 0.9724\n" + ] + } + ], + "source": [ + "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", + "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", + "#print('The one standard deviation on the estimate is ', str(np.round(np.sqrt(np.diag(pcov)[0]),5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 533, + "metadata": {}, + "outputs": [], + "source": [ + "zfit = one_param(xdata,popt)\n", + "Z_fit = zfit.reshape(size)" + ] + }, + { + "cell_type": "code", + "execution_count": 534, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y, Z_fit)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 535, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y,Zdata)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Two parameter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 541, + "metadata": {}, + "outputs": [], + "source": [ + "pguess2d = [0.0276, 0.01, 0.4]" + ] + }, + { + "cell_type": "code", + "execution_count": 542, + "metadata": {}, + "outputs": [], + "source": [ + "popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d , bounds=(0., 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 543, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00193651, 0.00070045, 0.02802694])" + ] + }, + "execution_count": 543, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "popt2d" + ] + }, + { + "cell_type": "code", + "execution_count": 544, + "metadata": {}, + "outputs": [], + "source": [ + "zfit2d = two_param(xdata,popt2d[0],popt2d[1])\n", + "Z_fit2d = zfit2d.reshape(size)" + ] + }, + { + "cell_type": "code", + "execution_count": 545, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y, Z_fit2d)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 486, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.897680214" + ] + }, + "execution_count": 486, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1-1.02319786e-01" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index e69de29b..20f7522a 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -0,0 +1,81 @@ +from typing import List +import networkx as nx +import random + +from pyquil.quilbase import Pragma +from pyquil.quil import Program +from pyquil.api import QuantumComputer +from pyquil.api import BenchmarkConnection +from pyquil.quil import address_qubits +from forest_benchmarking.rb import get_rb_gateset + + + +#=================================================================================================== +# Gate Sets +#=================================================================================================== +def random_single_qubit_gates(graph: nx.Graph, gates: list): + """Create a program comprised of single qubit gates randomly placed on the nodes + according to the specified graph. The gates are chosen uniformly from the list specified. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I, X, Z] or [I, X]. + :return: A program that randomly places single qubit gates on a graph. + """ + program = Program() + for q in graph.nodes: + gate = random.choice(gates) + program += gate(q) + return program + + +def random_two_qubit_gates(graph: nx.Graph, gates: list): + """Write a program to randomly place two qubit gates on edges of the graph. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + program = Program() + # do the two coloring with pragmas? + # no point until fencing is over + for a, b in graph.edges: + gate = random.choice(gates) + program += gate(a, b) + return program + +def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): + """Create a program comprised of single qubit Cliffords gates randomly placed on the nodes + according to the specified graph. The gates are chosen uniformly from the list specified. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that randomly places single qubit Clifford gates on a graph. + """ + gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q') + prog = Program() + for q in graph.nodes: + clif_n_inv = bm.generate_rb_sequence(depth=2,gateset=gateset_1q,seed=None) + # two elements are return for depth two. We take the first, the second is the inverse + gate = address_qubits(clif_n_inv[0],qubit_mapping={clif_n_inv[0].get_qubits().pop():q}) + prog += gate + return prog + +def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): + """Write a program to place random two qubit Cliffords gates on edges of the graph. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q') + prog = Program() + # do the two coloring with pragmas? + # no point until fencing is over + for a, b in graph.edges: + clif_n_inv = bm.generate_rb_sequence(depth=2,gateset=gateset_2q,seed=None) + qb1, qb2 = clif_n_inv[0].get_qubits() + # two elements are return for depth two. We take the first, the second is the inverse + gate = address_qubits(clif_n_inv[0],qubit_mapping={qb1: a, qb2: b,}) + prog += gate + return prog \ No newline at end of file From fa0d17d664a87a7d660da38592a1ac7cddfbc0bd Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Sun, 3 Mar 2019 15:52:55 -0800 Subject: [PATCH 03/49] some progress towards the goal --- examples/circuit_testing_josh.ipynb | 338 +++++++++++++++---------- forest_benchmarking/circuit_testing.py | 201 +++++++++++++-- 2 files changed, 386 insertions(+), 153 deletions(-) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb index 24bf90c0..daaf5b41 100644 --- a/examples/circuit_testing_josh.ipynb +++ b/examples/circuit_testing_josh.ipynb @@ -84,12 +84,28 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "#qc_perfect.device.get_specs()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -102,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -118,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -128,33 +144,33 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Z 0\n", - "Z 1\n", - "X 2\n", - "I 3\n", - "Z 4\n", - "X 5\n", + "I 0\n", + "X 1\n", + "I 2\n", + "X 3\n", + "I 4\n", + "I 5\n", "X 6\n", - "I 7\n", - "X 8\n", - "CZ 0 3\n", + "Z 7\n", + "I 8\n", + "I 0\n", + "I 3\n", "CZ 0 1\n", - "CZ 1 4\n", "I 1\n", - "I 2\n", + "I 4\n", + "CZ 1 2\n", "CZ 2 5\n", "CZ 3 6\n", "CZ 3 4\n", "CZ 4 7\n", - "I 4\n", - "I 5\n", + "CZ 4 5\n", "I 5\n", "I 8\n", "I 6\n", @@ -180,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -208,7 +224,7 @@ "'tcp://localhost:6000'" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -219,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -229,30 +245,29 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 0\n", "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", + "RX(pi/2) 0\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 1\n", "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RZ(pi/2) 2\n", - "RX(-pi/2) 3\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", + "RX(pi/2) 2\n", + "RX(-pi) 3\n", + "RX(-pi/2) 4\n", "RZ(-pi) 5\n", - "RX(-pi) 5\n", - "RZ(-pi) 6\n", + "RZ(-pi) 5\n", + "RZ(-pi/2) 6\n", "RX(-pi) 6\n", - "RX(-pi/2) 7\n", - "RZ(-pi) 8\n", - "RZ(-pi) 8\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RZ(pi/2) 8\n", + "RX(-pi) 8\n", "\n" ] } @@ -263,112 +278,179 @@ ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , , , , , , , , , , , , , , ]\n" - ] - } - ], "source": [ - "print(gateset_2q)" + "# Layer crap" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 14, "metadata": {}, + "outputs": [], + "source": [ + "def layer_1q_and_2q_rand_cliff(bm, G, layer_dagger: bool = False):\n", + " \n", + " \n", + " gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", + " gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')\n", + " \n", + " prog = Program()\n", + " prog += random_single_qubit_cliffords(bm, G)\n", + " prog += random_two_qubit_cliffords(bm, G)\n", + " \n", + " if layer_dagger:\n", + " prog +=prog.dagger()\n", + " \n", + " return prog" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def circuit_sandwich_clifford(bm: BenchmarkConnection,\n", + " graph: nx.Graph,\n", + " depth: int,\n", + " layer_dagger:bool = False,\n", + " sandwich_dagger:bool = False):\n", + " total_prog = Program()\n", + " \n", + " total_prog += pre_trival(graph)\n", + " \n", + " if sandwich_dagger:\n", + " depth = int(np.floor(depth/2))\n", + " \n", + " layer_progs = Program()\n", + " for ddx in range(1, depth + 1):\n", + " layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger)\n", + " if sandwich_dagger:\n", + " layer_progs += layer_progs.dagger()\n", + " \n", + " total_prog += layer_progs\n", + " total_prog += post_trival()\n", + " return total_prog" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def circuit_sandwich_rand_gates(graph: nx.Graph,\n", + " depth: int,\n", + " one_q_gates,\n", + " two_q_gates,\n", + " layer_dagger:bool = False,\n", + " sandwich_dagger:bool = False):\n", + " total_prog = Program()\n", + " total_prog += pre_trival(graph)\n", + " \n", + " if sandwich_dagger:\n", + " depth = int(np.floor(depth/2))\n", + " \n", + " layer_progs = Program()\n", + " for ddx in range(1, depth + 1):\n", + " layer_progs += layer_1q_and_2q_rand_gates(graph,\n", + " one_q_gates, \n", + " two_q_gates, \n", + " layer_dagger)\n", + " if sandwich_dagger:\n", + " layer_progs += layer_progs.dagger()\n", + " \n", + " total_prog += layer_progs\n", + " total_prog += post_trival()\n", + " return total_prog" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", - "CZ 3 0\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 3\n", - "RZ(pi/2) 0\n", - "RX(-pi) 0\n", - "RZ(-pi) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", - "CZ 1 4\n", - "RX(pi/2) 4\n", + "I 0\n", + "I 1\n", + "I 2\n", + "I 3\n", + "I 4\n", + "I 5\n", + "I 6\n", + "I 7\n", + "I 8\n", + "I 0\n", + "I 1\n", + "Z 2\n", + "X 3\n", + "X 4\n", + "I 5\n", + "Z 6\n", + "Z 7\n", + "Z 8\n", + "CZ 0 3\n", + "I 0\n", + "I 1\n", "CZ 1 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi) 4\n", - "RZ(pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 2 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "RX(pi/2) 2\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RX(pi/2) 2\n", - "RZ(-pi/2) 5\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RX(-pi/2) 2\n", - "RZ(-pi/2) 5\n", - "CZ 3 6\n", - "RX(pi/2) 6\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", + "CZ 1 2\n", + "CZ 2 5\n", "CZ 3 6\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 7 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 7\n", - "CZ 4 5\n", - "RX(-pi/2) 4\n", + "CZ 3 4\n", + "I 4\n", + "I 7\n", "CZ 4 5\n", - "RX(pi/2) 5\n", + "CZ 5 8\n", + "I 6\n", + "I 7\n", + "I 7\n", + "I 8\n", + "X 0\n", + "X 1\n", + "X 2\n", + "Z 3\n", + "Z 4\n", + "Z 5\n", + "Z 6\n", + "X 7\n", + "X 8\n", + "I 0\n", + "I 3\n", + "CZ 0 1\n", + "I 1\n", + "I 4\n", + "I 1\n", + "I 2\n", + "CZ 2 5\n", + "CZ 3 6\n", + "I 3\n", + "I 4\n", + "CZ 4 7\n", "CZ 4 5\n", - "RZ(pi/2) 5\n", - "RX(-pi/2) 4\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 5\n", - "RX(-pi/2) 8\n", - "CZ 8 5\n", - "RX(-pi/2) 5\n", - "RX(-pi/2) 8\n", - "CZ 8 5\n", - "RZ(-pi/2) 8\n", - "CZ 6 7\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 6\n", - "CZ 7 8\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 7\n", - "RZ(-pi/2) 7\n", + "I 5\n", + "I 8\n", + "I 6\n", + "I 7\n", + "I 7\n", + "I 8\n", "\n" ] } ], "source": [ - "print(random_two_qubit_cliffords(bm,G))" + "print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" ] }, { @@ -385,20 +467,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index 20f7522a..5b2c848f 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -1,22 +1,23 @@ from typing import List import networkx as nx import random +import itertools from pyquil.quilbase import Pragma from pyquil.quil import Program from pyquil.api import QuantumComputer from pyquil.api import BenchmarkConnection +from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET from pyquil.quil import address_qubits from forest_benchmarking.rb import get_rb_gateset - -#=================================================================================================== +# ================================================================================================== # Gate Sets -#=================================================================================================== +# ================================================================================================== def random_single_qubit_gates(graph: nx.Graph, gates: list): - """Create a program comprised of single qubit gates randomly placed on the nodes - according to the specified graph. The gates are chosen uniformly from the list specified. + """Create a program comprised of single qubit gates randomly placed on the nodes of the + specified graph. The gates are chosen uniformly from the list provided. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :param gates: A list of gates e.g. [I, X, Z] or [I, X]. @@ -30,7 +31,7 @@ def random_single_qubit_gates(graph: nx.Graph, gates: list): def random_two_qubit_gates(graph: nx.Graph, gates: list): - """Write a program to randomly place two qubit gates on edges of the graph. + """Write a program to randomly place two qubit gates on edges of the specified graph. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] @@ -44,23 +45,29 @@ def random_two_qubit_gates(graph: nx.Graph, gates: list): program += gate(a, b) return program + def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): - """Create a program comprised of single qubit Cliffords gates randomly placed on the nodes - according to the specified graph. The gates are chosen uniformly from the list specified. + """Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of + the specified graph. The gates are chosen uniformly from the list provided. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :return: A program that randomly places single qubit Clifford gates on a graph. """ + num_qubits = len(graph.nodes) gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q') + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) + rand_cliffords = clif_n_inv[0:num_qubits] + prog = Program() - for q in graph.nodes: - clif_n_inv = bm.generate_rb_sequence(depth=2,gateset=gateset_1q,seed=None) - # two elements are return for depth two. We take the first, the second is the inverse - gate = address_qubits(clif_n_inv[0],qubit_mapping={clif_n_inv[0].get_qubits().pop():q}) + for q, clif in zip(graph.nodes, rand_cliffords): + gate = address_qubits(clif, qubit_mapping={clif.get_qubits().pop(): q}) prog += gate return prog + def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): """Write a program to place random two qubit Cliffords gates on edges of the graph. @@ -68,14 +75,172 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :return: A program that has two qubit gates randomly placed on the graph edges. """ + num_2q_gates = len(graph.edges) gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q') + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) + rand_cliffords = clif_n_inv[0:num_2q_gates] + prog = Program() # do the two coloring with pragmas? # no point until fencing is over - for a, b in graph.edges: - clif_n_inv = bm.generate_rb_sequence(depth=2,gateset=gateset_2q,seed=None) - qb1, qb2 = clif_n_inv[0].get_qubits() - # two elements are return for depth two. We take the first, the second is the inverse - gate = address_qubits(clif_n_inv[0],qubit_mapping={qb1: a, qb2: b,}) + for edges, clif in zip(graph.edges, rand_cliffords): + qb1, qb2 = clif.get_qubits() + gate = address_qubits(clif, qubit_mapping={qb1: edges[0], qb2: edges[1], }) prog += gate - return prog \ No newline at end of file + return prog + + +# ================================================================================================== +# Prefix // Suffix programs; pre and post +# ================================================================================================== + +def pre_trival(graph: nx.Graph): + # Install identity on all qubits so that we can find all the qubits from prog.get_qubits(). + # Otherwise if the circuit happens to be identity on a particular qubit you will get + # not get that qubit from get_qubits. Worse, if the entire program is identity you will + # get the empty set. Do not delete this! + prep_gate = I + prog = Program() + prog += [prep_gate(qubit) for qubit in list(graph.nodes)] + return prog + +def post_trival(): + prog = Program() + return prog + + +# ================================================================================================== +# Layer tools +# ================================================================================================== + +def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, + graph: nx.Graph, + layer_dagger: bool = False): + ''' + Creates a layer of random one qubit Cliffords followed by random two qubit Cliffords. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param layer_dagger: Bool if true will add the dagger to the layer, making the layer + efectivley the identity + :return: program + ''' + prog = Program() + prog += random_single_qubit_cliffords(bm, graph) + prog += random_two_qubit_cliffords(bm, graph) + if layer_dagger: + prog += prog.dagger() + return prog + +def layer_1q_and_2q_rand_gates(graph: nx.Graph, + one_q_gates, + two_q_gates, + layer_dagger: bool = False): + ''' + You pass in two lists of one and two qubit gates. This function creates a layer of random one + qubit gates followed by random two qubit gates + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param one_q_gates: list of one qubit gates + :param two_q_gates: list of two qubit gates e.g. [CZ, ID] + :param layer_dagger: Bool if true will add the dagger to the layer, making the layer + efectivley the identity + :return: program + ''' + prog = Program() + prog += random_single_qubit_gates(graph, one_q_gates) + prog += random_two_qubit_gates(graph, two_q_gates) + if layer_dagger: + prog += prog.dagger() + return prog + +# ================================================================================================== +# Sandwitch tools +# ================================================================================================== +def circuit_sandwich_rand_gates(graph: nx.Graph, + depth: int, + one_q_gates, + two_q_gates, + layer_dagger: bool = False, + sandwich_dagger: bool = False): + ''' + + :param graph: + :param depth: + :param one_q_gates: + :param two_q_gates: + :param layer_dagger: + :param sandwich_dagger: + :return: + ''' + total_prog = Program() + total_prog += pre_trival(graph) + + if sandwich_dagger: + depth = int(np.floor(depth / 2)) + + layer_progs = Program() + for ddx in range(1, depth + 1): + layer_progs += layer_1q_and_2q_rand_gates(graph, + one_q_gates, + two_q_gates, + layer_dagger) + if sandwich_dagger: + layer_progs += layer_progs.dagger() + + total_prog += layer_progs + total_prog += post_trival() + return total_prog + + +def circuit_sandwich_clifford(bm: BenchmarkConnection, + graph: nx.Graph, + depth: int, + layer_dagger: bool = False, + sandwich_dagger: bool = False): + ''' + + :param bm: + :param graph: + :param depth: + :param layer_dagger: + :param sandwich_dagger: + :return: + ''' + total_prog = Program() + + total_prog += pre_trival(graph) + + if sandwich_dagger: + depth = int(np.floor(depth / 2)) + + layer_progs = Program() + for ddx in range(1, depth + 1): + layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) + if sandwich_dagger: + layer_progs += layer_progs.dagger() + + total_prog += layer_progs + total_prog += post_trival() + return total_prog + +# ================================================================================================== +# Graph tools +# ================================================================================================== +def generate_connected_subgraphs(G: nx.Graph, n_vert: int): + ''' + Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all + subgraphs with n_vert connect vertices. + + :params n_vert: number of verticies of connected subgraph. + :params G: networkx Graph + :returns: list of subgraphs with n_vert connected vertices + ''' + subgraph_list = [] + for sub_nodes in itertools.combinations(G.nodes(), n_vert): + subg = G.subgraph(sub_nodes) + if nx.is_connected(subg): + subgraph_list.append(subg) + return subgraph_list \ No newline at end of file From 57174a5bb8f0aed96a0c9ca9ff1c58edeec25cbb Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Sun, 3 Mar 2019 16:57:14 -0800 Subject: [PATCH 04/49] no message --- forest_benchmarking/circuit_testing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index 5b2c848f..de2baa08 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -161,8 +161,8 @@ def layer_1q_and_2q_rand_gates(graph: nx.Graph, # ================================================================================================== def circuit_sandwich_rand_gates(graph: nx.Graph, depth: int, - one_q_gates, - two_q_gates, + one_q_gates: list, + two_q_gates: list, layer_dagger: bool = False, sandwich_dagger: bool = False): ''' From d010da677e7c87d2438a2afa7b72eeac4361348f Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Mon, 11 Mar 2019 08:17:04 +1100 Subject: [PATCH 05/49] prototyping the sandwich --- examples/circuit_testing_josh.ipynb | 53 +++++++++++++++++++++++++- forest_benchmarking/circuit_testing.py | 4 +- 2 files changed, 53 insertions(+), 4 deletions(-) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb index daaf5b41..8d9f901b 100644 --- a/examples/circuit_testing_josh.ipynb +++ b/examples/circuit_testing_josh.ipynb @@ -371,7 +371,7 @@ " \n", " total_prog += layer_progs\n", " total_prog += post_trival()\n", - " return total_prog" + " return total_prog, " ] }, { @@ -458,7 +458,56 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer,\n", + " circuit_depth: int,\n", + " circuit_width: int,\n", + " circuit_sandwich: callable,\n", + " layer_dagger: bool = False,\n", + " sandwich_dagger: bool = False,\n", + " num_rand_subgraphs: int = 10,\n", + " num_shots_per_circuit: int = 100, #peter claims that no speed diff 800 shots\n", + " use_active_reset: bool = False) -> pd.DataFrame:\n", + " '''\n", + " Return a DataFrame where the rows contain all the information needed to run random circuits\n", + " of a certain width and depth on a particular lattice.\n", + "\n", + " :param qc_noisy: the noisy quantum resource (QPU or QVM)\n", + " :param circuit_depth: maximum depth of quantum circuit\n", + " :param circuit_width: maximum width of quantum circuit\n", + " :param num_rand_subgraphs: number of random circuits of circuit_width to be sampled\n", + " :param num_shots_per_circuit: number of shots per random circuit\n", + " :param use_active_reset: if True uses active reset. Doing so will speed up execution on a QPU.\n", + " :return: pandas DataFrame\n", + " '''\n", + " # get the networkx graph of the lattice\n", + " G = qc_noisy.qubit_topology()\n", + "\n", + " if circuit_width > len(G.nodes):\n", + " raise ValueError(\"You must have circuit widths less than or equal to the number of qubits on a lattice.\")\n", + "\n", + " experiment = []\n", + " # loop over different graph sizes\n", + " for depth, subgraph_size in itertools.product(range(1, circuit_depth+1),\n", + " range(1, circuit_width+1)):\n", + "\n", + " list_of_graphs = generate_connected_subgraphs(G, subgraph_size)\n", + " for kdx in range(1, num_rand_subgraphs+1):\n", + " # randomly choose a lattice from list\n", + " lattice = random.choice(list_of_graphs)\n", + " prog = circuit_sandwich(lattice, depth, layer_dagger, sandwich_dagger)\n", + "\n", + " experiment.append({'Depth': depth,\n", + " 'Width': subgraph_size,\n", + " 'Lattice':lattice,\n", + " 'Layer Dagger': layer_dagger,\n", + " 'Sandwich Dagger': sandwich_dagger,\n", + " 'Active Reset': use_active_reset,\n", + " 'Program': prog,\n", + " 'Trials': num_shots_per_circuit,\n", + " })\n", + " return pd.DataFrame(experiment)" + ] }, { "cell_type": "code", diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index de2baa08..7f94c621 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -157,7 +157,7 @@ def layer_1q_and_2q_rand_gates(graph: nx.Graph, return prog # ================================================================================================== -# Sandwitch tools +# Sandwich tools # ================================================================================================== def circuit_sandwich_rand_gates(graph: nx.Graph, depth: int, @@ -243,4 +243,4 @@ def generate_connected_subgraphs(G: nx.Graph, n_vert: int): subg = G.subgraph(sub_nodes) if nx.is_connected(subg): subgraph_list.append(subg) - return subgraph_list \ No newline at end of file + return subgraph_list \ No newline at end of file From ea6552ca6384da50a2ba320a305aafaf8f5de2cb Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Mon, 11 Mar 2019 14:57:45 +1100 Subject: [PATCH 06/49] first go --- examples/circuit_testing_josh.ipynb | 1514 +++++++++++++++++++----- forest_benchmarking/circuit_testing.py | 370 +++++- 2 files changed, 1597 insertions(+), 287 deletions(-) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb index 8d9f901b..8ac4a233 100644 --- a/examples/circuit_testing_josh.ipynb +++ b/examples/circuit_testing_josh.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -33,8 +33,8 @@ "import numpy as np\n", "import pandas as pd\n", "import time\n", - "from scipy.spatial.distance import hamming\n", - "import scipy.interpolate\n", + "# from scipy.spatial.distance import hamming\n", + "# import scipy.interpolate\n", "\n", "from matplotlib import pyplot as plt\n", "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -66,12 +66,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# if you want to run on a \"real lattice\"\n", - "#from pyquil import *\n", + "from pyquil import *\n", "#list_quantum_computers()\n", "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -98,14 +98,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -134,57 +134,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "one_q_gates = [X,Z,I]\n", - "two_q_gates = [two_q_id,CZ]" + "two_q_gates = [two_q_id,CZ]\n", + "\n", + "one_c_gates = [X,I]\n", + "two_c_gates = [two_q_id,CNOT]" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 0\n", - "X 1\n", - "I 2\n", - "X 3\n", - "I 4\n", - "I 5\n", - "X 6\n", - "Z 7\n", - "I 8\n", - "I 0\n", - "I 3\n", - "CZ 0 1\n", - "I 1\n", - "I 4\n", - "CZ 1 2\n", - "CZ 2 5\n", - "CZ 3 6\n", - "CZ 3 4\n", - "CZ 4 7\n", - "CZ 4 5\n", - "I 5\n", - "I 8\n", - "I 6\n", - "I 7\n", - "I 7\n", - "I 8\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "prog1 = random_single_qubit_gates(G, one_q_gates)\n", - "prog2 = random_two_qubit_gates(G, two_q_gates)\n", - "print(prog1+prog2)" + "#prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "#prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "#print(prog1+prog2)" ] }, { @@ -196,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -205,26 +174,26 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# my config has gone all cattywampus so i need to do this\n", - "bm = get_benchmarker(endpoint='tcp://localhost:6000')" + "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'tcp://localhost:6000'" + "'tcp://127.0.0.1:5555'" ] }, - "execution_count": 11, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -235,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -245,29 +214,29 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "RX(-pi/2) 0\n", "RZ(-pi/2) 0\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 1\n", - "RX(-pi/2) 1\n", - "RX(pi/2) 2\n", - "RX(-pi) 3\n", - "RX(-pi/2) 4\n", - "RZ(-pi) 5\n", + "RX(-pi/2) 0\n", + "RZ(-pi) 1\n", + "RZ(pi/2) 2\n", + "RX(-pi) 2\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 4\n", + "RX(-pi) 4\n", + "RX(-pi/2) 5\n", "RZ(-pi) 5\n", "RZ(-pi/2) 6\n", - "RX(-pi) 6\n", + "RZ(-pi/2) 7\n", "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RZ(pi/2) 8\n", - "RX(-pi) 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi/2) 8\n", "\n" ] } @@ -286,228 +255,1239 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" + ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "def layer_1q_and_2q_rand_cliff(bm, G, layer_dagger: bool = False):\n", - " \n", - " \n", - " gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", - " gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')\n", - " \n", - " prog = Program()\n", - " prog += random_single_qubit_cliffords(bm, G)\n", - " prog += random_two_qubit_cliffords(bm, G)\n", - " \n", - " if layer_dagger:\n", - " prog +=prog.dagger()\n", - " \n", - " return prog" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "def circuit_sandwich_clifford(bm: BenchmarkConnection,\n", - " graph: nx.Graph,\n", - " depth: int,\n", - " layer_dagger:bool = False,\n", - " sandwich_dagger:bool = False):\n", - " total_prog = Program()\n", - " \n", - " total_prog += pre_trival(graph)\n", - " \n", - " if sandwich_dagger:\n", - " depth = int(np.floor(depth/2))\n", - " \n", - " layer_progs = Program()\n", - " for ddx in range(1, depth + 1):\n", - " layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger)\n", - " if sandwich_dagger:\n", - " layer_progs += layer_progs.dagger()\n", - " \n", - " total_prog += layer_progs\n", - " total_prog += post_trival()\n", - " return total_prog" + "from functools import partial\n", + "\n", + "circuit_depth = 3\n", + "circuit_width = 3\n", + "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", + " one_q_gates = one_c_gates, \n", + " two_q_gates = two_c_gates)\n", + "layer_dagger = False\n", + "sandwich_dagger = False\n", + "num_rand_subgraphs = 2\n", + "num_shots_per_circuit = 2\n", + "use_active_reset= False" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "def circuit_sandwich_rand_gates(graph: nx.Graph,\n", - " depth: int,\n", - " one_q_gates,\n", - " two_q_gates,\n", - " layer_dagger:bool = False,\n", - " sandwich_dagger:bool = False):\n", - " total_prog = Program()\n", - " total_prog += pre_trival(graph)\n", - " \n", - " if sandwich_dagger:\n", - " depth = int(np.floor(depth/2))\n", - " \n", - " layer_progs = Program()\n", - " for ddx in range(1, depth + 1):\n", - " layer_progs += layer_1q_and_2q_rand_gates(graph,\n", - " one_q_gates, \n", - " two_q_gates, \n", - " layer_dagger)\n", - " if sandwich_dagger:\n", - " layer_progs += layer_progs.dagger()\n", - " \n", - " total_prog += layer_progs\n", - " total_prog += post_trival()\n", - " return total_prog, " + "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 41, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 0\n", - "I 1\n", - "I 2\n", - "I 3\n", - "I 4\n", - "I 5\n", - "I 6\n", - "I 7\n", - "I 8\n", - "I 0\n", - "I 1\n", - "Z 2\n", - "X 3\n", - "X 4\n", - "I 5\n", - "Z 6\n", - "Z 7\n", - "Z 8\n", - "CZ 0 3\n", - "I 0\n", - "I 1\n", - "CZ 1 4\n", - "CZ 1 2\n", - "CZ 2 5\n", - "CZ 3 6\n", - "CZ 3 4\n", - "I 4\n", - "I 7\n", - "CZ 4 5\n", - "CZ 5 8\n", - "I 6\n", - "I 7\n", - "I 7\n", - "I 8\n", - "X 0\n", - "X 1\n", - "X 2\n", - "Z 3\n", - "Z 4\n", - "Z 5\n", - "Z 6\n", - "X 7\n", - "X 8\n", - "I 0\n", - "I 3\n", - "CZ 0 1\n", - "I 1\n", - "I 4\n", - "I 1\n", - "I 2\n", - "CZ 2 5\n", - "CZ 3 6\n", - "I 3\n", - "I 4\n", - "CZ 4 7\n", - "CZ 4 5\n", - "I 5\n", - "I 8\n", - "I 6\n", - "I 7\n", - "I 7\n", - "I 8\n", - "\n" - ] - } - ], - "source": [ - "print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer,\n", - " circuit_depth: int,\n", - " circuit_width: int,\n", - " circuit_sandwich: callable,\n", - " layer_dagger: bool = False,\n", - " sandwich_dagger: bool = False,\n", - " num_rand_subgraphs: int = 10,\n", - " num_shots_per_circuit: int = 100, #peter claims that no speed diff 800 shots\n", - " use_active_reset: bool = False) -> pd.DataFrame:\n", - " '''\n", - " Return a DataFrame where the rows contain all the information needed to run random circuits\n", - " of a certain width and depth on a particular lattice.\n", - "\n", - " :param qc_noisy: the noisy quantum resource (QPU or QVM)\n", - " :param circuit_depth: maximum depth of quantum circuit\n", - " :param circuit_width: maximum width of quantum circuit\n", - " :param num_rand_subgraphs: number of random circuits of circuit_width to be sampled\n", - " :param num_shots_per_circuit: number of shots per random circuit\n", - " :param use_active_reset: if True uses active reset. Doing so will speed up execution on a QPU.\n", - " :return: pandas DataFrame\n", - " '''\n", - " # get the networkx graph of the lattice\n", - " G = qc_noisy.qubit_topology()\n", - "\n", - " if circuit_width > len(G.nodes):\n", - " raise ValueError(\"You must have circuit widths less than or equal to the number of qubits on a lattice.\")\n", - "\n", - " experiment = []\n", - " # loop over different graph sizes\n", - " for depth, subgraph_size in itertools.product(range(1, circuit_depth+1),\n", - " range(1, circuit_width+1)):\n", - "\n", - " list_of_graphs = generate_connected_subgraphs(G, subgraph_size)\n", - " for kdx in range(1, num_rand_subgraphs+1):\n", - " # randomly choose a lattice from list\n", - " lattice = random.choice(list_of_graphs)\n", - " prog = circuit_sandwich(lattice, depth, layer_dagger, sandwich_dagger)\n", - "\n", - " experiment.append({'Depth': depth,\n", - " 'Width': subgraph_size,\n", - " 'Lattice':lattice,\n", - " 'Layer Dagger': layer_dagger,\n", - " 'Sandwich Dagger': sandwich_dagger,\n", - " 'Active Reset': use_active_reset,\n", - " 'Program': prog,\n", - " 'Trials': num_shots_per_circuit,\n", - " })\n", - " return pd.DataFrame(experiment)" - ] + "data": { + "text/html": [ + "
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Active ResetDepthLatticeLayer DaggerProgramSandwich DaggerTrialsWidth
0False1(6)False(I 6, X 6)False21
1False1(6)False(I 6, I 6)False21
2False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
3False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
4False1(3, 6, 7)False(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...False23
5False1(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...False23
6False2(7)False(I 7, I 7, X 7)False21
7False2(7)False(I 7, X 7, I 7)False21
8False2(5, 8)False(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...False22
9False2(6, 7)False(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...False22
10False2(6, 7, 8)False(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...False23
11False2(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...False23
12False3(8)False(I 8, I 8, X 8, I 8)False21
13False3(0)False(I 0, X 0, I 0, I 0)False21
14False3(4, 7)False(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...False22
15False3(3, 4)False(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...False22
16False3(1, 3, 4)False(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...False23
17False3(3, 4, 6)False(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...False23
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Lattice Layer Dagger \\\n", + "0 False 1 (6) False \n", + "1 False 1 (6) False \n", + "2 False 1 (1, 2) False \n", + "3 False 1 (1, 2) False \n", + "4 False 1 (3, 6, 7) False \n", + "5 False 1 (4, 5, 7) False \n", + "6 False 2 (7) False \n", + "7 False 2 (7) False \n", + "8 False 2 (5, 8) False \n", + "9 False 2 (6, 7) False \n", + "10 False 2 (6, 7, 8) False \n", + "11 False 2 (4, 5, 7) False \n", + "12 False 3 (8) False \n", + "13 False 3 (0) False \n", + "14 False 3 (4, 7) False \n", + "15 False 3 (3, 4) False \n", + "16 False 3 (1, 3, 4) False \n", + "17 False 3 (3, 4, 6) False \n", + "\n", + " Program Sandwich Dagger \\\n", + "0 (I 6, X 6) False \n", + "1 (I 6, I 6) False \n", + "2 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", + "3 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", + "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... False \n", + "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... False \n", + "6 (I 7, I 7, X 7) False \n", + "7 (I 7, X 7, I 7) False \n", + "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... False \n", + "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... False \n", + "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... False \n", + "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... False \n", + "12 (I 8, I 8, X 8, I 8) False \n", + "13 (I 0, X 0, I 0, I 0) False \n", + "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... False \n", + "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... False \n", + "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... False \n", + "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... False \n", + "\n", + " Trials Width \n", + "0 2 1 \n", + "1 2 1 \n", + "2 2 2 \n", + "3 2 2 \n", + "4 2 3 \n", + "5 2 3 \n", + "6 2 1 \n", + "7 2 1 \n", + "8 2 2 \n", + "9 2 2 \n", + "10 2 3 \n", + "11 2 3 \n", + "12 2 1 \n", + "13 2 1 \n", + "14 2 2 \n", + "15 2 2 \n", + "16 2 3 \n", + "17 2 3 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "daty = acquire_circuit_sandwich_data(qc_noisy,exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthLatticeProgramSamplesTrialsWidth
0False1(6)(I 6, X 6)[[1], [1]]21
1False1(6)(I 6, I 6)[[0], [0]]21
2False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
3False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
4False1(3, 6, 7)(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]23
5False1(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]23
6False2(7)(I 7, I 7, X 7)[[1], [1]]21
7False2(7)(I 7, X 7, I 7)[[1], [1]]21
8False2(5, 8)(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]22
9False2(6, 7)(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]22
10False2(6, 7, 8)(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]23
11False2(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]23
12False3(8)(I 8, I 8, X 8, I 8)[[1], [1]]21
13False3(0)(I 0, X 0, I 0, I 0)[[1], [1]]21
14False3(4, 7)(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]22
15False3(3, 4)(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]22
16False3(1, 3, 4)(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]23
17False3(3, 4, 6)(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]23
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Lattice \\\n", + "0 False 1 (6) \n", + "1 False 1 (6) \n", + "2 False 1 (1, 2) \n", + "3 False 1 (1, 2) \n", + "4 False 1 (3, 6, 7) \n", + "5 False 1 (4, 5, 7) \n", + "6 False 2 (7) \n", + "7 False 2 (7) \n", + "8 False 2 (5, 8) \n", + "9 False 2 (6, 7) \n", + "10 False 2 (6, 7, 8) \n", + "11 False 2 (4, 5, 7) \n", + "12 False 3 (8) \n", + "13 False 3 (0) \n", + "14 False 3 (4, 7) \n", + "15 False 3 (3, 4) \n", + "16 False 3 (1, 3, 4) \n", + "17 False 3 (3, 4, 6) \n", + "\n", + " Program Samples \\\n", + "0 (I 6, X 6) [[1], [1]] \n", + "1 (I 6, I 6) [[0], [0]] \n", + "2 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", + "3 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", + "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... [[0, 0, 0], [0, 0, 0]] \n", + "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... [[0, 1, 1], [0, 1, 1]] \n", + "6 (I 7, I 7, X 7) [[1], [1]] \n", + "7 (I 7, X 7, I 7) [[1], [1]] \n", + "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... [[1, 1], [1, 1]] \n", + "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... [[0, 0], [0, 0]] \n", + "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... [[0, 0, 0], [0, 0, 0]] \n", + "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... [[0, 1, 0], [0, 1, 0]] \n", + "12 (I 8, I 8, X 8, I 8) [[1], [1]] \n", + "13 (I 0, X 0, I 0, I 0) [[1], [1]] \n", + "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... [[0, 1], [0, 1]] \n", + "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... [[0, 0], [0, 0]] \n", + "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... [[1, 0, 1], [1, 0, 1]] \n", + "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... [[0, 0, 0], [0, 0, 0]] \n", + "\n", + " Trials Width \n", + "0 2 1 \n", + "1 2 1 \n", + "2 2 2 \n", + "3 2 2 \n", + "4 2 3 \n", + "5 2 3 \n", + "6 2 1 \n", + "7 2 1 \n", + "8 2 2 \n", + "9 2 2 \n", + "10 2 3 \n", + "11 2 3 \n", + "12 2 1 \n", + "13 2 1 \n", + "14 2 2 \n", + "15 2 2 \n", + "16 2 3 \n", + "17 2 3 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "daty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetAnswerDepthHamming dist. dataHamming dist. idealHamming dist. randLatticePr. success dataPr. success loge dataPr. success loge randPr. success randProgramSamplesTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False[[1]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, X 6)[[1], [1]]0.00.500210
1False[[0]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, I 6)[[0], [0]]0.00.500210
2False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
3False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
4False[[0, 0, 0]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 6, 7)1.01.00.1250.125(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
5False[[0, 1, 1]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]0.00.875230
6False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, I 7, X 7)[[1], [1]]0.00.500210
7False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, X 7, I 7)[[1], [1]]0.00.500210
8False[[1, 1]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](5, 8)1.01.00.2500.250(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]0.00.750220
9False[[0, 0]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](6, 7)1.01.00.2500.250(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]0.00.750220
10False[[0, 0, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](6, 7, 8)1.01.00.1250.125(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
11False[[0, 1, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]0.00.875230
12False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](8)1.01.00.5000.500(I 8, I 8, X 8, I 8)[[1], [1]]0.00.500210
13False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](0)1.01.00.5000.500(I 0, X 0, I 0, I 0)[[1], [1]]0.00.500210
14False[[0, 1]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 7)1.01.00.2500.250(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]0.00.750220
15False[[0, 0]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](3, 4)1.01.00.2500.250(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]0.00.750220
16False[[1, 0, 1]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)1.01.00.1250.125(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]0.00.875230
17False[[0, 0, 0]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 4, 6)1.01.00.1250.125(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
\n", + "
" + ], + "text/plain": [ + " Active Reset Answer Depth Hamming dist. data \\\n", + "0 False [[1]] 1 [1.0, 0.0] \n", + "1 False [[0]] 1 [1.0, 0.0] \n", + "2 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", + "3 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", + "4 False [[0, 0, 0]] 1 [1.0, 0.0, 0.0, 0.0] \n", + "5 False [[0, 1, 1]] 1 [1.0, 0.0, 0.0, 0.0] \n", + "6 False [[1]] 2 [1.0, 0.0] \n", + "7 False [[1]] 2 [1.0, 0.0] \n", + "8 False [[1, 1]] 2 [1.0, 0.0, 0.0] \n", + "9 False [[0, 0]] 2 [1.0, 0.0, 0.0] \n", + "10 False [[0, 0, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", + "11 False [[0, 1, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", + "12 False [[1]] 3 [1.0, 0.0] \n", + "13 False [[1]] 3 [1.0, 0.0] \n", + "14 False [[0, 1]] 3 [1.0, 0.0, 0.0] \n", + "15 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", + "16 False [[1, 0, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", + "17 False [[0, 0, 0]] 3 [1.0, 0.0, 0.0, 0.0] \n", + "\n", + " Hamming dist. ideal Hamming dist. rand Lattice \\\n", + "0 [1.0, 0.0] [0.5, 0.5] (6) \n", + "1 [1.0, 0.0] [0.5, 0.5] (6) \n", + "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", + "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", + "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 6, 7) \n", + "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", + "6 [1.0, 0.0] [0.5, 0.5] (7) \n", + "7 [1.0, 0.0] [0.5, 0.5] (7) \n", + "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (5, 8) \n", + "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", + "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (6, 7, 8) \n", + "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", + "12 [1.0, 0.0] [0.5, 0.5] (8) \n", + "13 [1.0, 0.0] [0.5, 0.5] (0) \n", + "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", + "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (3, 4) \n", + "16 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", + "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 6) \n", + "\n", + " Pr. success data Pr. success loge data Pr. success loge rand \\\n", + "0 1.0 1.0 0.500 \n", + "1 1.0 1.0 0.500 \n", + "2 1.0 1.0 0.250 \n", + "3 1.0 1.0 0.250 \n", + "4 1.0 1.0 0.125 \n", + "5 1.0 1.0 0.125 \n", + "6 1.0 1.0 0.500 \n", + "7 1.0 1.0 0.500 \n", + "8 1.0 1.0 0.250 \n", + "9 1.0 1.0 0.250 \n", + "10 1.0 1.0 0.125 \n", + "11 1.0 1.0 0.125 \n", + "12 1.0 1.0 0.500 \n", + "13 1.0 1.0 0.500 \n", + "14 1.0 1.0 0.250 \n", + "15 1.0 1.0 0.250 \n", + "16 1.0 1.0 0.125 \n", + "17 1.0 1.0 0.125 \n", + "\n", + " Pr. success rand Program \\\n", + "0 0.500 (I 6, X 6) \n", + "1 0.500 (I 6, I 6) \n", + "2 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", + "3 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", + "4 0.125 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... \n", + "5 0.125 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... \n", + "6 0.500 (I 7, I 7, X 7) \n", + "7 0.500 (I 7, X 7, I 7) \n", + "8 0.250 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... \n", + "9 0.250 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... \n", + "10 0.125 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... \n", + "11 0.125 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... \n", + "12 0.500 (I 8, I 8, X 8, I 8) \n", + "13 0.500 (I 0, X 0, I 0, I 0) \n", + "14 0.250 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... \n", + "15 0.250 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... \n", + "16 0.125 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... \n", + "17 0.125 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... \n", + "\n", + " Samples TVD(data, ideal) TVD(data, rand) Trials Width \\\n", + "0 [[1], [1]] 0.0 0.500 2 1 \n", + "1 [[0], [0]] 0.0 0.500 2 1 \n", + "2 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", + "3 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", + "4 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "5 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", + "6 [[1], [1]] 0.0 0.500 2 1 \n", + "7 [[1], [1]] 0.0 0.500 2 1 \n", + "8 [[1, 1], [1, 1]] 0.0 0.750 2 2 \n", + "9 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", + "10 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "11 [[0, 1, 0], [0, 1, 0]] 0.0 0.875 2 3 \n", + "12 [[1], [1]] 0.0 0.500 2 1 \n", + "13 [[1], [1]] 0.0 0.500 2 1 \n", + "14 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", + "15 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", + "16 [[1, 0, 1], [1, 0, 1]] 0.0 0.875 2 3 \n", + "17 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "\n", + " loge = basement[log_2(Width)-1] \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 \n", + "10 0 \n", + "11 0 \n", + "12 0 \n", + "13 0 \n", + "14 0 \n", + "15 0 \n", + "16 0 \n", + "17 0 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimate_random_classical_circuit_errors(qc_perfect,daty)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "code", diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index 7f94c621..eb2a0e0e 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -1,7 +1,12 @@ from typing import List import networkx as nx +import numpy as np import random import itertools +import pandas as pd +from scipy.spatial.distance import hamming +import scipy.interpolate +from scipy.special import comb from pyquil.quilbase import Pragma from pyquil.quil import Program @@ -10,6 +15,7 @@ from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET from pyquil.quil import address_qubits from forest_benchmarking.rb import get_rb_gateset +from forest_benchmarking.distance_measures import total_variation_distance as tvd # ================================================================================================== @@ -160,29 +166,31 @@ def layer_1q_and_2q_rand_gates(graph: nx.Graph, # Sandwich tools # ================================================================================================== def circuit_sandwich_rand_gates(graph: nx.Graph, - depth: int, + circuit_depth: int, one_q_gates: list, two_q_gates: list, layer_dagger: bool = False, sandwich_dagger: bool = False): ''' + Create a sandwich circuit by adding layers. - :param graph: - :param depth: - :param one_q_gates: - :param two_q_gates: - :param layer_dagger: - :param sandwich_dagger: - :return: + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param circuit_depth: maximum depth of quantum circuit + :param one_q_gates: list of one qubit gates + :param two_q_gates: list of two qubit gates e.g. [CZ, ID] + :param layer_dagger: Bool if true will add the dagger to the layer, making the layer + :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of + the first. + :return: program ''' total_prog = Program() total_prog += pre_trival(graph) if sandwich_dagger: - depth = int(np.floor(depth / 2)) + circuit_depth = int(np.floor(circuit_depth / 2)) layer_progs = Program() - for ddx in range(1, depth + 1): + for ddx in range(1, circuit_depth + 1): layer_progs += layer_1q_and_2q_rand_gates(graph, one_q_gates, two_q_gates, @@ -197,27 +205,28 @@ def circuit_sandwich_rand_gates(graph: nx.Graph, def circuit_sandwich_clifford(bm: BenchmarkConnection, graph: nx.Graph, - depth: int, + circuit_depth: int, layer_dagger: bool = False, sandwich_dagger: bool = False): ''' - :param bm: - :param graph: - :param depth: - :param layer_dagger: - :param sandwich_dagger: - :return: + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param circuit_depth: maximum depth of quantum circuit + :param layer_dagger: Bool if true will add the dagger to the layer, making the layer + :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of + the first. + :return: program ''' total_prog = Program() total_prog += pre_trival(graph) if sandwich_dagger: - depth = int(np.floor(depth / 2)) + depth = int(np.floor(circuit_depth / 2)) layer_progs = Program() - for ddx in range(1, depth + 1): + for ddx in range(1, circuit_depth + 1): layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) if sandwich_dagger: layer_progs += layer_progs.dagger() @@ -226,6 +235,327 @@ def circuit_sandwich_clifford(bm: BenchmarkConnection, total_prog += post_trival() return total_prog + +# ================================================================================================== +# Generate and Acquire functions +# ================================================================================================== +def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, + circuit_depth: int, + circuit_width: int, + circuit_sandwich: callable, + layer_dagger: bool = False, + sandwich_dagger: bool = False, + num_rand_subgraphs: int = 10, + # peter claims that no speed diff 800 shots + num_shots_per_circuit: int = 100, + use_active_reset: bool = False) -> pd.DataFrame: + ''' + Return a DataFrame where the rows contain all the information needed to run random circuits + of a certain width and depth on a particular lattice. + + :param qc_noisy: the noisy quantum resource (QPU or QVM) + :param circuit_depth: maximum depth of quantum circuit + :param circuit_width: maximum width of quantum circuit + :param circuit_sandwich: callable. Regardless of the original arguments the function here + must only have graph, circuit_depth, layer_dagger, and sandwich_dagger as remainig keywords. + :param num_rand_subgraphs: number of random circuits of circuit_width to be sampled + :param num_shots_per_circuit: number of shots per random circuit + :param use_active_reset: if True uses active reset. Doing so will speed up execution on a QPU. + :return: pandas DataFrame + ''' + # get the networkx graph of the lattice + G = qc_noisy.qubit_topology() + + if circuit_width > len(G.nodes): + raise ValueError("You must have circuit widths less than or equal to the number of qubits on a lattice.") + + experiment = [] + # loop over different graph sizes + for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), + range(1, circuit_width+1)): + + list_of_graphs = generate_connected_subgraphs(G, subgraph_size) + for kdx in range(1, num_rand_subgraphs+1): + # randomly choose a lattice from list + lattice = random.choice(list_of_graphs) + prog = circuit_sandwich(graph=lattice, + circuit_depth=depth, + layer_dagger=layer_dagger, + sandwich_dagger=sandwich_dagger) + + experiment.append({'Depth': depth, + 'Width': subgraph_size, + 'Lattice':lattice, + 'Layer Dagger': layer_dagger, + 'Sandwich Dagger': sandwich_dagger, + 'Active Reset': use_active_reset, + 'Program': prog, + 'Trials': num_shots_per_circuit, + }) + return pd.DataFrame(experiment) + + +def acquire_circuit_sandwich_data(qc_noisy: QuantumComputer, + circ_sand_expt: pd.DataFrame) -> pd.DataFrame: + ''' + Convenient wrapper for collecting the results of running circuits sandwiches on a + particular lattice. + + It will run a series of random circuits with widths from [1, ...,circuit_width] and depths + from [1, ..., circuit_depth]. + + + :param qc_noisy: the noisy quantum resource (QPU or QVM) to + :param circ_sand_expt: pandas DataFrame where the rows contain experiments + :return: pandas DataFrame + ''' + #:param qc_perfect: the "perfect" quantum resource (QVM) to determine the true outcome. + # if qc_perfect.name == qc_noisy.name: + # raise ValueError("The noisy and perfect device can't be the same device.") + + # get the networkx graph of the lattice + G = qc_noisy.qubit_topology() + + data = [] + for index, row in circ_sand_expt.iterrows(): + prog = row['Program'] + use_active_reset = row['Active Reset'] + num_shots_per_circuit = row['Trials'] + + # run on perfect QVM or Wavefunction simulator + # perfect_bitstring = qc_perfect.run_and_measure(prog, trials=1) + # perfect_bitstring_array = np.vstack(perfect_bitstring[q] for q in prog.get_qubits()).T + + # add active reset + reset_prog = Program() + if use_active_reset: + reset_prog += RESET() + + # run on hardware or noisy QVM + # only need to pre append active reset on something that may run on the hardware + actual_bitstring = qc_noisy.run_and_measure(reset_prog + prog, trials=num_shots_per_circuit) + actual_bitstring_array = np.vstack(actual_bitstring[q] for q in prog.get_qubits()).T + + # list of dicts. + data.append({'Depth': row['Depth'], + 'Width': row['Width'], + 'Lattice': row['Lattice'], + # 'In X basis': row['In X basis'], + 'Active Reset': use_active_reset, + 'Program': prog, + 'Trials': num_shots_per_circuit, + # 'Answer': perfect_bitstring_array, + 'Samples': actual_bitstring_array, + }) + return pd.DataFrame(data) + + +# ================================================================================================== +# Analysis +# ================================================================================================== +def estimate_random_classical_circuit_errors(qc_perfect: QuantumComputer, + df: pd.DataFrame) -> pd.DataFrame: + ''' + asdf + + :param df: pandas DataFrame containing experimental results + :return: pandas DataFrame containing estiamted errors and experimental results + ''' + + results = [] + for _, row in df.iterrows(): + wt = [] + prog = row['Program'] + # run on perfect QVM or Wavefunction simulator + perfect_bitstring = qc_perfect.run_and_measure(prog, trials=1) + perfect_bitstring_array = np.vstack(perfect_bitstring[q] for q in prog.get_qubits()).T + # perfect_bitstring_array = np.asarray(row['Answer']) + actual_bitstring_array = np.asarray(row['Samples']) + wt.append(get_error_hamming_distance_from_results(perfect_bitstring_array, + actual_bitstring_array)) + wt_flat = flatten_list(wt) + + # Hamming weight distributions + wt_dist_data = np.asarray( + get_error_hamming_distributions_from_list(wt_flat, row['Width'])) # data + wt_dist_rand = np.asarray(hamming_dist_rand(row['Width'])) # random guessing + wt_dist_ideal = np.zeros_like(wt_dist_rand) # perfect + wt_dist_ideal[0] = 1 + + # Total variation distance + tvd_data_ideal = tvd(wt_dist_data, wt_dist_ideal) + tvd_data_rand = tvd(wt_dist_data, wt_dist_rand) + + # Probablity of success + pr_suc_data = wt_dist_data[0] + pr_suc_rand = wt_dist_rand[0] + + # Probablity of success with basement[ log_2(width) - 1 ] errors + # I.e. error when you allow for a logarithmic number of bit flips from the answer + num_bit_flips_allowed_from_answer = int(basement_function(np.log2(row['Width']) - 1)) + pr_suc_log_err_data = sum( + [wt_dist_data[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) + pr_suc_log_err_rand = sum( + [wt_dist_rand[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) + + results.append({'Depth': row['Depth'], + 'Width': row['Width'], + 'Lattice': row['Lattice'], + # 'In X basis': row['In X basis'], + 'Active Reset': row['Active Reset'], + 'Program': row['Program'], + 'Trials': row['Trials'], + 'Answer': perfect_bitstring_array, + 'Samples': actual_bitstring_array, + 'Hamming dist. data': wt_dist_data, + 'Hamming dist. rand': wt_dist_rand, + 'Hamming dist. ideal': wt_dist_ideal, + 'TVD(data, ideal)': tvd_data_ideal, + 'TVD(data, rand)': tvd_data_rand, + 'Pr. success data': pr_suc_data, + 'Pr. success rand': pr_suc_rand, + 'loge = basement[log_2(Width)-1]': num_bit_flips_allowed_from_answer, + 'Pr. success loge data': pr_suc_log_err_data, + 'Pr. success loge rand': pr_suc_log_err_rand, + }) + return pd.DataFrame(results) + +def get_error_hamming_distance_from_results(perfect_bit_string, results): + """Get the hamming weight of the error vector (number of bits flipped between output and + expected answer). + + :param perfect_bit_string: a np.ndarray with shape (1,number_of_bits) + :param results: a np.ndarray with shape (num_shots,number_of_bits) + :return: a list of length num_shots containing the hamming weight + """ + num_shots, n_bits = results.shape + _, pn_bits = perfect_bit_string.shape + if n_bits != pn_bits: + raise ValueError("Bit strings are not equal length, check you are runing on the same graph") + wt = [] + # loop over all results + for shot in results: + wt.append(n_bits * hamming(perfect_bit_string, shot)) + return wt + + +def get_error_hamming_distributions_from_list(wt_list, n_bits): + """ Get the distribution of the hamming weight of the error vector. + + :param wt_list: a list of length num_shots containing the hamming weight. + :param n_bits: the number of bit in the original binary strings. The hamming weight is an + integer between 0 and n_bits. + :return: the relative frequency of observing each hamming weight + """ + num_shots = len(wt_list) + + if n_bits < max(wt_list): + raise ValueError("Hamming weight can't be larger than the number of bits in a string.") + + hamming_wt_distrs = [] + hamming_wt_distr = [0. for _ in range(n_bits + 1)] + # record the fraction of shots that resulted in an error of the given weight + for wdx in range(n_bits): + hamming_wt_distr[int(wdx)] = wt_list.count(wdx) / num_shots + return hamming_wt_distr + + +def hamming_dist_rand(num_bits: int, pad: int = 0): + '''Return a list representing the Hamming distribution of + a particular bit string, of length num_bits, to randomly drawn bits. + + :param num_bits: number of bits in string + :param pad: number of zero elements to pad + returns: list of hamming weights with zero padding + ''' + N = 2 ** num_bits + pr = [comb(num_bits, ndx) / (2 ** num_bits) for ndx in range(0, num_bits + 1)] + padding = [0 for pdx in range(0, pad)] + return flatten_list([pr, padding]) + + +def flatten_list(xlist): + '''Flattens a list of lists. + + :param xlist: list of lists + :returns: a flattened list + ''' + return [item for sublist in xlist for item in sublist] + + +# helper functions to manipulate the dataframes +def get_hamming_dist(df: pd.DataFrame, depth_val: int, width_val: int): + ''' + Get Hamming distance from a dataframe for a particular depth and width. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param depth_val: depth of quantum circuit + :param width_val: width of quantum circuit + :return: smaller dataframe + ''' + idx = df.Depth == depth_val + jdx = df.Width == width_val + return df[idx & jdx].reset_index(drop=True) + + +def get_hamming_dists_fn_width(df: pd.DataFrame, depth_val: int): + ''' + Get Hamming distance from a dataframe for a particular depth. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param depth_val: depth of quantum circuit + :return: smaller dataframe + ''' + idx = df.Depth == depth_val + return df[idx].reset_index(drop=True) + + +def get_hamming_dists_fn_depth(df: pd.DataFrame, width_val: int): + ''' + Get Hamming distance from a dataframe for a particular width. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param width_val: width of quantum circuit + :return: smaller dataframe + ''' + jdx = df.Width == width_val + return df[jdx].reset_index(drop=True) + + +def basement_function(number: float): + ''' + Once you are in the basement you can't go lower. Defined as + + basement_function(number) = |floor(number)*heaviside(number,0)|, + + where heaviside(number,0) implies the value of the step function is + zero if number is zero. + + :param number: the basement function is applied to this number. + :returns: basement of the number + ''' + basement_of_number = np.abs(np.floor(number) * np.heaviside(number, 0)) + return basement_of_number + + +def CNOT_X_basis(control, target) -> Program: + """ + The CNOT in the X basis, i.e. + + CNOTX = |+X+| * I + |-X-| * Z + + where |+> and |-> are the +/- eigenstate of the Pauli X operator and * denotes a tensor product. + + :param control: qubit label + :param target: qubit label + :return: program + """ + prog = Program() + prog += H(control) + prog += CZ(control, target) + prog += H(control) + return prog + # ================================================================================================== # Graph tools # ================================================================================================== @@ -234,7 +564,7 @@ def generate_connected_subgraphs(G: nx.Graph, n_vert: int): Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all subgraphs with n_vert connect vertices. - :params n_vert: number of verticies of connected subgraph. + :params n_vert: number of vertices of connected subgraph. :params G: networkx Graph :returns: list of subgraphs with n_vert connected vertices ''' From f2d16141683fe6371ec5cf9c4382b9834d30fc69 Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Tue, 12 Mar 2019 08:53:55 +1100 Subject: [PATCH 07/49] no message --- examples/circuit_testing_josh.ipynb | 726 ++++++++++++++-------------- 1 file changed, 365 insertions(+), 361 deletions(-) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb index 8ac4a233..462eaa7a 100644 --- a/examples/circuit_testing_josh.ipynb +++ b/examples/circuit_testing_josh.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -98,14 +98,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ "'tcp://127.0.0.1:5555'" ] }, - "execution_count": 35, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -214,29 +214,33 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "RX(pi/2) 0\n", + "RZ(pi/2) 0\n", "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi) 1\n", - "RZ(pi/2) 2\n", - "RX(-pi) 2\n", + "RX(-pi) 1\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", "RX(-pi/2) 3\n", "RZ(pi/2) 4\n", - "RX(-pi) 4\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 5\n", + "RZ(-pi/2) 5\n", "RX(-pi/2) 5\n", - "RZ(-pi) 5\n", "RZ(-pi/2) 6\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", - "RX(-pi/2) 8\n", - "RZ(-pi/2) 8\n", + "RX(-pi) 6\n", + "RZ(-pi) 7\n", + "RX(-pi) 7\n", + "RX(pi/2) 8\n", + "RZ(-pi) 8\n", "\n" ] } @@ -255,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -291,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -300,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -339,9 +343,9 @@ " 0\n", " False\n", " 1\n", - " (6)\n", + " (3)\n", " False\n", - " (I 6, X 6)\n", + " (I 3, I 3)\n", " False\n", " 2\n", " 1\n", @@ -352,7 +356,7 @@ " 1\n", " (6)\n", " False\n", - " (I 6, I 6)\n", + " (I 6, X 6)\n", " False\n", " 2\n", " 1\n", @@ -361,9 +365,9 @@ " 2\n", " False\n", " 1\n", - " (1, 2)\n", + " (6, 7)\n", " False\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", + " (I 6, I 7, I 6, X 7, I 6, I 7)\n", " False\n", " 2\n", " 2\n", @@ -372,9 +376,9 @@ " 3\n", " False\n", " 1\n", - " (1, 2)\n", + " (7, 8)\n", " False\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", + " (I 7, I 8, I 7, I 8, I 7, I 8)\n", " False\n", " 2\n", " 2\n", @@ -383,9 +387,9 @@ " 4\n", " False\n", " 1\n", - " (3, 6, 7)\n", + " (1, 3, 4)\n", " False\n", - " (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...\n", " False\n", " 2\n", " 3\n", @@ -394,9 +398,9 @@ " 5\n", " False\n", " 1\n", - " (4, 5, 7)\n", + " (1, 3, 4)\n", " False\n", - " (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...\n", " False\n", " 2\n", " 3\n", @@ -405,9 +409,9 @@ " 6\n", " False\n", " 2\n", - " (7)\n", + " (1)\n", " False\n", - " (I 7, I 7, X 7)\n", + " (I 1, I 1, I 1)\n", " False\n", " 2\n", " 1\n", @@ -416,9 +420,9 @@ " 7\n", " False\n", " 2\n", - " (7)\n", + " (5)\n", " False\n", - " (I 7, X 7, I 7)\n", + " (I 5, X 5, X 5)\n", " False\n", " 2\n", " 1\n", @@ -427,9 +431,9 @@ " 8\n", " False\n", " 2\n", - " (5, 8)\n", + " (4, 7)\n", " False\n", - " (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...\n", + " (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...\n", " False\n", " 2\n", " 2\n", @@ -438,9 +442,9 @@ " 9\n", " False\n", " 2\n", - " (6, 7)\n", + " (2, 5)\n", " False\n", - " (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...\n", + " (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...\n", " False\n", " 2\n", " 2\n", @@ -449,9 +453,9 @@ " 10\n", " False\n", " 2\n", - " (6, 7, 8)\n", + " (0, 1, 3)\n", " False\n", - " (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...\n", + " (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...\n", " False\n", " 2\n", " 3\n", @@ -460,9 +464,9 @@ " 11\n", " False\n", " 2\n", - " (4, 5, 7)\n", + " (0, 3, 4)\n", " False\n", - " (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...\n", + " (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...\n", " False\n", " 2\n", " 3\n", @@ -473,7 +477,7 @@ " 3\n", " (8)\n", " False\n", - " (I 8, I 8, X 8, I 8)\n", + " (I 8, X 8, I 8, X 8)\n", " False\n", " 2\n", " 1\n", @@ -482,9 +486,9 @@ " 13\n", " False\n", " 3\n", - " (0)\n", + " (2)\n", " False\n", - " (I 0, X 0, I 0, I 0)\n", + " (I 2, X 2, I 2, I 2)\n", " False\n", " 2\n", " 1\n", @@ -493,9 +497,9 @@ " 14\n", " False\n", " 3\n", - " (4, 7)\n", + " (4, 5)\n", " False\n", - " (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...\n", + " (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...\n", " False\n", " 2\n", " 2\n", @@ -504,9 +508,9 @@ " 15\n", " False\n", " 3\n", - " (3, 4)\n", + " (4, 7)\n", " False\n", - " (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...\n", + " (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...\n", " False\n", " 2\n", " 2\n", @@ -517,7 +521,7 @@ " 3\n", " (1, 3, 4)\n", " False\n", - " (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...\n", + " (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...\n", " False\n", " 2\n", " 3\n", @@ -526,9 +530,9 @@ " 17\n", " False\n", " 3\n", - " (3, 4, 6)\n", + " (3, 4, 5)\n", " False\n", - " (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...\n", + " (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...\n", " False\n", " 2\n", " 3\n", @@ -539,44 +543,44 @@ ], "text/plain": [ " Active Reset Depth Lattice Layer Dagger \\\n", - "0 False 1 (6) False \n", + "0 False 1 (3) False \n", "1 False 1 (6) False \n", - "2 False 1 (1, 2) False \n", - "3 False 1 (1, 2) False \n", - "4 False 1 (3, 6, 7) False \n", - "5 False 1 (4, 5, 7) False \n", - "6 False 2 (7) False \n", - "7 False 2 (7) False \n", - "8 False 2 (5, 8) False \n", - "9 False 2 (6, 7) False \n", - "10 False 2 (6, 7, 8) False \n", - "11 False 2 (4, 5, 7) False \n", + "2 False 1 (6, 7) False \n", + "3 False 1 (7, 8) False \n", + "4 False 1 (1, 3, 4) False \n", + "5 False 1 (1, 3, 4) False \n", + "6 False 2 (1) False \n", + "7 False 2 (5) False \n", + "8 False 2 (4, 7) False \n", + "9 False 2 (2, 5) False \n", + "10 False 2 (0, 1, 3) False \n", + "11 False 2 (0, 3, 4) False \n", "12 False 3 (8) False \n", - "13 False 3 (0) False \n", - "14 False 3 (4, 7) False \n", - "15 False 3 (3, 4) False \n", + "13 False 3 (2) False \n", + "14 False 3 (4, 5) False \n", + "15 False 3 (4, 7) False \n", "16 False 3 (1, 3, 4) False \n", - "17 False 3 (3, 4, 6) False \n", + "17 False 3 (3, 4, 5) False \n", "\n", " Program Sandwich Dagger \\\n", - "0 (I 6, X 6) False \n", - "1 (I 6, I 6) False \n", - "2 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", - "3 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", - "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... False \n", - "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... False \n", - "6 (I 7, I 7, X 7) False \n", - "7 (I 7, X 7, I 7) False \n", - "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... False \n", - "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... False \n", - "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... False \n", - "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... False \n", - "12 (I 8, I 8, X 8, I 8) False \n", - "13 (I 0, X 0, I 0, I 0) False \n", - "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... False \n", - "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... False \n", - "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... False \n", - "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... False \n", + "0 (I 3, I 3) False \n", + "1 (I 6, X 6) False \n", + "2 (I 6, I 7, I 6, X 7, I 6, I 7) False \n", + "3 (I 7, I 8, I 7, I 8, I 7, I 8) False \n", + "4 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ... False \n", + "5 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ... False \n", + "6 (I 1, I 1, I 1) False \n", + "7 (I 5, X 5, X 5) False \n", + "8 (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ... False \n", + "9 (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ... False \n", + "10 (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ... False \n", + "11 (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ... False \n", + "12 (I 8, X 8, I 8, X 8) False \n", + "13 (I 2, X 2, I 2, I 2) False \n", + "14 (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ... False \n", + "15 (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ... False \n", + "16 (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ... False \n", + "17 (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ... False \n", "\n", " Trials Width \n", "0 2 1 \n", @@ -599,7 +603,7 @@ "17 2 3 " ] }, - "execution_count": 41, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -617,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -664,9 +668,9 @@ " 0\n", " False\n", " 1\n", - " (6)\n", - " (I 6, X 6)\n", - " [[1], [1]]\n", + " (3)\n", + " (I 3, I 3)\n", + " [[0], [0]]\n", " 2\n", " 1\n", " \n", @@ -675,8 +679,8 @@ " False\n", " 1\n", " (6)\n", - " (I 6, I 6)\n", - " [[0], [0]]\n", + " (I 6, X 6)\n", + " [[1], [1]]\n", " 2\n", " 1\n", " \n", @@ -684,9 +688,9 @@ " 2\n", " False\n", " 1\n", - " (1, 2)\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", - " [[1, 0], [1, 0]]\n", + " (6, 7)\n", + " (I 6, I 7, I 6, X 7, I 6, I 7)\n", + " [[0, 1], [0, 1]]\n", " 2\n", " 2\n", " \n", @@ -694,9 +698,9 @@ " 3\n", " False\n", " 1\n", - " (1, 2)\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", - " [[1, 0], [1, 0]]\n", + " (7, 8)\n", + " (I 7, I 8, I 7, I 8, I 7, I 8)\n", + " [[0, 0], [0, 0]]\n", " 2\n", " 2\n", " \n", @@ -704,9 +708,9 @@ " 4\n", " False\n", " 1\n", - " (3, 6, 7)\n", - " (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", + " (1, 3, 4)\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...\n", + " [[0, 0, 0], [0, 1, 0]]\n", " 2\n", " 3\n", " \n", @@ -714,9 +718,9 @@ " 5\n", " False\n", " 1\n", - " (4, 5, 7)\n", - " (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...\n", - " [[0, 1, 1], [0, 1, 1]]\n", + " (1, 3, 4)\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...\n", + " [[0, 1, 0], [0, 1, 1]]\n", " 2\n", " 3\n", " \n", @@ -724,9 +728,9 @@ " 6\n", " False\n", " 2\n", - " (7)\n", - " (I 7, I 7, X 7)\n", - " [[1], [1]]\n", + " (1)\n", + " (I 1, I 1, I 1)\n", + " [[0], [0]]\n", " 2\n", " 1\n", " \n", @@ -734,9 +738,9 @@ " 7\n", " False\n", " 2\n", - " (7)\n", - " (I 7, X 7, I 7)\n", - " [[1], [1]]\n", + " (5)\n", + " (I 5, X 5, X 5)\n", + " [[0], [0]]\n", " 2\n", " 1\n", " \n", @@ -744,9 +748,9 @@ " 8\n", " False\n", " 2\n", - " (5, 8)\n", - " (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...\n", - " [[1, 1], [1, 1]]\n", + " (4, 7)\n", + " (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...\n", + " [[0, 1], [0, 0]]\n", " 2\n", " 2\n", " \n", @@ -754,9 +758,9 @@ " 9\n", " False\n", " 2\n", - " (6, 7)\n", - " (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...\n", - " [[0, 0], [0, 0]]\n", + " (2, 5)\n", + " (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...\n", + " [[0, 1], [1, 1]]\n", " 2\n", " 2\n", " \n", @@ -764,9 +768,9 @@ " 10\n", " False\n", " 2\n", - " (6, 7, 8)\n", - " (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", + " (0, 1, 3)\n", + " (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...\n", + " [[1, 1, 1], [0, 1, 1]]\n", " 2\n", " 3\n", " \n", @@ -774,9 +778,9 @@ " 11\n", " False\n", " 2\n", - " (4, 5, 7)\n", - " (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...\n", - " [[0, 1, 0], [0, 1, 0]]\n", + " (0, 3, 4)\n", + " (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...\n", + " [[1, 1, 0], [1, 0, 0]]\n", " 2\n", " 3\n", " \n", @@ -785,8 +789,8 @@ " False\n", " 3\n", " (8)\n", - " (I 8, I 8, X 8, I 8)\n", - " [[1], [1]]\n", + " (I 8, X 8, I 8, X 8)\n", + " [[0], [0]]\n", " 2\n", " 1\n", " \n", @@ -794,8 +798,8 @@ " 13\n", " False\n", " 3\n", - " (0)\n", - " (I 0, X 0, I 0, I 0)\n", + " (2)\n", + " (I 2, X 2, I 2, I 2)\n", " [[1], [1]]\n", " 2\n", " 1\n", @@ -804,9 +808,9 @@ " 14\n", " False\n", " 3\n", - " (4, 7)\n", - " (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...\n", - " [[0, 1], [0, 1]]\n", + " (4, 5)\n", + " (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...\n", + " [[0, 0], [0, 0]]\n", " 2\n", " 2\n", " \n", @@ -814,8 +818,8 @@ " 15\n", " False\n", " 3\n", - " (3, 4)\n", - " (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...\n", + " (4, 7)\n", + " (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...\n", " [[0, 0], [0, 0]]\n", " 2\n", " 2\n", @@ -825,8 +829,8 @@ " False\n", " 3\n", " (1, 3, 4)\n", - " (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...\n", - " [[1, 0, 1], [1, 0, 1]]\n", + " (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...\n", + " [[0, 0, 0], [0, 0, 0]]\n", " 2\n", " 3\n", " \n", @@ -834,9 +838,9 @@ " 17\n", " False\n", " 3\n", - " (3, 4, 6)\n", - " (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", + " (3, 4, 5)\n", + " (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...\n", + " [[0, 1, 1], [0, 1, 1]]\n", " 2\n", " 3\n", " \n", @@ -846,44 +850,44 @@ ], "text/plain": [ " Active Reset Depth Lattice \\\n", - "0 False 1 (6) \n", + "0 False 1 (3) \n", "1 False 1 (6) \n", - "2 False 1 (1, 2) \n", - "3 False 1 (1, 2) \n", - "4 False 1 (3, 6, 7) \n", - "5 False 1 (4, 5, 7) \n", - "6 False 2 (7) \n", - "7 False 2 (7) \n", - "8 False 2 (5, 8) \n", - "9 False 2 (6, 7) \n", - "10 False 2 (6, 7, 8) \n", - "11 False 2 (4, 5, 7) \n", + "2 False 1 (6, 7) \n", + "3 False 1 (7, 8) \n", + "4 False 1 (1, 3, 4) \n", + "5 False 1 (1, 3, 4) \n", + "6 False 2 (1) \n", + "7 False 2 (5) \n", + "8 False 2 (4, 7) \n", + "9 False 2 (2, 5) \n", + "10 False 2 (0, 1, 3) \n", + "11 False 2 (0, 3, 4) \n", "12 False 3 (8) \n", - "13 False 3 (0) \n", - "14 False 3 (4, 7) \n", - "15 False 3 (3, 4) \n", + "13 False 3 (2) \n", + "14 False 3 (4, 5) \n", + "15 False 3 (4, 7) \n", "16 False 3 (1, 3, 4) \n", - "17 False 3 (3, 4, 6) \n", + "17 False 3 (3, 4, 5) \n", "\n", " Program Samples \\\n", - "0 (I 6, X 6) [[1], [1]] \n", - "1 (I 6, I 6) [[0], [0]] \n", - "2 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", - "3 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", - "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... [[0, 0, 0], [0, 0, 0]] \n", - "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... [[0, 1, 1], [0, 1, 1]] \n", - "6 (I 7, I 7, X 7) [[1], [1]] \n", - "7 (I 7, X 7, I 7) [[1], [1]] \n", - "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... [[1, 1], [1, 1]] \n", - "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... [[0, 0], [0, 0]] \n", - "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... [[0, 0, 0], [0, 0, 0]] \n", - "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... [[0, 1, 0], [0, 1, 0]] \n", - "12 (I 8, I 8, X 8, I 8) [[1], [1]] \n", - "13 (I 0, X 0, I 0, I 0) [[1], [1]] \n", - "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... [[0, 1], [0, 1]] \n", - "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... [[0, 0], [0, 0]] \n", - "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... [[1, 0, 1], [1, 0, 1]] \n", - "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... [[0, 0, 0], [0, 0, 0]] \n", + "0 (I 3, I 3) [[0], [0]] \n", + "1 (I 6, X 6) [[1], [1]] \n", + "2 (I 6, I 7, I 6, X 7, I 6, I 7) [[0, 1], [0, 1]] \n", + "3 (I 7, I 8, I 7, I 8, I 7, I 8) [[0, 0], [0, 0]] \n", + "4 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ... [[0, 0, 0], [0, 1, 0]] \n", + "5 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ... [[0, 1, 0], [0, 1, 1]] \n", + "6 (I 1, I 1, I 1) [[0], [0]] \n", + "7 (I 5, X 5, X 5) [[0], [0]] \n", + "8 (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ... [[0, 1], [0, 0]] \n", + "9 (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ... [[0, 1], [1, 1]] \n", + "10 (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ... [[1, 1, 1], [0, 1, 1]] \n", + "11 (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ... [[1, 1, 0], [1, 0, 0]] \n", + "12 (I 8, X 8, I 8, X 8) [[0], [0]] \n", + "13 (I 2, X 2, I 2, I 2) [[1], [1]] \n", + "14 (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ... [[0, 0], [0, 0]] \n", + "15 (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ... [[0, 0], [0, 0]] \n", + "16 (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ... [[0, 0, 0], [0, 0, 0]] \n", + "17 (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ... [[0, 1, 1], [0, 1, 1]] \n", "\n", " Trials Width \n", "0 2 1 \n", @@ -906,7 +910,7 @@ "17 2 3 " ] }, - "execution_count": 43, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -924,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -972,18 +976,18 @@ " \n", " 0\n", " False\n", - " [[1]]\n", + " [[0]]\n", " 1\n", " [1.0, 0.0]\n", " [1.0, 0.0]\n", " [0.5, 0.5]\n", - " (6)\n", + " (3)\n", " 1.0\n", " 1.0\n", " 0.500\n", " 0.500\n", - " (I 6, X 6)\n", - " [[1], [1]]\n", + " (I 3, I 3)\n", + " [[0], [0]]\n", " 0.0\n", " 0.500\n", " 2\n", @@ -993,7 +997,7 @@ " \n", " 1\n", " False\n", - " [[0]]\n", + " [[1]]\n", " 1\n", " [1.0, 0.0]\n", " [1.0, 0.0]\n", @@ -1003,8 +1007,8 @@ " 1.0\n", " 0.500\n", " 0.500\n", - " (I 6, I 6)\n", - " [[0], [0]]\n", + " (I 6, X 6)\n", + " [[1], [1]]\n", " 0.0\n", " 0.500\n", " 2\n", @@ -1014,18 +1018,18 @@ " \n", " 2\n", " False\n", - " [[1, 0]]\n", + " [[0, 1]]\n", " 1\n", " [1.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (1, 2)\n", + " (6, 7)\n", " 1.0\n", " 1.0\n", " 0.250\n", " 0.250\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", - " [[1, 0], [1, 0]]\n", + " (I 6, I 7, I 6, X 7, I 6, I 7)\n", + " [[0, 1], [0, 1]]\n", " 0.0\n", " 0.750\n", " 2\n", @@ -1035,18 +1039,18 @@ " \n", " 3\n", " False\n", - " [[1, 0]]\n", + " [[0, 0]]\n", " 1\n", " [1.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (1, 2)\n", + " (7, 8)\n", " 1.0\n", " 1.0\n", " 0.250\n", " 0.250\n", - " (I 1, I 2, X 1, X 2, CNOT 1 2)\n", - " [[1, 0], [1, 0]]\n", + " (I 7, I 8, I 7, I 8, I 7, I 8)\n", + " [[0, 0], [0, 0]]\n", " 0.0\n", " 0.750\n", " 2\n", @@ -1056,20 +1060,20 @@ " \n", " 4\n", " False\n", - " [[0, 0, 0]]\n", + " [[0, 1, 0]]\n", " 1\n", - " [1.0, 0.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", - " (3, 6, 7)\n", - " 1.0\n", - " 1.0\n", + " (1, 3, 4)\n", + " 0.5\n", + " 0.5\n", " 0.125\n", " 0.125\n", - " (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", - " 0.0\n", - " 0.875\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...\n", + " [[0, 0, 0], [0, 1, 0]]\n", + " 0.5\n", + " 0.500\n", " 2\n", " 3\n", " 0\n", @@ -1079,18 +1083,18 @@ " False\n", " [[0, 1, 1]]\n", " 1\n", - " [1.0, 0.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", - " (4, 5, 7)\n", - " 1.0\n", - " 1.0\n", + " (1, 3, 4)\n", + " 0.5\n", + " 0.5\n", " 0.125\n", " 0.125\n", - " (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...\n", - " [[0, 1, 1], [0, 1, 1]]\n", - " 0.0\n", - " 0.875\n", + " (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...\n", + " [[0, 1, 0], [0, 1, 1]]\n", + " 0.5\n", + " 0.500\n", " 2\n", " 3\n", " 0\n", @@ -1098,18 +1102,18 @@ " \n", " 6\n", " False\n", - " [[1]]\n", + " [[0]]\n", " 2\n", " [1.0, 0.0]\n", " [1.0, 0.0]\n", " [0.5, 0.5]\n", - " (7)\n", + " (1)\n", " 1.0\n", " 1.0\n", " 0.500\n", " 0.500\n", - " (I 7, I 7, X 7)\n", - " [[1], [1]]\n", + " (I 1, I 1, I 1)\n", + " [[0], [0]]\n", " 0.0\n", " 0.500\n", " 2\n", @@ -1119,18 +1123,18 @@ " \n", " 7\n", " False\n", - " [[1]]\n", + " [[0]]\n", " 2\n", " [1.0, 0.0]\n", " [1.0, 0.0]\n", " [0.5, 0.5]\n", - " (7)\n", + " (5)\n", " 1.0\n", " 1.0\n", " 0.500\n", " 0.500\n", - " (I 7, X 7, I 7)\n", - " [[1], [1]]\n", + " (I 5, X 5, X 5)\n", + " [[0], [0]]\n", " 0.0\n", " 0.500\n", " 2\n", @@ -1140,20 +1144,20 @@ " \n", " 8\n", " False\n", - " [[1, 1]]\n", + " [[0, 0]]\n", " 2\n", - " [1.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (5, 8)\n", - " 1.0\n", - " 1.0\n", + " (4, 7)\n", + " 0.5\n", + " 0.5\n", " 0.250\n", " 0.250\n", - " (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...\n", - " [[1, 1], [1, 1]]\n", - " 0.0\n", - " 0.750\n", + " (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...\n", + " [[0, 1], [0, 0]]\n", + " 0.5\n", + " 0.250\n", " 2\n", " 2\n", " 0\n", @@ -1161,20 +1165,20 @@ " \n", " 9\n", " False\n", - " [[0, 0]]\n", + " [[1, 1]]\n", " 2\n", - " [1.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (6, 7)\n", - " 1.0\n", - " 1.0\n", + " (2, 5)\n", + " 0.5\n", + " 0.5\n", " 0.250\n", " 0.250\n", - " (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...\n", - " [[0, 0], [0, 0]]\n", - " 0.0\n", - " 0.750\n", + " (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...\n", + " [[0, 1], [1, 1]]\n", + " 0.5\n", + " 0.250\n", " 2\n", " 2\n", " 0\n", @@ -1182,20 +1186,20 @@ " \n", " 10\n", " False\n", - " [[0, 0, 0]]\n", + " [[1, 1, 1]]\n", " 2\n", - " [1.0, 0.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", - " (6, 7, 8)\n", - " 1.0\n", - " 1.0\n", + " (0, 1, 3)\n", + " 0.5\n", + " 0.5\n", " 0.125\n", " 0.125\n", - " (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", - " 0.0\n", - " 0.875\n", + " (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...\n", + " [[1, 1, 1], [0, 1, 1]]\n", + " 0.5\n", + " 0.500\n", " 2\n", " 3\n", " 0\n", @@ -1203,20 +1207,20 @@ " \n", " 11\n", " False\n", - " [[0, 1, 0]]\n", + " [[1, 1, 0]]\n", " 2\n", - " [1.0, 0.0, 0.0, 0.0]\n", + " [0.5, 0.5, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", - " (4, 5, 7)\n", - " 1.0\n", - " 1.0\n", + " (0, 3, 4)\n", + " 0.5\n", + " 0.5\n", " 0.125\n", " 0.125\n", - " (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...\n", - " [[0, 1, 0], [0, 1, 0]]\n", - " 0.0\n", - " 0.875\n", + " (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...\n", + " [[1, 1, 0], [1, 0, 0]]\n", + " 0.5\n", + " 0.500\n", " 2\n", " 3\n", " 0\n", @@ -1224,7 +1228,7 @@ " \n", " 12\n", " False\n", - " [[1]]\n", + " [[0]]\n", " 3\n", " [1.0, 0.0]\n", " [1.0, 0.0]\n", @@ -1234,8 +1238,8 @@ " 1.0\n", " 0.500\n", " 0.500\n", - " (I 8, I 8, X 8, I 8)\n", - " [[1], [1]]\n", + " (I 8, X 8, I 8, X 8)\n", + " [[0], [0]]\n", " 0.0\n", " 0.500\n", " 2\n", @@ -1250,12 +1254,12 @@ " [1.0, 0.0]\n", " [1.0, 0.0]\n", " [0.5, 0.5]\n", - " (0)\n", + " (2)\n", " 1.0\n", " 1.0\n", " 0.500\n", " 0.500\n", - " (I 0, X 0, I 0, I 0)\n", + " (I 2, X 2, I 2, I 2)\n", " [[1], [1]]\n", " 0.0\n", " 0.500\n", @@ -1266,18 +1270,18 @@ " \n", " 14\n", " False\n", - " [[0, 1]]\n", + " [[0, 0]]\n", " 3\n", " [1.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (4, 7)\n", + " (4, 5)\n", " 1.0\n", " 1.0\n", " 0.250\n", " 0.250\n", - " (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...\n", - " [[0, 1], [0, 1]]\n", + " (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...\n", + " [[0, 0], [0, 0]]\n", " 0.0\n", " 0.750\n", " 2\n", @@ -1292,12 +1296,12 @@ " [1.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0]\n", " [0.25, 0.5, 0.25]\n", - " (3, 4)\n", + " (4, 7)\n", " 1.0\n", " 1.0\n", " 0.250\n", " 0.250\n", - " (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...\n", + " (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...\n", " [[0, 0], [0, 0]]\n", " 0.0\n", " 0.750\n", @@ -1308,20 +1312,20 @@ " \n", " 16\n", " False\n", - " [[1, 0, 1]]\n", + " [[1, 0, 0]]\n", " 3\n", - " [1.0, 0.0, 0.0, 0.0]\n", + " [0.0, 1.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", " (1, 3, 4)\n", - " 1.0\n", - " 1.0\n", + " 0.0\n", + " 0.0\n", " 0.125\n", " 0.125\n", - " (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...\n", - " [[1, 0, 1], [1, 0, 1]]\n", - " 0.0\n", - " 0.875\n", + " (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...\n", + " [[0, 0, 0], [0, 0, 0]]\n", + " 1.0\n", + " 0.625\n", " 2\n", " 3\n", " 0\n", @@ -1329,18 +1333,18 @@ " \n", " 17\n", " False\n", - " [[0, 0, 0]]\n", + " [[0, 1, 1]]\n", " 3\n", " [1.0, 0.0, 0.0, 0.0]\n", " [1.0, 0.0, 0.0, 0.0]\n", " [0.125, 0.375, 0.375, 0.125]\n", - " (3, 4, 6)\n", + " (3, 4, 5)\n", " 1.0\n", " 1.0\n", " 0.125\n", " 0.125\n", - " (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...\n", - " [[0, 0, 0], [0, 0, 0]]\n", + " (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...\n", + " [[0, 1, 1], [0, 1, 1]]\n", " 0.0\n", " 0.875\n", " 2\n", @@ -1353,104 +1357,104 @@ ], "text/plain": [ " Active Reset Answer Depth Hamming dist. data \\\n", - "0 False [[1]] 1 [1.0, 0.0] \n", - "1 False [[0]] 1 [1.0, 0.0] \n", - "2 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", - "3 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", - "4 False [[0, 0, 0]] 1 [1.0, 0.0, 0.0, 0.0] \n", - "5 False [[0, 1, 1]] 1 [1.0, 0.0, 0.0, 0.0] \n", - "6 False [[1]] 2 [1.0, 0.0] \n", - "7 False [[1]] 2 [1.0, 0.0] \n", - "8 False [[1, 1]] 2 [1.0, 0.0, 0.0] \n", - "9 False [[0, 0]] 2 [1.0, 0.0, 0.0] \n", - "10 False [[0, 0, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", - "11 False [[0, 1, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", - "12 False [[1]] 3 [1.0, 0.0] \n", + "0 False [[0]] 1 [1.0, 0.0] \n", + "1 False [[1]] 1 [1.0, 0.0] \n", + "2 False [[0, 1]] 1 [1.0, 0.0, 0.0] \n", + "3 False [[0, 0]] 1 [1.0, 0.0, 0.0] \n", + "4 False [[0, 1, 0]] 1 [0.5, 0.5, 0.0, 0.0] \n", + "5 False [[0, 1, 1]] 1 [0.5, 0.5, 0.0, 0.0] \n", + "6 False [[0]] 2 [1.0, 0.0] \n", + "7 False [[0]] 2 [1.0, 0.0] \n", + "8 False [[0, 0]] 2 [0.5, 0.5, 0.0] \n", + "9 False [[1, 1]] 2 [0.5, 0.5, 0.0] \n", + "10 False [[1, 1, 1]] 2 [0.5, 0.5, 0.0, 0.0] \n", + "11 False [[1, 1, 0]] 2 [0.5, 0.5, 0.0, 0.0] \n", + "12 False [[0]] 3 [1.0, 0.0] \n", "13 False [[1]] 3 [1.0, 0.0] \n", - "14 False [[0, 1]] 3 [1.0, 0.0, 0.0] \n", + "14 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", "15 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", - "16 False [[1, 0, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", - "17 False [[0, 0, 0]] 3 [1.0, 0.0, 0.0, 0.0] \n", + "16 False [[1, 0, 0]] 3 [0.0, 1.0, 0.0, 0.0] \n", + "17 False [[0, 1, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", "\n", " Hamming dist. ideal Hamming dist. rand Lattice \\\n", - "0 [1.0, 0.0] [0.5, 0.5] (6) \n", + "0 [1.0, 0.0] [0.5, 0.5] (3) \n", "1 [1.0, 0.0] [0.5, 0.5] (6) \n", - "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", - "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", - "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 6, 7) \n", - "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", - "6 [1.0, 0.0] [0.5, 0.5] (7) \n", - "7 [1.0, 0.0] [0.5, 0.5] (7) \n", - "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (5, 8) \n", - "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", - "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (6, 7, 8) \n", - "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", + "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", + "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (7, 8) \n", + "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", + "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", + "6 [1.0, 0.0] [0.5, 0.5] (1) \n", + "7 [1.0, 0.0] [0.5, 0.5] (5) \n", + "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", + "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (2, 5) \n", + "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (0, 1, 3) \n", + "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (0, 3, 4) \n", "12 [1.0, 0.0] [0.5, 0.5] (8) \n", - "13 [1.0, 0.0] [0.5, 0.5] (0) \n", - "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", - "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (3, 4) \n", + "13 [1.0, 0.0] [0.5, 0.5] (2) \n", + "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 5) \n", + "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", "16 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", - "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 6) \n", + "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 5) \n", "\n", " Pr. success data Pr. success loge data Pr. success loge rand \\\n", "0 1.0 1.0 0.500 \n", "1 1.0 1.0 0.500 \n", "2 1.0 1.0 0.250 \n", "3 1.0 1.0 0.250 \n", - "4 1.0 1.0 0.125 \n", - "5 1.0 1.0 0.125 \n", + "4 0.5 0.5 0.125 \n", + "5 0.5 0.5 0.125 \n", "6 1.0 1.0 0.500 \n", "7 1.0 1.0 0.500 \n", - "8 1.0 1.0 0.250 \n", - "9 1.0 1.0 0.250 \n", - "10 1.0 1.0 0.125 \n", - "11 1.0 1.0 0.125 \n", + "8 0.5 0.5 0.250 \n", + "9 0.5 0.5 0.250 \n", + "10 0.5 0.5 0.125 \n", + "11 0.5 0.5 0.125 \n", "12 1.0 1.0 0.500 \n", "13 1.0 1.0 0.500 \n", "14 1.0 1.0 0.250 \n", "15 1.0 1.0 0.250 \n", - "16 1.0 1.0 0.125 \n", + "16 0.0 0.0 0.125 \n", "17 1.0 1.0 0.125 \n", "\n", " Pr. success rand Program \\\n", - "0 0.500 (I 6, X 6) \n", - "1 0.500 (I 6, I 6) \n", - "2 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", - "3 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", - "4 0.125 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... \n", - "5 0.125 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... \n", - "6 0.500 (I 7, I 7, X 7) \n", - "7 0.500 (I 7, X 7, I 7) \n", - "8 0.250 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... \n", - "9 0.250 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... \n", - "10 0.125 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... \n", - "11 0.125 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... \n", - "12 0.500 (I 8, I 8, X 8, I 8) \n", - "13 0.500 (I 0, X 0, I 0, I 0) \n", - "14 0.250 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... \n", - "15 0.250 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... \n", - "16 0.125 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... \n", - "17 0.125 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... \n", + "0 0.500 (I 3, I 3) \n", + "1 0.500 (I 6, X 6) \n", + "2 0.250 (I 6, I 7, I 6, X 7, I 6, I 7) \n", + "3 0.250 (I 7, I 8, I 7, I 8, I 7, I 8) \n", + "4 0.125 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ... \n", + "5 0.125 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ... \n", + "6 0.500 (I 1, I 1, I 1) \n", + "7 0.500 (I 5, X 5, X 5) \n", + "8 0.250 (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ... \n", + "9 0.250 (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ... \n", + "10 0.125 (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ... \n", + "11 0.125 (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ... \n", + "12 0.500 (I 8, X 8, I 8, X 8) \n", + "13 0.500 (I 2, X 2, I 2, I 2) \n", + "14 0.250 (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ... \n", + "15 0.250 (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ... \n", + "16 0.125 (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ... \n", + "17 0.125 (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ... \n", "\n", " Samples TVD(data, ideal) TVD(data, rand) Trials Width \\\n", - "0 [[1], [1]] 0.0 0.500 2 1 \n", - "1 [[0], [0]] 0.0 0.500 2 1 \n", - "2 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", - "3 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", - "4 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", - "5 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", - "6 [[1], [1]] 0.0 0.500 2 1 \n", - "7 [[1], [1]] 0.0 0.500 2 1 \n", - "8 [[1, 1], [1, 1]] 0.0 0.750 2 2 \n", - "9 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "10 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", - "11 [[0, 1, 0], [0, 1, 0]] 0.0 0.875 2 3 \n", - "12 [[1], [1]] 0.0 0.500 2 1 \n", + "0 [[0], [0]] 0.0 0.500 2 1 \n", + "1 [[1], [1]] 0.0 0.500 2 1 \n", + "2 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", + "3 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", + "4 [[0, 0, 0], [0, 1, 0]] 0.5 0.500 2 3 \n", + "5 [[0, 1, 0], [0, 1, 1]] 0.5 0.500 2 3 \n", + "6 [[0], [0]] 0.0 0.500 2 1 \n", + "7 [[0], [0]] 0.0 0.500 2 1 \n", + "8 [[0, 1], [0, 0]] 0.5 0.250 2 2 \n", + "9 [[0, 1], [1, 1]] 0.5 0.250 2 2 \n", + "10 [[1, 1, 1], [0, 1, 1]] 0.5 0.500 2 3 \n", + "11 [[1, 1, 0], [1, 0, 0]] 0.5 0.500 2 3 \n", + "12 [[0], [0]] 0.0 0.500 2 1 \n", "13 [[1], [1]] 0.0 0.500 2 1 \n", - "14 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", + "14 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", "15 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "16 [[1, 0, 1], [1, 0, 1]] 0.0 0.875 2 3 \n", - "17 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "16 [[0, 0, 0], [0, 0, 0]] 1.0 0.625 2 3 \n", + "17 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", "\n", " loge = basement[log_2(Width)-1] \n", "0 0 \n", @@ -1473,7 +1477,7 @@ "17 0 " ] }, - "execution_count": 44, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1505,24 +1509,24 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[16, 18, 24, 35, 52, 76, 108, 135, 156, 166, 164, 149, 120, 76, 16, 1]" + "[9, 12, 22, 36, 49, 48, 32, 9, 1]" ] }, - "execution_count": 5, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, From b4e444f8e5a7f9acaeadbfd229dabf6d5725a33f Mon Sep 17 00:00:00 2001 From: Kyle Date: Tue, 12 Mar 2019 19:55:28 -0400 Subject: [PATCH 08/49] Added notebook for personal dev. --- examples/circuit_testing_kyle.ipynb | 5344 +++++++++++++++++++++++++++ 1 file changed, 5344 insertions(+) create mode 100644 examples/circuit_testing_kyle.ipynb diff --git a/examples/circuit_testing_kyle.ipynb b/examples/circuit_testing_kyle.ipynb new file mode 100644 index 00000000..889d6446 --- /dev/null +++ b/examples/circuit_testing_kyle.ipynb @@ -0,0 +1,5344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Circuit testing\n", + "\n", + "\n", + "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", + "\n", + "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", + "\n", + "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import itertools\n", + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "import time\n", + "# from scipy.spatial.distance import hamming\n", + "# import scipy.interpolate\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", + "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", + "from pyquil.quilbase import Pragma\n", + "\n", + "from forest_benchmarking.circuit_testing import *" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def two_q_id(qb1,qb2):\n", + " prog = Program()\n", + " prog +=I(qb1)\n", + " prog +=I(qb2)\n", + " return prog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# if you want to run on a \"real lattice\"\n", + "from pyquil import *\n", + "#list_quantum_computers()\n", + "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", + "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", + "\n", + "qc_perfect = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", + "qc_noisy = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#qc_perfect.device.get_specs()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw(qc_perfect.qubit_topology(),with_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "G = qc_perfect.qubit_topology()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# gate sets" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "one_q_gates = [X,Z,I]\n", + "two_q_gates = [two_q_id,CZ]\n", + "\n", + "one_c_gates = [X,I]\n", + "two_c_gates = [two_q_id,CNOT]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "#prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "#print(prog1+prog2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# random cliffords" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from forest_benchmarking.rb import get_rb_gateset" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# my config has gone all cattywampus so i need to do this\n", + "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'tcp://127.0.0.1:5555'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm.client.endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", + "gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 2\n", + "RZ(-pi) 3\n", + "RX(-pi) 3\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "RZ(-pi/2) 7\n", + "RX(-pi) 7\n", + "RX(-pi/2) 8\n", + "\n" + ] + } + ], + "source": [ + "progy = random_single_qubit_cliffords(bm,G)\n", + "print(progy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Layer crap" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "#print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "circuit_depth = 3\n", + "circuit_width = 3\n", + "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", + " one_q_gates = one_c_gates, \n", + " two_q_gates = two_c_gates)\n", + "layer_dagger = False\n", + "sandwich_dagger = False\n", + "num_rand_subgraphs = 2\n", + "num_shots_per_circuit = 2\n", + "use_active_reset= False" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthLatticeLayer DaggerProgramSandwich DaggerTrialsWidth
0False1(6)False(I 6, X 6)False21
1False1(6)False(I 6, I 6)False21
2False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
3False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
4False1(3, 6, 7)False(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...False23
5False1(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...False23
6False2(7)False(I 7, I 7, X 7)False21
7False2(7)False(I 7, X 7, I 7)False21
8False2(5, 8)False(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...False22
9False2(6, 7)False(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...False22
10False2(6, 7, 8)False(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...False23
11False2(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...False23
12False3(8)False(I 8, I 8, X 8, I 8)False21
13False3(0)False(I 0, X 0, I 0, I 0)False21
14False3(4, 7)False(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...False22
15False3(3, 4)False(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...False22
16False3(1, 3, 4)False(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...False23
17False3(3, 4, 6)False(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...False23
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Lattice Layer Dagger \\\n", + "0 False 1 (6) False \n", + "1 False 1 (6) False \n", + "2 False 1 (1, 2) False \n", + "3 False 1 (1, 2) False \n", + "4 False 1 (3, 6, 7) False \n", + "5 False 1 (4, 5, 7) False \n", + "6 False 2 (7) False \n", + "7 False 2 (7) False \n", + "8 False 2 (5, 8) False \n", + "9 False 2 (6, 7) False \n", + "10 False 2 (6, 7, 8) False \n", + "11 False 2 (4, 5, 7) False \n", + "12 False 3 (8) False \n", + "13 False 3 (0) False \n", + "14 False 3 (4, 7) False \n", + "15 False 3 (3, 4) False \n", + "16 False 3 (1, 3, 4) False \n", + "17 False 3 (3, 4, 6) False \n", + "\n", + " Program Sandwich Dagger \\\n", + "0 (I 6, X 6) False \n", + "1 (I 6, I 6) False \n", + "2 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", + "3 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", + "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... False \n", + "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... False \n", + "6 (I 7, I 7, X 7) False \n", + "7 (I 7, X 7, I 7) False \n", + "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... False \n", + "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... False \n", + "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... False \n", + "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... False \n", + "12 (I 8, I 8, X 8, I 8) False \n", + "13 (I 0, X 0, I 0, I 0) False \n", + "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... False \n", + "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... False \n", + "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... False \n", + "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... False \n", + "\n", + " Trials Width \n", + "0 2 1 \n", + "1 2 1 \n", + "2 2 2 \n", + "3 2 2 \n", + "4 2 3 \n", + "5 2 3 \n", + "6 2 1 \n", + "7 2 1 \n", + "8 2 2 \n", + "9 2 2 \n", + "10 2 3 \n", + "11 2 3 \n", + "12 2 1 \n", + "13 2 1 \n", + "14 2 2 \n", + "15 2 2 \n", + "16 2 3 \n", + "17 2 3 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "daty = acquire_circuit_sandwich_data(qc_noisy,exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthLatticeProgramSamplesTrialsWidth
0False1(6)(I 6, X 6)[[1], [1]]21
1False1(6)(I 6, I 6)[[0], [0]]21
2False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
3False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
4False1(3, 6, 7)(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]23
5False1(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]23
6False2(7)(I 7, I 7, X 7)[[1], [1]]21
7False2(7)(I 7, X 7, I 7)[[1], [1]]21
8False2(5, 8)(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]22
9False2(6, 7)(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]22
10False2(6, 7, 8)(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]23
11False2(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]23
12False3(8)(I 8, I 8, X 8, I 8)[[1], [1]]21
13False3(0)(I 0, X 0, I 0, I 0)[[1], [1]]21
14False3(4, 7)(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]22
15False3(3, 4)(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]22
16False3(1, 3, 4)(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]23
17False3(3, 4, 6)(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]23
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Lattice \\\n", + "0 False 1 (6) \n", + "1 False 1 (6) \n", + "2 False 1 (1, 2) \n", + "3 False 1 (1, 2) \n", + "4 False 1 (3, 6, 7) \n", + "5 False 1 (4, 5, 7) \n", + "6 False 2 (7) \n", + "7 False 2 (7) \n", + "8 False 2 (5, 8) \n", + "9 False 2 (6, 7) \n", + "10 False 2 (6, 7, 8) \n", + "11 False 2 (4, 5, 7) \n", + "12 False 3 (8) \n", + "13 False 3 (0) \n", + "14 False 3 (4, 7) \n", + "15 False 3 (3, 4) \n", + "16 False 3 (1, 3, 4) \n", + "17 False 3 (3, 4, 6) \n", + "\n", + " Program Samples \\\n", + "0 (I 6, X 6) [[1], [1]] \n", + "1 (I 6, I 6) [[0], [0]] \n", + "2 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", + "3 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", + "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... [[0, 0, 0], [0, 0, 0]] \n", + "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... [[0, 1, 1], [0, 1, 1]] \n", + "6 (I 7, I 7, X 7) [[1], [1]] \n", + "7 (I 7, X 7, I 7) [[1], [1]] \n", + "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... [[1, 1], [1, 1]] \n", + "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... [[0, 0], [0, 0]] \n", + "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... [[0, 0, 0], [0, 0, 0]] \n", + "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... [[0, 1, 0], [0, 1, 0]] \n", + "12 (I 8, I 8, X 8, I 8) [[1], [1]] \n", + "13 (I 0, X 0, I 0, I 0) [[1], [1]] \n", + "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... [[0, 1], [0, 1]] \n", + "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... [[0, 0], [0, 0]] \n", + "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... [[1, 0, 1], [1, 0, 1]] \n", + "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... [[0, 0, 0], [0, 0, 0]] \n", + "\n", + " Trials Width \n", + "0 2 1 \n", + "1 2 1 \n", + "2 2 2 \n", + "3 2 2 \n", + "4 2 3 \n", + "5 2 3 \n", + "6 2 1 \n", + "7 2 1 \n", + "8 2 2 \n", + "9 2 2 \n", + "10 2 3 \n", + "11 2 3 \n", + "12 2 1 \n", + "13 2 1 \n", + "14 2 2 \n", + "15 2 2 \n", + "16 2 3 \n", + "17 2 3 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "daty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetAnswerDepthHamming dist. dataHamming dist. idealHamming dist. randLatticePr. success dataPr. success loge dataPr. success loge randPr. success randProgramSamplesTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False[[1]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, X 6)[[1], [1]]0.00.500210
1False[[0]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, I 6)[[0], [0]]0.00.500210
2False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
3False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
4False[[0, 0, 0]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 6, 7)1.01.00.1250.125(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
5False[[0, 1, 1]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]0.00.875230
6False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, I 7, X 7)[[1], [1]]0.00.500210
7False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, X 7, I 7)[[1], [1]]0.00.500210
8False[[1, 1]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](5, 8)1.01.00.2500.250(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]0.00.750220
9False[[0, 0]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](6, 7)1.01.00.2500.250(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]0.00.750220
10False[[0, 0, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](6, 7, 8)1.01.00.1250.125(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
11False[[0, 1, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]0.00.875230
12False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](8)1.01.00.5000.500(I 8, I 8, X 8, I 8)[[1], [1]]0.00.500210
13False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](0)1.01.00.5000.500(I 0, X 0, I 0, I 0)[[1], [1]]0.00.500210
14False[[0, 1]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 7)1.01.00.2500.250(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]0.00.750220
15False[[0, 0]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](3, 4)1.01.00.2500.250(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]0.00.750220
16False[[1, 0, 1]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)1.01.00.1250.125(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]0.00.875230
17False[[0, 0, 0]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 4, 6)1.01.00.1250.125(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
\n", + "
" + ], + "text/plain": [ + " Active Reset Answer Depth Hamming dist. data \\\n", + "0 False [[1]] 1 [1.0, 0.0] \n", + "1 False [[0]] 1 [1.0, 0.0] \n", + "2 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", + "3 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", + "4 False [[0, 0, 0]] 1 [1.0, 0.0, 0.0, 0.0] \n", + "5 False [[0, 1, 1]] 1 [1.0, 0.0, 0.0, 0.0] \n", + "6 False [[1]] 2 [1.0, 0.0] \n", + "7 False [[1]] 2 [1.0, 0.0] \n", + "8 False [[1, 1]] 2 [1.0, 0.0, 0.0] \n", + "9 False [[0, 0]] 2 [1.0, 0.0, 0.0] \n", + "10 False [[0, 0, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", + "11 False [[0, 1, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", + "12 False [[1]] 3 [1.0, 0.0] \n", + "13 False [[1]] 3 [1.0, 0.0] \n", + "14 False [[0, 1]] 3 [1.0, 0.0, 0.0] \n", + "15 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", + "16 False [[1, 0, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", + "17 False [[0, 0, 0]] 3 [1.0, 0.0, 0.0, 0.0] \n", + "\n", + " Hamming dist. ideal Hamming dist. rand Lattice \\\n", + "0 [1.0, 0.0] [0.5, 0.5] (6) \n", + "1 [1.0, 0.0] [0.5, 0.5] (6) \n", + "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", + "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", + "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 6, 7) \n", + "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", + "6 [1.0, 0.0] [0.5, 0.5] (7) \n", + "7 [1.0, 0.0] [0.5, 0.5] (7) \n", + "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (5, 8) \n", + "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", + "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (6, 7, 8) \n", + "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", + "12 [1.0, 0.0] [0.5, 0.5] (8) \n", + "13 [1.0, 0.0] [0.5, 0.5] (0) \n", + "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", + "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (3, 4) \n", + "16 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", + "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 6) \n", + "\n", + " Pr. success data Pr. success loge data Pr. success loge rand \\\n", + "0 1.0 1.0 0.500 \n", + "1 1.0 1.0 0.500 \n", + "2 1.0 1.0 0.250 \n", + "3 1.0 1.0 0.250 \n", + "4 1.0 1.0 0.125 \n", + "5 1.0 1.0 0.125 \n", + "6 1.0 1.0 0.500 \n", + "7 1.0 1.0 0.500 \n", + "8 1.0 1.0 0.250 \n", + "9 1.0 1.0 0.250 \n", + "10 1.0 1.0 0.125 \n", + "11 1.0 1.0 0.125 \n", + "12 1.0 1.0 0.500 \n", + "13 1.0 1.0 0.500 \n", + "14 1.0 1.0 0.250 \n", + "15 1.0 1.0 0.250 \n", + "16 1.0 1.0 0.125 \n", + "17 1.0 1.0 0.125 \n", + "\n", + " Pr. success rand Program \\\n", + "0 0.500 (I 6, X 6) \n", + "1 0.500 (I 6, I 6) \n", + "2 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", + "3 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", + "4 0.125 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... \n", + "5 0.125 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... \n", + "6 0.500 (I 7, I 7, X 7) \n", + "7 0.500 (I 7, X 7, I 7) \n", + "8 0.250 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... \n", + "9 0.250 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... \n", + "10 0.125 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... \n", + "11 0.125 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... \n", + "12 0.500 (I 8, I 8, X 8, I 8) \n", + "13 0.500 (I 0, X 0, I 0, I 0) \n", + "14 0.250 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... \n", + "15 0.250 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... \n", + "16 0.125 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... \n", + "17 0.125 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... \n", + "\n", + " Samples TVD(data, ideal) TVD(data, rand) Trials Width \\\n", + "0 [[1], [1]] 0.0 0.500 2 1 \n", + "1 [[0], [0]] 0.0 0.500 2 1 \n", + "2 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", + "3 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", + "4 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "5 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", + "6 [[1], [1]] 0.0 0.500 2 1 \n", + "7 [[1], [1]] 0.0 0.500 2 1 \n", + "8 [[1, 1], [1, 1]] 0.0 0.750 2 2 \n", + "9 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", + "10 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "11 [[0, 1, 0], [0, 1, 0]] 0.0 0.875 2 3 \n", + "12 [[1], [1]] 0.0 0.500 2 1 \n", + "13 [[1], [1]] 0.0 0.500 2 1 \n", + "14 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", + "15 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", + "16 [[1, 0, 1], [1, 0, 1]] 0.0 0.875 2 3 \n", + "17 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", + "\n", + " loge = basement[log_2(Width)-1] \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 \n", + "10 0 \n", + "11 0 \n", + "12 0 \n", + "13 0 \n", + "14 0 \n", + "15 0 \n", + "16 0 \n", + "17 0 " + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimate_random_classical_circuit_errors(qc_perfect,daty)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot the distribution of sublattice widths" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[16, 18, 24, 35, 52, 76, 108, 135, 156, 166, 164, 149, 120, 76, 16, 1]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = qc_perfect.qubit_topology()\n", + "len(qc_perfect.qubit_topology())\n", + "# distribution of graph lengths\n", + "disty = []\n", + "for gdx in range(1,len(G.nodes)+1):\n", + " listg = generate_connected_subgraphs(G,gdx)\n", + " disty.append(len(listg))\n", + "\n", + "cir_wid = list(range(1,len(G.nodes)+1))\n", + "plt.bar(cir_wid, disty, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "disty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acquire data in Z basis" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# # with these parameters the cell below takes about 1 hour 40 minutes\n", + "# num_shots_per_circuit = 400\n", + "# num_rand_subgraphs = 16\n", + "# circuit_depth = 18\n", + "# circuit_width = 15 #max = len(G.nodes)\n", + "# x_basis = False\n", + "# active_reset = True\n", + "# total == 6077" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# with these parameters the cell below takes about 5 minutes\n", + "num_shots_per_circuit = 1000\n", + "num_rand_subgraphs = 20\n", + "circuit_depth = 6\n", + "circuit_width = 4 #max = len(G.nodes)\n", + "x_basis = False\n", + "active_reset = False" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthIn X basisLatticeProgramTrialsWidth
0False1False(13)(I 13)10001
1False1False(1)(I 1, X 1)10001
2False1False(7)(I 7)10001
3False1False(7)(I 7, X 7)10001
4False1False(2)(I 2, X 2)10001
5False1False(10)(I 10, X 10)10001
6False1False(7)(I 7)10001
7False1False(4)(I 4)10001
8False1False(13)(I 13)10001
9False1False(11)(I 11)10001
10False1False(10)(I 10, X 10)10001
11False1False(14)(I 14)10001
12False1False(11)(I 11)10001
13False1False(2)(I 2)10001
14False1False(12)(I 12)10001
15False1False(10)(I 10)10001
16False1False(2)(I 2, X 2)10001
17False1False(16)(I 16)10001
18False1False(15)(I 15, X 15)10001
19False1False(11)(I 11, X 11)10001
20False1False(13, 14)(I 13, I 14, X 13)10002
21False1False(17, 10)(I 17, I 10, X 17)10002
22False1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)10002
23False1False(16, 17)(I 16, I 17, X 16)10002
24False1False(1, 2)(I 1, I 2, CNOT 1 2)10002
25False1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)10002
26False1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)10002
27False1False(17, 10)(I 17, I 10, CNOT 17 10)10002
28False1False(16, 15)(I 16, I 15, X 16)10002
29False1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)10002
........................
450False6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...10003
451False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...10003
452False6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...10003
453False6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...10003
454False6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...10003
455False6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...10003
456False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...10003
457False6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...10003
458False6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...10003
459False6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...10003
460False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...10004
461False6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...10004
462False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...10004
463False6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...10004
464False6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...10004
465False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...10004
466False6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...10004
467False6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...10004
468False6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...10004
469False6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...10004
470False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...10004
471False6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...10004
472False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...10004
473False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...10004
474False6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...10004
475False6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...10004
476False6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...10004
477False6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...10004
478False6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...10004
479False6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...10004
\n", + "

480 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Active Reset Depth In X basis Lattice \\\n", + "0 False 1 False (13) \n", + "1 False 1 False (1) \n", + "2 False 1 False (7) \n", + "3 False 1 False (7) \n", + "4 False 1 False (2) \n", + "5 False 1 False (10) \n", + "6 False 1 False (7) \n", + "7 False 1 False (4) \n", + "8 False 1 False (13) \n", + "9 False 1 False (11) \n", + "10 False 1 False (10) \n", + "11 False 1 False (14) \n", + "12 False 1 False (11) \n", + "13 False 1 False (2) \n", + "14 False 1 False (12) \n", + "15 False 1 False (10) \n", + "16 False 1 False (2) \n", + "17 False 1 False (16) \n", + "18 False 1 False (15) \n", + "19 False 1 False (11) \n", + "20 False 1 False (13, 14) \n", + "21 False 1 False (17, 10) \n", + "22 False 1 False (4, 5) \n", + "23 False 1 False (16, 17) \n", + "24 False 1 False (1, 2) \n", + "25 False 1 False (3, 4) \n", + "26 False 1 False (0, 7) \n", + "27 False 1 False (17, 10) \n", + "28 False 1 False (16, 15) \n", + "29 False 1 False (17, 10) \n", + ".. ... ... ... ... \n", + "450 False 6 False (17, 10, 11) \n", + "451 False 6 False (4, 5, 6) \n", + "452 False 6 False (16, 14, 15) \n", + "453 False 6 False (13, 14, 15) \n", + "454 False 6 False (16, 14, 15) \n", + "455 False 6 False (16, 14, 15) \n", + "456 False 6 False (4, 5, 6) \n", + "457 False 6 False (0, 1, 2) \n", + "458 False 6 False (0, 6, 7) \n", + "459 False 6 False (16, 2, 15) \n", + "460 False 6 False (0, 1, 2, 15) \n", + "461 False 6 False (4, 5, 6, 7) \n", + "462 False 6 False (16, 1, 14, 15) \n", + "463 False 6 False (16, 1, 10, 17) \n", + "464 False 6 False (2, 3, 4, 15) \n", + "465 False 6 False (16, 1, 14, 15) \n", + "466 False 6 False (2, 13, 14, 15) \n", + "467 False 6 False (11, 12, 13, 14) \n", + "468 False 6 False (16, 17, 2, 15) \n", + "469 False 6 False (0, 1, 6, 7) \n", + "470 False 6 False (10, 11, 12, 13) \n", + "471 False 6 False (0, 1, 16, 15) \n", + "472 False 6 False (10, 11, 12, 13) \n", + "473 False 6 False (0, 1, 2, 15) \n", + "474 False 6 False (16, 1, 2, 3) \n", + "475 False 6 False (17, 10, 11, 12) \n", + "476 False 6 False (16, 17, 14, 15) \n", + "477 False 6 False (16, 17, 10, 15) \n", + "478 False 6 False (16, 13, 14, 15) \n", + "479 False 6 False (2, 3, 4, 5) \n", + "\n", + " Program Trials Width \n", + "0 (I 13) 1000 1 \n", + "1 (I 1, X 1) 1000 1 \n", + "2 (I 7) 1000 1 \n", + "3 (I 7, X 7) 1000 1 \n", + "4 (I 2, X 2) 1000 1 \n", + "5 (I 10, X 10) 1000 1 \n", + "6 (I 7) 1000 1 \n", + "7 (I 4) 1000 1 \n", + "8 (I 13) 1000 1 \n", + "9 (I 11) 1000 1 \n", + "10 (I 10, X 10) 1000 1 \n", + "11 (I 14) 1000 1 \n", + "12 (I 11) 1000 1 \n", + "13 (I 2) 1000 1 \n", + "14 (I 12) 1000 1 \n", + "15 (I 10) 1000 1 \n", + "16 (I 2, X 2) 1000 1 \n", + "17 (I 16) 1000 1 \n", + "18 (I 15, X 15) 1000 1 \n", + "19 (I 11, X 11) 1000 1 \n", + "20 (I 13, I 14, X 13) 1000 2 \n", + "21 (I 17, I 10, X 17) 1000 2 \n", + "22 (I 4, I 5, X 4, X 5, CNOT 4 5) 1000 2 \n", + "23 (I 16, I 17, X 16) 1000 2 \n", + "24 (I 1, I 2, CNOT 1 2) 1000 2 \n", + "25 (I 3, I 4, X 3, CNOT 3 4) 1000 2 \n", + "26 (I 0, I 7, X 7, CNOT 0 7) 1000 2 \n", + "27 (I 17, I 10, CNOT 17 10) 1000 2 \n", + "28 (I 16, I 15, X 16) 1000 2 \n", + "29 (I 17, I 10, X 10, CNOT 17 10) 1000 2 \n", + ".. ... ... ... \n", + "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... 1000 3 \n", + "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... 1000 3 \n", + "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... 1000 3 \n", + "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... 1000 3 \n", + "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... 1000 3 \n", + "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... 1000 3 \n", + "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... 1000 3 \n", + "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... 1000 3 \n", + "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... 1000 3 \n", + "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... 1000 3 \n", + "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... 1000 4 \n", + "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... 1000 4 \n", + "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... 1000 4 \n", + "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... 1000 4 \n", + "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... 1000 4 \n", + "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... 1000 4 \n", + "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... 1000 4 \n", + "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... 1000 4 \n", + "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... 1000 4 \n", + "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... 1000 4 \n", + "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... 1000 4 \n", + "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... 1000 4 \n", + "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... 1000 4 \n", + "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... 1000 4 \n", + "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... 1000 4 \n", + "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... 1000 4 \n", + "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... 1000 4 \n", + "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... 1000 4 \n", + "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... 1000 4 \n", + "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... 1000 4 \n", + "\n", + "[480 rows x 7 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "exp =generate_rand_cir_for_rand_lattices_experiments(qc_noisy, \n", + " circuit_depth, \n", + " circuit_width,\n", + " num_rand_subgraphs, \n", + " num_shots_per_circuit, \n", + " in_x_basis=x_basis, \n", + " use_active_reset=active_reset)\n", + "exp" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Collect data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "257.87861728668213\n" + ] + } + ], + "source": [ + "t0 = time.time()\n", + "data_zbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp)\n", + "t1 = time.time()\n", + "total = t1-t0\n", + "print(total)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetAnswerDepthIn X basisLatticeProgramSamplesTrialsWidth
0False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
1False[[1]]1False(1)(I 1, X 1)[[1], [1], [1], [1], [1], [1], [1], [0], [0], ...10001
2False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
3False[[1]]1False(7)(I 7, X 7)[[1], [1], [0], [1], [1], [1], [0], [1], [1], ...10001
4False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
5False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [1], [1], [1], ...10001
6False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
7False[[0]]1False(4)(I 4)[[0], [0], [0], [1], [0], [0], [0], [0], [0], ...10001
8False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
9False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
10False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [0], [1], [1], ...10001
11False[[0]]1False(14)(I 14)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
12False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
13False[[0]]1False(2)(I 2)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
14False[[0]]1False(12)(I 12)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
15False[[0]]1False(10)(I 10)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
16False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [1], [1], [0], [0], [1], ...10001
17False[[0]]1False(16)(I 16)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
18False[[1]]1False(15)(I 15, X 15)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
19False[[1]]1False(11)(I 11, X 11)[[1], [1], [0], [1], [1], [1], [1], [1], [1], ...10001
20False[[1, 0]]1False(13, 14)(I 13, I 14, X 13)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
21False[[1, 0]]1False(17, 10)(I 17, I 10, X 17)[[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0...10002
22False[[1, 0]]1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)[[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0...10002
23False[[1, 0]]1False(16, 17)(I 16, I 17, X 16)[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0...10002
24False[[0, 0]]1False(1, 2)(I 1, I 2, CNOT 1 2)[[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0...10002
25False[[1, 1]]1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)[[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0...10002
26False[[0, 1]]1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
27False[[0, 0]]1False(17, 10)(I 17, I 10, CNOT 17 10)[[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0...10002
28False[[1, 0]]1False(16, 15)(I 16, I 15, X 16)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
29False[[0, 1]]1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)[[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
..............................
450False[[0, 0, 0]]6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [...10003
451False[[1, 1, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...[[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [...10003
452False[[0, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
453False[[0, 1, 0]]6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...[[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [...10003
454False[[1, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...[[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
455False[[1, 0, 0]]6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...[[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [...10003
456False[[0, 0, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...[[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [...10003
457False[[0, 1, 1]]6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...[[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [...10003
458False[[1, 0, 1]]6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...[[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
459False[[0, 0, 1]]6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
460False[[1, 0, 1, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...[[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,...10004
461False[[0, 1, 1, 1]]6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...[[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,...10004
462False[[1, 0, 0, 1]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...[[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,...10004
463False[[1, 0, 0, 0]]6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,...10004
464False[[1, 1, 1, 1]]6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...[[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,...10004
465False[[0, 1, 0, 0]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...[[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,...10004
466False[[1, 1, 1, 0]]6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...[[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,...10004
467False[[0, 0, 1, 0]]6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...[[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,...10004
468False[[0, 1, 0, 0]]6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...[[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
469False[[0, 1, 1, 1]]6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...[[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
470False[[1, 0, 0, 0]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...[[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,...10004
471False[[0, 0, 1, 1]]6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...[[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,...10004
472False[[0, 1, 1, 1]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...[[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,...10004
473False[[1, 1, 0, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...[[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,...10004
474False[[0, 0, 0, 0]]6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
475False[[0, 0, 1, 1]]6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,...10004
476False[[1, 0, 0, 1]]6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...[[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,...10004
477False[[0, 0, 0, 0]]6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
478False[[1, 1, 1, 0]]6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...[[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,...10004
479False[[0, 0, 0, 1]]6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...[[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,...10004
\n", + "

480 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Active Reset Answer Depth In X basis Lattice \\\n", + "0 False [[0]] 1 False (13) \n", + "1 False [[1]] 1 False (1) \n", + "2 False [[0]] 1 False (7) \n", + "3 False [[1]] 1 False (7) \n", + "4 False [[1]] 1 False (2) \n", + "5 False [[1]] 1 False (10) \n", + "6 False [[0]] 1 False (7) \n", + "7 False [[0]] 1 False (4) \n", + "8 False [[0]] 1 False (13) \n", + "9 False [[0]] 1 False (11) \n", + "10 False [[1]] 1 False (10) \n", + "11 False [[0]] 1 False (14) \n", + "12 False [[0]] 1 False (11) \n", + "13 False [[0]] 1 False (2) \n", + "14 False [[0]] 1 False (12) \n", + "15 False [[0]] 1 False (10) \n", + "16 False [[1]] 1 False (2) \n", + "17 False [[0]] 1 False (16) \n", + "18 False [[1]] 1 False (15) \n", + "19 False [[1]] 1 False (11) \n", + "20 False [[1, 0]] 1 False (13, 14) \n", + "21 False [[1, 0]] 1 False (17, 10) \n", + "22 False [[1, 0]] 1 False (4, 5) \n", + "23 False [[1, 0]] 1 False (16, 17) \n", + "24 False [[0, 0]] 1 False (1, 2) \n", + "25 False [[1, 1]] 1 False (3, 4) \n", + "26 False [[0, 1]] 1 False (0, 7) \n", + "27 False [[0, 0]] 1 False (17, 10) \n", + "28 False [[1, 0]] 1 False (16, 15) \n", + "29 False [[0, 1]] 1 False (17, 10) \n", + ".. ... ... ... ... ... \n", + "450 False [[0, 0, 0]] 6 False (17, 10, 11) \n", + "451 False [[1, 1, 1]] 6 False (4, 5, 6) \n", + "452 False [[0, 0, 1]] 6 False (16, 14, 15) \n", + "453 False [[0, 1, 0]] 6 False (13, 14, 15) \n", + "454 False [[1, 0, 1]] 6 False (16, 14, 15) \n", + "455 False [[1, 0, 0]] 6 False (16, 14, 15) \n", + "456 False [[0, 0, 1]] 6 False (4, 5, 6) \n", + "457 False [[0, 1, 1]] 6 False (0, 1, 2) \n", + "458 False [[1, 0, 1]] 6 False (0, 6, 7) \n", + "459 False [[0, 0, 1]] 6 False (16, 2, 15) \n", + "460 False [[1, 0, 1, 1]] 6 False (0, 1, 2, 15) \n", + "461 False [[0, 1, 1, 1]] 6 False (4, 5, 6, 7) \n", + "462 False [[1, 0, 0, 1]] 6 False (16, 1, 14, 15) \n", + "463 False [[1, 0, 0, 0]] 6 False (16, 1, 10, 17) \n", + "464 False [[1, 1, 1, 1]] 6 False (2, 3, 4, 15) \n", + "465 False [[0, 1, 0, 0]] 6 False (16, 1, 14, 15) \n", + "466 False [[1, 1, 1, 0]] 6 False (2, 13, 14, 15) \n", + "467 False [[0, 0, 1, 0]] 6 False (11, 12, 13, 14) \n", + "468 False [[0, 1, 0, 0]] 6 False (16, 17, 2, 15) \n", + "469 False [[0, 1, 1, 1]] 6 False (0, 1, 6, 7) \n", + "470 False [[1, 0, 0, 0]] 6 False (10, 11, 12, 13) \n", + "471 False [[0, 0, 1, 1]] 6 False (0, 1, 16, 15) \n", + "472 False [[0, 1, 1, 1]] 6 False (10, 11, 12, 13) \n", + "473 False [[1, 1, 0, 1]] 6 False (0, 1, 2, 15) \n", + "474 False [[0, 0, 0, 0]] 6 False (16, 1, 2, 3) \n", + "475 False [[0, 0, 1, 1]] 6 False (17, 10, 11, 12) \n", + "476 False [[1, 0, 0, 1]] 6 False (16, 17, 14, 15) \n", + "477 False [[0, 0, 0, 0]] 6 False (16, 17, 10, 15) \n", + "478 False [[1, 1, 1, 0]] 6 False (16, 13, 14, 15) \n", + "479 False [[0, 0, 0, 1]] 6 False (2, 3, 4, 5) \n", + "\n", + " Program \\\n", + "0 (I 13) \n", + "1 (I 1, X 1) \n", + "2 (I 7) \n", + "3 (I 7, X 7) \n", + "4 (I 2, X 2) \n", + "5 (I 10, X 10) \n", + "6 (I 7) \n", + "7 (I 4) \n", + "8 (I 13) \n", + "9 (I 11) \n", + "10 (I 10, X 10) \n", + "11 (I 14) \n", + "12 (I 11) \n", + "13 (I 2) \n", + "14 (I 12) \n", + "15 (I 10) \n", + "16 (I 2, X 2) \n", + "17 (I 16) \n", + "18 (I 15, X 15) \n", + "19 (I 11, X 11) \n", + "20 (I 13, I 14, X 13) \n", + "21 (I 17, I 10, X 17) \n", + "22 (I 4, I 5, X 4, X 5, CNOT 4 5) \n", + "23 (I 16, I 17, X 16) \n", + "24 (I 1, I 2, CNOT 1 2) \n", + "25 (I 3, I 4, X 3, CNOT 3 4) \n", + "26 (I 0, I 7, X 7, CNOT 0 7) \n", + "27 (I 17, I 10, CNOT 17 10) \n", + "28 (I 16, I 15, X 16) \n", + "29 (I 17, I 10, X 10, CNOT 17 10) \n", + ".. ... \n", + "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... \n", + "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... \n", + "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... \n", + "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... \n", + "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... \n", + "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... \n", + "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... \n", + "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... \n", + "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... \n", + "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... \n", + "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... \n", + "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... \n", + "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... \n", + "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... \n", + "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... \n", + "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... \n", + "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... \n", + "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... \n", + "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... \n", + "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... \n", + "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... \n", + "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... \n", + "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... \n", + "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... \n", + "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... \n", + "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... \n", + "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... \n", + "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... \n", + "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... \n", + "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... \n", + "\n", + " Samples Trials Width \n", + "0 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", + "1 [[1], [1], [1], [1], [1], [1], [1], [0], [0], ... 1000 1 \n", + "2 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "3 [[1], [1], [0], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", + "4 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", + "5 [[0], [1], [1], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", + "6 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "7 [[0], [0], [0], [1], [0], [0], [0], [0], [0], ... 1000 1 \n", + "8 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", + "9 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "10 [[0], [1], [1], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", + "11 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "12 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "13 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "14 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "15 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "16 [[1], [1], [1], [1], [1], [1], [0], [0], [1], ... 1000 1 \n", + "17 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", + "18 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", + "19 [[1], [1], [0], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", + "20 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "21 [[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0... 1000 2 \n", + "22 [[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0... 1000 2 \n", + "23 [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "24 [[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0... 1000 2 \n", + "25 [[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0... 1000 2 \n", + "26 [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", + "27 [[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0... 1000 2 \n", + "28 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", + "29 [[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", + ".. ... ... ... \n", + "450 [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [... 1000 3 \n", + "451 [[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [... 1000 3 \n", + "452 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", + "453 [[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [... 1000 3 \n", + "454 [[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", + "455 [[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [... 1000 3 \n", + "456 [[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [... 1000 3 \n", + "457 [[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [... 1000 3 \n", + "458 [[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", + "459 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", + "460 [[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,... 1000 4 \n", + "461 [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,... 1000 4 \n", + "462 [[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,... 1000 4 \n", + "463 [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,... 1000 4 \n", + "464 [[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,... 1000 4 \n", + "465 [[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,... 1000 4 \n", + "466 [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,... 1000 4 \n", + "467 [[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,... 1000 4 \n", + "468 [[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", + "469 [[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", + "470 [[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,... 1000 4 \n", + "471 [[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,... 1000 4 \n", + "472 [[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,... 1000 4 \n", + "473 [[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,... 1000 4 \n", + "474 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", + "475 [[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,... 1000 4 \n", + "476 [[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,... 1000 4 \n", + "477 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", + "478 [[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,... 1000 4 \n", + "479 [[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,... 1000 4 \n", + "\n", + "[480 rows x 9 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_zbasis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#data_zbasis.to_pickle(\"data_z_Aspen-1-16Q-A_2019_02_16.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "data_zbasis = pd.read_pickle('data_z_Aspen-1-16Q-A_2019_02_16.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# circuit_width = df['Width'].max()\n", + "# circuit_depth = df['Depth'].max()\n", + "# for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", + "# print(depth,subgraph_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfz = pd.DataFrame(data_zbasis)\n", + "dfz.to_pickle(\"data_z_Aspen_1_15Q_A_2019_02_09.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_pickle('data_z_Aspen_1_15Q_A_2019_02_09.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acquire data in X basis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exp_xbasis = exp.copy()\n", + "exp_xbasis['In X basis']=True" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t0x = time.time()\n", + "data_xbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp_xbasis)\n", + "t1x = time.time()\n", + "totalx = t1x-t0x\n", + "print(totalx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dfx = pd.DataFrame(data_xbasis)\n", + "dfx.to_pickle(\"data_x_Aspen_1_15Q_A_2019_02_09.pkl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now put the data into a dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#data_xbasis.to_pickle(\"data_x_Aspen-1-16Q-A_2019_02_16.pkl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "#data_xbasis = pd.read_pickle('data_x_Aspen-1-16Q-A_2019_02_16.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data processing and estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "res_df = estimate_random_classical_circuit_errors(data_zbasis)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "circuit_width = res_df['Width'].max()\n", + "\n", + "for subgraph_size in range(1, circuit_width+1):\n", + " wdx = data_zbasis['Width']==subgraph_size\n", + " res_df[wdx]\n", + " \n", + " df.append(df2, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "circuit_width = res_df['Width'].max()\n", + "circuit_depth = res_df['Depth'].max()\n", + "results = []\n", + "for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", + " wdx = data_zbasis['Width']==subgraph_size\n", + " ddx = data_zbasis['Depth']==depth\n", + " ndf= res_df[wdx&ddx].copy()\n", + " results.append({'Depth': depth,\n", + " 'Width': subgraph_size,\n", + " 'In X basis': ndf['In X basis'].iloc[0],\n", + " 'Active Reset': ndf['Active Reset'].iloc[0],\n", + " 'Trials': ndf['Trials'].iloc[0],\n", + " 'Hamming dist. data': ndf['Hamming dist. data'].mean(),\n", + " 'Hamming dist. rand': ndf['Hamming dist. rand'].mean(),\n", + " 'Hamming dist. ideal': ndf['Hamming dist. ideal'].mean(),\n", + " 'TVD(data, ideal)': ndf['TVD(data, ideal)'].mean(),\n", + " 'TVD(data, rand)': ndf['TVD(data, rand)'].mean(),\n", + " 'Pr. success data': ndf['Pr. success data'].mean(),\n", + " 'Pr. success rand': ndf['Pr. success rand'].mean(),\n", + " 'loge = basement[log_2(Width)-1]': ndf['loge = basement[log_2(Width)-1]'].mean(),\n", + " 'Pr. success loge data': ndf['Pr. success loge data'].mean(),\n", + " 'Pr. success loge rand': ndf['Pr. success loge rand'].mean(),\n", + " }) \n", + "munged = pd.DataFrame(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Active ResetDepthHamming dist. dataHamming dist. idealHamming dist. randIn X basisPr. success dataPr. success loge dataPr. success loge randPr. success randTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False1[0.9251000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925100.925100.50000.50000.0374500.462550100010.0
1False1[0.8674, 0.12184999999999999, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.867400.867400.25000.25000.1272250.622775100020.0
2False1[0.73105, 0.21615, 0.046950000000000006, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.731050.731050.12500.12500.2660250.608975100030.0
3False1[0.7171500000000001, 0.23810000000000003, 0.03...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.717150.955250.31250.06250.2826750.675675100041.0
4False2[0.9201, 0.0][1.0, 0.0][0.5, 0.5]False0.920100.920100.50000.50000.0399500.460050100010.0
5False2[0.8482000000000003, 0.1441, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.848200.848200.25000.25000.1479500.602050100020.0
6False2[0.7371000000000001, 0.21269999999999997, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.737100.737100.12500.12500.2596750.615325100030.0
7False2[0.67555, 0.24490000000000003, 0.0602499999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.675550.920450.31250.06250.3220000.645700100041.0
8False3[0.9037499999999999, 0.0][1.0, 0.0][0.5, 0.5]False0.903750.903750.50000.50000.0481250.451875100010.0
9False3[0.8446999999999999, 0.14550000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.844700.844700.25000.25000.1504000.599600100020.0
10False3[0.75855, 0.20669999999999997, 0.0327500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.758550.758550.12500.12500.2404500.634550100030.0
11False3[0.6030999999999999, 0.2619, 0.103649999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.603100.865000.31250.06250.3947000.581100100041.0
12False4[0.9255500000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925550.925550.50000.50000.0372250.462775100010.0
13False4[0.8305999999999999, 0.15719999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.830600.830600.25000.25000.1633000.586700100020.0
14False4[0.76205, 0.19485000000000002, 0.0389500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.762050.762050.12500.12500.2358750.639125100030.0
15False4[0.5921999999999998, 0.26195, 0.10720000000000...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.592200.854150.31250.06250.4059000.565650100041.0
16False5[0.9231000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.923100.923100.50000.50000.0384500.461550100010.0
17False5[0.85725, 0.13285000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.857250.857250.25000.25000.1378000.612200100020.0
18False5[0.7151500000000002, 0.23395000000000002, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.715150.715150.12500.12500.2831250.592275100030.0
19False5[0.5072000000000001, 0.29245, 0.14304999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.507200.799650.31250.06250.4882250.505625100041.0
20False6[0.9045, 0.0][1.0, 0.0][0.5, 0.5]False0.904500.904500.50000.50000.0477500.452250100010.0
21False6[0.8439, 0.14684999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.843900.843900.25000.25000.1514750.598525100020.0
22False6[0.7076000000000001, 0.23464999999999997, 0.05...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.707600.707600.12500.12500.2908500.592950100030.0
23False6[0.54185, 0.28845, 0.12315000000000001, 0.0422...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.541850.830300.31250.06250.4560000.537950100041.0
\n", + "
" + ], + "text/plain": [ + " Active Reset Depth Hamming dist. data \\\n", + "0 False 1 [0.9251000000000001, 0.0] \n", + "1 False 1 [0.8674, 0.12184999999999999, 0.0] \n", + "2 False 1 [0.73105, 0.21615, 0.046950000000000006, 0.0] \n", + "3 False 1 [0.7171500000000001, 0.23810000000000003, 0.03... \n", + "4 False 2 [0.9201, 0.0] \n", + "5 False 2 [0.8482000000000003, 0.1441, 0.0] \n", + "6 False 2 [0.7371000000000001, 0.21269999999999997, 0.04... \n", + "7 False 2 [0.67555, 0.24490000000000003, 0.0602499999999... \n", + "8 False 3 [0.9037499999999999, 0.0] \n", + "9 False 3 [0.8446999999999999, 0.14550000000000002, 0.0] \n", + "10 False 3 [0.75855, 0.20669999999999997, 0.0327500000000... \n", + "11 False 3 [0.6030999999999999, 0.2619, 0.103649999999999... \n", + "12 False 4 [0.9255500000000001, 0.0] \n", + "13 False 4 [0.8305999999999999, 0.15719999999999998, 0.0] \n", + "14 False 4 [0.76205, 0.19485000000000002, 0.0389500000000... \n", + "15 False 4 [0.5921999999999998, 0.26195, 0.10720000000000... \n", + "16 False 5 [0.9231000000000001, 0.0] \n", + "17 False 5 [0.85725, 0.13285000000000002, 0.0] \n", + "18 False 5 [0.7151500000000002, 0.23395000000000002, 0.04... \n", + "19 False 5 [0.5072000000000001, 0.29245, 0.14304999999999... \n", + "20 False 6 [0.9045, 0.0] \n", + "21 False 6 [0.8439, 0.14684999999999998, 0.0] \n", + "22 False 6 [0.7076000000000001, 0.23464999999999997, 0.05... \n", + "23 False 6 [0.54185, 0.28845, 0.12315000000000001, 0.0422... \n", + "\n", + " Hamming dist. ideal Hamming dist. rand \\\n", + "0 [1.0, 0.0] [0.5, 0.5] \n", + "1 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "2 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "3 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "4 [1.0, 0.0] [0.5, 0.5] \n", + "5 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "6 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "7 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "8 [1.0, 0.0] [0.5, 0.5] \n", + "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "11 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "12 [1.0, 0.0] [0.5, 0.5] \n", + "13 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "14 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "15 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "16 [1.0, 0.0] [0.5, 0.5] \n", + "17 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "18 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "19 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "20 [1.0, 0.0] [0.5, 0.5] \n", + "21 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", + "22 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", + "23 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", + "\n", + " In X basis Pr. success data Pr. success loge data \\\n", + "0 False 0.92510 0.92510 \n", + "1 False 0.86740 0.86740 \n", + "2 False 0.73105 0.73105 \n", + "3 False 0.71715 0.95525 \n", + "4 False 0.92010 0.92010 \n", + "5 False 0.84820 0.84820 \n", + "6 False 0.73710 0.73710 \n", + "7 False 0.67555 0.92045 \n", + "8 False 0.90375 0.90375 \n", + "9 False 0.84470 0.84470 \n", + "10 False 0.75855 0.75855 \n", + "11 False 0.60310 0.86500 \n", + "12 False 0.92555 0.92555 \n", + "13 False 0.83060 0.83060 \n", + "14 False 0.76205 0.76205 \n", + "15 False 0.59220 0.85415 \n", + "16 False 0.92310 0.92310 \n", + "17 False 0.85725 0.85725 \n", + "18 False 0.71515 0.71515 \n", + "19 False 0.50720 0.79965 \n", + "20 False 0.90450 0.90450 \n", + "21 False 0.84390 0.84390 \n", + "22 False 0.70760 0.70760 \n", + "23 False 0.54185 0.83030 \n", + "\n", + " Pr. success loge rand Pr. success rand TVD(data, ideal) \\\n", + "0 0.5000 0.5000 0.037450 \n", + "1 0.2500 0.2500 0.127225 \n", + "2 0.1250 0.1250 0.266025 \n", + "3 0.3125 0.0625 0.282675 \n", + "4 0.5000 0.5000 0.039950 \n", + "5 0.2500 0.2500 0.147950 \n", + "6 0.1250 0.1250 0.259675 \n", + "7 0.3125 0.0625 0.322000 \n", + "8 0.5000 0.5000 0.048125 \n", + "9 0.2500 0.2500 0.150400 \n", + "10 0.1250 0.1250 0.240450 \n", + "11 0.3125 0.0625 0.394700 \n", + "12 0.5000 0.5000 0.037225 \n", + "13 0.2500 0.2500 0.163300 \n", + "14 0.1250 0.1250 0.235875 \n", + "15 0.3125 0.0625 0.405900 \n", + "16 0.5000 0.5000 0.038450 \n", + "17 0.2500 0.2500 0.137800 \n", + "18 0.1250 0.1250 0.283125 \n", + "19 0.3125 0.0625 0.488225 \n", + "20 0.5000 0.5000 0.047750 \n", + "21 0.2500 0.2500 0.151475 \n", + "22 0.1250 0.1250 0.290850 \n", + "23 0.3125 0.0625 0.456000 \n", + "\n", + " TVD(data, rand) Trials Width loge = basement[log_2(Width)-1] \n", + "0 0.462550 1000 1 0.0 \n", + "1 0.622775 1000 2 0.0 \n", + "2 0.608975 1000 3 0.0 \n", + "3 0.675675 1000 4 1.0 \n", + "4 0.460050 1000 1 0.0 \n", + "5 0.602050 1000 2 0.0 \n", + "6 0.615325 1000 3 0.0 \n", + "7 0.645700 1000 4 1.0 \n", + "8 0.451875 1000 1 0.0 \n", + "9 0.599600 1000 2 0.0 \n", + "10 0.634550 1000 3 0.0 \n", + "11 0.581100 1000 4 1.0 \n", + "12 0.462775 1000 1 0.0 \n", + "13 0.586700 1000 2 0.0 \n", + "14 0.639125 1000 3 0.0 \n", + "15 0.565650 1000 4 1.0 \n", + "16 0.461550 1000 1 0.0 \n", + "17 0.612200 1000 2 0.0 \n", + "18 0.592275 1000 3 0.0 \n", + "19 0.505625 1000 4 1.0 \n", + "20 0.452250 1000 1 0.0 \n", + "21 0.598525 1000 2 0.0 \n", + "22 0.592950 1000 3 0.0 \n", + "23 0.537950 1000 4 1.0 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "munged" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.54185, 0.28845, 0.12315, 0.04225, 0. ])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_df[wdx&ddx]['Hamming dist. data'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0625, 0.25 , 0.375 , 0.25 , 0.0625])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_df[wdx&ddx]['Hamming dist. rand'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot a particular depth and width" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dep = 6\n", + "wid = 4\n", + "\n", + "distz = get_hamming_dist(res_df, dep, wid)\n", + "\n", + "\n", + "# combine data from different subgraphs\n", + "avg_dist = distz['Hamming dist. data'].mean()\n", + "\n", + "# rand data\n", + "rand_dist = distz['Hamming dist. rand'][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_labels = np.arange(0, len(avg_dist))\n", + "plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + "plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + "plt.xticks(x_labels)\n", + "plt.xlabel('Hamming Weight of Error')\n", + "plt.ylabel('Relative Frequency of Occurence')\n", + "plt.ylim([0,1])\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.legend(['data','random'])\n", + "plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# For a particular width plot all depths" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "wid = 4\n", + "df_fn_depth = get_hamming_dists_fn_depth(res_df, wid)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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VVVcB8Pa3v51HPOIRHHTQQTz96U/n1ltv5VOf+hTr16/nj//4j1mzZg1f+cpX5lxP0uLYolSHPUp12KNUhz1KNSyHFt05q2Xj0ksv5YwzzmDDhg2cc845XHzxxQA87WlP4+KLL+YLX/gC++67L+94xzt49KMfzdFHH81f//Vfs2HDBh760IfOuZ6khbNFqQ57lOqwR6kOe5RqWC4tes5ZLRsXXHABxxxzDPe73/0AOProowH40pe+xKte9SpuvPFGfvjDH/Irv/Irc96+7XqSBrNFqQ57lOqwR6kOe5RqWC4tunNWy96xxx7LWWedxUEHHcSpp57Kxz/+8S1aT9Li2KJUhz1KddijVIc9SjUstRY9rYGWjcc85jGcddZZ/OhHP+Lmm2/m/e9/PwA333wzu+yyC7fffjunnXba3evvsMMO3HzzzXdfnm89SQtji1Id9ijVYY9SHfYo1bBcWvTIWU3FppOOmvh9HnLIITzzmc/koIMO4sEPfjCPeMQjAHjd617HIx/5SB70oAfxyEc+8u6Qn/WsZ/GCF7yAv/3bv+W9733vvOtJXTfpHm1Rmp89SjX4WlWqwx6lOnytOh6RmdOeYUHWrl2bl1xyybTH0AJdccUV7LvvvtMeoxPmeqwi4tLMXDulkeZlj91kj+3Zo8bNHtuxRY2bLbZnjxo3e2zPHjVu9tjOlrboaQ0kSZIkSZIkaQrcOStJkiRJkiRJU+DOWUmSJEmSJEmaAnfOSpIkSZIkSdIUuHNWkiRJkiRJkqbAnbOSJEmSJEmSNAVbT3sALVPrdhzx9m4a7fZaWL16NZdccgk777zzxO9bGil7lOqwR6kGW5TqsEepDnscC4+c1bKUmdx1113THkMS9ihVYo9SDbYo1WGPUh1LtUd3zmrZ2LRpE/vssw+/9Vu/xQEHHMBxxx3H2rVr2X///TnxxBPvXm/16tWceOKJHHLIIRx44IFceeWVAFx//fX88i//Mvvvvz/Pf/7zycy7b/PmN7+ZAw44gAMOOIC3vOUtd9/fwx72MI499lj23ntvnvvc5/LhD3+Yww47jL322ovPfvazk30ApELsUarDHqUabFGqwx6lOpZDj+6c1bLy5S9/mRe+8IVs3LiRN73pTVxyySVcdtllfOITn+Cyyy67e72dd96Zz33uc/z+7/8+J598MgCvec1rOPzww9m4cSPHHHMM1157LQCXXnop//zP/8xFF13EZz7zGd7+9rfz+c9/HoCrr76al7/85Vx55ZVceeWVnH766Vx44YWcfPLJvP71r5/8AyAVYo9SHfYo1WCLUh32KNWx1Ht056yWlT322INHPepRAJx55pkccsghHHzwwWzcuJHLL7/87vWe9rSnAfDwhz+cTZs2AXD++efzvOc9D4CjjjqKnXbaCYALL7yQY445hu22247tt9+epz3taVxwwQUA7Lnnnhx44IFstdVW7L///jz+8Y8nIjjwwAPv3q60XNmjVIc9SjXYolSHPUp1LPUe/UAwLSvbbbcdAF/96lc5+eSTufjii9lpp5049thjue222+5e7z73uQ8AK1as4I477lj0/c1sB2Crrba6+/JWW221RduVlgJ7lOqwR6kGW5TqsEepjqXeo0fOaln6wQ9+wHbbbceOO+7Id77zHT74wQ8Ovc1jHvMYTj/9dAA++MEPcsMNNwBwxBFHcNZZZ3Hrrbdyyy238L73vY8jjjhirPNLS4k9SnXYo1SDLUp12KNUx1Lt0SNnNR3rbprq3R900EEcfPDBPOxhD2O33XbjsMMOG3qbE088kWc/+9nsv//+PPrRj2b33XcH4JBDDuHYY4/l0EMPBeD5z38+Bx98sP/0RN1hj1Id9ijVYItSHfYo1WGPYxH9n1LWBWvXrs1LLrlk2mNoga644gr23XffaY/RCXM9VhFxaWaundJI87LHbrLH9uxR42aP7diixs0W27NHjZs9tmePGjd7bGdLW2x1WoOIuG9E7LOI+SSNmD1KddijVIMtSnXYo1SHPUrdMHTnbEQ8GdgAnNtcXhMR68c9mKR7s0epDnuUarBFqQ57lOqwR6k72hw5uw44FLgRIDM3AHuOcSYtUV07hcY0tHiM1mGPGgF7HM4eNSn2OJgtalJscTh71KTY43D2qEmxx8FG8fi02Tl7e2bOPuOv/2e0ICtXruT666836gEyk+uvv56VK1cOWs0etcXscTh71KTY42C2qEmxxeHsUZNij8PZoybFHgdr2eJQW7dYZ2NEPAdYERF7AS8BPrVF96plZ9WqVWzevJnvfe970x6ltJUrV7Jq1apBq9ijtpg9tmOPmgR7HM4WNQm22I49ahLssR171CTY43AtWhyqzc7ZFwOvBH4MnA6cB/zFFt2rlp1tttmGPff0X1CMgD1qi9njyNijtpg9joQtaovZ4sjYo7aYPY6MPWqL2eNkDN05m5m30gv6leMfR9Ig9ijVYY9SDbYo1WGPUh32KHXH0HPORsSHIuIBfZd3iojzxjuWpLnYo1SHPUo12KJUhz1Kddij1B1tPhBs58y8ceZCZt4APHh8I0kawB6lOuxRqsEWpTrsUarDHqWOaLNz9q6I2H3mQkTsQctP+IuIIyPiqoi4OiJOmOP63SPiYxHx+Yi4LCJ+tf3o0rJkj1Idi+rRFqWR87lRqsMepTrsUeqINh8I9krgwoj4BBDAEcDxw24UESuAtwFPBDYDF0fE+sy8vG+1VwFnZubfR8R+wDnA6oX9CNKyYo9SHQvu0RalsfC5UarDHqU67FHqiDYfCHZuRBwCPKpZ9LLMvK7Ftg8Frs7MawAi4gzgKUB/0Ancv/l+R+CbbQeXliN7lOpYZI+2KI2Yz41SHfYo1WGPUne0OXIW4D7A95v194sIMvP8IbfZFfh63+XNwCNnrbMO+K+IeDGwHfCEuTYUEcfTvMOzyy67sGHDhpZjS0uSPUp1LLTHkbUI9ij18blRqsMepTrsUeqAoTtnI+INwDOBjcBdzeIEhgXdxrOBUzPzTRHxi8C7IuKAzLyrf6XMPAU4BWDt2rW5Zs2aEdy11D32KNUxxh5btQj2KIHPjVIl9ijVYY9Sd7Q5cvapwD6Z+eMFbvsbwG59l1c1y/odBxwJkJmfjoiVwM7Adxd4X9JyYY9SHYvp0Ral0fO5UarDHqU67FHqiK1arHMNsM0itn0xsFdE7BkR2wLPAtbPWuda4PEAEbEvsBL43iLuS1ou7FGqYzE92qI0ej43SnXYo1SHPUod0ebI2VuBDRHxEeDud1wy8yWDbpSZd0TEi4DzgBXAOzNzY0S8FrgkM9cDLwfeHhF/SO/w+mMzMxf5s0jLgT1KdSy4R1uUxsLnRqkOe5TqsEepI9rsnF3Pvd8laSUzzwHOmbXs1X3fXw4ctphtS8uUPUp1LKpHW5RGzudGqQ57lOqwR6kjhu6czcx/iYj7Artn5lUTmEnSPOxRqsMepRpsUarDHqU67FHqjqHnnI2IJwMbgHOby2siYlHvvkjaMvYo1WGPUg22KNVhj1Id9ih1R5sPBFsHHArcCJCZG4CHjHEmSfNbhz1KVazDHqUK1mGLUhXrsEepinXYo9QJbXbO3p6ZN81adtc4hpE0lD1KddijVIMtSnXYo1SHPUod0eYDwTZGxHOAFRGxF/AS4FPjHUvSPOxRqsMepRpsUarDHqU67FHqiDZHzr4Y2B/4MXA6cBPwsnEOJWle9ijVYY9SDbYo1WGPUh32KHXEwCNnI2IF8NrM/CPglZMZSdJc7FGqwx6lGmxRqsMepTrsUeqWgUfOZuadwOETmkXSAPYo1WGPUg22KNVhj1Id9ih1S5tzzn4+ItYD7wFumVmYmf85tqkkzccepTrsUarBFqU67FGqwx6ljmizc3YlcD3wS33LEjBoafLsUarDHqUabFGqwx6lOuxR6oihO2cz83cmMYik4exRqsMepRpsUarDHqU67FHqjqE7ZyPin+m9u3IPmfm7Y5lI0rzsUarDHqUabFGqwx6lOuxR6o42pzX4QN/3K4FjgG+OZxxJQ9ijVIc9SjXYolSHPUp12KPUEW1Oa/Af/Zcj4t+BC8c2kaR52aNUhz1KNdiiVIc9SnXYo9QdbY6cnW0v4MGjHmSUVp9w9ti2vemko8a2bWkRyvcoLSP2KNVgi1Id9ijVYY9SUW3OOXsz9zxPybeBV4xtIknzskepDnuUarBFqQ57lOqwR6k72pzWYIdJDCJpOHuU6rBHqQZblOqwR6kOe5S6Y6thK0TEMRGxY9/lB0TEU8c7lqS52KNUhz1KNdiiVIc9SnXYo9QdQ3fOAidm5k0zFzLzRuDE8Y0kaQB7lOqwR6kGW5TqsEepDnuUOqLNztm51lnMB4lJ2nL2KNVhj1INtijVYY9SHfYodUSbnbOXRMSbI+KhzdebgUvHPZikOdmjVIc9SjXYolSHPUp12KPUEW12zr4Y+AnwbuAM4DbgD8Y5lKR52aNUhz1KNdiiVIc9SnXYo9QRQw9pz8xbgBMmMIukIexRqsMepRpsUarDHqU67FHqjqE7ZyPiQ8BvNCePJiJ2As7IzF8Z93CS7qlrPa4+4eyxbXvTSUeNbdtSG13rUVqqbFGqwx6lOuxR6o42pzXYeSZmgMy8AXjw+EaSNIA9SnXYo1SDLUp12KNUhz1KHdFm5+xdEbH7zIWI2API8Y0kaQB7lOqwR6kGW5TqsEepDnuUOmLoaQ2AVwIXRsQngACOAI4f61SS5mOPUh32KNVgi1Id9ijVYY9SR7T5QLBzI+IQ4FHNopdl5nXjHUvSXOxRqsMepRpsUarDHqU67FHqjoE7ZyNiW+C5wP7Noo3AzeMeStK92aNUhz1KNdiiVIc9SnXYo9Qt855zNiL2Ay4HHgtc23w9FtjYXCdpclZij1IV9ijVYItSHfYo1WGPUscMOnL274Dfz8wP9S+MiCcAbwMeN87BJN3D7sDT7VEqwR6lGmxRqsMepTrsUeqYeY+cBXadHTNAZn4Y+LnxjSRpDtvYo1SGPUo12KJUhz1Kddij1DGDds5uFRH3mb0wIlbS4oPEJI1U2KNUhj1KNdiiVIc9SnXYo9Qxg3bO/ivwHxGxx8yCiFgNnAm8a7xjSZrleuxRqsIepRpsUarDHqU67FHqmHnfNcnMv4iIFwEXRMT9msW3ACdn5t9NZDpJM74FnIs9ShXYo1SDLUp12KNUhz1KHTPwkPbMfCvw1ojYobl880SmknQv9ijVYY9SDbYo1WGPUh32KHVLq/ONGLJUhz1KddijVIMtSnXYo1SHPUrdMOics1ssIo6MiKsi4uqIOGGedZ4REZdHxMaIOH2c80jLmT1KNdiiVIc9SnXYo1SDLUqTN++RsxHxG5n5nojYMzO/utANR8QK4G3AE4HNwMURsT4zL+9bZy/gT4HDMvOGiHjwwn8EaVnYCcAepRIW3aMtSiPlc6NUhz1KdfhaVeqYQUfO/mnz3/9Y5LYPBa7OzGsy8yfAGcBTZq3zAuBtmXkDQGZ+d5H3JS11P9f81x6l6duSHm1RGh2fG6U67FGqw9eqUscMOufs9RHxX8CeEbF+9pWZefSQbe8KfL3v8mbgkbPW2RsgIj4JrADWZea5Q6eWlp877FEqY0t6nFqLq084e0s3Ma9NJx01tm1LA/jcKNVhj1IdnXytKi1ng3bOHgUcArwLeNMY738v4LHAKuD8iDgwM2/sXykijgeOB9hll13YsGHDwI0+4yF3jmVYYOh9S2NyNfBqOtajLWqJGnePrVoEe9Sy18nnRmmJskepjk6+VpWWs3l3zjaHsH8mIh6dmd+LiO2b5T9sue1vALv1XV7VLOu3GbgoM28HvhoR/00v8otnzXIKcArA2rVrc82aNQPv+KlnzL6b0Xnj8YPvWxqTzMzO9WiLWqK2pMeRtdjcpz1qOevkcyN4JLuWpM72KC1BnXytKi1ng845O+NnI+LzwEbg8oi4NCIOaHG7i4G9ImLPiNgWeBYw+5D6s+i920JE7Ezv8Phr2g4vLUP2KNWxmB5tURo9nxulOuxRqsPXqlJHtNk5ewrwvzNzj8zcHXh5s2ygzLwDeBFwHnAFcGZmboyI10bEzDlOzqN3btvLgY8Bf5yZ1y/mB5GWCXuU6lhwj7ZOEJlzAAAgAElEQVQojYXPjVId9ijV4WtVqSMGnXN2xnaZ+bGZC5n58YjYrs3GM/Mc4JxZy17d930C/7v5kjScPUp1LKpHW5RGzudGqQ57lOrwtarUEW12zl4TEX9O72TSAM/DQ9alabFHqQ57lGqwRakOe5TqsEepI9qc1uB3gQcB/wn8B7Bzs0zS5NmjVIc9SjXYolSHPUp12KPUEUOPnM3MG4CXTGAWSUPYo1SHPUo12KJUhz1Kddij1B1tjpyVJEmSJEmSJI2YO2clSZIkSZIkaQqG7pyNiAdOYhBJw9mjVIc9SjXYolSHPUp12KPUHW2OnP1MRLwnIn41ImLsE0kaxB6lOuxRqsEWpTrsUarDHqWOaLNzdm/gFOA3gS9HxOsjYu/xjiVpHvYo1WGPUg22KNVhj1Id9ih1xNbDVsjMBD4EfCgiHgf8G/DCiPgCcEJmfnrMM0pq2KNUhz1KNdiiVIc9SnV0rcfVJ5w9tm1vOumosW1bGoWhO2eb85Q8j967Ld8BXgysB9YA7wH2HOeAkn7KHqU67FGqwRalOuxRqsMepe4YunMW+DTwLuCpmbm5b/klEfEP4xlL0jzsUarDHqUabFGqwx6lOuxR6og2O2f3aQ6Hv5fMfMOI55E0mD1KddijVIMtSnXYo1SHPUod0eYDwf4rIh4wcyEidoqI88Y4k6T52aNUhz1KNdiiVIc9SnXYo9QRbXbOPigzb5y5kJk3AA8e30iSBrBHqQ57lGqwRakOe5TqsEepI9rsnL0zInafuRARewBzHhovaezsUarDHqUabFGqwx6lOuxR6og255x9JXBhRHwCCOAI4PixTiVpPvYo1WGPUg22KNVhj1Id9ih1xNCds5l5bkQcAjyqWfSyzLxuvGNJmos9SnXYo1SDLUp1dLHH1SecPZbtbjrpqLFsV2qriz1Ky1WbI2cB7gN8v1l/v4ggM88f31iSBrBHqQ57lGqwRakOe5TqsEepA4bunI2INwDPBDYCdzWLEzBoacLsUarDHqUabFGqwx6lOuxR6o42R84+FdgnM3887mEkDWWPUh32KNVgi1Id9ijVYY9SR2zVYp1rgG3GPYikVuxRqsMepRpsUarDHqU67FHqiDZHzt4KbIiIjwB3v+OSmS8Z21SS5mOPUh32KNVgi1Id9ijVYY9SR7TZObu++ZI0ffYo1WGPUg22KNVhj1Id9ih1xNCds5n5LxFxX2D3zLxqAjNJmoc9SnXYo1SDLUp12KNUhz1K3TH0nLMR8WRgA3Buc3lNRPjuizQF9ijVYY9SDbYo1WGPUh32KHVHmw8EWwccCtwIkJkbgIeMcSZJ81uHPUpVrMMepQrWYYtSFeuwR6mKddij1Altds7enpk3zVp21ziGkTSUPUp12KNUgy1KddijVIc9Sh3R5gPBNkbEc4AVEbEX8BLgU+MdS9I87FGqwx6lGmxRqsMepTrsUeqINkfOvhjYH/gx8O/AD4CXjXMoSfOyR6kOe5RqsEWpDnuU6rBHqSOGHjmbmbcCr2y+JE2RPUp12KNUgy1KddijVIc9St0xdOdsRHwMyNnLM/OXxjKRpHnZo1SHPUo12KJUhz1Kddij1B1tzjn7R33frwSeDtwxnnEkDWGPUh32KNVgi1Id9ijVYY9SR7Q5rcGlsxZ9MiI+O6Z5JA1gj1Id9ijVYItSHfYo1WGPUne0Oa3Bz/Rd3Ap4OLDj2CaSNC97lOqwR6kGW5TqsEepDnuUuqPNaQ0upXeekqB3CPxXgePGOZSkedmjVIc9SjXYolSHPUp12KPUEW1Oa7DnJAaRNJw9SnXYo1SDLUp12KNUhz1K3dHmtAZPG3R9Zv7n6MaRNIg9SnXYo1SDLUp12KNUhz1K3dHmtAbHAY8GPtpcfhzwKeB79A6RnzfoiDgS+D/ACuCfMvOkedZ7OvBe4BGZeUnr6aXlxx6lOhbVoy1KI+dzo1SHPUp12KPUEW12zm4D7JeZ3wKIiF2AUzPzdwbdKCJWAG8DnghsBi6OiPWZefms9XYAXgpctIj5peXGHqU6FtyjLUpj4XOjVIc9SnXYo9QRW7VYZ7eZmBvfAXZvcbtDgasz85rM/AlwBvCUOdZ7HfAG4LYW25SWO3uU6lhMj7YojZ7PjVId9ijVYY9SR7TZOfuRiDgvIo6NiGOBs4EPt7jdrsDX+y5vbpbdLSIOofcHxtkt55WWO3uU6lhMj7YojZ7PjVId9ijVYY9SRww9rUFmvigijgEe0yw6JTPft6V3HBFbAW8Gjm2x7vHA8QC77LILGzZsGLj+Mx5y55aON69h9y2NU9d6tEUtZePocSEtNuvbo5a9rj03gj1q6bLHn7JFTVvXevS5UctZm3POAnwOuDkzPxwR94uIHTLz5iG3+QawW9/lVc2yGTsABwAfjwiAnwPWR8TRs08knZmnAKcArF27NtesWTPwjp96xjcGXr8l3nj84PuWJqAzPdqiloGF9jiyFsEepT6deW4Ee9SSZ4/YosroTI8+N2o5G3pag4h4Ab1P3/vHZtGuwFkttn0xsFdE7BkR2wLPAtbPXJmZN2Xmzpm5OjNXA58B5vzLp6Qee5TqWGSPtiiNmM+NUh32KNVhj1J3tDnn7B8AhwE/AMjMLwMPHnajzLwDeBFwHnAFcGZmboyI10bE0YsfWVrW7FGqY8E92qI0Fj43SnXYo1SHPUod0ea0Bj/OzJ80h6sTEVsD2WbjmXkOcM6sZa+eZ93HttmmtMzZo1THonq0RWnkfG6U6rBHqQ57lDqizc7ZT0TEnwH3jYgnAi8E3j/esTRV63Zc4Po3jWcOzcUel5uF9GiLk2aPy409VmWLy42vVSuzx+XGHiuzx+XG16qd1ea0BicA3wO+CPwveu+evGqcQ0malz1KddijVIMtSnXYo1SHPUodMfDI2YhYAfxrZj4XePtkRpI0F3uU6rBHqQZblOqwR6kOe5S6ZeCRs5l5J7BH8wl9kqbIHqU67FGqwRalOuxRqsMepW5pc87Za4BPRsR64JaZhZn55rFNJWk+9ijVYY9SDbYo1WGPUh32KHVEm52zX2m+tgJ2GO84koawR6kOe5RqsEWpDnuU6rBHqSPm3TkbEVtn5h2Z+ZpJDiRpfvYo1WGPUg22KNVhj1Id9ih1x6Bzzn525puI+LsJzCJpfvvOfGOP0tTZo1SDLUp12KNUhz1KHTNo52z0fX/YuAeRNJA9SnXYo1SDLUp12KNUhz1KHTNo52xObApJw9ijVIc9SjXYolSHPUp12KPUMYM+EOxhEXEZvXddHtp8T3M5M/MXxj6dpBkr7VEqwx6lGmxRqsMepTrsUeqYQTtn9x1wnaTJ2gg8edpDSALsUarCFqU67FGqwx6ljpl352xmfm2Sg0ga6Cc2KZVhj1INtijVYY9SHfYodcygc85KkiRJkiRJksbEnbOSJEmSJEmSNAWtds5GxH0jYp9xDyNpOHuU6rBHqQZblOqwR6kOe5S6YejO2Yh4MrABOLe5vCYi1o97MEn3Zo9SHfYo1WCLUh32KNVhj1J3tDlydh1wKHAjQGZuAPYc40yS5rcOe5SqWIc9ShWswxalKtZhj1IV67BHqRPa7Jy9PTNvmrUsxzGMpKHsUarDHqUabFGqwx6lOuxR6oitW6yzMSKeA6yIiL2AlwCfGu9YkuZhj1Id9ijVYItSHfY4AqtPOHts29500lFj27bKsUepI9rsnH0x8Ergx8DpwHnAX4xzKEnzskepDnuUarBFqQ57lOqwxxHwzRJNQpudsw/LzFfSi1rSdNmjVIc9SjXYolSHPUp12KPUEW3OOfumiLgiIl4XEQeMfSJJg9ijVIc9SjXYolSHPUp12KPUEUN3zmbm44DHAd8D/jEivhgRrxr7ZJLuxR6lOuxRqsEWpTrsUarDHqXuaHPkLJn57cz8W+D3gA3Aq8c6laR52aNUhz1KNdiiVIc9SnXYo9QNQ3fORsS+EbEuIr4I/B29T/dbNfbJJN2LPUp12KNUgy1KddijVIc9St3R5gPB3gm8G/iVzPzmmOeRNJg9SnXY4wiM6xNw/fTbZcUWR8BPo9aI2KNUhz1KHTF052xm/uIkBpE0nD1KddijVIMtSnXYo1SHPUrdMe/O2Yg4MzOf0RwCn/1XAZmZvzD26STNeAiAPUol2KNUgy1KddijVIc9Sh0z6MjZlzb//bVJDLJsrdtxAeveNL45umB5P1Zfb/5rj+OykN8vWIq/Y+35WNnjmG1a+ZwFrb/6ttPHNEkHLO8ebXEC7HEBfK0K9jhWC+nRFheyvj1qYXxuXAB7bGXenbOZ+a3m2xdm5iv6r4uINwCvuPetJI3J7c1/7XEE5jqv3qaVW74N8Lx6y4Q9SjXYolSHPUp12KPUMVu1WOeJcyx70qgHkdSKPUp12KNUgy1KddijVIc9Sh0x6Jyzvw+8EHhIRFzWd9UOwCfHPdhSM+9Rdgs4Ws8j9Za1BzXnDLJHafrsUarBFqU67FGqwx6ljhl0ztnTgQ8CfwWc0Lf85sz8/linkjTb94FjsEepAnuUarBFqQ57lOqwR6ljBp1z9ibgJuDZABHxYGAlsH1EbJ+Z105mREnAnZm5CXuUKrBHqQZblOqwR6kOe5Q6Zug5ZyPiyRHxZeCrwCeATfSOqJU0YfYo1WGPUg22KNVhj1Id9ih1R5sPBPsL4FHAf2fmnsDjgc+MdSpJ87FHqQ57lGqwRakOe5TqsEepI9rsnL09M68HtoqIrTLzY8DaMc8laW72KNVhj1INtijVYY9SHfYodUSbnbM3RsT2wPnAaRHxf4Bb2mw8Io6MiKsi4uqIOGGO6/93RFweEZdFxEciYo+FjS8tO/Yo1bGoHm1RGjmfG6U67FGqw9eqUke02Tn7FOBHwB8C5wJfAZ487EYRsQJ4G/AkYD/g2RGx36zVPg+szcxfAN4LvLH96NKyZI9SHQvu0RalsfC5UarDHqU6fK0qdcTWw1bIzP53Vv5lAds+FLg6M68BiIgz6P3hcHnftj/Wt/5ngOctYPvSsmOPUh2L7NEWpRHzuVGqwx6lOnytKnXHvDtnI+JmIPsXNZcDyMy8/5Bt7wp8ve/yZuCRA9Y/jnk+OTAijgeOB9hll13YsGHDwDt+xkPuHDLa4g277/nMN9OGFce238ad82xjkTPNa7djF7b+qO9/PguZa1IzTc7BEfGDvsud6LFiizD3XAtpEYr2WLFFsMd7GlmLYI93b2OOHkfeIthjPZ18boSaPY7itSpMqMeqv/cV/4yYHHucZdTPjeDfHVurONNk+Vp1loqvVcG/O85p6fXYyrw7ZzNzh0kNERHPo3di6v85zyynAKcArF27NtesWTNwe0894xujHvFubzx+8H3PZ76Z3rjy1PbbuO2X597GImea11mnLmz94/7PaO9/PguZa1IzTc7nM3MiJ28fZY8VW4S551pIi1C0x4otgj0u0rAWwR7v3sYcPY68RbDHejr53Ag1exzFa1WYUI9Vf+8r/hkxOfY4y6ifG8G/O7ZWcabJ8rXqLBVfq4J/d5zT0uuxlTbnnCUiDo+I32m+3zki9mxxs28Au/VdXtUsm73tJwCvBI7OzB+3mUdazuxRqmMRPdqiNAY+N0p12KNUh69VpW4YunM2Ik4EXgH8abNoW+DfWmz7YmCviNgzIrYFngWsn7Xtg4F/pBf0dxcyuLQc2aNUxyJ7tEVpxHxulOqwR6kOX6tK3dHmyNljgKOBWwAy85vA0FMeZOYdwIuA84ArgDMzc2NEvDYijm5W+2tge+A9EbEhItbPszlJPfYo1bHgHm1RGgufG6U67FGqw9eqUkfMe87ZPj/JzIyIBIiI7dpuPDPPAc6ZtezVfd8/oe22JAH2KFWyqB5tURo5nxulOuxRqsPXqlJHtDly9syI+EfgARHxAuDDwD+NdyxJ87BHqQ57lGqwRakOe5TqsEepI4YeOZuZJ0fEE4EfAPsAr87MD419Mkn3Yo9SHfYo1WCLUh32KNVhj1J3tDmtAU3AHwKIiK0i4rmZedpYJ5M0J3uU6rBHqQZblOqwR6kOe5S6Yd7TGkTE/SPiTyPirRHxy9HzIuAa4BmTG1ESsJU9SmXYo1SDLUp12KNUhz1KHTPoyNl3ATcAnwaeD/wZEMBTM3PDBGaT9FN70vunKPYoTZ89SjXYolSHPUp12KPUMYN2zj4kMw8EiIh/Ar4F7J6Zt01kMkn97pOZx4I9SgXYo1SDLUp12KNUhz1KHTPvaQ2A22e+ycw7gc3GLE1N3v2NPUrTZo9SDbYo1WGPUh32KHXMoCNnD4qIHzTfB3Df5nIAmZn3H/t0kmbczx6lMuxRqsEWpTrscYlbfcLZcy7ftHLLt7PppKMWM5LmZ49Sx8y7czYzV0xyEEkDXZqZa6c9hCTAHqUqbFGqwx6lOuxR6phBpzWQJEmSJEmSJI2JO2clSZIkSZIkaQoGnXNWkiRJkiRJUhGjOAf0vNvwHNBT4ZGzkiRJkiRJkjQF7pyVJEmSJEmSpClw56wkSZIkSZIkTYHnnJUkSZJU0lznxFvIOfXm2wZ4Xj1JklSDO2clSZL6+CELkiRJkibF0xpIkiRJkiRJ0hS4c1aSJEmSJEmSpsCds5IkSZIkSZI0Be6clSRJkiRJkqQpcOesJEmSJEmSJE2BO2clSZIkSZIkaQrcOStJkiRJkiRJU+DOWUmSJEmSJEmagq2nPcCkbFr5nAWtv/q208c0iZaUdTsucP2bxjNHx9ijxmIhPdri3RbSoy2qFZ8bF8XnRo2FPS6KPWos7HFR7FFjUezvjstm56wkVbb6hLPnXL5p5Qi2cdJRixlJkiRJkiSNmac1kCRJkiRJkqQpcOesJEmSJEmSJE2BpzWQJEmSJEmdM9dpvRZyWrD5tgGeGkzS5LhzVpIkSZIkSdKijevNkuXwRok7Z5c545EkSZIkSZKmw52zkiRJktTSvP8EegEHOPjPqCVJ0gw/EEySJEmSJEmSpsAjZyVJkoobxZF6823HI/UkSZKk6fHIWUmSJEmSJEmaAo+clSTNa1wfGggerSdJkiRJ0liPnI2IIyPiqoi4OiJOmOP6+0TEu5vrL4qI1eOcR1rO7FGqwRalOuxRqsMepTrsUZqsse2cjYgVwNuAJwH7Ac+OiP1mrXYccENm/g/gb4A3jGseaTmzR6kGW5TqsEepDnuU6rBHafLGeVqDQ4GrM/MagIg4A3gKcHnfOk8B1jXfvxd4a0REZuYY51Jxo/jQE/8Z9b3Yo1SDLUp12KNUhz1KddijNGExrnYi4teBIzPz+c3l3wQemZkv6lvnS806m5vLX2nWuW7Wto4Hjm8u7gNcNcJRdwauG7rW5FWcy5naG/Vce2TmgxZ7Y3vcIhVngppzVZwJCvU4yhab6+xx+irOBDXnKtMi+Nw4AhXnqjgT1JzLHhdnOfy/HJWKc1WcCexxsSr+/6w4E9ScaznM1LrFTnwgWGaeApwyjm1HxCWZuXYc294SFedypvaqzjUKy63HijNBzbkqzgR15xoFe5y+ijNBzbkqzjQqy61FqDlXxZmg5lwVZxqV5dZjxZmg5lwVZ4K6c42CPdZQcS5nuqdxfiDYN4Dd+i6vapbNuU5EbA3sCFw/xpmk5coepRpsUarDHqU67FGqwx6lCRvnztmLgb0iYs+I2BZ4FrB+1jrrgd9uvv914KOeo0QaC3uUarBFqQ57lOqwR6kOe5QmbGynNcjMOyLiRcB5wArgnZm5MSJeC1ySmeuBdwDvioirge/Ti37SxnKI/QhUnMuZ2is1lz1ukYozQc25Ks4EhebqUItQ6HHr40ztVZyr1Ewd6rHU49an4lwVZ4Kac5WayR63SMWZoOZcFWeCYnPZ4xapOBPUnMuZ+oztA8EkSZIkSZIkSfMb52kNJEmSJEmSJEnzcOesJEmSJEmSJE3Bst45GxFHRsRVEXF1RJxQYJ53RsR3I+JL056lX0TsFhEfi4jLI2JjRLy0wEwrI+KzEfGFZqbXTHumGRGxIiI+HxEfmPYsXVGtRajZY8UWwR6XGntsp2KPlVsEe1wMe2zHHhfGFhenWo+22J49Li3VWgR7XAh7nNuy3TkbESuAtwFPAvYDnh0R+013Kk4FjpzyDHO5A3h5Zu4HPAr4gwKP1Y+BX8rMg4A1wJER8agpzzTjpcAV0x6iK4q2CDV7rNgi2OOSYY8LUrHHyi2CPS6IPS6IPS6MLS5Q0R5PxRbbssclomiLYI8LYY9zWLY7Z4FDgasz85rM/AlwBvCUaQ6UmefT+6TDUjLzW5n5ueb7m+n9su465ZkyM3/YXNym+Zr6p9tFxCrgKOCfpj1Lh5RrEWr2WLHFZhZ7XDrssaWKPVZtEexxkeyxJXtszxYXrVyPttiePS4p5VoEe1wIe5zbct45uyvw9b7Lmynwi1pdRKwGDgYumu4kdx9yvgH4LvChzJz6TMBbgD8B7pr2IB1ii4tQqUWwxyXEHhehUo9FWwR7XAx7XAR7HMoWF8ceF6hSi2CPS4gtLoI9tjLVHpfzzlktUERsD/wH8LLM/MG058nMOzNzDbAKODQiDpjmPBHxa8B3M/PSac6hpa9ai2CPWr6q9VitRbBHTY49DmaLmpRqLYI9avmyx+Eq9Licd85+A9it7/KqZpnmEBHb0Av6tMz8z2nP0y8zbwQ+xvTP8XIYcHREbKL3zyt+KSL+bbojdYItLkDlFsEelwB7XIDKPRZqEexxsexxAeyxFVtcPHtsqXKLYI9LgC0ugD22NvUel/PO2YuBvSJiz4jYFngWsH7KM5UUEQG8A7giM9887XkAIuJBEfGA5vv7Ak8ErpzmTJn5p5m5KjNX0/t9+mhmPm+aM3WELbZUsUWwxyXGHluq2GPFFsEet4A9tmSP7djiFrHHFiq2CPa4xNhiS/bYXoUel+3O2cy8A3gRcB69EyOfmZkbpzlTRPw78Glgn4jYHBHHTXOePocBv0nv3YMNzdevTnmmXYCPRcRl9P6A/lBmfmDKM2kRKrYIZXus2CLY45JhjwtSsUdbXELscUHsUWNVsUdbXBB7XCIqtgj2uED2OIfInPqHokmSJEmSJEnSsrNsj5yVJEmSJEmSpGly56wkSZIkSZIkTYE7ZyVJkiRJkiRpCtw5K0mSJEmSJElT4M5ZSZIkSZIkSZoCd84uUET8cNblYyPirRO8/5+PiPeOYDsREddFxE7N5V0iIiPi8L51vhcRDxywjaMj4oQh9/PYiPjAPNe9LCLut8C5j4iIjRGxISLuO+u6O5vlM18DZ1P32eM9tmGPmip7vMc27FFTY4v32IYtaqrs8R7bsEdNlT3eYxv2WIw7ZzsmM7+Zmb8+gu0k8BngF5tFjwY+3/yXiNgHuD4zrx+wjfWZedIWjPEyYEFBA88F/ioz12Tmj2Zd96Nm+czXvWaLiBWzLm/d5k7brqflxR7tUXXYoz2qBlu0RdVhj/aoOuzRHgdx5+wIRcSTI+KiiPh8RHw4In62Wb4uIv4lIi6IiK9FxNMi4o0R8cWIODcitmnW2xQRf9W8U3BJRBwSEedFxFci4veadVZHxJea74+NiP9stvHliHhj3yzHRcR/R8RnI+Lt87wj9CmagJv//g33DPyTzbYeFBH/EREXN1+H9d3/W5vvHxoRn2l+pr+Y9a7U9hHx3oi4MiJOa97peQnw88DHIuJjczyWj28exy9GxDsj4j4R8XzgGcDrIuK0Bfx/2RQRb4iIzwG/EREfj4i3RMQlwEubx/SjEXFZRHwkInZvbndqRPxDRFwEvHHgnagce7RH1WGP9qgabNEWVYc92qPqsEd7nLrM9GsBX8CdwIa+r2uBtzbX7QRE8/3zgTc1368DLgS2AQ4CbgWe1Fz3PuCpzfebgN9vvv8b4DJgB+BBwHea5auBLzXfHwtcA+wIrAS+BuxGL5RNwM8093nBzIyzfpb/CXy0+f4CYHvgkuby24Hjmu9PBw5vvt8duKLv/md+9g8Az26+/z3gh833jwVuAlbRezPg033b2gTsPMdcK4GvA3s3l/8VeFnz/anAr7f8f/PMvvv5k771Pg78377L7wd+u/n+d4Gz+u7rA8CKaf/e+WWP9miP1b/s0R79qvFli7boV50ve7RHv+p82aM9Vv7qxOG9xfwoM9fMXIiIY4G1zcVVwLsjYhdgW+Crfbf7YGbeHhFfBFYA5zbLv0gv0hnr+5Zvn5k3AzdHxI8j4gFzzPORzLypmeVyYA9gZ+ATmfn9Zvl7gL3nuO3FwMERsR2wTWb+MCKuiYj/Qe/dljc16z0B2C8iZm53/4j4/+3dsWvUYBiA8ecVF6UgLs4ign+ADnbSwclF0aUgRZylKv4FNzmJCDqIgyCCIB0UJ+kmaFsqFlEQqoMOdnMQRLGI/Ry+HE3DcT3KtfnaPj8oXNLkkvZ4bkiub0cazzUKnK0ePwZu1r43l1L6Vp3Lu+rnfdXjfLqOAF9SSp+q5YfAZeB2n32g8do0POmzPAqcqx4/YvWdlcmU0r81jqv22KM9qhz2aI8qgy3aosphj/aoctijPRbLi7PDdQe4lVJ6HhEnyXdZupYAUkrLEfE3VZfzgWVWvw5LtfVLtfXN7ZrbQ77bMPBrmlL6HRGfyXcY5qvVs8Bp4ACwUK3bBRxPKf2p718LfC3rPsch+rXG8qD7aeuwx97sUW2wx97sUZvNFnuzRbXBHnuzR7XBHnuzx03izNnh2gcsVo8vtngeb4ATEbE/8vDj8322nSYPc56plmeAq8Bs7U1nCpjo7hARve5ozNaOMzbgef4kf9S/aQE4WN31ARgHXg74nOsxzco5XyD/WYC2PnvM7FElsMfMHtU2W8xsUSWwx8weVQJ7zOyxJV6cHa4OMBkRb4HvbZ1ESmkRuAHMkQdBfyXPCunlNXCIlaDnyR/pn65tcwU4FnnI8kfyHJKma8D1iHgPHO5zvLr7wItoDJGu7upcIv8uP6ljaXsAAADqSURBVJDvNN0b4Pn2RB7A3f0a9L8PTgCXqnMfJ7+haevrYI/2qFJ0sEd7VAk62KItqhQd7NEeVYoO9miPLYqVC+raTiJipJo7sps8qPpBSunpBh5vL3lOSIqIMfJA6TMbdTxpK7FHqRz2KJXBFqVy2KNUDnvcmZw5u311IuIU+b/lTQHPNvh4R4G7kYeX/CDPPpGU2aNUDnuUymCLUjnsUSqHPe5AfnJWkiRJkiRJklrgzFlJkiRJkiRJaoEXZyVJkiRJkiSpBV6clSRJkiRJkqQWeHFWkiRJkiRJklrgxVlJkiRJkiRJasF/8JRwQ5RXJSYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for dep in range(1, df_fn_depth.Depth.max()+1):\n", + " idx = df_fn_depth['Depth']== dep\n", + " avg_dist = df_fn_depth[idx]['Hamming dist. data'].mean() \n", + " rand_dist = df_fn_depth[idx]['Hamming dist. rand'].mean() \n", + " x_labels = np.arange(0, len(avg_dist))\n", + " plt.subplot(1,df_fn_depth.Depth.max(),dep)\n", + " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + " plt.xticks(x_labels)\n", + " plt.xlabel('Hamming Weight of Error')\n", + " plt.ylabel('Relative Frequency of Occurence')\n", + " plt.ylim([0,1])\n", + " plt.grid(axis='y', alpha=0.75)\n", + " plt.legend(['data','random'])\n", + " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data fron the data frame." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "depth_vec = []\n", + "pcheck = []\n", + "pcheck_rand = []\n", + "pcheck_log_errors = []\n", + "pcheck_log_errors_rand = []\n", + "tvd_rand = []\n", + "tvd_ideal = []\n", + "\n", + "for dep in range(1, df_fn_depth.Depth.max()+1):\n", + " idx = df_fn_depth['Depth']== dep\n", + " depth_vec.append(dep)\n", + " pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", + " pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", + " pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", + " pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", + " tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", + " tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Success probablity and success probablity including a small number of errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will plot the success probablity of a circuit with a certain width as a function of depth. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", + "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Pr(success)')\n", + "plt.title('Pr(success) vs Depth for Width = {}'.format(wid))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sucess if we allow for a small number of errors**\n", + "\n", + "Some near term algorithms have robustness to noise. In light of that we might want to consider answers that are only a little wrong successes.\n", + "\n", + "To make this notion formal we allow a logarithmic number of bits to flip from the correct answer and call all such instances \"success\".\n", + "\n", + "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", + "\n", + "$\\log_2 ({\\rm number\\ of\\ bits}) -1$\n", + "\n", + "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", + "\n", + "\n", + "Supose we have a circuit of width 4, this means correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4)-1 = 1$.\n", + "\n", + "So any string with hamming weight zero or one counts as a success.\n", + "\n", + "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", + "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", + "plt.scatter(depth_vec,pcheck_log_errors,label='Sucess Probablity + log errors')\n", + "plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Pr(success+log errors)')\n", + "plt.title('Pr(success+log errors) vs Depth for Width = {}'.format(wid))\n", + "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Total variation distance from ideal answer and random distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(depth_vec,tvd_ideal,label='TVD(data, ideal)')\n", + "plt.scatter(depth_vec,tvd_rand,label='TVD(data, rand)')\n", + "plt.scatter(depth_vec,1-np.asarray(pcheck),label='1-Sucess Probablity',alpha=0.33,marker='^',s=80)\n", + "#plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Total variation distance')\n", + "plt.title('Width = {}'.format(wid))\n", + "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot depth = width" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "max_idx = min([max(res_df['Depth']),max(res_df['Width'])])\n", + "\n", + "for idx in range(1,max_idx+1):\n", + " distz = get_hamming_dist(res_df, idx, idx)\n", + " # combine data from different subgraphs\n", + " avg_dist = distz['Hamming dist. data'].mean()\n", + " # rand data\n", + " rand_dist = distz['Hamming dist. rand'][0]\n", + " dep = idx\n", + " wid = idx\n", + " x_labels = np.arange(0, len(avg_dist))\n", + " plt.subplot(1,max_idx,idx)\n", + " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", + " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + " plt.xticks(x_labels)\n", + " plt.xlabel('Hamming Weight of Error')\n", + " plt.ylabel('Relative Frequency of Occurence')\n", + " plt.ylim([0,1])\n", + " plt.grid(axis='y', alpha=0.75)\n", + " plt.legend(['data','random'])\n", + " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot success probablity landscape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just the success probablity as a function of depth and width." + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.9251 , 0.8674 , 0.73105, 0.71715, 0.9201 , 0.8482 , 0.7371 ,\n", + " 0.67555, 0.90375, 0.8447 , 0.75855, 0.6031 , 0.92555, 0.8306 ,\n", + " 0.76205, 0.5922 , 0.9231 , 0.85725, 0.71515, 0.5072 , 0.9045 ,\n", + " 0.8439 , 0.7076 , 0.54185])" + ] + }, + "execution_count": 326, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values = np.asarray([munged['Pr. success data'][idx] for idx in munged.index])\n", + "values" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", + " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", + " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625])" + ] + }, + "execution_count": 327, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "values_rand = np.asarray([munged['Pr. success rand'][idx] for idx in munged.index])\n", + "values_rand" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.arange(min(res_df['Depth']), max(res_df['Depth'])+1)\n", + "\n", + "y = np.arange(min(res_df['Width']), max(res_df['Width'])+1)\n", + "\n", + "X, Y = np.meshgrid(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [], + "source": [ + "(x1,x2) = X.shape\n", + "Zdata = np.reshape(values,(x2,x1)).T\n", + "Zrand = np.reshape(values_rand,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.9251 , 0.9201 , 0.90375, 0.92555, 0.9231 , 0.9045 ],\n", + " [0.8674 , 0.8482 , 0.8447 , 0.8306 , 0.85725, 0.8439 ],\n", + " [0.73105, 0.7371 , 0.75855, 0.76205, 0.71515, 0.7076 ],\n", + " [0.71715, 0.67555, 0.6031 , 0.5922 , 0.5072 , 0.54185]])" + ] + }, + "execution_count": 331, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Zdata" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": {}, + "outputs": [], + "source": [ + "tvd_rand_values = np.asarray([munged['TVD(data, rand)'][idx] for idx in munged.index])\n", + "tvd_ideal_values = np.asarray([munged['TVD(data, ideal)'][idx] for idx in munged.index])\n", + "Ztvd_rand = np.reshape(tvd_rand_values,(x2,x1)).T\n", + "Ztvd_ideal = np.reshape(tvd_ideal_values,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.46255 , 0.622775, 0.608975, 0.675675, 0.46005 , 0.60205 ,\n", + " 0.615325, 0.6457 , 0.451875, 0.5996 , 0.63455 , 0.5811 ,\n", + " 0.462775, 0.5867 , 0.639125, 0.56565 , 0.46155 , 0.6122 ,\n", + " 0.592275, 0.505625, 0.45225 , 0.598525, 0.59295 , 0.53795 ])" + ] + }, + "execution_count": 357, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tvd_ideal_values\n", + "tvd_rand_values" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 362, + "metadata": {}, + "outputs": [], + "source": [ + "loge_rand_values = np.asarray([munged['Pr. success loge rand'][idx] for idx in munged.index])\n", + "loge_data_values = np.asarray([munged['Pr. success loge data'][idx] for idx in munged.index])\n", + "Zlge_rand = np.reshape(loge_rand_values,(x2,x1)).T\n", + "Zlge_data = np.reshape(loge_data_values,(x2,x1)).T" + ] + }, + { + "cell_type": "code", + "execution_count": 363, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zlge_data, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zlge_rand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "ax.set_xticks(xticks)\n", + "ax.set_xticklabels(map(str, xticks))\n", + "\n", + "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "ax.set_yticks(yticks)\n", + "ax.set_yticklabels(map(str, yticks))\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Depth')\n", + "plt.ylabel('Width')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 432, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": {}, + "outputs": [], + "source": [ + "size = Y.shape\n", + "width_1d = Y.reshape((1,np.prod(size)))\n", + "depth_1d = X.reshape((1,np.prod(size)))" + ] + }, + { + "cell_type": "code", + "execution_count": 441, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 24)" + ] + }, + "execution_count": 441, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_1d = Zdata.reshape((1,np.prod(size)))\n", + "data_1d.shape\n", + "width_1d.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 435, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0],\n", + " [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,\n", + " 4, 4],\n", + " [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n", + " 5, 6]])" + ] + }, + "execution_count": 435, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims = np.zeros_like(width_1d)\n", + "dims[0,0] = size[0]\n", + "dims[0,1] = size[1]\n", + "\n", + "xdata = np.vstack((dims,width_1d, depth_1d))\n", + "\n", + "\n", + "\n", + "xdata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two parameter model \n", + "\n", + "\n", + "$f(W,D,p_W,p_D) = (1-p_W)^W * (1-p_D)^D $\n", + "\n", + "The fidelity is proporional to $1 - p$" + ] + }, + { + "cell_type": "code", + "execution_count": 455, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param(x,pw,pd):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = (1-pw)**(width) * (1-pd)**depth\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One parameter model\n", + "\n", + "$f(W,D,p) = (1-p)^{W * D} $" + ] + }, + { + "cell_type": "code", + "execution_count": 447, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param(x,p):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = (1-p)**(width*depth)\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From my prior work a better model to fit to is\n", + "\n", + "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" + ] + }, + { + "cell_type": "code", + "execution_count": 510, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param_exp(x,p,a,b):\n", + " temp = x[0]\n", + " wid = temp[0]\n", + " dep = temp[1]\n", + " width = x[1].reshape(wid,dep)\n", + " depth = x[2].reshape(wid,dep)\n", + " pcheck = np.exp(-(a*p + b) * width * depth)\n", + " rpcheck = pcheck.reshape((1,wid*dep))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Start with one paramter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 531, + "metadata": {}, + "outputs": [], + "source": [ + "pguess = 0.1\n", + "popt, pcov = curve_fit(one_param, xdata, data_1d.ravel(), p0=pguess, bounds=(0, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 532, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The estimated error is p = 0.0276\n", + "The estimated product of the one and two qubit fidelity is F = 0.9724\n" + ] + } + ], + "source": [ + "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", + "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", + "#print('The one standard deviation on the estimate is ', str(np.round(np.sqrt(np.diag(pcov)[0]),5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 533, + "metadata": {}, + "outputs": [], + "source": [ + "zfit = one_param(xdata,popt)\n", + "Z_fit = zfit.reshape(size)" + ] + }, + { + "cell_type": "code", + "execution_count": 534, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y, Z_fit)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 535, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y,Zdata)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Two parameter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 541, + "metadata": {}, + "outputs": [], + "source": [ + "pguess2d = [0.0276, 0.01, 0.4]" + ] + }, + { + "cell_type": "code", + "execution_count": 542, + "metadata": {}, + "outputs": [], + "source": [ + "popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d , bounds=(0., 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 543, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00193651, 0.00070045, 0.02802694])" + ] + }, + "execution_count": 543, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "popt2d" + ] + }, + { + "cell_type": "code", + "execution_count": 544, + "metadata": {}, + "outputs": [], + "source": [ + "zfit2d = two_param(xdata,popt2d[0],popt2d[1])\n", + "Z_fit2d = zfit2d.reshape(size)" + ] + }, + { + "cell_type": "code", + "execution_count": 545, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pcolormesh(X,Y, Z_fit2d)\n", + "plt.xticks(list(range(1,circuit_depth+1)))\n", + "plt.yticks(list(range(1,circuit_width+1)))\n", + "plt.colorbar()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 486, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.897680214" + ] + }, + "execution_count": 486, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1-1.02319786e-01" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 52e5129f389f20609ef0110f6cfaa5a92c06297e Mon Sep 17 00:00:00 2001 From: Kyle Date: Wed, 13 Mar 2019 11:36:07 -0400 Subject: [PATCH 09/49] Minor formatting changes. --- forest_benchmarking/circuit_testing.py | 135 +++++++++++++------------ 1 file changed, 72 insertions(+), 63 deletions(-) diff --git a/forest_benchmarking/circuit_testing.py b/forest_benchmarking/circuit_testing.py index eb2a0e0e..efba29da 100644 --- a/forest_benchmarking/circuit_testing.py +++ b/forest_benchmarking/circuit_testing.py @@ -22,7 +22,8 @@ # Gate Sets # ================================================================================================== def random_single_qubit_gates(graph: nx.Graph, gates: list): - """Create a program comprised of single qubit gates randomly placed on the nodes of the + """ + Create a program comprised of single qubit gates randomly placed on the nodes of the specified graph. The gates are chosen uniformly from the list provided. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -37,7 +38,8 @@ def random_single_qubit_gates(graph: nx.Graph, gates: list): def random_two_qubit_gates(graph: nx.Graph, gates: list): - """Write a program to randomly place two qubit gates on edges of the specified graph. + """ + Write a program to randomly place two qubit gates on edges of the specified graph. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] @@ -53,8 +55,10 @@ def random_two_qubit_gates(graph: nx.Graph, gates: list): def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): - """Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of - the specified graph. The gates are chosen uniformly from the list provided. + """ + Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of + the specified graph. Each uniformly random choice of Clifford is implemented in the native + gateset. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -75,7 +79,8 @@ def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): - """Write a program to place random two qubit Cliffords gates on edges of the graph. + """ + Write a program to place random two qubit Cliffords gates on edges of the graph. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -112,6 +117,7 @@ def pre_trival(graph: nx.Graph): prog += [prep_gate(qubit) for qubit in list(graph.nodes)] return prog + def post_trival(): prog = Program() return prog @@ -124,7 +130,7 @@ def post_trival(): def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, graph: nx.Graph, layer_dagger: bool = False): - ''' + """ Creates a layer of random one qubit Cliffords followed by random two qubit Cliffords. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords @@ -132,7 +138,7 @@ def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, :param layer_dagger: Bool if true will add the dagger to the layer, making the layer efectivley the identity :return: program - ''' + """ prog = Program() prog += random_single_qubit_cliffords(bm, graph) prog += random_two_qubit_cliffords(bm, graph) @@ -140,11 +146,12 @@ def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, prog += prog.dagger() return prog + def layer_1q_and_2q_rand_gates(graph: nx.Graph, one_q_gates, two_q_gates, layer_dagger: bool = False): - ''' + """ You pass in two lists of one and two qubit gates. This function creates a layer of random one qubit gates followed by random two qubit gates @@ -154,7 +161,7 @@ def layer_1q_and_2q_rand_gates(graph: nx.Graph, :param layer_dagger: Bool if true will add the dagger to the layer, making the layer efectivley the identity :return: program - ''' + """ prog = Program() prog += random_single_qubit_gates(graph, one_q_gates) prog += random_two_qubit_gates(graph, two_q_gates) @@ -162,6 +169,7 @@ def layer_1q_and_2q_rand_gates(graph: nx.Graph, prog += prog.dagger() return prog + # ================================================================================================== # Sandwich tools # ================================================================================================== @@ -171,7 +179,7 @@ def circuit_sandwich_rand_gates(graph: nx.Graph, two_q_gates: list, layer_dagger: bool = False, sandwich_dagger: bool = False): - ''' + """ Create a sandwich circuit by adding layers. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -182,7 +190,7 @@ def circuit_sandwich_rand_gates(graph: nx.Graph, :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of the first. :return: program - ''' + """ total_prog = Program() total_prog += pre_trival(graph) @@ -190,7 +198,7 @@ def circuit_sandwich_rand_gates(graph: nx.Graph, circuit_depth = int(np.floor(circuit_depth / 2)) layer_progs = Program() - for ddx in range(1, circuit_depth + 1): + for _ in range(circuit_depth): layer_progs += layer_1q_and_2q_rand_gates(graph, one_q_gates, two_q_gates, @@ -208,7 +216,7 @@ def circuit_sandwich_clifford(bm: BenchmarkConnection, circuit_depth: int, layer_dagger: bool = False, sandwich_dagger: bool = False): - ''' + """ :param bm: A benchmark connection that will do the grunt work of generating the Cliffords :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -217,16 +225,16 @@ def circuit_sandwich_clifford(bm: BenchmarkConnection, :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of the first. :return: program - ''' + """ total_prog = Program() total_prog += pre_trival(graph) if sandwich_dagger: - depth = int(np.floor(circuit_depth / 2)) + circuit_depth = int(np.floor(circuit_depth / 2)) layer_progs = Program() - for ddx in range(1, circuit_depth + 1): + for _ in range(circuit_depth): layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) if sandwich_dagger: layer_progs += layer_progs.dagger() @@ -249,7 +257,7 @@ def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, # peter claims that no speed diff 800 shots num_shots_per_circuit: int = 100, use_active_reset: bool = False) -> pd.DataFrame: - ''' + """ Return a DataFrame where the rows contain all the information needed to run random circuits of a certain width and depth on a particular lattice. @@ -262,42 +270,43 @@ def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, :param num_shots_per_circuit: number of shots per random circuit :param use_active_reset: if True uses active reset. Doing so will speed up execution on a QPU. :return: pandas DataFrame - ''' + """ # get the networkx graph of the lattice G = qc_noisy.qubit_topology() if circuit_width > len(G.nodes): - raise ValueError("You must have circuit widths less than or equal to the number of qubits on a lattice.") + raise ValueError("You must have circuit widths less than or equal to the number of qubits " + "on a lattice.") experiment = [] # loop over different graph sizes - for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), - range(1, circuit_width+1)): - + for subgraph_size in range(1, circuit_width + 1): list_of_graphs = generate_connected_subgraphs(G, subgraph_size) - for kdx in range(1, num_rand_subgraphs+1): - # randomly choose a lattice from list - lattice = random.choice(list_of_graphs) - prog = circuit_sandwich(graph=lattice, - circuit_depth=depth, - layer_dagger=layer_dagger, - sandwich_dagger=sandwich_dagger) - - experiment.append({'Depth': depth, - 'Width': subgraph_size, - 'Lattice':lattice, - 'Layer Dagger': layer_dagger, - 'Sandwich Dagger': sandwich_dagger, - 'Active Reset': use_active_reset, - 'Program': prog, - 'Trials': num_shots_per_circuit, - }) + + for depth in range(1, circuit_depth + 1): + for _ in range(num_rand_subgraphs): + # randomly choose a lattice from list + lattice = random.choice(list_of_graphs) + prog = circuit_sandwich(graph=lattice, + circuit_depth=depth, + layer_dagger=layer_dagger, + sandwich_dagger=sandwich_dagger) + + experiment.append({'Depth': depth, + 'Width': subgraph_size, + 'Lattice': lattice, + 'Layer Dagger': layer_dagger, + 'Sandwich Dagger': sandwich_dagger, + 'Active Reset': use_active_reset, + 'Program': prog, + 'Trials': num_shots_per_circuit, + }) return pd.DataFrame(experiment) def acquire_circuit_sandwich_data(qc_noisy: QuantumComputer, circ_sand_expt: pd.DataFrame) -> pd.DataFrame: - ''' + """ Convenient wrapper for collecting the results of running circuits sandwiches on a particular lattice. @@ -308,14 +317,11 @@ def acquire_circuit_sandwich_data(qc_noisy: QuantumComputer, :param qc_noisy: the noisy quantum resource (QPU or QVM) to :param circ_sand_expt: pandas DataFrame where the rows contain experiments :return: pandas DataFrame - ''' + """ #:param qc_perfect: the "perfect" quantum resource (QVM) to determine the true outcome. # if qc_perfect.name == qc_noisy.name: # raise ValueError("The noisy and perfect device can't be the same device.") - # get the networkx graph of the lattice - G = qc_noisy.qubit_topology() - data = [] for index, row in circ_sand_expt.iterrows(): prog = row['Program'] @@ -355,12 +361,12 @@ def acquire_circuit_sandwich_data(qc_noisy: QuantumComputer, # ================================================================================================== def estimate_random_classical_circuit_errors(qc_perfect: QuantumComputer, df: pd.DataFrame) -> pd.DataFrame: - ''' + """ asdf :param df: pandas DataFrame containing experimental results :return: pandas DataFrame containing estiamted errors and experimental results - ''' + """ results = [] for _, row in df.iterrows(): @@ -420,6 +426,7 @@ def estimate_random_classical_circuit_errors(qc_perfect: QuantumComputer, }) return pd.DataFrame(results) + def get_error_hamming_distance_from_results(perfect_bit_string, results): """Get the hamming weight of the error vector (number of bits flipped between output and expected answer). @@ -452,7 +459,6 @@ def get_error_hamming_distributions_from_list(wt_list, n_bits): if n_bits < max(wt_list): raise ValueError("Hamming weight can't be larger than the number of bits in a string.") - hamming_wt_distrs = [] hamming_wt_distr = [0. for _ in range(n_bits + 1)] # record the fraction of shots that resulted in an error of the given weight for wdx in range(n_bits): @@ -461,69 +467,69 @@ def get_error_hamming_distributions_from_list(wt_list, n_bits): def hamming_dist_rand(num_bits: int, pad: int = 0): - '''Return a list representing the Hamming distribution of + """Return a list representing the Hamming distribution of a particular bit string, of length num_bits, to randomly drawn bits. :param num_bits: number of bits in string :param pad: number of zero elements to pad returns: list of hamming weights with zero padding - ''' + """ N = 2 ** num_bits pr = [comb(num_bits, ndx) / (2 ** num_bits) for ndx in range(0, num_bits + 1)] - padding = [0 for pdx in range(0, pad)] + padding = [0 for _ in range(pad)] return flatten_list([pr, padding]) def flatten_list(xlist): - '''Flattens a list of lists. + """Flattens a list of lists. :param xlist: list of lists :returns: a flattened list - ''' + """ return [item for sublist in xlist for item in sublist] # helper functions to manipulate the dataframes def get_hamming_dist(df: pd.DataFrame, depth_val: int, width_val: int): - ''' + """ Get Hamming distance from a dataframe for a particular depth and width. :param df: dataframe generated from data from 'get_random_classical_circuit_results' :param depth_val: depth of quantum circuit :param width_val: width of quantum circuit :return: smaller dataframe - ''' + """ idx = df.Depth == depth_val jdx = df.Width == width_val return df[idx & jdx].reset_index(drop=True) def get_hamming_dists_fn_width(df: pd.DataFrame, depth_val: int): - ''' + """ Get Hamming distance from a dataframe for a particular depth. :param df: dataframe generated from data from 'get_random_classical_circuit_results' :param depth_val: depth of quantum circuit :return: smaller dataframe - ''' + """ idx = df.Depth == depth_val return df[idx].reset_index(drop=True) def get_hamming_dists_fn_depth(df: pd.DataFrame, width_val: int): - ''' + """ Get Hamming distance from a dataframe for a particular width. :param df: dataframe generated from data from 'get_random_classical_circuit_results' :param width_val: width of quantum circuit :return: smaller dataframe - ''' + """ jdx = df.Width == width_val return df[jdx].reset_index(drop=True) def basement_function(number: float): - ''' + """ Once you are in the basement you can't go lower. Defined as basement_function(number) = |floor(number)*heaviside(number,0)|, @@ -533,7 +539,7 @@ def basement_function(number: float): :param number: the basement function is applied to this number. :returns: basement of the number - ''' + """ basement_of_number = np.abs(np.floor(number) * np.heaviside(number, 0)) return basement_of_number @@ -556,21 +562,24 @@ def CNOT_X_basis(control, target) -> Program: prog += H(control) return prog + # ================================================================================================== # Graph tools # ================================================================================================== + + def generate_connected_subgraphs(G: nx.Graph, n_vert: int): - ''' + """ Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all subgraphs with n_vert connect vertices. :params n_vert: number of vertices of connected subgraph. :params G: networkx Graph :returns: list of subgraphs with n_vert connected vertices - ''' + """ subgraph_list = [] for sub_nodes in itertools.combinations(G.nodes(), n_vert): subg = G.subgraph(sub_nodes) if nx.is_connected(subg): subgraph_list.append(subg) - return subgraph_list \ No newline at end of file + return subgraph_list From d6dc3df238602a159ba11adc568dc6d33801d8ac Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 18 Mar 2019 10:34:46 -0400 Subject: [PATCH 10/49] Copied dataclass definition over to nb. --- examples/circuit_testing_kyle.ipynb | 45 +++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/examples/circuit_testing_kyle.ipynb b/examples/circuit_testing_kyle.ipynb index 889d6446..e763804c 100644 --- a/examples/circuit_testing_kyle.ipynb +++ b/examples/circuit_testing_kyle.ipynb @@ -14,6 +14,51 @@ "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from dataclasses import dataclass\n", + "\n", + "\n", + "@dataclass()\n", + "class Component:\n", + " sequence: Tuple[Program]\n", + " # add list of experiment settings here? Good for unitarity and RPE, though not necessary for RB.\n", + " # Would allow natural use of measure_observables, labeling measurement types, symmetrized ro.\n", + " measure_qubits: Tuple[int]\n", + " num_shots: int = None\n", + " results: np.ndarray = None\n", + " mean: int = None\n", + " stddev: int = None\n", + "\n", + "\n", + " def __str__(self):\n", + " return '[' + ', '.join([str(instr) for instr in self.sequence[0]]) + '] ... [' + \\\n", + " ', '.join([str(instr) for instr in self.sequence[-1]]) + ']'\n", + "\n", + "\n", + "@dataclass(order=True)\n", + "class Layer:\n", + " depth: int\n", + " components: Tuple[Component]\n", + "\n", + " def __str__(self):\n", + " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", + "\n", + "\n", + "@dataclass\n", + "class StratifiedExperiment:\n", + " layers: Tuple[Layer]\n", + " qubits: Tuple[Layer]\n", + " exp_type: str\n", + "\n", + " def __str__(self):\n", + " return '\\n'.join([str(lyr) for lyr in self.layers]) + '\\n'" + ] + }, { "cell_type": "markdown", "metadata": {}, From 19eaed675c395c7e2ae8f2e6f186bac55853bd17 Mon Sep 17 00:00:00 2001 From: Joshua Combes Date: Thu, 21 Mar 2019 14:31:40 +1000 Subject: [PATCH 11/49] update for new repo name --- examples/circuit_testing_josh.ipynb | 4 ++-- examples/circuit_testing_kyle.ipynb | 4 ++-- forest/benchmarking/circuit_testing.py | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb index 462eaa7a..79900a30 100644 --- a/examples/circuit_testing_josh.ipynb +++ b/examples/circuit_testing_josh.ipynb @@ -41,7 +41,7 @@ "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", "from pyquil.quilbase import Pragma\n", "\n", - "from forest_benchmarking.circuit_testing import *" + "from forest.benchmarking.circuit_testing import *" ] }, { @@ -169,7 +169,7 @@ "metadata": {}, "outputs": [], "source": [ - "from forest_benchmarking.rb import get_rb_gateset" + "from forest.benchmarking.rb import get_rb_gateset" ] }, { diff --git a/examples/circuit_testing_kyle.ipynb b/examples/circuit_testing_kyle.ipynb index 889d6446..87924811 100644 --- a/examples/circuit_testing_kyle.ipynb +++ b/examples/circuit_testing_kyle.ipynb @@ -41,7 +41,7 @@ "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", "from pyquil.quilbase import Pragma\n", "\n", - "from forest_benchmarking.circuit_testing import *" + "from forest.benchmarking.circuit_testing import *" ] }, { @@ -162,7 +162,7 @@ "metadata": {}, "outputs": [], "source": [ - "from forest_benchmarking.rb import get_rb_gateset" + "from forest.benchmarking.rb import get_rb_gateset" ] }, { diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/circuit_testing.py index efba29da..4dd19675 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/circuit_testing.py @@ -14,8 +14,8 @@ from pyquil.api import BenchmarkConnection from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET from pyquil.quil import address_qubits -from forest_benchmarking.rb import get_rb_gateset -from forest_benchmarking.distance_measures import total_variation_distance as tvd +from forest.benchmarking.rb import get_rb_gateset +from forest.benchmarking.distance_measures import total_variation_distance as tvd # ================================================================================================== From 64d94236a61825a0d5e7ec4fdd1f6d3eb1e9fa96 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 25 Mar 2019 17:43:15 -0400 Subject: [PATCH 12/49] Add dataclasses. --- examples/circuit_testing_kyle.ipynb | 86 +++++++++++++- forest/benchmarking/circuit_testing.py | 152 +++++++++++++++++++++++-- 2 files changed, 226 insertions(+), 12 deletions(-) diff --git a/examples/circuit_testing_kyle.ipynb b/examples/circuit_testing_kyle.ipynb index 99c923cd..69ad5497 100644 --- a/examples/circuit_testing_kyle.ipynb +++ b/examples/circuit_testing_kyle.ipynb @@ -20,7 +20,41 @@ "metadata": {}, "outputs": [], "source": [ + "def single_q_rand_slice(graph):\n", + " for node in graph.nodes:\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 3, 2, 0])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.permutation(range(4))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Tuple, Callable\n", "from dataclasses import dataclass\n", + "import networkx as nx\n", "\n", "\n", "@dataclass()\n", @@ -38,22 +72,64 @@ " def __str__(self):\n", " return '[' + ', '.join([str(instr) for instr in self.sequence[0]]) + '] ... [' + \\\n", " ', '.join([str(instr) for instr in self.sequence[-1]]) + ']'\n", + " \n", + "@dataclass(order=True)\n", + "class Slice:\n", + " index: int\n", + " gates: Tuple[Program]\n", + " needs_compilation: bool = True\n", "\n", - "\n", + " def __str__(self):\n", + " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", + " \n", + " \n", "@dataclass(order=True)\n", "class Layer:\n", " depth: int\n", - " components: Tuple[Component]\n", + " slices: Tuple[Slice]\n", + " needs_compilation: bool = True\n", "\n", " def __str__(self):\n", " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", "\n", "\n", "@dataclass\n", - "class StratifiedExperiment:\n", + "class Circuit:\n", " layers: Tuple[Layer]\n", - " qubits: Tuple[Layer]\n", - " exp_type: str\n", + " graph: nx.Graph\n", + " needs_compilation: bool = True\n", + " name: str = None\n", + "\n", + " def __str__(self):\n", + " return '\\n'.join([str(lyr) for lyr in self.layers]) + '\\n'\n", + "\n", + " \n", + "@dataclass(order=True)\n", + "class SliceTemplate:\n", + " index: int\n", + " generator: Callable\n", + " sandwich: bool = False\n", + "\n", + " def __str__(self):\n", + " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", + " \n", + " \n", + "@dataclass(order=True)\n", + "class LayerTemplate:\n", + " depth: int\n", + " slices: Tuple[SliceTemplate]\n", + " sandwich: bool = False\n", + "\n", + " def __str__(self):\n", + " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", + "\n", + "\n", + "@dataclass\n", + "class CircuitTemplate:\n", + " layers: Tuple[LayerTemplate]\n", + " graph: nx.Graph\n", + " sandwich: bool = False\n", + " name: str = None\n", "\n", " def __str__(self):\n", " return '\\n'.join([str(lyr) for lyr in self.layers]) + '\\n'" diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/circuit_testing.py index 4dd19675..7641c08a 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/circuit_testing.py @@ -1,14 +1,14 @@ -from typing import List +from typing import Tuple, Sequence, Callable, Any, List import networkx as nx import numpy as np import random import itertools import pandas as pd from scipy.spatial.distance import hamming -import scipy.interpolate from scipy.special import comb +from dataclasses import dataclass -from pyquil.quilbase import Pragma +from pyquil.quilbase import Pragma, Gate, DefGate from pyquil.quil import Program from pyquil.api import QuantumComputer from pyquil.api import BenchmarkConnection @@ -16,12 +16,75 @@ from pyquil.quil import address_qubits from forest.benchmarking.rb import get_rb_gateset from forest.benchmarking.distance_measures import total_variation_distance as tvd +from forest.benchmarking.random_operators import haar_rand_unitary +@dataclass(order=True) +class Slice: + index: int + gates: Tuple[Program] + needs_compilation: bool = True + + # def __str__(self): + # return f'Index {self.index}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' + + +@dataclass(order=True) +class Layer: + depth: int + slices: Tuple[Slice] + needs_compilation: bool = True + + # def __str__(self): + # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' + + +@dataclass +class Circuit: + layers: Tuple[Layer] + graph: nx.Graph + needs_compilation: bool = True + name: str = None + + # def __str__(self): + # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' + + +@dataclass(order=True) +class SliceTemplate: + index: int + generator: Callable + args = Sequence[Any] + sandwich: bool = False + + # def __str__(self): + # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' + + +@dataclass(order=True) +class LayerTemplate: + depth: int + slices: Tuple[SliceTemplate] + sandwich: bool = False + + # def __str__(self): + # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' + + +@dataclass +class CircuitTemplate: + layers: Tuple[LayerTemplate] + graph: nx.Graph + sandwich: bool = False + name: str = None + + # def __str__(self): + # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' + # ================================================================================================== # Gate Sets # ================================================================================================== -def random_single_qubit_gates(graph: nx.Graph, gates: list): +def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): """ Create a program comprised of single qubit gates randomly placed on the nodes of the specified graph. The gates are chosen uniformly from the list provided. @@ -37,7 +100,7 @@ def random_single_qubit_gates(graph: nx.Graph, gates: list): return program -def random_two_qubit_gates(graph: nx.Graph, gates: list): +def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): """ Write a program to randomly place two qubit gates on edges of the specified graph. @@ -54,7 +117,7 @@ def random_two_qubit_gates(graph: nx.Graph, gates: list): return program -def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): +def random_single_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): """ Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of the specified graph. Each uniformly random choice of Clifford is implemented in the native @@ -78,7 +141,7 @@ def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog -def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): +def random_two_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): """ Write a program to place random two qubit Cliffords gates on edges of the graph. @@ -103,6 +166,38 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog +def random_permutation(graph: nx.Graph): + qubits = graph.nodes + permutation = np.random_permutation(range(len(qubits))) + + matrix = [] + for target in permutation: + row = [0 for _ in permutation] + row[target] = 1 + matrix.append(row) + + gate_definition = DefGate("".join([str(qubits[idx]) for idx in permutation]), matrix) + PERMUTE = gate_definition.get_constructor() + p = Program() + p += gate_definition + p += PERMUTE(*qubits) + return p + + +def random_su2_pairs(graph: nx.Graph): + qubits = graph.nodes + gates = [] + for q1, q2 in zip(qubits[::2], qubits[1::2]): + matrix = haar_rand_unitary(4) + gate_definition = DefGate("RSU2(" + str(q1) + str(q2) + ")", matrix) + RSU2 = gate_definition.get_constructor() + p = Program() + p += gate_definition + p += RSU2(q1, q2) + gates.append(p) + return gates + + # ================================================================================================== # Prefix // Suffix programs; pre and post # ================================================================================================== @@ -127,6 +222,18 @@ def post_trival(): # Layer tools # ================================================================================================== +def slice_templates_1q_and_2q_rand_cliff(bm: BenchmarkConnection): + slice_1q = SliceTemplate(0, random_single_qubit_cliffords, (bm, )) + slice_2q = SliceTemplate(1, random_two_qubit_cliffords, (bm, )) + return slice_1q, slice_2q + + +def slice_templates_1q_and_2q_rand_rand_gates(one_q_gates, two_q_gates): + slice_1q = SliceTemplate(0, random_single_qubit_gates, (one_q_gates, )) + slice_2q = SliceTemplate(1, random_two_qubit_gates, (two_q_gates, )) + return slice_1q, slice_2q + + def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, graph: nx.Graph, layer_dagger: bool = False): @@ -247,6 +354,37 @@ def circuit_sandwich_clifford(bm: BenchmarkConnection, # ================================================================================================== # Generate and Acquire functions # ================================================================================================== +def generate_repeated_layer_circuit_template(lattice: nx.Graph, circuit_depth: int, + circuit_width: int, + slice_templates: Sequence[SliceTemplate], + layer_sandwich: bool = False, + circuit_sandwich: bool = False) -> CircuitTemplate: + """ + Return the template needed to generate random circuits of a certain width and depth using a + particular lattice connectivity. + + :param lattice: + :param circuit_depth: depth of quantum circuit + :param circuit_width: width of quantum circuit + :param slice_templates: + :param layer_sandwich: + :param circuit_sandwich: + :return: + """ + if circuit_width > len(lattice.nodes): + raise ValueError("You must have circuit widths less than or equal to the number of qubits " + "on a lattice.") + layers = (LayerTemplate(depth, slice_templates, layer_sandwich) \ + for depth in range(1, circuit_depth + 1)) + + return CircuitTemplate(layers, lattice, circuit_sandwich, "UniformLayers") + + +# def generate_circuit_experiments(): +# +# yield Circuit(layers, graph, needs_compilation, name) + + def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, circuit_depth: int, circuit_width: int, From 5384910727875bcd4ea753cc3cd51319cb156acb Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 19 Apr 2019 11:42:40 -0400 Subject: [PATCH 13/49] Refactored toward generative approach --- examples/circuit_testing_kyle.ipynb | 4387 ++---------------------- forest/benchmarking/circuit_testing.py | 469 +-- forest/benchmarking/compilation.py | 2 + 3 files changed, 517 insertions(+), 4341 deletions(-) diff --git a/examples/circuit_testing_kyle.ipynb b/examples/circuit_testing_kyle.ipynb index 69ad5497..48a55a04 100644 --- a/examples/circuit_testing_kyle.ipynb +++ b/examples/circuit_testing_kyle.ipynb @@ -14,127 +14,6 @@ "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def single_q_rand_slice(graph):\n", - " for node in graph.nodes:\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 2, 0])" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.permutation(range(4))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Tuple, Callable\n", - "from dataclasses import dataclass\n", - "import networkx as nx\n", - "\n", - "\n", - "@dataclass()\n", - "class Component:\n", - " sequence: Tuple[Program]\n", - " # add list of experiment settings here? Good for unitarity and RPE, though not necessary for RB.\n", - " # Would allow natural use of measure_observables, labeling measurement types, symmetrized ro.\n", - " measure_qubits: Tuple[int]\n", - " num_shots: int = None\n", - " results: np.ndarray = None\n", - " mean: int = None\n", - " stddev: int = None\n", - "\n", - "\n", - " def __str__(self):\n", - " return '[' + ', '.join([str(instr) for instr in self.sequence[0]]) + '] ... [' + \\\n", - " ', '.join([str(instr) for instr in self.sequence[-1]]) + ']'\n", - " \n", - "@dataclass(order=True)\n", - "class Slice:\n", - " index: int\n", - " gates: Tuple[Program]\n", - " needs_compilation: bool = True\n", - "\n", - " def __str__(self):\n", - " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", - " \n", - " \n", - "@dataclass(order=True)\n", - "class Layer:\n", - " depth: int\n", - " slices: Tuple[Slice]\n", - " needs_compilation: bool = True\n", - "\n", - " def __str__(self):\n", - " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", - "\n", - "\n", - "@dataclass\n", - "class Circuit:\n", - " layers: Tuple[Layer]\n", - " graph: nx.Graph\n", - " needs_compilation: bool = True\n", - " name: str = None\n", - "\n", - " def __str__(self):\n", - " return '\\n'.join([str(lyr) for lyr in self.layers]) + '\\n'\n", - "\n", - " \n", - "@dataclass(order=True)\n", - "class SliceTemplate:\n", - " index: int\n", - " generator: Callable\n", - " sandwich: bool = False\n", - "\n", - " def __str__(self):\n", - " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", - " \n", - " \n", - "@dataclass(order=True)\n", - "class LayerTemplate:\n", - " depth: int\n", - " slices: Tuple[SliceTemplate]\n", - " sandwich: bool = False\n", - "\n", - " def __str__(self):\n", - " return f'Depth {self.depth}:\\n' + '\\n'.join([str(comp) for comp in self.components]) + '\\n'\n", - "\n", - "\n", - "@dataclass\n", - "class CircuitTemplate:\n", - " layers: Tuple[LayerTemplate]\n", - " graph: nx.Graph\n", - " sandwich: bool = False\n", - " name: str = None\n", - "\n", - " def __str__(self):\n", - " return '\\n'.join([str(lyr) for lyr in self.layers]) + '\\n'" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -144,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -165,19 +44,6 @@ "from forest.benchmarking.circuit_testing import *" ] }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def two_q_id(qb1,qb2):\n", - " prog = Program()\n", - " prog +=I(qb1)\n", - " prog +=I(qb2)\n", - " return prog" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -187,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -203,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -212,14 +78,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -232,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +114,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def two_q_id(qb1,qb2):\n", + " prog = Program()\n", + " prog +=I(qb1)\n", + " prog +=I(qb2)\n", + " return prog" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -261,13 +140,47 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Z 0\n", + "Z 1\n", + "Z 2\n", + "I 3\n", + "I 4\n", + "I 5\n", + "X 6\n", + "I 7\n", + "X 8\n", + "CZ 0 3\n", + "I 0\n", + "I 1\n", + "CZ 1 4\n", + "CZ 1 2\n", + "CZ 2 5\n", + "CZ 3 6\n", + "CZ 3 4\n", + "I 4\n", + "I 7\n", + "I 4\n", + "I 5\n", + "CZ 5 8\n", + "I 6\n", + "I 7\n", + "I 7\n", + "I 8\n", + "\n" + ] + } + ], "source": [ - "#prog1 = random_single_qubit_gates(G, one_q_gates)\n", - "#prog2 = random_two_qubit_gates(G, two_q_gates)\n", - "#print(prog1+prog2)" + "prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "print(prog1+prog2)" ] }, { @@ -279,16 +192,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "from forest.benchmarking.rb import get_rb_gateset" + "from forest.benchmarking.randomized_benchmarking import get_rb_gateset" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -298,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -307,7 +220,7 @@ "'tcp://127.0.0.1:5555'" ] }, - "execution_count": 25, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -328,37 +241,35 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", + "RZ(pi/2) 0\n", + "RX(-pi) 0\n", + "RZ(pi/2) 1\n", "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RX(pi/2) 2\n", "RZ(-pi/2) 2\n", - "RZ(-pi) 3\n", - "RX(-pi) 3\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 5\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RX(-pi) 4\n", + "RX(-pi/2) 5\n", "RZ(-pi/2) 5\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "RZ(-pi/2) 7\n", - "RX(-pi) 7\n", - "RX(-pi/2) 8\n", + "RX(-pi/2) 6\n", + "RZ(-pi) 6\n", + "RX(pi/2) 7\n", + "RZ(-pi/2) 8\n", + "RX(-pi) 8\n", "\n" ] } ], "source": [ - "progy = random_single_qubit_cliffords(bm,G)\n", + "progy = random_single_qubit_cliffords(G, bm)\n", "print(progy)" ] }, @@ -366,362 +277,103 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Layer crap" + "# Make a Template" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "#print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" + "def oneq_twoq_rand_layer(qc, graph, width, depth, sequence, index):\n", + " prog1 = random_single_qubit_gates(graph, one_q_gates)\n", + " prog2 = random_two_qubit_gates(graph, two_q_gates)\n", + " return prog1 + prog2, index+1\n", + "\n", + "rand_gate_sandwich_circuit = CircuitTemplate((oneq_twoq_rand_layer, ))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X 0\n", + "X 1\n", + "Z 2\n", + "I 3\n", + "I 4\n", + "X 5\n", + "Z 6\n", + "Z 7\n", + "X 8\n", + "I 0\n", + "I 3\n", + "CZ 0 1\n", + "I 1\n", + "I 4\n", + "I 1\n", + "I 2\n", + "CZ 2 5\n", + "CZ 3 6\n", + "I 3\n", + "I 4\n", + "CZ 4 7\n", + "I 4\n", + "I 5\n", + "I 5\n", + "I 8\n", + "CZ 6 7\n", + "CZ 7 8\n", + "\n" + ] + } + ], + "source": [ + "print(rand_gate_sandwich_circuit.sample(qc_noisy, G, 2, 2)[0])" + ] }, { - "cell_type": "code", - "execution_count": 39, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from functools import partial\n", - "\n", - "circuit_depth = 3\n", - "circuit_width = 3\n", - "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", - " one_q_gates = one_c_gates, \n", - " two_q_gates = two_c_gates)\n", - "layer_dagger = False\n", - "sandwich_dagger = False\n", - "num_rand_subgraphs = 2\n", - "num_shots_per_circuit = 2\n", - "use_active_reset= False" + "# Quantum Volume" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" + "def quantum_volume_layer(qc, graph, width, depth, sequence, index):\n", + " prog1 = random_permutation(graph, width)\n", + " prog2 = random_su2_pairs(graph, width)\n", + " return prog1 + prog2, index+1\n", + "\n", + "qv_template = CircuitTemplate((quantum_volume_layer, ))" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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Active ResetDepthLatticeLayer DaggerProgramSandwich DaggerTrialsWidth
0False1(6)False(I 6, X 6)False21
1False1(6)False(I 6, I 6)False21
2False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
3False1(1, 2)False(I 1, I 2, X 1, X 2, CNOT 1 2)False22
4False1(3, 6, 7)False(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...False23
5False1(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...False23
6False2(7)False(I 7, I 7, X 7)False21
7False2(7)False(I 7, X 7, I 7)False21
8False2(5, 8)False(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...False22
9False2(6, 7)False(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...False22
10False2(6, 7, 8)False(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...False23
11False2(4, 5, 7)False(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...False23
12False3(8)False(I 8, I 8, X 8, I 8)False21
13False3(0)False(I 0, X 0, I 0, I 0)False21
14False3(4, 7)False(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...False22
15False3(3, 4)False(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...False22
16False3(1, 3, 4)False(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...False23
17False3(3, 4, 6)False(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...False23
\n", - "
" - ], - "text/plain": [ - " Active Reset Depth Lattice Layer Dagger \\\n", - "0 False 1 (6) False \n", - "1 False 1 (6) False \n", - "2 False 1 (1, 2) False \n", - "3 False 1 (1, 2) False \n", - "4 False 1 (3, 6, 7) False \n", - "5 False 1 (4, 5, 7) False \n", - "6 False 2 (7) False \n", - "7 False 2 (7) False \n", - "8 False 2 (5, 8) False \n", - "9 False 2 (6, 7) False \n", - "10 False 2 (6, 7, 8) False \n", - "11 False 2 (4, 5, 7) False \n", - "12 False 3 (8) False \n", - "13 False 3 (0) False \n", - "14 False 3 (4, 7) False \n", - "15 False 3 (3, 4) False \n", - "16 False 3 (1, 3, 4) False \n", - "17 False 3 (3, 4, 6) False \n", - "\n", - " Program Sandwich Dagger \\\n", - "0 (I 6, X 6) False \n", - "1 (I 6, I 6) False \n", - "2 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", - "3 (I 1, I 2, X 1, X 2, CNOT 1 2) False \n", - "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... False \n", - "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... False \n", - "6 (I 7, I 7, X 7) False \n", - "7 (I 7, X 7, I 7) False \n", - "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... False \n", - "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... False \n", - "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... False \n", - "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... False \n", - "12 (I 8, I 8, X 8, I 8) False \n", - "13 (I 0, X 0, I 0, I 0) False \n", - "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... False \n", - "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... False \n", - "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... False \n", - "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... False \n", - "\n", - " Trials Width \n", - "0 2 1 \n", - "1 2 1 \n", - "2 2 2 \n", - "3 2 2 \n", - "4 2 3 \n", - "5 2 3 \n", - "6 2 1 \n", - "7 2 1 \n", - "8 2 2 \n", - "9 2 2 \n", - "10 2 3 \n", - "11 2 3 \n", - "12 2 1 \n", - "13 2 1 \n", - "14 2 2 \n", - "15 2 2 \n", - "16 2 3 \n", - "17 2 3 " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "[, ]\n" + ] } ], "source": [ - "exp" + "print(qv_template.sample(qc_noisy, G, 2, 2))" ] }, { @@ -733,302 +385,37 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "daty = acquire_circuit_sandwich_data(qc_noisy,exp)" + "\n", + "circuit_depth = 3\n", + "circuit_width = 3\n", + "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", + " one_q_gates = one_c_gates, \n", + " two_q_gates = two_c_gates)\n", + "layer_dagger = False\n", + "sandwich_dagger = False\n", + "num_rand_subgraphs = 2\n", + "num_shots_per_circuit = 2\n", + "use_active_reset= False" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetDepthLatticeProgramSamplesTrialsWidth
0False1(6)(I 6, X 6)[[1], [1]]21
1False1(6)(I 6, I 6)[[0], [0]]21
2False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
3False1(1, 2)(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]22
4False1(3, 6, 7)(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]23
5False1(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]23
6False2(7)(I 7, I 7, X 7)[[1], [1]]21
7False2(7)(I 7, X 7, I 7)[[1], [1]]21
8False2(5, 8)(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]22
9False2(6, 7)(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]22
10False2(6, 7, 8)(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]23
11False2(4, 5, 7)(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]23
12False3(8)(I 8, I 8, X 8, I 8)[[1], [1]]21
13False3(0)(I 0, X 0, I 0, I 0)[[1], [1]]21
14False3(4, 7)(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]22
15False3(3, 4)(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]22
16False3(1, 3, 4)(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]23
17False3(3, 4, 6)(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]23
\n", - "
" - ], - "text/plain": [ - " Active Reset Depth Lattice \\\n", - "0 False 1 (6) \n", - "1 False 1 (6) \n", - "2 False 1 (1, 2) \n", - "3 False 1 (1, 2) \n", - "4 False 1 (3, 6, 7) \n", - "5 False 1 (4, 5, 7) \n", - "6 False 2 (7) \n", - "7 False 2 (7) \n", - "8 False 2 (5, 8) \n", - "9 False 2 (6, 7) \n", - "10 False 2 (6, 7, 8) \n", - "11 False 2 (4, 5, 7) \n", - "12 False 3 (8) \n", - "13 False 3 (0) \n", - "14 False 3 (4, 7) \n", - "15 False 3 (3, 4) \n", - "16 False 3 (1, 3, 4) \n", - "17 False 3 (3, 4, 6) \n", - "\n", - " Program Samples \\\n", - "0 (I 6, X 6) [[1], [1]] \n", - "1 (I 6, I 6) [[0], [0]] \n", - "2 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", - "3 (I 1, I 2, X 1, X 2, CNOT 1 2) [[1, 0], [1, 0]] \n", - "4 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... [[0, 0, 0], [0, 0, 0]] \n", - "5 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... [[0, 1, 1], [0, 1, 1]] \n", - "6 (I 7, I 7, X 7) [[1], [1]] \n", - "7 (I 7, X 7, I 7) [[1], [1]] \n", - "8 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... [[1, 1], [1, 1]] \n", - "9 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... [[0, 0], [0, 0]] \n", - "10 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... [[0, 0, 0], [0, 0, 0]] \n", - "11 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... [[0, 1, 0], [0, 1, 0]] \n", - "12 (I 8, I 8, X 8, I 8) [[1], [1]] \n", - "13 (I 0, X 0, I 0, I 0) [[1], [1]] \n", - "14 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... [[0, 1], [0, 1]] \n", - "15 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... [[0, 0], [0, 0]] \n", - "16 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... [[1, 0, 1], [1, 0, 1]] \n", - "17 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... [[0, 0, 0], [0, 0, 0]] \n", - "\n", - " Trials Width \n", - "0 2 1 \n", - "1 2 1 \n", - "2 2 2 \n", - "3 2 2 \n", - "4 2 3 \n", - "5 2 3 \n", - "6 2 1 \n", - "7 2 1 \n", - "8 2 2 \n", - "9 2 2 \n", - "10 2 3 \n", - "11 2 3 \n", - "12 2 1 \n", - "13 2 1 \n", - "14 2 2 \n", - "15 2 2 \n", - "16 2 3 \n", - "17 2 3 " - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "daty" + "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" ] }, { @@ -1036,566 +423,17 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "exp" + ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetAnswerDepthHamming dist. dataHamming dist. idealHamming dist. randLatticePr. success dataPr. success loge dataPr. success loge randPr. success randProgramSamplesTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False[[1]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, X 6)[[1], [1]]0.00.500210
1False[[0]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, I 6)[[0], [0]]0.00.500210
2False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
3False[[1, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](1, 2)1.01.00.2500.250(I 1, I 2, X 1, X 2, CNOT 1 2)[[1, 0], [1, 0]]0.00.750220
4False[[0, 0, 0]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 6, 7)1.01.00.1250.125(I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
5False[[0, 1, 1]]1[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ...[[0, 1, 1], [0, 1, 1]]0.00.875230
6False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, I 7, X 7)[[1], [1]]0.00.500210
7False[[1]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](7)1.01.00.5000.500(I 7, X 7, I 7)[[1], [1]]0.00.500210
8False[[1, 1]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](5, 8)1.01.00.2500.250(I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ...[[1, 1], [1, 1]]0.00.750220
9False[[0, 0]]2[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](6, 7)1.01.00.2500.250(I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ...[[0, 0], [0, 0]]0.00.750220
10False[[0, 0, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](6, 7, 8)1.01.00.1250.125(I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
11False[[0, 1, 0]]2[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](4, 5, 7)1.01.00.1250.125(I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ...[[0, 1, 0], [0, 1, 0]]0.00.875230
12False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](8)1.01.00.5000.500(I 8, I 8, X 8, I 8)[[1], [1]]0.00.500210
13False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](0)1.01.00.5000.500(I 0, X 0, I 0, I 0)[[1], [1]]0.00.500210
14False[[0, 1]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 7)1.01.00.2500.250(I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ...[[0, 1], [0, 1]]0.00.750220
15False[[0, 0]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](3, 4)1.01.00.2500.250(I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ...[[0, 0], [0, 0]]0.00.750220
16False[[1, 0, 1]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)1.01.00.1250.125(I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ...[[1, 0, 1], [1, 0, 1]]0.00.875230
17False[[0, 0, 0]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 4, 6)1.01.00.1250.125(I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ...[[0, 0, 0], [0, 0, 0]]0.00.875230
\n", - "
" - ], - "text/plain": [ - " Active Reset Answer Depth Hamming dist. data \\\n", - "0 False [[1]] 1 [1.0, 0.0] \n", - "1 False [[0]] 1 [1.0, 0.0] \n", - "2 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", - "3 False [[1, 0]] 1 [1.0, 0.0, 0.0] \n", - "4 False [[0, 0, 0]] 1 [1.0, 0.0, 0.0, 0.0] \n", - "5 False [[0, 1, 1]] 1 [1.0, 0.0, 0.0, 0.0] \n", - "6 False [[1]] 2 [1.0, 0.0] \n", - "7 False [[1]] 2 [1.0, 0.0] \n", - "8 False [[1, 1]] 2 [1.0, 0.0, 0.0] \n", - "9 False [[0, 0]] 2 [1.0, 0.0, 0.0] \n", - "10 False [[0, 0, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", - "11 False [[0, 1, 0]] 2 [1.0, 0.0, 0.0, 0.0] \n", - "12 False [[1]] 3 [1.0, 0.0] \n", - "13 False [[1]] 3 [1.0, 0.0] \n", - "14 False [[0, 1]] 3 [1.0, 0.0, 0.0] \n", - "15 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", - "16 False [[1, 0, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", - "17 False [[0, 0, 0]] 3 [1.0, 0.0, 0.0, 0.0] \n", - "\n", - " Hamming dist. ideal Hamming dist. rand Lattice \\\n", - "0 [1.0, 0.0] [0.5, 0.5] (6) \n", - "1 [1.0, 0.0] [0.5, 0.5] (6) \n", - "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", - "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (1, 2) \n", - "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 6, 7) \n", - "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", - "6 [1.0, 0.0] [0.5, 0.5] (7) \n", - "7 [1.0, 0.0] [0.5, 0.5] (7) \n", - "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (5, 8) \n", - "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", - "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (6, 7, 8) \n", - "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (4, 5, 7) \n", - "12 [1.0, 0.0] [0.5, 0.5] (8) \n", - "13 [1.0, 0.0] [0.5, 0.5] (0) \n", - "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", - "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (3, 4) \n", - "16 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", - "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 6) \n", - "\n", - " Pr. success data Pr. success loge data Pr. success loge rand \\\n", - "0 1.0 1.0 0.500 \n", - "1 1.0 1.0 0.500 \n", - "2 1.0 1.0 0.250 \n", - "3 1.0 1.0 0.250 \n", - "4 1.0 1.0 0.125 \n", - "5 1.0 1.0 0.125 \n", - "6 1.0 1.0 0.500 \n", - "7 1.0 1.0 0.500 \n", - "8 1.0 1.0 0.250 \n", - "9 1.0 1.0 0.250 \n", - "10 1.0 1.0 0.125 \n", - "11 1.0 1.0 0.125 \n", - "12 1.0 1.0 0.500 \n", - "13 1.0 1.0 0.500 \n", - "14 1.0 1.0 0.250 \n", - "15 1.0 1.0 0.250 \n", - "16 1.0 1.0 0.125 \n", - "17 1.0 1.0 0.125 \n", - "\n", - " Pr. success rand Program \\\n", - "0 0.500 (I 6, X 6) \n", - "1 0.500 (I 6, I 6) \n", - "2 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", - "3 0.250 (I 1, I 2, X 1, X 2, CNOT 1 2) \n", - "4 0.125 (I 3, I 6, I 7, I 3, I 6, I 7, CNOT 3 6, I 6, ... \n", - "5 0.125 (I 4, I 5, I 7, I 4, X 5, X 7, I 4, I 7, CNOT ... \n", - "6 0.500 (I 7, I 7, X 7) \n", - "7 0.500 (I 7, X 7, I 7) \n", - "8 0.250 (I 5, I 8, X 5, X 8, CNOT 5 8, I 5, X 8, I 5, ... \n", - "9 0.250 (I 6, I 7, I 6, I 7, CNOT 6 7, I 6, I 7, I 6, ... \n", - "10 0.125 (I 6, I 7, I 8, I 6, X 7, I 8, I 6, I 7, I 7, ... \n", - "11 0.125 (I 4, I 5, I 7, I 4, X 5, I 7, CNOT 4 7, I 4, ... \n", - "12 0.500 (I 8, I 8, X 8, I 8) \n", - "13 0.500 (I 0, X 0, I 0, I 0) \n", - "14 0.250 (I 4, I 7, I 4, I 7, I 4, I 7, X 4, X 7, CNOT ... \n", - "15 0.250 (I 3, I 4, I 3, X 4, I 3, I 4, I 3, I 4, CNOT ... \n", - "16 0.125 (I 1, I 3, I 4, X 1, X 3, X 4, CNOT 1 4, CNOT ... \n", - "17 0.125 (I 3, I 4, I 6, I 3, I 4, I 6, I 3, I 6, I 3, ... \n", - "\n", - " Samples TVD(data, ideal) TVD(data, rand) Trials Width \\\n", - "0 [[1], [1]] 0.0 0.500 2 1 \n", - "1 [[0], [0]] 0.0 0.500 2 1 \n", - "2 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", - "3 [[1, 0], [1, 0]] 0.0 0.750 2 2 \n", - "4 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", - "5 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", - "6 [[1], [1]] 0.0 0.500 2 1 \n", - "7 [[1], [1]] 0.0 0.500 2 1 \n", - "8 [[1, 1], [1, 1]] 0.0 0.750 2 2 \n", - "9 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "10 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", - "11 [[0, 1, 0], [0, 1, 0]] 0.0 0.875 2 3 \n", - "12 [[1], [1]] 0.0 0.500 2 1 \n", - "13 [[1], [1]] 0.0 0.500 2 1 \n", - "14 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", - "15 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "16 [[1, 0, 1], [1, 0, 1]] 0.0 0.875 2 3 \n", - "17 [[0, 0, 0], [0, 0, 0]] 0.0 0.875 2 3 \n", - "\n", - " loge = basement[log_2(Width)-1] \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 \n", - "12 0 \n", - "13 0 \n", - "14 0 \n", - "15 0 \n", - "16 0 \n", - "17 0 " - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "estimate_random_classical_circuit_errors(qc_perfect,daty)" + "dat = acquire_circuit_sandwich_data(qc_noisy,exp)" ] }, { @@ -1603,7 +441,18 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "dat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "estimate_random_classical_circuit_errors(qc_perfect,daty)" + ] }, { "cell_type": "code", @@ -1621,30 +470,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[16, 18, 24, 35, 52, 76, 108, 135, 156, 166, 164, 149, 120, 76, 16, 1]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Active ResetDepthIn X basisLatticeProgramTrialsWidth
0False1False(13)(I 13)10001
1False1False(1)(I 1, X 1)10001
2False1False(7)(I 7)10001
3False1False(7)(I 7, X 7)10001
4False1False(2)(I 2, X 2)10001
5False1False(10)(I 10, X 10)10001
6False1False(7)(I 7)10001
7False1False(4)(I 4)10001
8False1False(13)(I 13)10001
9False1False(11)(I 11)10001
10False1False(10)(I 10, X 10)10001
11False1False(14)(I 14)10001
12False1False(11)(I 11)10001
13False1False(2)(I 2)10001
14False1False(12)(I 12)10001
15False1False(10)(I 10)10001
16False1False(2)(I 2, X 2)10001
17False1False(16)(I 16)10001
18False1False(15)(I 15, X 15)10001
19False1False(11)(I 11, X 11)10001
20False1False(13, 14)(I 13, I 14, X 13)10002
21False1False(17, 10)(I 17, I 10, X 17)10002
22False1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)10002
23False1False(16, 17)(I 16, I 17, X 16)10002
24False1False(1, 2)(I 1, I 2, CNOT 1 2)10002
25False1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)10002
26False1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)10002
27False1False(17, 10)(I 17, I 10, CNOT 17 10)10002
28False1False(16, 15)(I 16, I 15, X 16)10002
29False1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)10002
........................
450False6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...10003
451False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...10003
452False6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...10003
453False6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...10003
454False6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...10003
455False6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...10003
456False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...10003
457False6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...10003
458False6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...10003
459False6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...10003
460False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...10004
461False6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...10004
462False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...10004
463False6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...10004
464False6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...10004
465False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...10004
466False6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...10004
467False6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...10004
468False6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...10004
469False6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...10004
470False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...10004
471False6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...10004
472False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...10004
473False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...10004
474False6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...10004
475False6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...10004
476False6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...10004
477False6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...10004
478False6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...10004
479False6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...10004
\n", - "

480 rows × 7 columns

\n", - "
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"450 False 6 False (17, 10, 11) \n", - "451 False 6 False (4, 5, 6) \n", - "452 False 6 False (16, 14, 15) \n", - "453 False 6 False (13, 14, 15) \n", - "454 False 6 False (16, 14, 15) \n", - "455 False 6 False (16, 14, 15) \n", - "456 False 6 False (4, 5, 6) \n", - "457 False 6 False (0, 1, 2) \n", - "458 False 6 False (0, 6, 7) \n", - "459 False 6 False (16, 2, 15) \n", - "460 False 6 False (0, 1, 2, 15) \n", - "461 False 6 False (4, 5, 6, 7) \n", - "462 False 6 False (16, 1, 14, 15) \n", - "463 False 6 False (16, 1, 10, 17) \n", - "464 False 6 False (2, 3, 4, 15) \n", - "465 False 6 False (16, 1, 14, 15) \n", - "466 False 6 False (2, 13, 14, 15) \n", - "467 False 6 False (11, 12, 13, 14) \n", - "468 False 6 False (16, 17, 2, 15) \n", - "469 False 6 False (0, 1, 6, 7) \n", - "470 False 6 False (10, 11, 12, 13) \n", - "471 False 6 False (0, 1, 16, 15) \n", - "472 False 6 False (10, 11, 12, 13) \n", - "473 False 6 False (0, 1, 2, 15) \n", - "474 False 6 False (16, 1, 2, 3) \n", - "475 False 6 False (17, 10, 11, 12) \n", - "476 False 6 False (16, 17, 14, 15) \n", - "477 False 6 False (16, 17, 10, 15) \n", - "478 False 6 False (16, 13, 14, 15) \n", - "479 False 6 False (2, 3, 4, 5) \n", - "\n", - " Program Trials Width \n", - "0 (I 13) 1000 1 \n", - "1 (I 1, X 1) 1000 1 \n", - "2 (I 7) 1000 1 \n", - "3 (I 7, X 7) 1000 1 \n", - "4 (I 2, X 2) 1000 1 \n", - "5 (I 10, X 10) 1000 1 \n", - "6 (I 7) 1000 1 \n", - "7 (I 4) 1000 1 \n", - "8 (I 13) 1000 1 \n", - "9 (I 11) 1000 1 \n", - "10 (I 10, X 10) 1000 1 \n", - "11 (I 14) 1000 1 \n", - "12 (I 11) 1000 1 \n", - "13 (I 2) 1000 1 \n", - "14 (I 12) 1000 1 \n", - "15 (I 10) 1000 1 \n", - "16 (I 2, X 2) 1000 1 \n", - "17 (I 16) 1000 1 \n", - "18 (I 15, X 15) 1000 1 \n", - "19 (I 11, X 11) 1000 1 \n", - "20 (I 13, I 14, X 13) 1000 2 \n", - "21 (I 17, I 10, X 17) 1000 2 \n", - "22 (I 4, I 5, X 4, X 5, CNOT 4 5) 1000 2 \n", - "23 (I 16, I 17, X 16) 1000 2 \n", - "24 (I 1, I 2, CNOT 1 2) 1000 2 \n", - "25 (I 3, I 4, X 3, CNOT 3 4) 1000 2 \n", - "26 (I 0, I 7, X 7, CNOT 0 7) 1000 2 \n", - "27 (I 17, I 10, CNOT 17 10) 1000 2 \n", - "28 (I 16, I 15, X 16) 1000 2 \n", - "29 (I 17, I 10, X 10, CNOT 17 10) 1000 2 \n", - ".. ... ... ... \n", - "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... 1000 3 \n", - "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... 1000 3 \n", - "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... 1000 3 \n", - "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... 1000 3 \n", - "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... 1000 3 \n", - "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... 1000 3 \n", - "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... 1000 3 \n", - "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... 1000 3 \n", - "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... 1000 3 \n", - "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... 1000 3 \n", - "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... 1000 4 \n", - "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... 1000 4 \n", - "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... 1000 4 \n", - "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... 1000 4 \n", - "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... 1000 4 \n", - "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... 1000 4 \n", - "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... 1000 4 \n", - "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... 1000 4 \n", - "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... 1000 4 \n", - "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... 1000 4 \n", - "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... 1000 4 \n", - "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... 1000 4 \n", - "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... 1000 4 \n", - "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... 1000 4 \n", - "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... 1000 4 \n", - "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... 1000 4 \n", - "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... 1000 4 \n", - "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... 1000 4 \n", - "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... 1000 4 \n", - "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... 1000 4 \n", - "\n", - "[480 rows x 7 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "exp =generate_rand_cir_for_rand_lattices_experiments(qc_noisy, \n", " circuit_depth, \n", @@ -2508,17 +555,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "257.87861728668213\n" - ] - } - ], + "outputs": [], "source": [ "t0 = time.time()\n", "data_zbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp)\n", @@ -2529,977 +568,9 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetAnswerDepthIn X basisLatticeProgramSamplesTrialsWidth
0False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
1False[[1]]1False(1)(I 1, X 1)[[1], [1], [1], [1], [1], [1], [1], [0], [0], ...10001
2False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
3False[[1]]1False(7)(I 7, X 7)[[1], [1], [0], [1], [1], [1], [0], [1], [1], ...10001
4False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
5False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [1], [1], [1], ...10001
6False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
7False[[0]]1False(4)(I 4)[[0], [0], [0], [1], [0], [0], [0], [0], [0], ...10001
8False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
9False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
10False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [0], [1], [1], ...10001
11False[[0]]1False(14)(I 14)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
12False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
13False[[0]]1False(2)(I 2)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
14False[[0]]1False(12)(I 12)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
15False[[0]]1False(10)(I 10)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
16False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [1], [1], [0], [0], [1], ...10001
17False[[0]]1False(16)(I 16)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
18False[[1]]1False(15)(I 15, X 15)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
19False[[1]]1False(11)(I 11, X 11)[[1], [1], [0], [1], [1], [1], [1], [1], [1], ...10001
20False[[1, 0]]1False(13, 14)(I 13, I 14, X 13)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
21False[[1, 0]]1False(17, 10)(I 17, I 10, X 17)[[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0...10002
22False[[1, 0]]1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)[[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0...10002
23False[[1, 0]]1False(16, 17)(I 16, I 17, X 16)[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0...10002
24False[[0, 0]]1False(1, 2)(I 1, I 2, CNOT 1 2)[[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0...10002
25False[[1, 1]]1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)[[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0...10002
26False[[0, 1]]1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
27False[[0, 0]]1False(17, 10)(I 17, I 10, CNOT 17 10)[[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0...10002
28False[[1, 0]]1False(16, 15)(I 16, I 15, X 16)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
29False[[0, 1]]1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)[[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
..............................
450False[[0, 0, 0]]6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [...10003
451False[[1, 1, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...[[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [...10003
452False[[0, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
453False[[0, 1, 0]]6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...[[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [...10003
454False[[1, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...[[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
455False[[1, 0, 0]]6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...[[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [...10003
456False[[0, 0, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...[[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [...10003
457False[[0, 1, 1]]6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...[[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [...10003
458False[[1, 0, 1]]6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...[[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
459False[[0, 0, 1]]6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
460False[[1, 0, 1, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...[[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,...10004
461False[[0, 1, 1, 1]]6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...[[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,...10004
462False[[1, 0, 0, 1]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...[[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,...10004
463False[[1, 0, 0, 0]]6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,...10004
464False[[1, 1, 1, 1]]6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...[[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,...10004
465False[[0, 1, 0, 0]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...[[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,...10004
466False[[1, 1, 1, 0]]6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...[[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,...10004
467False[[0, 0, 1, 0]]6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...[[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,...10004
468False[[0, 1, 0, 0]]6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...[[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
469False[[0, 1, 1, 1]]6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...[[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
470False[[1, 0, 0, 0]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...[[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,...10004
471False[[0, 0, 1, 1]]6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...[[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,...10004
472False[[0, 1, 1, 1]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...[[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,...10004
473False[[1, 1, 0, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...[[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,...10004
474False[[0, 0, 0, 0]]6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
475False[[0, 0, 1, 1]]6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,...10004
476False[[1, 0, 0, 1]]6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...[[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,...10004
477False[[0, 0, 0, 0]]6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
478False[[1, 1, 1, 0]]6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...[[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,...10004
479False[[0, 0, 0, 1]]6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...[[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,...10004
\n", - "

480 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Active Reset Answer Depth In X basis Lattice \\\n", - "0 False [[0]] 1 False (13) \n", - "1 False [[1]] 1 False (1) \n", - "2 False [[0]] 1 False (7) \n", - "3 False [[1]] 1 False (7) \n", - "4 False [[1]] 1 False (2) \n", - "5 False [[1]] 1 False (10) \n", - "6 False [[0]] 1 False (7) \n", - "7 False [[0]] 1 False (4) \n", - "8 False [[0]] 1 False (13) \n", - "9 False [[0]] 1 False (11) \n", - "10 False [[1]] 1 False (10) \n", - "11 False [[0]] 1 False (14) \n", - "12 False [[0]] 1 False (11) \n", - "13 False [[0]] 1 False (2) \n", - "14 False [[0]] 1 False (12) \n", - "15 False [[0]] 1 False (10) \n", - "16 False [[1]] 1 False (2) \n", - "17 False [[0]] 1 False (16) \n", - "18 False [[1]] 1 False (15) \n", - "19 False [[1]] 1 False (11) \n", - "20 False [[1, 0]] 1 False (13, 14) \n", - "21 False [[1, 0]] 1 False (17, 10) \n", - "22 False [[1, 0]] 1 False (4, 5) \n", - "23 False [[1, 0]] 1 False (16, 17) \n", - "24 False [[0, 0]] 1 False (1, 2) \n", - "25 False [[1, 1]] 1 False (3, 4) \n", - "26 False [[0, 1]] 1 False (0, 7) \n", - "27 False [[0, 0]] 1 False (17, 10) \n", - "28 False [[1, 0]] 1 False (16, 15) \n", - "29 False [[0, 1]] 1 False (17, 10) \n", - ".. ... ... ... ... ... \n", - "450 False [[0, 0, 0]] 6 False (17, 10, 11) \n", - "451 False [[1, 1, 1]] 6 False (4, 5, 6) \n", - "452 False [[0, 0, 1]] 6 False (16, 14, 15) \n", - "453 False [[0, 1, 0]] 6 False (13, 14, 15) \n", - "454 False [[1, 0, 1]] 6 False (16, 14, 15) \n", - "455 False [[1, 0, 0]] 6 False (16, 14, 15) \n", - "456 False [[0, 0, 1]] 6 False (4, 5, 6) \n", - "457 False [[0, 1, 1]] 6 False (0, 1, 2) \n", - "458 False [[1, 0, 1]] 6 False (0, 6, 7) \n", - "459 False [[0, 0, 1]] 6 False (16, 2, 15) \n", - "460 False [[1, 0, 1, 1]] 6 False (0, 1, 2, 15) \n", - "461 False [[0, 1, 1, 1]] 6 False (4, 5, 6, 7) \n", - "462 False [[1, 0, 0, 1]] 6 False (16, 1, 14, 15) \n", - "463 False [[1, 0, 0, 0]] 6 False (16, 1, 10, 17) \n", - "464 False [[1, 1, 1, 1]] 6 False (2, 3, 4, 15) \n", - "465 False [[0, 1, 0, 0]] 6 False (16, 1, 14, 15) \n", - "466 False [[1, 1, 1, 0]] 6 False (2, 13, 14, 15) \n", - "467 False [[0, 0, 1, 0]] 6 False (11, 12, 13, 14) \n", - "468 False [[0, 1, 0, 0]] 6 False (16, 17, 2, 15) \n", - "469 False [[0, 1, 1, 1]] 6 False (0, 1, 6, 7) \n", - "470 False [[1, 0, 0, 0]] 6 False (10, 11, 12, 13) \n", - "471 False [[0, 0, 1, 1]] 6 False (0, 1, 16, 15) \n", - "472 False [[0, 1, 1, 1]] 6 False (10, 11, 12, 13) \n", - "473 False [[1, 1, 0, 1]] 6 False (0, 1, 2, 15) \n", - "474 False [[0, 0, 0, 0]] 6 False (16, 1, 2, 3) \n", - "475 False [[0, 0, 1, 1]] 6 False (17, 10, 11, 12) \n", - "476 False [[1, 0, 0, 1]] 6 False (16, 17, 14, 15) \n", - "477 False [[0, 0, 0, 0]] 6 False (16, 17, 10, 15) \n", - "478 False [[1, 1, 1, 0]] 6 False (16, 13, 14, 15) \n", - "479 False [[0, 0, 0, 1]] 6 False (2, 3, 4, 5) \n", - "\n", - " Program \\\n", - "0 (I 13) \n", - "1 (I 1, X 1) \n", - "2 (I 7) \n", - "3 (I 7, X 7) \n", - "4 (I 2, X 2) \n", - "5 (I 10, X 10) \n", - "6 (I 7) \n", - "7 (I 4) \n", - "8 (I 13) \n", - "9 (I 11) \n", - "10 (I 10, X 10) \n", - "11 (I 14) \n", - "12 (I 11) \n", - "13 (I 2) \n", - "14 (I 12) \n", - "15 (I 10) \n", - "16 (I 2, X 2) \n", - "17 (I 16) \n", - "18 (I 15, X 15) \n", - "19 (I 11, X 11) \n", - "20 (I 13, I 14, X 13) \n", - "21 (I 17, I 10, X 17) \n", - "22 (I 4, I 5, X 4, X 5, CNOT 4 5) \n", - "23 (I 16, I 17, X 16) \n", - "24 (I 1, I 2, CNOT 1 2) \n", - "25 (I 3, I 4, X 3, CNOT 3 4) \n", - "26 (I 0, I 7, X 7, CNOT 0 7) \n", - "27 (I 17, I 10, CNOT 17 10) \n", - "28 (I 16, I 15, X 16) \n", - "29 (I 17, I 10, X 10, CNOT 17 10) \n", - ".. ... \n", - "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... \n", - "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... \n", - "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... \n", - "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... \n", - "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... \n", - "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... \n", - "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... \n", - "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... \n", - "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... \n", - "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... \n", - "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... \n", - "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... \n", - "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... \n", - "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... \n", - "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... \n", - "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... \n", - "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... \n", - "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... \n", - "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... \n", - "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... \n", - "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... \n", - "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... \n", - "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... \n", - "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... \n", - "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... \n", - "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... \n", - "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... \n", - "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... \n", - "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... \n", - "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... \n", - "\n", - " Samples Trials Width \n", - "0 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", - "1 [[1], [1], [1], [1], [1], [1], [1], [0], [0], ... 1000 1 \n", - "2 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "3 [[1], [1], [0], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", - "4 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", - "5 [[0], [1], [1], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", - "6 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "7 [[0], [0], [0], [1], [0], [0], [0], [0], [0], ... 1000 1 \n", - "8 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", - "9 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "10 [[0], [1], [1], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", - "11 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "12 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "13 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "14 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "15 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "16 [[1], [1], [1], [1], [1], [1], [0], [0], [1], ... 1000 1 \n", - "17 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "18 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", - "19 [[1], [1], [0], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", - "20 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "21 [[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0... 1000 2 \n", - "22 [[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0... 1000 2 \n", - "23 [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "24 [[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0... 1000 2 \n", - "25 [[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0... 1000 2 \n", - "26 [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", - "27 [[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0... 1000 2 \n", - "28 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "29 [[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", - ".. ... ... ... \n", - "450 [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [... 1000 3 \n", - "451 [[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [... 1000 3 \n", - "452 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", - "453 [[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [... 1000 3 \n", - "454 [[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", - "455 [[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [... 1000 3 \n", - "456 [[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [... 1000 3 \n", - "457 [[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [... 1000 3 \n", - "458 [[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", - "459 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", - "460 [[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,... 1000 4 \n", - "461 [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,... 1000 4 \n", - "462 [[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,... 1000 4 \n", - "463 [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,... 1000 4 \n", - "464 [[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,... 1000 4 \n", - "465 [[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,... 1000 4 \n", - "466 [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,... 1000 4 \n", - "467 [[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,... 1000 4 \n", - "468 [[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", - "469 [[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", - "470 [[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,... 1000 4 \n", - "471 [[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,... 1000 4 \n", - "472 [[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,... 1000 4 \n", - "473 [[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,... 1000 4 \n", - "474 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", - "475 [[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,... 1000 4 \n", - "476 [[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,... 1000 4 \n", - "477 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", - "478 [[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,... 1000 4 \n", - "479 [[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,... 1000 4 \n", - "\n", - "[480 rows x 9 columns]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data_zbasis" ] @@ -3513,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3522,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3625,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3641,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -3665,7 +736,7 @@ }, { "cell_type": "code", - 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Active ResetDepthHamming dist. dataHamming dist. idealHamming dist. randIn X basisPr. success dataPr. success loge dataPr. success loge randPr. success randTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False1[0.9251000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925100.925100.50000.50000.0374500.462550100010.0
1False1[0.8674, 0.12184999999999999, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.867400.867400.25000.25000.1272250.622775100020.0
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3False1[0.7171500000000001, 0.23810000000000003, 0.03...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.717150.955250.31250.06250.2826750.675675100041.0
4False2[0.9201, 0.0][1.0, 0.0][0.5, 0.5]False0.920100.920100.50000.50000.0399500.460050100010.0
5False2[0.8482000000000003, 0.1441, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.848200.848200.25000.25000.1479500.602050100020.0
6False2[0.7371000000000001, 0.21269999999999997, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.737100.737100.12500.12500.2596750.615325100030.0
7False2[0.67555, 0.24490000000000003, 0.0602499999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.675550.920450.31250.06250.3220000.645700100041.0
8False3[0.9037499999999999, 0.0][1.0, 0.0][0.5, 0.5]False0.903750.903750.50000.50000.0481250.451875100010.0
9False3[0.8446999999999999, 0.14550000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.844700.844700.25000.25000.1504000.599600100020.0
10False3[0.75855, 0.20669999999999997, 0.0327500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.758550.758550.12500.12500.2404500.634550100030.0
11False3[0.6030999999999999, 0.2619, 0.103649999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.603100.865000.31250.06250.3947000.581100100041.0
12False4[0.9255500000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.925550.925550.50000.50000.0372250.462775100010.0
13False4[0.8305999999999999, 0.15719999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.830600.830600.25000.25000.1633000.586700100020.0
14False4[0.76205, 0.19485000000000002, 0.0389500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.762050.762050.12500.12500.2358750.639125100030.0
15False4[0.5921999999999998, 0.26195, 0.10720000000000...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.592200.854150.31250.06250.4059000.565650100041.0
16False5[0.9231000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.923100.923100.50000.50000.0384500.461550100010.0
17False5[0.85725, 0.13285000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.857250.857250.25000.25000.1378000.612200100020.0
18False5[0.7151500000000002, 0.23395000000000002, 0.04...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.715150.715150.12500.12500.2831250.592275100030.0
19False5[0.5072000000000001, 0.29245, 0.14304999999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.507200.799650.31250.06250.4882250.505625100041.0
20False6[0.9045, 0.0][1.0, 0.0][0.5, 0.5]False0.904500.904500.50000.50000.0477500.452250100010.0
21False6[0.8439, 0.14684999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.843900.843900.25000.25000.1514750.598525100020.0
22False6[0.7076000000000001, 0.23464999999999997, 0.05...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.707600.707600.12500.12500.2908500.592950100030.0
23False6[0.54185, 0.28845, 0.12315000000000001, 0.0422...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.541850.830300.31250.06250.4560000.537950100041.0
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" - ], - "text/plain": [ - " Active Reset Depth Hamming dist. data \\\n", - "0 False 1 [0.9251000000000001, 0.0] \n", - "1 False 1 [0.8674, 0.12184999999999999, 0.0] \n", - "2 False 1 [0.73105, 0.21615, 0.046950000000000006, 0.0] \n", - "3 False 1 [0.7171500000000001, 0.23810000000000003, 0.03... \n", - "4 False 2 [0.9201, 0.0] \n", - "5 False 2 [0.8482000000000003, 0.1441, 0.0] \n", - "6 False 2 [0.7371000000000001, 0.21269999999999997, 0.04... \n", - "7 False 2 [0.67555, 0.24490000000000003, 0.0602499999999... \n", - "8 False 3 [0.9037499999999999, 0.0] \n", - "9 False 3 [0.8446999999999999, 0.14550000000000002, 0.0] \n", - "10 False 3 [0.75855, 0.20669999999999997, 0.0327500000000... \n", - "11 False 3 [0.6030999999999999, 0.2619, 0.103649999999999... \n", - "12 False 4 [0.9255500000000001, 0.0] \n", - "13 False 4 [0.8305999999999999, 0.15719999999999998, 0.0] \n", - "14 False 4 [0.76205, 0.19485000000000002, 0.0389500000000... \n", - "15 False 4 [0.5921999999999998, 0.26195, 0.10720000000000... \n", - "16 False 5 [0.9231000000000001, 0.0] \n", - "17 False 5 [0.85725, 0.13285000000000002, 0.0] \n", - "18 False 5 [0.7151500000000002, 0.23395000000000002, 0.04... \n", - "19 False 5 [0.5072000000000001, 0.29245, 0.14304999999999... \n", - "20 False 6 [0.9045, 0.0] \n", - "21 False 6 [0.8439, 0.14684999999999998, 0.0] \n", - "22 False 6 [0.7076000000000001, 0.23464999999999997, 0.05... \n", - "23 False 6 [0.54185, 0.28845, 0.12315000000000001, 0.0422... \n", - "\n", - " Hamming dist. ideal Hamming dist. rand \\\n", - "0 [1.0, 0.0] [0.5, 0.5] \n", - "1 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "2 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "3 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "4 [1.0, 0.0] [0.5, 0.5] \n", - "5 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "6 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "7 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "8 [1.0, 0.0] [0.5, 0.5] \n", - "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "11 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "12 [1.0, 0.0] [0.5, 0.5] \n", - "13 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "14 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "15 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "16 [1.0, 0.0] [0.5, 0.5] \n", - "17 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "18 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "19 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "20 [1.0, 0.0] [0.5, 0.5] \n", - "21 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "22 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "23 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "\n", - " In X basis Pr. success data Pr. success loge data \\\n", - "0 False 0.92510 0.92510 \n", - "1 False 0.86740 0.86740 \n", - "2 False 0.73105 0.73105 \n", - "3 False 0.71715 0.95525 \n", - "4 False 0.92010 0.92010 \n", - "5 False 0.84820 0.84820 \n", - "6 False 0.73710 0.73710 \n", - "7 False 0.67555 0.92045 \n", - "8 False 0.90375 0.90375 \n", - "9 False 0.84470 0.84470 \n", - "10 False 0.75855 0.75855 \n", - "11 False 0.60310 0.86500 \n", - "12 False 0.92555 0.92555 \n", - "13 False 0.83060 0.83060 \n", - "14 False 0.76205 0.76205 \n", - "15 False 0.59220 0.85415 \n", - "16 False 0.92310 0.92310 \n", - "17 False 0.85725 0.85725 \n", - "18 False 0.71515 0.71515 \n", - "19 False 0.50720 0.79965 \n", - "20 False 0.90450 0.90450 \n", - "21 False 0.84390 0.84390 \n", - "22 False 0.70760 0.70760 \n", - "23 False 0.54185 0.83030 \n", - "\n", - " Pr. success loge rand Pr. success rand TVD(data, ideal) \\\n", - "0 0.5000 0.5000 0.037450 \n", - "1 0.2500 0.2500 0.127225 \n", - "2 0.1250 0.1250 0.266025 \n", - "3 0.3125 0.0625 0.282675 \n", - "4 0.5000 0.5000 0.039950 \n", - "5 0.2500 0.2500 0.147950 \n", - "6 0.1250 0.1250 0.259675 \n", - "7 0.3125 0.0625 0.322000 \n", - "8 0.5000 0.5000 0.048125 \n", - "9 0.2500 0.2500 0.150400 \n", - "10 0.1250 0.1250 0.240450 \n", - "11 0.3125 0.0625 0.394700 \n", - "12 0.5000 0.5000 0.037225 \n", - "13 0.2500 0.2500 0.163300 \n", - "14 0.1250 0.1250 0.235875 \n", - "15 0.3125 0.0625 0.405900 \n", - "16 0.5000 0.5000 0.038450 \n", - "17 0.2500 0.2500 0.137800 \n", - "18 0.1250 0.1250 0.283125 \n", - "19 0.3125 0.0625 0.488225 \n", - "20 0.5000 0.5000 0.047750 \n", - "21 0.2500 0.2500 0.151475 \n", - "22 0.1250 0.1250 0.290850 \n", - "23 0.3125 0.0625 0.456000 \n", - "\n", - " TVD(data, rand) Trials Width loge = basement[log_2(Width)-1] \n", - "0 0.462550 1000 1 0.0 \n", - "1 0.622775 1000 2 0.0 \n", - "2 0.608975 1000 3 0.0 \n", - "3 0.675675 1000 4 1.0 \n", - "4 0.460050 1000 1 0.0 \n", - "5 0.602050 1000 2 0.0 \n", - "6 0.615325 1000 3 0.0 \n", - "7 0.645700 1000 4 1.0 \n", - "8 0.451875 1000 1 0.0 \n", - "9 0.599600 1000 2 0.0 \n", - "10 0.634550 1000 3 0.0 \n", - "11 0.581100 1000 4 1.0 \n", - "12 0.462775 1000 1 0.0 \n", - "13 0.586700 1000 2 0.0 \n", - "14 0.639125 1000 3 0.0 \n", - "15 0.565650 1000 4 1.0 \n", - "16 0.461550 1000 1 0.0 \n", - "17 0.612200 1000 2 0.0 \n", - "18 0.592275 1000 3 0.0 \n", - "19 0.505625 1000 4 1.0 \n", - "20 0.452250 1000 1 0.0 \n", - "21 0.598525 1000 2 0.0 \n", - "22 0.592950 1000 3 0.0 \n", - "23 0.537950 1000 4 1.0 " - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "munged" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.54185, 0.28845, 0.12315, 0.04225, 0. ])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "res_df[wdx&ddx]['Hamming dist. data'].mean()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0625, 0.25 , 0.375 , 0.25 , 0.0625])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "res_df[wdx&ddx]['Hamming dist. rand'].mean()" ] @@ -4372,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4391,20 +828,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x_labels = np.arange(0, len(avg_dist))\n", "plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", @@ -4428,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4438,20 +864,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for dep in range(1, df_fn_depth.Depth.max()+1):\n", " idx = df_fn_depth['Depth']== dep\n", @@ -4481,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4520,20 +935,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", @@ -4570,21 +974,10 @@ ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.figure()\n", "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", @@ -4608,20 +1001,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure()\n", "plt.scatter(depth_vec,tvd_ideal,label='TVD(data, ideal)')\n", @@ -4645,20 +1027,9 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "max_idx = min([max(res_df['Depth']),max(res_df['Width'])])\n", "\n", @@ -4701,23 +1072,9 @@ }, { "cell_type": "code", - "execution_count": 326, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.9251 , 0.8674 , 0.73105, 0.71715, 0.9201 , 0.8482 , 0.7371 ,\n", - " 0.67555, 0.90375, 0.8447 , 0.75855, 0.6031 , 0.92555, 0.8306 ,\n", - " 0.76205, 0.5922 , 0.9231 , 0.85725, 0.71515, 0.5072 , 0.9045 ,\n", - " 0.8439 , 0.7076 , 0.54185])" - ] - }, - "execution_count": 326, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "values = np.asarray([munged['Pr. success data'][idx] for idx in munged.index])\n", "values" @@ -4725,22 +1082,9 @@ }, { "cell_type": "code", - "execution_count": 327, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", - " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", - " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625])" - ] - }, - "execution_count": 327, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "values_rand = np.asarray([munged['Pr. success rand'][idx] for idx in munged.index])\n", "values_rand" @@ -4748,7 +1092,7 @@ }, { "cell_type": "code", - "execution_count": 328, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4761,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4772,43 +1116,18 @@ }, { "cell_type": "code", - "execution_count": 331, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.9251 , 0.9201 , 0.90375, 0.92555, 0.9231 , 0.9045 ],\n", - " [0.8674 , 0.8482 , 0.8447 , 0.8306 , 0.85725, 0.8439 ],\n", - " [0.73105, 0.7371 , 0.75855, 0.76205, 0.71515, 0.7076 ],\n", - " [0.71715, 0.67555, 0.6031 , 0.5922 , 0.5072 , 0.54185]])" - ] - }, - "execution_count": 331, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Zdata" ] }, { "cell_type": "code", - "execution_count": 332, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -4833,20 +1152,9 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -4871,7 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 355, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4883,23 +1191,9 @@ }, { "cell_type": "code", - "execution_count": 357, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.46255 , 0.622775, 0.608975, 0.675675, 0.46005 , 0.60205 ,\n", - " 0.615325, 0.6457 , 0.451875, 0.5996 , 0.63455 , 0.5811 ,\n", - " 0.462775, 0.5867 , 0.639125, 0.56565 , 0.46155 , 0.6122 ,\n", - " 0.592275, 0.505625, 0.45225 , 0.598525, 0.59295 , 0.53795 ])" - ] - }, - "execution_count": 357, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "tvd_ideal_values\n", "tvd_rand_values" @@ -4907,20 +1201,9 @@ }, { "cell_type": "code", - "execution_count": 358, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -4945,20 +1228,9 @@ }, { "cell_type": "code", - "execution_count": 359, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -4983,7 +1255,7 @@ }, { "cell_type": "code", - "execution_count": 362, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4995,20 +1267,9 @@ }, { "cell_type": "code", - "execution_count": 363, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -5033,20 +1294,9 @@ }, { "cell_type": "code", - "execution_count": 365, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", @@ -5078,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 432, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5087,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 433, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5098,20 +1348,9 @@ }, { "cell_type": "code", - "execution_count": 441, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 24)" - ] - }, - "execution_count": 441, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "data_1d = Zdata.reshape((1,np.prod(size)))\n", "data_1d.shape\n", @@ -5120,25 +1359,9 @@ }, { "cell_type": "code", - "execution_count": 435, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0],\n", - " [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,\n", - " 4, 4],\n", - " [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n", - " 5, 6]])" - ] - }, - "execution_count": 435, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dims = np.zeros_like(width_1d)\n", "dims[0,0] = size[0]\n", @@ -5172,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 455, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5198,7 +1421,7 @@ }, { "cell_type": "code", - "execution_count": 447, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5224,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 510, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5248,7 +1471,7 @@ }, { "cell_type": "code", - "execution_count": 531, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5258,18 +1481,9 @@ }, { "cell_type": "code", - "execution_count": 532, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The estimated error is p = 0.0276\n", - "The estimated product of the one and two qubit fidelity is F = 0.9724\n" - ] - } - ], + "outputs": [], "source": [ "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", @@ -5278,7 +1492,7 @@ }, { "cell_type": "code", - "execution_count": 533, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5288,20 +1502,9 @@ }, { "cell_type": "code", - "execution_count": 534, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.pcolormesh(X,Y, Z_fit)\n", "plt.xticks(list(range(1,circuit_depth+1)))\n", @@ -5312,20 +1515,9 @@ }, { "cell_type": "code", - "execution_count": 535, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.pcolormesh(X,Y,Zdata)\n", "plt.xticks(list(range(1,circuit_depth+1)))\n", @@ -5343,7 +1535,7 @@ }, { "cell_type": "code", - "execution_count": 541, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5352,7 +1544,7 @@ }, { "cell_type": "code", - "execution_count": 542, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5361,27 +1553,16 @@ }, { "cell_type": "code", - "execution_count": 543, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00193651, 0.00070045, 0.02802694])" - ] - }, - "execution_count": 543, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "popt2d" ] }, { "cell_type": "code", - "execution_count": 544, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5391,20 +1572,9 @@ }, { "cell_type": "code", - "execution_count": 545, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.pcolormesh(X,Y, Z_fit2d)\n", "plt.xticks(list(range(1,circuit_depth+1)))\n", @@ -5415,20 +1585,9 @@ }, { "cell_type": "code", - "execution_count": 486, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.897680214" - ] - }, - "execution_count": 486, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "1-1.02319786e-01" ] diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/circuit_testing.py index 7641c08a..a4959d93 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/circuit_testing.py @@ -1,4 +1,5 @@ from typing import Tuple, Sequence, Callable, Any, List +from copy import copy import networkx as nx import numpy as np import random @@ -9,74 +10,110 @@ from dataclasses import dataclass from pyquil.quilbase import Pragma, Gate, DefGate -from pyquil.quil import Program -from pyquil.api import QuantumComputer -from pyquil.api import BenchmarkConnection +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.api import QuantumComputer, BenchmarkConnection from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET -from pyquil.quil import address_qubits -from forest.benchmarking.rb import get_rb_gateset +from pyquil.unitary_tools import permutation_arbitrary +from rpcq.messages import TargetDevice +from rpcq._utils import RPCErrorError + +from forest.benchmarking.randomized_benchmarking import get_rb_gateset from forest.benchmarking.distance_measures import total_variation_distance as tvd from forest.benchmarking.random_operators import haar_rand_unitary +from forest.benchmarking.compilation import basic_compile - -@dataclass(order=True) -class Slice: - index: int - gates: Tuple[Program] - needs_compilation: bool = True +# +# @dataclass(order=True) +# class Slice: +# index: int +# gates: Tuple[Program] +# needs_compilation: bool = True # def __str__(self): # return f'Index {self.index}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' +#TODO: make concatenation of slices possible. -@dataclass(order=True) -class Layer: - depth: int - slices: Tuple[Slice] - needs_compilation: bool = True +# @dataclass(order=True) +# class Layer: +# depth: int +# slices: Tuple[Slice] +# needs_compilation: bool = True # def __str__(self): # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' -@dataclass -class Circuit: - layers: Tuple[Layer] - graph: nx.Graph - needs_compilation: bool = True - name: str = None +# @dataclass +# class Circuit: +# layers: Tuple[Layer] +# graph: nx.Graph +# needs_compilation: bool = True +# name: str = None # def __str__(self): # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' -@dataclass(order=True) -class SliceTemplate: - index: int - generator: Callable - args = Sequence[Any] - sandwich: bool = False +@dataclass +class CircuitTemplate: + generators: List[Callable] + #TODO: could allow CircuitTemplates, allow definition of depth, subunits... + #TODO: add compilation? + + def append(self, other): + self.generators += other.generators + + def __add__(self, other): + """ + Concatenate two circuits together, returning a new one. + + :param Circuit other: Another circuit to add to this one. + :return: A newly concatenated circuit. + :rtype: Program + """ + ckt = CircuitTemplate(self.generators) + ckt.append(other) + return ckt + + def __iadd__(self, other): + """ + Concatenate two circuits together using +=, returning a new one. + """ + self.append(other) + return self + + def sample(self, qc, graph, width, depth, sequence = None, index=0): + if sequence is None: + sequence = [] + while index < depth: + for generator in self.generators: + if index == depth: + break + prog, index = generator(qc, graph, width, depth, sequence, index) + sequence.append(prog) + return sequence # def __str__(self): # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' -@dataclass(order=True) -class LayerTemplate: - depth: int - slices: Tuple[SliceTemplate] - sandwich: bool = False +# @dataclass(order=True) +# class LayerTemplate: +# depth: int +# slices: Tuple[SliceTemplate] +# sandwich: bool = False # def __str__(self): # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' -@dataclass -class CircuitTemplate: - layers: Tuple[LayerTemplate] - graph: nx.Graph - sandwich: bool = False - name: str = None +# @dataclass +# class CircuitTemplate: +# slices: Tuple[SliceTemplate] +# graph: nx.Graph +# sandwich: bool = False +# name: str = None # def __str__(self): # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' @@ -166,30 +203,27 @@ def random_two_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): return prog -def random_permutation(graph: nx.Graph): - qubits = graph.nodes - permutation = np.random_permutation(range(len(qubits))) +def random_permutation(graph: nx.Graph, width): + #TODO: find another way; this is too slow + qubits = list(graph.nodes) + measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes + permutation = np.random.permutation(range(len(measure_qubits))) + matrix = permutation_arbitrary(permutation, len(measure_qubits))[0] - matrix = [] - for target in permutation: - row = [0 for _ in permutation] - row[target] = 1 - matrix.append(row) - - gate_definition = DefGate("".join([str(qubits[idx]) for idx in permutation]), matrix) + gate_definition = DefGate("Perm" + "".join([str(measure_qubits[idx]) for idx in permutation]), matrix) PERMUTE = gate_definition.get_constructor() p = Program() p += gate_definition - p += PERMUTE(*qubits) + p += PERMUTE(*measure_qubits) return p -def random_su2_pairs(graph: nx.Graph): - qubits = graph.nodes +def random_su2_pairs(graph: nx.Graph, width): + qubits = list(graph.nodes)[:width] # arbitrarily pick the first width-many nodes gates = [] for q1, q2 in zip(qubits[::2], qubits[1::2]): matrix = haar_rand_unitary(4) - gate_definition = DefGate("RSU2(" + str(q1) + str(q2) + ")", matrix) + gate_definition = DefGate(f"RSU2({q1},{q2})", matrix) RSU2 = gate_definition.get_constructor() p = Program() p += gate_definition @@ -198,191 +232,172 @@ def random_su2_pairs(graph: nx.Graph): return gates -# ================================================================================================== -# Prefix // Suffix programs; pre and post -# ================================================================================================== - -def pre_trival(graph: nx.Graph): - # Install identity on all qubits so that we can find all the qubits from prog.get_qubits(). - # Otherwise if the circuit happens to be identity on a particular qubit you will get - # not get that qubit from get_qubits. Worse, if the entire program is identity you will - # get the empty set. Do not delete this! - prep_gate = I - prog = Program() - prog += [prep_gate(qubit) for qubit in list(graph.nodes)] - return prog - - -def post_trival(): - prog = Program() - return prog - - -# ================================================================================================== +def quantum_volume_compilation(qc, graph, width, depth, sequence): + prog = merge_programs(sequence) + qubits = list(graph.nodes) + measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes + + ro = prog.declare("ro", "BIT", len(measure_qubits)) + for idx, qubit in enumerate(measure_qubits): + prog.measure(qubit, ro[idx]) + + # restrict compilation to chosen qubits + isa_dict = qc.device.get_isa().to_dict() + single_qs = isa_dict['1Q'] + two_qs = isa_dict['2Q'] + + new_1q = {} + for key, val in single_qs.items(): + if int(key) in qubits: + new_1q[key] = val + new_2q = {} + for key, val in two_qs.items(): + q1, q2 = key.split('-') + if int(q1) in qubits and int(q2) in qubits: + new_2q[key] = val + + new_isa = {'1Q': new_1q, '2Q': new_2q} + + new_compiler = copy(qc.compiler) + new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) + # try to compile with the restricted qubit topology + try: + native_quil = new_compiler.quil_to_native_quil(prog) + except RPCErrorError as e: + if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ + in str(e): + raise ValueError("naive_program_generator could not generate a program using only the " + "qubits supplied; expand the set of allowed qubits or supply " + "a custom program_generator.") + raise + + return native_quil + +# =========================================== # Layer tools # ================================================================================================== - -def slice_templates_1q_and_2q_rand_cliff(bm: BenchmarkConnection): - slice_1q = SliceTemplate(0, random_single_qubit_cliffords, (bm, )) - slice_2q = SliceTemplate(1, random_two_qubit_cliffords, (bm, )) - return slice_1q, slice_2q - - -def slice_templates_1q_and_2q_rand_rand_gates(one_q_gates, two_q_gates): - slice_1q = SliceTemplate(0, random_single_qubit_gates, (one_q_gates, )) - slice_2q = SliceTemplate(1, random_two_qubit_gates, (two_q_gates, )) - return slice_1q, slice_2q - - -def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, - graph: nx.Graph, - layer_dagger: bool = False): - """ - Creates a layer of random one qubit Cliffords followed by random two qubit Cliffords. - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param layer_dagger: Bool if true will add the dagger to the layer, making the layer - efectivley the identity - :return: program - """ - prog = Program() - prog += random_single_qubit_cliffords(bm, graph) - prog += random_two_qubit_cliffords(bm, graph) - if layer_dagger: - prog += prog.dagger() - return prog - - -def layer_1q_and_2q_rand_gates(graph: nx.Graph, - one_q_gates, - two_q_gates, - layer_dagger: bool = False): - """ - You pass in two lists of one and two qubit gates. This function creates a layer of random one - qubit gates followed by random two qubit gates - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param one_q_gates: list of one qubit gates - :param two_q_gates: list of two qubit gates e.g. [CZ, ID] - :param layer_dagger: Bool if true will add the dagger to the layer, making the layer - efectivley the identity - :return: program - """ - prog = Program() - prog += random_single_qubit_gates(graph, one_q_gates) - prog += random_two_qubit_gates(graph, two_q_gates) - if layer_dagger: - prog += prog.dagger() - return prog +# +# +# def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, +# graph: nx.Graph, +# layer_dagger: bool = False): +# """ +# Creates a layer of random one qubit Cliffords followed by random two qubit Cliffords. +# +# :param bm: A benchmark connection that will do the grunt work of generating the Cliffords +# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. +# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer +# effectively the identity +# :return: program +# """ +# prog = Program() +# prog += random_single_qubit_cliffords(bm, graph) +# prog += random_two_qubit_cliffords(bm, graph) +# if layer_dagger: +# prog += prog.dagger() +# return prog +# +# +# def layer_1q_and_2q_rand_gates(graph: nx.Graph, +# one_q_gates, +# two_q_gates, +# layer_dagger: bool = False): +# """ +# You pass in two lists of one and two qubit gates. This function creates a layer of random one +# qubit gates followed by random two qubit gates +# +# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. +# :param one_q_gates: list of one qubit gates +# :param two_q_gates: list of two qubit gates e.g. [CZ, ID] +# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer +# effectively the identity +# :return: program +# """ +# prog = Program() +# prog += random_single_qubit_gates(graph, one_q_gates) +# prog += random_two_qubit_gates(graph, two_q_gates) +# if layer_dagger: +# prog += prog.dagger() +# return prog # ================================================================================================== # Sandwich tools # ================================================================================================== -def circuit_sandwich_rand_gates(graph: nx.Graph, - circuit_depth: int, - one_q_gates: list, - two_q_gates: list, - layer_dagger: bool = False, - sandwich_dagger: bool = False): - """ - Create a sandwich circuit by adding layers. - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param circuit_depth: maximum depth of quantum circuit - :param one_q_gates: list of one qubit gates - :param two_q_gates: list of two qubit gates e.g. [CZ, ID] - :param layer_dagger: Bool if true will add the dagger to the layer, making the layer - :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of - the first. - :return: program - """ - total_prog = Program() - total_prog += pre_trival(graph) - - if sandwich_dagger: - circuit_depth = int(np.floor(circuit_depth / 2)) - - layer_progs = Program() - for _ in range(circuit_depth): - layer_progs += layer_1q_and_2q_rand_gates(graph, - one_q_gates, - two_q_gates, - layer_dagger) - if sandwich_dagger: - layer_progs += layer_progs.dagger() - - total_prog += layer_progs - total_prog += post_trival() - return total_prog - - -def circuit_sandwich_clifford(bm: BenchmarkConnection, - graph: nx.Graph, - circuit_depth: int, - layer_dagger: bool = False, - sandwich_dagger: bool = False): - """ - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param circuit_depth: maximum depth of quantum circuit - :param layer_dagger: Bool if true will add the dagger to the layer, making the layer - :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of - the first. - :return: program - """ - total_prog = Program() - - total_prog += pre_trival(graph) - - if sandwich_dagger: - circuit_depth = int(np.floor(circuit_depth / 2)) - - layer_progs = Program() - for _ in range(circuit_depth): - layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) - if sandwich_dagger: - layer_progs += layer_progs.dagger() - - total_prog += layer_progs - total_prog += post_trival() - return total_prog - +# def circuit_sandwich_rand_gates(graph: nx.Graph, +# circuit_depth: int, +# one_q_gates: list, +# two_q_gates: list, +# layer_dagger: bool = False, +# sandwich_dagger: bool = False): +# """ +# Create a sandwich circuit by adding layers. +# +# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. +# :param circuit_depth: maximum depth of quantum circuit +# :param one_q_gates: list of one qubit gates +# :param two_q_gates: list of two qubit gates e.g. [CZ, ID] +# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer +# :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of +# the first. +# :return: program +# """ +# total_prog = Program() +# total_prog += pre_trival(graph) +# +# if sandwich_dagger: +# circuit_depth = int(np.floor(circuit_depth / 2)) +# +# layer_progs = Program() +# for _ in range(circuit_depth): +# layer_progs += layer_1q_and_2q_rand_gates(graph, +# one_q_gates, +# two_q_gates, +# layer_dagger) +# if sandwich_dagger: +# layer_progs += layer_progs.dagger() +# +# total_prog += layer_progs +# total_prog += post_trival() +# return total_prog +# +# +# def circuit_sandwich_clifford(bm: BenchmarkConnection, +# graph: nx.Graph, +# circuit_depth: int, +# layer_dagger: bool = False, +# sandwich_dagger: bool = False): +# """ +# +# :param bm: A benchmark connection that will do the grunt work of generating the Cliffords +# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. +# :param circuit_depth: maximum depth of quantum circuit +# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer +# :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of +# the first. +# :return: program +# """ +# total_prog = Program() +# +# total_prog += pre_trival(graph) +# +# if sandwich_dagger: +# circuit_depth = int(np.floor(circuit_depth / 2)) +# +# layer_progs = Program() +# for _ in range(circuit_depth): +# layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) +# if sandwich_dagger: +# layer_progs += layer_progs.dagger() +# +# total_prog += layer_progs +# total_prog += post_trival() +# return total_prog +# # ================================================================================================== # Generate and Acquire functions # ================================================================================================== -def generate_repeated_layer_circuit_template(lattice: nx.Graph, circuit_depth: int, - circuit_width: int, - slice_templates: Sequence[SliceTemplate], - layer_sandwich: bool = False, - circuit_sandwich: bool = False) -> CircuitTemplate: - """ - Return the template needed to generate random circuits of a certain width and depth using a - particular lattice connectivity. - - :param lattice: - :param circuit_depth: depth of quantum circuit - :param circuit_width: width of quantum circuit - :param slice_templates: - :param layer_sandwich: - :param circuit_sandwich: - :return: - """ - if circuit_width > len(lattice.nodes): - raise ValueError("You must have circuit widths less than or equal to the number of qubits " - "on a lattice.") - layers = (LayerTemplate(depth, slice_templates, layer_sandwich) \ - for depth in range(1, circuit_depth + 1)) - - return CircuitTemplate(layers, lattice, circuit_sandwich, "UniformLayers") - - -# def generate_circuit_experiments(): -# -# yield Circuit(layers, graph, needs_compilation, name) def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, diff --git a/forest/benchmarking/compilation.py b/forest/benchmarking/compilation.py index 4939659a..38a807b0 100644 --- a/forest/benchmarking/compilation.py +++ b/forest/benchmarking/compilation.py @@ -184,6 +184,8 @@ def basic_compile(program): new_prog += _H(inst.qubits[0]) elif inst.name == "X": new_prog += _X(inst.qubits[0]) + elif inst.name == "Z": + new_prog += RZ(pi, inst.qubits[0]) elif inst.name in [gate.name for gate in new_prog.defined_gates]: new_prog += inst else: From 920324205a8a08e2d0e1d914acffd86681347707 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 10 Jun 2019 11:49:30 -0400 Subject: [PATCH 14/49] simplify template --- forest/benchmarking/circuit_testing.py | 30 +++++++++++++++++++++----- 1 file changed, 25 insertions(+), 5 deletions(-) diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/circuit_testing.py index a4959d93..fae710cc 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/circuit_testing.py @@ -8,6 +8,7 @@ from scipy.spatial.distance import hamming from scipy.special import comb from dataclasses import dataclass +from functools import partial from pyquil.quilbase import Pragma, Gate, DefGate from pyquil.quil import Program, address_qubits, merge_programs @@ -61,6 +62,12 @@ class CircuitTemplate: #TODO: could allow CircuitTemplates, allow definition of depth, subunits... #TODO: add compilation? + + # def create_unit(self): + # return lambda qc, graph, width, depth, sequence: sum(gen(qc, graph, width, depth, + # sequence) for gen in + # self.generators) + def append(self, other): self.generators += other.generators @@ -83,17 +90,23 @@ def __iadd__(self, other): self.append(other) return self - def sample(self, qc, graph, width, depth, sequence = None, index=0): + def sample(self, qc, graph, width, repetitions, sequence = None): if sequence is None: sequence = [] - while index < depth: + for _ in range(repetitions): for generator in self.generators: - if index == depth: - break - prog, index = generator(qc, graph, width, depth, sequence, index) + prog, index = generator(qc, graph, width, sequence) sequence.append(prog) return sequence + # repetitions = [([1, 1, ([2,1], 2), 3], 4), 1] + # For four times do: + # the first gen, second gen, + # for two times do + # two third gen, the fourth gen + # the fifth gen 3 times + # do the final 6th gen once + # def __str__(self): # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' @@ -137,6 +150,13 @@ def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): return program +def get_rand_1q_template(gates: Sequence[Gate]): + def func(qc, graph, width, sequence): + partial_func = partial(random_single_qubit_gates, gates = gates) + return partial_func(graph) + return CircuitTemplate([func]) + + def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): """ Write a program to randomly place two qubit gates on edges of the specified graph. From d70d5f6105ca6ea9c783ae91e0e23599fc048d75 Mon Sep 17 00:00:00 2001 From: Kyle Date: Wed, 17 Jul 2019 12:04:10 -0400 Subject: [PATCH 15/49] Update rb functions and remove old code. --- forest/benchmarking/circuit_testing.py | 110 ++++++++++++------------- 1 file changed, 51 insertions(+), 59 deletions(-) diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/circuit_testing.py index fae710cc..baa1e5d4 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/circuit_testing.py @@ -11,6 +11,7 @@ from functools import partial from pyquil.quilbase import Pragma, Gate, DefGate +from pyquil.quilatom import QubitPlaceholder from pyquil.quil import Program, address_qubits, merge_programs from pyquil.api import QuantumComputer, BenchmarkConnection from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET @@ -21,28 +22,6 @@ from forest.benchmarking.randomized_benchmarking import get_rb_gateset from forest.benchmarking.distance_measures import total_variation_distance as tvd from forest.benchmarking.random_operators import haar_rand_unitary -from forest.benchmarking.compilation import basic_compile - -# -# @dataclass(order=True) -# class Slice: -# index: int -# gates: Tuple[Program] -# needs_compilation: bool = True - - # def __str__(self): - # return f'Index {self.index}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' - -#TODO: make concatenation of slices possible. - -# @dataclass(order=True) -# class Layer: -# depth: int -# slices: Tuple[Slice] -# needs_compilation: bool = True - - # def __str__(self): - # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' # @dataclass @@ -110,27 +89,6 @@ def sample(self, qc, graph, width, repetitions, sequence = None): # def __str__(self): # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' - -# @dataclass(order=True) -# class LayerTemplate: -# depth: int -# slices: Tuple[SliceTemplate] -# sandwich: bool = False - - # def __str__(self): - # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' - - -# @dataclass -# class CircuitTemplate: -# slices: Tuple[SliceTemplate] -# graph: nx.Graph -# sandwich: bool = False -# name: str = None - - # def __str__(self): - # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' - # ================================================================================================== # Gate Sets # ================================================================================================== @@ -150,13 +108,6 @@ def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): return program -def get_rand_1q_template(gates: Sequence[Gate]): - def func(qc, graph, width, sequence): - partial_func = partial(random_single_qubit_gates, gates = gates) - return partial_func(graph) - return CircuitTemplate([func]) - - def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): """ Write a program to randomly place two qubit gates on edges of the specified graph. @@ -174,10 +125,10 @@ def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): return program -def random_single_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): +def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): """ Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of - the specified graph. Each uniformly random choice of Clifford is implemented in the native + the specified graph. Each uniformly random choice of Clifford is implemented in the native gateset. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords @@ -185,7 +136,9 @@ def random_single_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): :return: A program that randomly places single qubit Clifford gates on a graph. """ num_qubits = len(graph.nodes) - gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q') + + q_placeholder = QubitPlaceholder() + gateset_1q = get_rb_gateset([q_placeholder]) # the +1 is because the depth includes the inverse clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) @@ -193,12 +146,12 @@ def random_single_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): prog = Program() for q, clif in zip(graph.nodes, rand_cliffords): - gate = address_qubits(clif, qubit_mapping={clif.get_qubits().pop(): q}) + gate = address_qubits(clif, qubit_mapping={q_placeholder: q}) prog += gate return prog -def random_two_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): +def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): """ Write a program to place random two qubit Cliffords gates on edges of the graph. @@ -207,7 +160,8 @@ def random_two_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): :return: A program that has two qubit gates randomly placed on the graph edges. """ num_2q_gates = len(graph.edges) - gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q') + q_placeholders = QubitPlaceholder.register(n=2) + gateset_2q = get_rb_gateset(q_placeholders) # the +1 is because the depth includes the inverse clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) @@ -217,8 +171,8 @@ def random_two_qubit_cliffords(graph: nx.Graph, bm: BenchmarkConnection): # do the two coloring with pragmas? # no point until fencing is over for edges, clif in zip(graph.edges, rand_cliffords): - qb1, qb2 = clif.get_qubits() - gate = address_qubits(clif, qubit_mapping={qb1: edges[0], qb2: edges[1], }) + gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], + q_placeholders[1]: edges[1]}) prog += gate return prog @@ -227,6 +181,7 @@ def random_permutation(graph: nx.Graph, width): #TODO: find another way; this is too slow qubits = list(graph.nodes) measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes + # TODO: use native permutations permutation = np.random.permutation(range(len(measure_qubits))) matrix = permutation_arbitrary(permutation, len(measure_qubits))[0] @@ -252,7 +207,7 @@ def random_su2_pairs(graph: nx.Graph, width): return gates -def quantum_volume_compilation(qc, graph, width, depth, sequence): +def quantum_volume_compilation(qc, graph, width, sequence): prog = merge_programs(sequence) qubits = list(graph.nodes) measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes @@ -293,6 +248,43 @@ def quantum_volume_compilation(qc, graph, width, depth, sequence): return native_quil + +### +# Templates +### + +def get_rand_1q_template(gates: Sequence[Gate]): + def func(qc, graph, width, sequence): + partial_func = partial(random_single_qubit_gates, gates = gates) + return partial_func(graph) + return CircuitTemplate([func]) + +def get_rand_2q_template(gates: Sequence[Gate]): + def func(qc, graph, width, sequence): + partial_func = partial(random_two_qubit_gates, gates = gates) + return partial_func(graph) + return CircuitTemplate([func]) + +def get_rand_1q_cliff_template(bm: BenchmarkConnection): + def func(qc, graph, width, sequence): + partial_func = partial(random_single_qubit_cliffords, bm =bm) + return partial_func(graph) + return CircuitTemplate([func]) + +def get_rand_2q_cliff_template(bm: BenchmarkConnection): + def func(qc, graph, width, sequence): + partial_func = partial(random_two_qubit_cliffords, bm =bm) + return partial_func(graph) + return CircuitTemplate([func]) + +def get_rand_perm_template(bm: BenchmarkConnection): + def func(qc, graph, width, sequence): + prog = random_permutation(graph, width) + native_quil = qc.compiler.quil_to_native_quil(prog) + return partial_func(graph) + return CircuitTemplate([func]) + + # =========================================== # Layer tools # ================================================================================================== From d5384fa802755a48b63a3b150cd00bfbad267f80 Mon Sep 17 00:00:00 2001 From: Kyle Date: Wed, 17 Jul 2019 17:14:04 -0400 Subject: [PATCH 16/49] Get basic templates working. --- examples/circuit_testing_josh.ipynb | 5353 ----------------- ...t_testing_kyle.ipynb => volumetrics.ipynb} | 331 +- .../{circuit_testing.py => volumetrics.py} | 252 +- 3 files changed, 175 insertions(+), 5761 deletions(-) delete mode 100644 examples/circuit_testing_josh.ipynb rename examples/{circuit_testing_kyle.ipynb => volumetrics.ipynb} (62%) rename forest/benchmarking/{circuit_testing.py => volumetrics.py} (73%) diff --git a/examples/circuit_testing_josh.ipynb b/examples/circuit_testing_josh.ipynb deleted file mode 100644 index 79900a30..00000000 --- a/examples/circuit_testing_josh.ipynb +++ /dev/null @@ -1,5353 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Circuit testing\n", - "\n", - "\n", - "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", - "\n", - "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", - "\n", - "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import itertools\n", - "import networkx as nx\n", - "import numpy as np\n", - "import pandas as pd\n", - "import time\n", - "# from scipy.spatial.distance import hamming\n", - "# import scipy.interpolate\n", - "\n", - "from matplotlib import pyplot as plt\n", - "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", - "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", - "from pyquil.quilbase import Pragma\n", - "\n", - "from forest.benchmarking.circuit_testing import *" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def two_q_id(qb1,qb2):\n", - " prog = Program()\n", - " prog +=I(qb1)\n", - " prog +=I(qb2)\n", - " return prog" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get lattice" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# if you want to run on a \"real lattice\"\n", - "from pyquil import *\n", - "#list_quantum_computers()\n", - "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", - "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", - "\n", - "qc_perfect = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", - "qc_noisy = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "#qc_perfect.device.get_specs()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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egDOA/gCWAujwj295ubaTkQmQy+UIDw/Hrl27REchiZPJZFi2bBnWrl0rOorJYwm35eCg8cOOAFwBtJ1n1OlsI0dH7WUik7JgwQJs3LhRdAwyAY8//jjOnTuH06dPi45i0ljCbQUHAzY2Gl+aC+A/AEoAlAN4F8BETZ9oawsEBekqIUncpEmTkJGRgaysLNFRSOIsLS0RGxuLf/3rX6KjmDRuzGqrk93RDQBeAPAZABsA0wCsvvnfKmxsgIICYTOlyfj9+c9/BgCsXr1acBKSumvXrsHT0xPJyclwdXUVHccksYTb6+A54S5p85wwUU9lZmZizJgxKCwshJWVleg4JHFLliyBlZUV/vnPf4qOYpJ4Obq9FSuUl5R7wtZW+fVEt8HPzw9Dhw7Ft99+KzoKmYAXXngBmzdvRmVlpegoJokl3F54uHIGdHcnybTMju5gZCVRd8TExGDTpk2iY5AJcHd3x9ixY7FlyxbRUUwSL0d3RNApSkQAUFNTA1dXV5w+fRpyuVx0HJK4EydOYObMmcjOzoa5ubnoOCaFK+GOLFqkPIwhKkq52ardJep6c3M0WlgoXz9yhAVMWmVra4uZM2dydUJ6cdddd8HZ2Rl79uwRHcXkcCXcFaWlwNatQEqKchCHoyOybWyw6MQJHExKEp2OJCopKQmTJk3C+fPnuTohndu9ezfWrFmD48ePi45iUljCPdTY2AhXV1f8+uuv8Pb2Fh2HJCo8PBwrV65EZGSk6CgkcU1NTfD19cX27dtx9913i45jMng5uocsLCzw2GOP4csvvxQdhSSME7RIX8zNzfHiiy9yeIeecSV8G44ePYrY2FicOXNGdBSSqOvXr0MulyM9PR1OTk6i45DE3bhxA+7u7oiPj4eHh4foOCaBK+HbcO+996KkpASZmZmio5BE9enTB9HR0di2bZvoKGQC7O3tERMTg3//+9+io5gMroRv0wsvvIB+/frh1VdfFR2FJOq3337Dk08+iaysLMhknR4dQnTbLly4gKCgIOTm5qJv376i40geV8K3afr06dixY4foGCRho0aNgrW1NY4cOSI6CpmAwYMH45FHHuFeBD1hCd+mUaNGobKyEqmpqaKjkETJZDJO0CK9Wrp0Kd577z00NDSIjiJ5LOHbZGZmhqlTp3I1TDo1e/Zs7Nu3D1evXhUdhUxAaGgofHx8+PSHHrCEtaDlkjRvr5Ou9OvXD5GRkfj0009FRyETsWzZMqxdu5Z/r+kYS1gLwsPD0dDQgCROzyIdanlmmH8pkj5ERkaiqqqKexF0jCWsBTKZDNOmTcPOnTtFRyEJi4iIQFVVFeLj40VHIRNgZmaGpUuXcniHjrGEtWTatGm8JE06ZWZmhpiYGO5aJb2ZPXs2Tpw4wVkIOsQS1pLQ0FCYmZnh1KlToqOQhM2ZMwdfffUVD2AnvbC1tcUzzzyDdevWiY4iWSxhLZHJZHxmmHTO2dkZ999/P/+ckd4sXrwYO3bsQFlZmegoksQS1qLp06dj586dvCRNOsVnhkmfBg0ahOjoaHz44Yeio0gSS1iLhg0bBjs7O5w4cUJ0FJKw8ePHo6ioCCkpKaKjkIlYsmQJ3n//fdTW1oqOIjksYS3iJWnSBwsLC8ydO5erYdKbwMBADB8+HJ9//rnoKJLDAxy0LD09HePGjUNBQQHMzPhvHNKNvLw8hIWFoaioCDY2NqLjkAk4ePAglixZguTkZB4kokVsCS3z9/fHHXfcgWPHjomOQhLm7u6OESNGYPfu3aKjkIl48MEHYWZmhgMHDoiOIiksYR3g4A7Sh5YJWkT6IJPJ/hjeUVICrF4NzJoFTJqk/Hn1aqC0VHRMo8PL0TqQnZ2NMWPGoKioCObm5qLjkETV1dXBzc0Nx44dg4+Pj+g4ZAIajh/Hj2PHYrxCobzd1najlq0toFAAkZHAihVAeLi4oEaEK2Ed8PHxgbOzM44ePSo6CkmYtbU1nnzySWzevFl0FDIF69fDctw4jK+vh1l9vWoBA0BNjfJje/YAERHA+vVCYhoblrCOcJc06cP8+fOxbds2nvtKurV+PbB8OVBdDbNbXTxVKIDqauXns4hviZejdSQ3NxejRo1CcXExLCwsRMchCbvvvvuwdOlSREVFiY5CUhQfr1zZVld3/2vt7IAjR4CwMK3HkgquhHXE09MT7u7uOHTokOgoJHELFizgM8OkO6tWKS81t/NfAGEArAHM6ehra2qUX08d4kpYh9auXYv09HT+BUk6VV1dDTc3N5w5cwZubm6i45CUlJQAcrn6/V8Au6Fcxf0AoAbA1o7ew8YGKCgABgzQVUqjxpWwDk2bNg179uxBfX296CgkYXZ2dpgxYwa2bNkiOgpJzdatHb4UDWAKgH63eg+ZrNP3MXUsYR1yc3ODn58fDh48KDoKSVxMTAw2b96MpqYm0VFISpKTNa6Cu6WmBuCc8w6xhHWMgztIH0JDQzFgwAD+g4+0q6JCO+9TXq6d95EglrCOTZ06FXv37kVdXZ3oKCRxMTExnKBFt02hUKCgoADffPMNkvLztfOmjo7aeR8J4rMzOubi4oKgoCD88MMPmDx5sug4JGH/93//h5dffhklJSUYOHCg6DhkBJqampCZmYnExEQkJibizJkzSExMhKWlJUJDQxHbrx8CLS1hcTvPodvaAkFB2gstMdwdrQcffPABjh07hk8//VR0FJK4uXPnIiAgAC+99JLoKGRgamtrkZKS0lq4iYmJSElJgZOTE0JDQ1V+ODs7K7+ok93RjTd/vAGgCMBGKFd1ais77o7uFEtYDy5fvgw/Pz9cvHgRtra2ouOQhB0/fhxz585FRkYGj5szYdeuXWtd1bb8yMnJgY+Pj0rZDh8+HA4ODp2/WXS0chRlu6p4HcoCbuu1mx9vJZMBUVHArl23+1uSLJawnjz44IN49tlnER0dLToKSZhCoUBgYCA+/PBDjBkzRnQc0jGFQoGLFy+qlG1iYiJKSkoQHBysUriBgYE9O3uaE7N0iiWsJxs2bMBPP/3EedKkc++++y4SExPx8ccfi45CWtTc3Ixz586pFW5TU5Pa5WQfHx/tnuDWZnZ0l9nZAWvWAIsWaS+HBLGE9aSsrAxeXl4oLi5Gr169RMchCSsrK4O3tzfOnz8PR+5KNUr19fVIS0tTKdukpCQ4OjqqFa6rq6t+bj20FHFNjdqlaRUymXIzFgu4S1jCevSnP/0J8+bNw/Tp00VHIYmbMWMG7rvvPjz77LOio9At3LhxA0lJSSqFm5GRAXd3d7X7t/363XI+lW4lJChnQcfFKcu2zUzpOnNzmMtksJg8WXmeMC9BdwlLWI8++ugjfPfdd9jFTQqkYwcPHsTy5cuRmJjIDVoGpLS0VO1ycmFhIQIDA1UKNzg4GHZ2dqLjdqy0VDmKMiVFOYjD0RHplpZYmpSE/QkJotMZFZawHpWXl8Pd3R1FRUXo3bu36DgkYc3NzfD29sbOnTsRxhWJ3ikUCuTl5amU7ZkzZ3Djxg0MHz5cpXCHDh0KS0tL0ZFvW0NDA5ydnXHq1CnI5XLRcYwGS1jPJk6ciJkzZ+KJJ54QHYUk7q233kJBQQH+97//iY4iaY2NjcjIyFArXFtbW7X7tx4eHpK+MrFgwQL4+vryOfVuYAnr2ccff4yvvvoKe/fuFR2FJK64uBiBgYEoLCyEvb296DiSUFNTg+TkZJXCTU1NhYuLi1rhDho0SHRcvTt48CBefvllJPCSdJexhPWsoqICbm5uKCgoQN++fUXHIYmbPHkypkyZgnnz5omOYnTKy8vV7t/m5ubCz89PpWxDQkLQp08f0XENQmNjIwYPHoxjx47B29tbdByjwBIWYMqUKYiKisJTTz0lOgpJ3LfffotVq1bh+PHjoqMYLIVCgQsXLqgV7pUrVxASEqJSuAEBAbC2thYd2aAtXrwYbm5uWLFihegoRoElLMDnn3+OTz75BHFxcaKjkMQ1NjZCLpfjwIEDCAwMFB1HuObmZmRnZ6sVrkwmU7uc7O3tDTMzHjTXXUeOHMELL7yAM2fOiI5iFFjCAlRWVsLV1RW5ubnin/sjyfv73/+OqqoqvPvuu6Kj6FVdXR1SU1NVyjY5ORn9+/dXK1wXFxdJb5jSp6amJri5ueHQoUPw8/MTHcfgsYQFefzxxzF+/HjExMSIjkISl5ubi7vuuguFhYU9mx1sBK5fv6428CIzMxNeXl5qAy84RUz3YmNj0b9/f7z66quioxg8lrAgX375JTZu3IgDBw6IjkImYNy4cZg/fz5mzJghOsptu3z5strl5OLiYgwbNkylcIOCgnhqmSDHjh3DwoULkZqaKjqKwWMJC1JdXQ1nZ2fk5ORgAM/ZJB3bsWOH8hCRzz9XTjpKTgYqKgAHByA4GJg71+DOe1UoFMjNzVU7cL6mpkbtcrKfnx8sLNROsiVBmpubIZfLsX//fgwbNkx0HIPGEhZoxowZiIiIwDPPPCM6Cklc/bFj+HHsWETKZDCTyVQPabe1VQ7kj4xUzvwND9d7voaGBqSnp6sdWGBvb69WuHK5nPdvjcCyZctgZ2eHN998U3QUg8YSFujrr7/Ge++9h0OHDomOQlJ28/Sb5upqdLrXV0+n31RVVakNvEhLS4Obm5ta4fIqkfE6efIkZs2ahczMTP6jqRMsYYFqa2vh7OyM9PR0ODk5iY5DUiT4HNgrV66o3b/Nz8+Hv79/60aploEXnOolLQqFAl5eXti1axdCQ0NFxzFYLGHBZs+ejbvuugvPPfec6CgkNfHxQERE9wq4hZ0dcORIl4+jUygUKCwsVCvca9euqR1Y4O/vDysrq+5nIqPz8ssvAwDeeecdwUkMF0tYsH379uGf//wnfvnlF9FRSGqio4E9ezo8gD0bQBCAxwFsb/+iTAZERQEajt1sampCVlaWWuFaWlqqXU729PTkwAsTlpiYiOjoaOTm5vKSdAdYwoLV1dXB2dkZycnJcHV1FR2HpKKkBJDLVTdgtfMwgBoAcmgoYQCwsUFtVhbOtnskKCUlBYMGDVIrXGdnZ938XshoKRQK+Pn5Yfv27bjzzjtFxzFI3NMvmLW1NR599FF8+eWXWLJkieg4JBVbt3b68hcA+gK4B0BOB59TU1eHlR4e+C4goLVop0+fjuHDh8PBwUG7eUmSZDIZpk+fjh07drCEO8CVsAH4/vvv8cYbb+C3334THYWkYtYs4NNPNb50HUAYgJ8BbIKyhDWuhAE0zpwJi88+00lEMg1nz55FZGQk8vPzeWtCA/4vYgAefPBBZGdnIz8/X3QUkoqKig5fegXAfABduflhUVmprURkooYNG4Y+ffpwkdEBlrABsLS0RFRUFHbu3Ck6ChkphUKBixcv4ocffsDq1avx69mzGj/vDICDALp844NzlkkLWi5JkzreEzYQ06dPx8svv4yXXnpJdBQycHV1dUhPT0dycjKSkpJaf25qakJISAhCQkJgEx6O5osXYVZXp/K1hwHkARhy89c3ADQBSANwuv03srUFgoJ0+nsh0zB9+nRERETg3Xffhbm5ueg4BoX3hA1EY2MjBg8ejOPHj8PLy0t0HDIQly5dUina5ORkZGdnw9PTE8HBwQgJCWn9WeU4vg52R1dDeU+4xRooS3k9ALXZVDY2QEGBwc2UJuM0fPhwrFu3DhEREaKjGBSWsAFZvHgx3NzcsGLFCtFRSM/q6+tVVrcthdvY2KhStMHBwQgMDOzakYS3eE4YAF5HBxuzOnlOmKgnVq1ahYKCAqxfv150FIPCEjYghw8fxosvvogzZ86IjkI6dPnyZZXVbVJSErKzs+Hh4aFSuGqr2+7S48QsolvJzc3FqFGjUFxczBOv2mAJG5Cmpia4ubnh0KFD8PPzEx2HblN9fT0yMjLUCrehoaG1ZFsKNyAgQDdn3wqeHU3UVnh4ON5++22MGzdOdBSDwRI2MLGxsRgwYABeeeUV0VGoG0pKSlQuIyclJSErKwvu7u5qhTt48GD9jvBrKeKamk4vTevrFCUyXWvWrEFmZiY2btwoOorBYAkbmGPHjuHpp5/G2Q4eMSGx6uvrkZmZqVa4dXV1rWXb9t6tTla3PZGQAKxaBcTFKcu2pqb1pTpzc5jLZLCYPFkQU/eMAAAcI0lEQVR5njAvQZOO5OfnY+TIkbh48SIsLS1FxzEILGED09zcjCFDhuCHH35AYGCg6DgmraSkRG2jVFZWFuRyucp92+DgYLi6uhrHgPrSUuVIy5QUoLwccHREprU1Fp84gZ+Sk0WnIxNw991349VXX0VkZKToKAaBJWyAli5dCnt7e6xcuVJ0FJPQ0NCAjIwMtcKtra1V2ygVEBAAOzs70ZG1qrm5GZ6enti9ezdGjBghOg5J3Lp163DmzBlsvcV8c1PBEjZAJ06cwJNPPomMjAzjWF0ZkdLSUrXnbjMzMzFkyBC1wjWa1a0WvPnmm7h48SI++OAD0VFI4i5cuICgoCBcvHgR1tbWouMIxxI2QAqFAh4eHvjmm28QEhIiOo5RamhoaL1327Zwa2pq1IZcBAYGSm51211FRUUIDg5GYWEhevXqJToOSdyYMWOwfPlyTJ48WXQU4VjCBurPf/4zLCws8Pbbb4uOYvDKysrUNkplZmbCzc1NbWeym5ubyaxuu2vixImYOnUqnnrqKdFRSOLef/99HD9+HJ92cNKXKWEJG6hTp05h2rRpyMnJYWnc1NDQgKysLLXnbqurq9U2Sg0bNszkV7fdtWfPHqxZswa//vqr6CgkcZcvX4afnx8uXrxoOE8QCMISNlAKhQI+Pj744osvEGaCj4yUlZWpHVCQkZEBNzc3tcIdMmQI/6GiBQ0NDRgyZAh+/vln+Pv7i45DEvfggw9i8eLFeOyxx0RHEYolbMD+9re/oaGhAatXrxYdRWcaGxuRmZmpVrhVVVUIDg5WKdzAwEDer9SxFStWoL6+HmvXrhUdhSRuw4YNOHjwoMkf4coSNmBJSUl49NFHcf78eUms9K5cuaL2GFB6ejpcXV3VdiZzdStGTk4O7rnnHhQWFnLnKulUWVkZvLy8cOHCBdjb24uOIwxL2IApFAr4+/tj+7/+hbCzZ4HkZKCiAnBwAIKDgblzDfKYucbGRmRlZakVbmVlpdrO5GHDhnF1a2DGjh2LZ555BtOmTRMdhSRu/PjxmDNnDmbMmCE6ijAsYUMWH4+02bPhk5OjHPHW9mxYW1vlHODISOWowfBwIRGvXr2qtlGqZXXbvnDlcjlXt0bg888/x5YtW3DgwAHRUUjitmzZgr179+Lrr78WHUUYlrChujl0X1FTA5kBDN1vbGxEdna22nO3169fR1BQkNrOZFO+vGTsamtr4ebmhpMnT8LDw0N0HJKw8vJyyOVyFBUVoU+fPqLjCMESNkSCj5+7evWq2kap9PR0uLi4qO1Mdnd35+pWgl588UX07t0bb775pugoJHGTJk3CtGnTMHv2bNFRhGAJGxo9HsTe1NTUurptW7gVFRVqO5O5ujUtKSkpiIyMRF5eHg9gJ53avn07vvjiC+zbt090FCFYwoYmOhrYs0fl3Nf21VcDYDGA/7T/WpkMiIoCdu1Se9vy8nK11W1aWhqcnZ3VdibL5XKYmZlp+TdGxmbUqFH4+9//jokTJ4qOQhJ2/fp1uLm5IS8vD46OjqLj6B1L2JCUlAByueoGrHZuAHACEAdgjIbXFTY2yPnpJyQWFakU7rVr11rv3bYUblBQEFe31KHNmzfj22+/xZ49e0RHIYmLjo7GxIkTMW/ePNFR9I4lbEhWrwZee63TEt4G4A0A5wBouhNbDeC9O+7AyfvvVylcd3d3rm6pW27cuAE3N7fWKyZEurJjxw589NFH+OGHH0RH0TuWsCGZNQu4xUDzsVCugF/v7JNmzwY+/lh7uchkLVy4EB4eHlixYoXoKCRhVVVVcHFxQU5ODgYY4OwDXeLSyJBUVHT6cj6AIwBuecZNebmWApGpi4mJwaZNm9Dc3Cw6CklYr169MH78eOzevVt0FL1jCRsSB4dOX/4EwGgAt3xy0wQ3N5BuhIeHw97eHocPHxYdhSRu2rRpJjlHmiVsSIKDARubDl/+GF1YBdvaAkFB2kxFJkwmkyEmJgYbN24UHYUkbsKECTh16hQuX74sOope8Z6wIelkd/RxAOMAXALQu7P3sLEBCgoMcqY0Gafy8nJ4eHjg3Llz6Nevn+g4JGFPPPEE7rnnHjz77LOio+gNV8KGZOBA5SxoDROotgGIxi0KWCYDJkxgAZNWOTo6YuLEifjkk09ERyGJmz59Onbs2CE6hl5xJWxo9Dgxi6irjhw5gmeffRYpKSkcU0o6U1dXB2dnZ6SkpGDw4MGi4+gFV8KGJjxcOQPazq57X9cyO5oFTDowZswY1NXV4ffffxcdhSTM2toakydPxpdffik6it6whA3RokV/FPGtVh0ymVYPbyDSpGWD1qZNm0RHIYkztUvSvBxtyBISgFWrgLg4ZdnW1LS+1GRtjYb6elhNmQKzv/6VK2DSuUuXLsHf3x/5+fkme+wc6V5DQwOcnZ1x6tQpyOVy0XF0zvz1119/XXQI6oCLCzB9OvD008pnf52cgCFDgLvugtmMGZh45Qp6x8Zi6NixopOSCbC3t8fJkydRW1uLkSNHio5DEmVubo7s7GxcunQJ9957r+g4OseVsBHbuXMn/vvf/+Lo0aOio5CJ2L9/P1577TWcPHlSdBSSsIMHD+Lll19GQkKC6Cg6x3vCRiw6Ohr5+fkm8QeVDMPDDz+MS5cuISkpSXQUkrCIiAgUFhYiJydHdBSdYwkbMQsLC8TGxuLdd98VHYVMhLm5OebNm8cNWqRTFhYWeOyxx0xijCUvRxu5iooKeHh4IDk5Ga6urqLjkAnIz8/HiBEjUFRUBFtbW9FxSKKOHDmC2NhYyV914UrYyDk4OODJJ5/Ef//7X9FRyETI5XLceeed2LVrl+goJGGjR49GaWkpMjIyREfRKZawBMTGxmLTpk24ceOG6ChkInioA+maubk5pk6dKvlnhlnCEuDp6Yn7778f27ZtEx2FTMSkSZOQmZmJrKws0VFIwloGd0j5rilLWCKWLl2KdevWoampSXQUMgFWVlZ48sknuUGLdGrUqFG4ceMGzp49KzqKzrCEJeKee+6Bo6Mj9u3bJzoKmYj58+dj27ZtqK+vFx2FJMrMzAzTpk2T9CVplrBEyGQyLF26lI8rkd74+flh6NCh+Pbbb0VHIQmT+iVplrCEPPbYY8jNzcXp06dFRyETwUMdSNfCwsLQ1NSExMRE0VF0giUsIZaWlnj++ee5Gia9efzxx3Hy5EkUFBSIjkISJZPJJH1JmsM6JObatWvw9PQ0qUOxSaznnnsO/fv3B8+CIV1JTExEdHQ0cnNzIbvV8a5Ghithienbty9mzZrF4R2kNwsWLMBHH33EnfmkM8OHD4elpSXi4+NFR9E6lrAEvfDCC9i0aROqqqpERyETEBISgkGDBuHAgQOio5BEyWSy1g1aUsMSliAvLy/cd999HN5BerNgwQJu0CKdmj59Onbu3Inm5mbRUbSK94Ql6pdffsH8+fORkZEBMzP+W4t06/r165DL5cjIyMCgQYNExyGJCgwMxIYNG3DvvfeKjqI1/NtZokaPHg0HBwd89913oqOQCejTpw+io6N59YV0SoqXpLkSlrDPPvsMGzduxKFDh0RHIRPw22+/4amnnkJmZqbkdrCSYcjMzERERASKiopgbm4uOo5WcCUsYVOnTkVOTo5kH3InwzJq1ChYWlri6NGjoqOQRPn5+WHQoEH45ZdfREfRGvPX+XCfZJmbm6OxsRF79+5FVFSU6DgkcTKZDA0NDdi3bx+io6NFxyGJKi8vR8pPP2FcVhawfj2wZQsQFwfk5AC+vkCvXqIjdgsvR0tceXk5PD09kZqaChcXF9FxSOLKysrg7e2N8+fPw9HRUXQckpr4eNz4299g8eOPsLaxgay29o/XbG0BhQKIjARWrADCw8Xl7AZejpY4R0dHzJo1C++//77oKGQC+vfvj8jISGzfvl10FJKa9euBiAjYHzwIG0C1gAGgpgaorQX27AEiIpSfbwS4EjYBOTk5uPvuu5Gfnw87OzvRcUjifvrpJyxZsgRJSUncoEXasX49sHw5UF3d9a+xswPWrAEWLdJdLi3gStgEeHt7495778XHH38sOgqZgAceeABVVVVISEgQHYWkID5erYDrAMwHIAfQG8BwAPvbf111tfLrDPzPIUvYRLScNSy1aTNkeMzMzDB//nxs3LhRdBSSglWrlJea22gE4AbgCIAKAP8AMA1AXvuvralRfr0B4+VoE6FQKBAWFoY33ngDEydOFB2HJK64uBiBgYEoLCyEvb296DhkrEpKALlcea/3FoIBvAbgsfYv2NgABQXAgAE6CHj7uBI2ETKZrHU1TKRrLi4uGDNmjOSmG5Gebd3apU+7DCALQKCmF2WyLr+PCCxhEzJ16lRkZmbizJkzoqOQCeChDnTbkpNvuQpuAPAEgKcADNX0CTU1QEqK9rNpCUvYhFhZWeG5557DunXrREchEzB+/HgUFBTg7NmzoqOQsaqo6PTlZgCzAVgB6PQE9fJy7WXSMpawiVm4cCH27t2Lixcvio5CEmdhYYG5c+dyNUxdplAoUFhYiK+//hp//etfcbCTnc0KKHdIXwawC4BlZ29swINjWMIm5o477sDMmTM5vIP0Yv78+di+fTtqu7CxhkxPSUkJ4uLiWjeMOjs7Y+TIkdi0aROsrKwwePx4NNvYaPzaRQDSAXwLwLazb2JrCwQFaT+8lnB3tAnKysrC6NGjkZeXx+EdpHPjxo3DvHnzMHPmTNFRSKBr167h1KlTiI+PR0JCAuLj41FRUYGwsDCEh4e3/uzm5vbHkJcOdkfnA3AHYA3Aos3H/wfl/WEVBr47miVsoiZPnoxHHnkETz/9tOgoJHE7d+7Ehx9+iJ9//ll0FNKTqqoqJCYmtpZtfHw8iouLERoaqlK4Xl5eMDO7xQXZ6GjlKMqeVJVMBkRFAbt29ew3ogcsYRN1+PBhLFq0CKmpqbf+PwHRbairq4ObmxuOHz8Ob29v0XFIy+rq6pCcnKxSuOfOncOwYcNayzY8PBxDhw6FhYXFrd+wvfh45Szo7oysbGFnBxw5AoSFdf9r9YQlbKIUCgVGjhyJf/zjH5gwYYLoOCRxy5Ytg5WVFVYZ+PQi6lxjYyPS0tJaCzchIQGpqanw8fFRKdxhw4bB2tpae99YwrOjWcIm7JNPPsG2bdtw8OBB0VFI4tLT0zF27FgUFBTA0rLTfaxkIJqbm5GTk9O6uk1ISMCZM2cwePDg1rINCwtDaGiofvaWtBRxTU3nl6ZlMuVmLCMoYIAlbNLq6+vh4eGB/fv3Izg4WHQckrjRo0fjpZdewqOPPio6CrWjUChQUFCgUrinTp1C3759VQp35MiRcHBwEBc0IUE5CzouTlm2bWdKt5wnPGGC8jxhA74E3RZL2MS9/fbbyM7OxpYtW0RHIYnbunUrvvrqK+zbt090FJN36dIllV3KCQkJMDc3V9k0FRYWhgEGuqMYpaXKUZQpKcpBHI6OyseQ5swx2F3QHWEJm7grV67A29sb6enpcHJyEh2HJKyqqgpubm5ITk6Gq6ur6Dgm4+rVq62PBrUUblVVlVrhDh48mOc/C8ASJixevBj9+/fHypUrRUchiVu0aBFcXFzwyiuviI4iSZWVlUhMTFQp3MuXL2PEiBEql5U9PT1ZuAaCJUzIzMzEfffdh/z8fNjadjp7hui2nDp1Co899hhyc3P5aNxtqq2tRVJSkspl5by8PAQFBamscv38/GBubi46LnWAJUwAgEmTJmHy5MlYsGCB6CgkcSNGjMA777yDhx9+WHQUo9HQ0IDU1FSVws3IyICfn59K4QYGBsLKykp0XOoGljABAH7++Wc899xzSE1N5WUq0qkPPvgAhw8fxs6dO0VHMUjNzc3IzMxUGX6RnJwMuVyuUrghISG8ciUBLGECoHxEITQ0FO+88w7Gjx8vOg5J2LVr1+Du7o7s7GzD3X2rJwqFAufPn1fZpXz69Gn0799fZfhFaGgo+vTpIzou6QBLmFp9/PHH2L59Ow4cOCA6CkncU089heDgYCxbtkx0FL0qLi5W2TSVkJAAGxsblV3KYWFh6Nevn+iopCcsYWpVX18Pd3d3/PDDDwgy4KO/yPj9+uuviImJQXp6umRvf5SVlbUWbUvx1tfXq+xSDgsLg4uLi+ioJBBLmFS89dZbyM3NxebNm0VHIQlTKBQICAjAxo0bMXr0aNFxbtv169dx6tQplcK9evUqRo4cqXJZWS6XS/YfHdQzLGFSUVZWBh8fH2RkZGDQoEGi45CErV27FsnJydi2bZvoKN1SU1OjckxfQkICCgsLERISonJZ2dfXl49h0S2xhEnNM888AycnJ7z++uuio5CElZaWwsfHB/nx8XD4+msgORmoqAAcHIDgYGDuXOEjCOvr63H27FmV+7hZWVnw9/dXuawcGBjYs2P6yOSxhElNRkYG7r//fuTl5fERCNKd+HicmDIFI0tKlAVWW/vHay3D+CMjlcP4w8N1HqepqQkZGRkqz+KePXsWHh4eKo8GBQcHw8bGRud5yDSwhEmjRx55BFFRUYiJiREdhaTo5rF0zTU1MBNwLJ1CocC5c+dUCjcxMRFOTk4qhRsaGgp7e3utfV+i9ljCpNFPP/2E2NhYnD17lhtJSLv0fEC7QqFAUVGRyqapU6dOwd7eXmXT1MiRI+Ho6Njt9ye6HSxh0kihUGD48OFYvXo1/vSnP4mOQ1IRHw9ERKgVcASA3wG03FUdDCCz/dfa2QFHjtzynNiSkhKVwk1ISEBzc3Nr2basdLnxkAwBS5g6tHXrVnzxxRf4/vvvRUchqYiOBvbsUd7vbSMCwCwAnd78kMmAqChg167WD127dq31mL6W4q2oqFDZpRweHg43Nzde0SGDxBKmDtXV1cHd3R0//vgjhg0bJjoOGbuSEkAuV92AdVMEulDCAJqtrLDxlVdwND0d8fHxKC4uRmhoqErhenl58dEgMhosYerUm2++ifz8fGzatEl0FDJ2q1cDr73WYQmnAlAA8APw1s2PtVcrkyHurrtQsXAhwsPDMXToUD4aREaNJUydKi0tha+vLzIzMzFw4EDRcciYzZoFfPqpxpdOAAgAYAXgCwDPATgDwEvTJ8+eDXz8sY5CEukXr9lQpwYMGICpU6di/fr1oqOQkasvLe3wtbsA9AZgDeApAPcCiOvok8vLtR2NSBhex6FbevHFFzF27Fj85S9/4ZACuqWysjKkpaUhLS0NqamprT+vu3IFM7r4HjIoL01rxMeISEJYwnRLAQEBGDFiBD777DPMmzdPdBwyECUlJWplm5aWhrq6OgQEBCAgIACBgYGYOHEiAgIC4Pr55xrvCV+D8nL0/VD+hbQDwFEA/9b0TW1tAZ7wRRLCe8LUJT/++COWLFmClJQUPuphQhQKBUpKSlRKtuW/GxsbERgY2Fq2LcXr4uKi+c9IB7ujSwFMAJABwBzAUABvAhinKZCNDVBQIHymNJG2sISpSxQKBYKDg/Gvf/0L48Zp/OuRjJhCocClS5c0li0AjWXr5OTU/X+QdfCccJdoeE6YyNixhKnLPvroI3z55ZfYv3+/6CjUQwqFAsXFxRrL1sLCQqVkW/574MCB2rv60cHErC7p4sQsImPCEqYuq62thbu7O37++WcEBASIjkOdaJmX3P5+bVpaGqytrTWW7QB9XeLV8+xoIkPGEqZuWblyJYqKirBhwwbRUQhAc3MzCgsLNZZtr1691MrW398f/fv3Fx37jyKuqen80rSOTlEiMhQsYeqWkpIS+Pn5ISsrS38rJ0JzczPy8/PVyjY9PR19+vRRu18bEBCAO+64Q3TsziUkAKtWAXFxyrKtqfnjtZbzhCdMUJ4nzEvQJFEsYeq2BQsWYMiQIXjllVdER5Gc5uZmnD9/Xu1+bUZGBhwdHTWWbd++fUXHvj2lpcDWrUBKinIQh6Oj8jGkOXO4C5okjyVM3ZaamoqHHnoIeXl5sLa2Fh3HKDU1NSE3N1etbDMzM9G/f3+1svX394eDg4Po2ESkZSxh6pHx48djxowZmDNnjugoBq2xsRG5ubkqk6PS0tKQlZWFQYMGaSzb3r17i45NRHrCEqYeOXDgAJYvX46kpCQO7wDQ0NCAc+fOqT36k52dDRcXF7WdyP7+/ujVq5fo2EQkGEuYekShUCAoKAjr1q3DQw89JDqO3tTX1yMnJ0etbM+dOwdXV1e1sh06dCjs7OxExyYiA8USph7bvHkzdu/eje+++050FK2rq6tDdna22m7k3NxcDBkyRK1s/fz8YGtrKzo2ERkZljD1WG1tLeRyOX7ZtQu+x48DyclARQXg4AAEBwNz5xr87tba2lpkZWWplW1eXh7c3d3VytbX15cnSRGR1rCEqefi45H+5JPwzs6GpaWl6mD+luc8IyOVz3mGh4vLCWXZZmRkqJVtQUEBPDw81IZa+Pj4cOc3EekcS5h65ubEI0VNDWQGNPGourq6tWzbFm5RURG8vLzUytbb2xtWVlY6z0VEpAlLmLrPAGb/VlVVIT09Xa1si4uL4ePjo1a2Xl5eytU6EZEBYQlT93RwCk4egMUAfgNgDeBxAOugPKS9VQ9Owblx4wbS09PVnrO9fPkyfH191Z6z9fLygoWFxa3fmIjIALCEqXs6OA92AoCBAD4EcA3KA9kXAIht+0mdnAd7/fp1lbJtKdyysjL4+fmpla2npyfMzc119bskItILljB1XUkJIJerbsC6yR/AWijLGABeAnAdwP/afZ7C2hoJu3cjqbhYpWyvXr0Kf39/tbJ1d3dn2RKRZPG6HXXd1q0dvvQigC8ARAAoB7AfwJsaPq+mrg6/Pf00Tj/4IAICAvDgzZ/lcjnMzMx0EJqIyHCxhKnrkpM1roIBYAyADQD6AGgC8BSAKRo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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw(qc_perfect.qubit_topology(),with_labels=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "G = qc_perfect.qubit_topology()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# gate sets" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "one_q_gates = [X,Z,I]\n", - "two_q_gates = [two_q_id,CZ]\n", - "\n", - "one_c_gates = [X,I]\n", - "two_c_gates = [two_q_id,CNOT]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#prog1 = random_single_qubit_gates(G, one_q_gates)\n", - "#prog2 = random_two_qubit_gates(G, two_q_gates)\n", - "#print(prog1+prog2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# random cliffords" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from forest.benchmarking.rb import get_rb_gateset" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# my config has gone all cattywampus so i need to do this\n", - "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tcp://127.0.0.1:5555'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm.client.endpoint" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", - "gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RX(pi/2) 0\n", - "RZ(pi/2) 0\n", - "RX(-pi/2) 0\n", - "RX(-pi) 1\n", - "RX(pi/2) 2\n", - "RZ(-pi/2) 2\n", - "RX(pi/2) 3\n", - "RZ(pi/2) 3\n", - "RX(-pi/2) 3\n", - "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 5\n", - "RZ(-pi/2) 5\n", - "RX(-pi/2) 5\n", - "RZ(-pi/2) 6\n", - "RX(-pi) 6\n", - "RZ(-pi) 7\n", - "RX(-pi) 7\n", - "RX(pi/2) 8\n", - "RZ(-pi) 8\n", - "\n" - ] - } - ], - "source": [ - "progy = random_single_qubit_cliffords(bm,G)\n", - "print(progy)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Layer crap" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "#print(circuit_sandwich_rand_gates(G,2, one_q_gates,two_q_gates))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from functools import partial\n", - "\n", - "circuit_depth = 3\n", - "circuit_width = 3\n", - "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", - " one_q_gates = one_c_gates, \n", - " two_q_gates = two_c_gates)\n", - "layer_dagger = False\n", - "sandwich_dagger = False\n", - "num_rand_subgraphs = 2\n", - "num_shots_per_circuit = 2\n", - "use_active_reset= False" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetDepthLatticeLayer DaggerProgramSandwich DaggerTrialsWidth
0False1(3)False(I 3, I 3)False21
1False1(6)False(I 6, X 6)False21
2False1(6, 7)False(I 6, I 7, I 6, X 7, I 6, I 7)False22
3False1(7, 8)False(I 7, I 8, I 7, I 8, I 7, I 8)False22
4False1(1, 3, 4)False(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...False23
5False1(1, 3, 4)False(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...False23
6False2(1)False(I 1, I 1, I 1)False21
7False2(5)False(I 5, X 5, X 5)False21
8False2(4, 7)False(I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...False22
9False2(2, 5)False(I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...False22
10False2(0, 1, 3)False(I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...False23
11False2(0, 3, 4)False(I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...False23
12False3(8)False(I 8, X 8, I 8, X 8)False21
13False3(2)False(I 2, X 2, I 2, I 2)False21
14False3(4, 5)False(I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...False22
15False3(4, 7)False(I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...False22
16False3(1, 3, 4)False(I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...False23
17False3(3, 4, 5)False(I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...False23
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" - ], - "text/plain": [ - " Active Reset Depth Lattice Layer Dagger \\\n", - "0 False 1 (3) False \n", - "1 False 1 (6) False \n", - "2 False 1 (6, 7) False \n", - "3 False 1 (7, 8) False \n", - "4 False 1 (1, 3, 4) False \n", - "5 False 1 (1, 3, 4) False \n", - "6 False 2 (1) False \n", - "7 False 2 (5) False \n", - "8 False 2 (4, 7) False \n", - "9 False 2 (2, 5) False \n", - "10 False 2 (0, 1, 3) False \n", - "11 False 2 (0, 3, 4) False \n", - "12 False 3 (8) False \n", - "13 False 3 (2) False \n", - "14 False 3 (4, 5) False \n", - "15 False 3 (4, 7) False \n", - "16 False 3 (1, 3, 4) False \n", - "17 False 3 (3, 4, 5) False \n", - "\n", - " Program Sandwich Dagger \\\n", - "0 (I 3, I 3) False \n", - "1 (I 6, X 6) False \n", - "2 (I 6, I 7, I 6, X 7, I 6, I 7) False \n", - "3 (I 7, I 8, I 7, I 8, I 7, I 8) False \n", - "4 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ... False \n", - "5 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ... False \n", - "6 (I 1, I 1, I 1) False \n", - "7 (I 5, X 5, X 5) False \n", - "8 (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ... False \n", - "9 (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ... False \n", - "10 (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ... False \n", - "11 (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ... False \n", - "12 (I 8, X 8, I 8, X 8) False \n", - "13 (I 2, X 2, I 2, I 2) False \n", - "14 (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ... False \n", - "15 (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ... False \n", - "16 (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ... False \n", - "17 (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ... False \n", - "\n", - " Trials Width \n", - "0 2 1 \n", - "1 2 1 \n", - "2 2 2 \n", - "3 2 2 \n", - "4 2 3 \n", - "5 2 3 \n", - "6 2 1 \n", - "7 2 1 \n", - "8 2 2 \n", - "9 2 2 \n", - "10 2 3 \n", - "11 2 3 \n", - "12 2 1 \n", - "13 2 1 \n", - "14 2 2 \n", - "15 2 2 \n", - "16 2 3 \n", - "17 2 3 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "daty = acquire_circuit_sandwich_data(qc_noisy,exp)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetDepthLatticeProgramSamplesTrialsWidth
0False1(3)(I 3, I 3)[[0], [0]]21
1False1(6)(I 6, X 6)[[1], [1]]21
2False1(6, 7)(I 6, I 7, I 6, X 7, I 6, I 7)[[0, 1], [0, 1]]22
3False1(7, 8)(I 7, I 8, I 7, I 8, I 7, I 8)[[0, 0], [0, 0]]22
4False1(1, 3, 4)(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...[[0, 0, 0], [0, 1, 0]]23
5False1(1, 3, 4)(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...[[0, 1, 0], [0, 1, 1]]23
6False2(1)(I 1, I 1, I 1)[[0], [0]]21
7False2(5)(I 5, X 5, X 5)[[0], [0]]21
8False2(4, 7)(I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...[[0, 1], [0, 0]]22
9False2(2, 5)(I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...[[0, 1], [1, 1]]22
10False2(0, 1, 3)(I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...[[1, 1, 1], [0, 1, 1]]23
11False2(0, 3, 4)(I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...[[1, 1, 0], [1, 0, 0]]23
12False3(8)(I 8, X 8, I 8, X 8)[[0], [0]]21
13False3(2)(I 2, X 2, I 2, I 2)[[1], [1]]21
14False3(4, 5)(I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...[[0, 0], [0, 0]]22
15False3(4, 7)(I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...[[0, 0], [0, 0]]22
16False3(1, 3, 4)(I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...[[0, 0, 0], [0, 0, 0]]23
17False3(3, 4, 5)(I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...[[0, 1, 1], [0, 1, 1]]23
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Active ResetAnswerDepthHamming dist. dataHamming dist. idealHamming dist. randLatticePr. success dataPr. success loge dataPr. success loge randPr. success randProgramSamplesTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
0False[[0]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](3)1.01.00.5000.500(I 3, I 3)[[0], [0]]0.00.500210
1False[[1]]1[1.0, 0.0][1.0, 0.0][0.5, 0.5](6)1.01.00.5000.500(I 6, X 6)[[1], [1]]0.00.500210
2False[[0, 1]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](6, 7)1.01.00.2500.250(I 6, I 7, I 6, X 7, I 6, I 7)[[0, 1], [0, 1]]0.00.750220
3False[[0, 0]]1[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](7, 8)1.01.00.2500.250(I 7, I 8, I 7, I 8, I 7, I 8)[[0, 0], [0, 0]]0.00.750220
4False[[0, 1, 0]]1[0.5, 0.5, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)0.50.50.1250.125(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ...[[0, 0, 0], [0, 1, 0]]0.50.500230
5False[[0, 1, 1]]1[0.5, 0.5, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)0.50.50.1250.125(I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ...[[0, 1, 0], [0, 1, 1]]0.50.500230
6False[[0]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](1)1.01.00.5000.500(I 1, I 1, I 1)[[0], [0]]0.00.500210
7False[[0]]2[1.0, 0.0][1.0, 0.0][0.5, 0.5](5)1.01.00.5000.500(I 5, X 5, X 5)[[0], [0]]0.00.500210
8False[[0, 0]]2[0.5, 0.5, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 7)0.50.50.2500.250(I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ...[[0, 1], [0, 0]]0.50.250220
9False[[1, 1]]2[0.5, 0.5, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](2, 5)0.50.50.2500.250(I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ...[[0, 1], [1, 1]]0.50.250220
10False[[1, 1, 1]]2[0.5, 0.5, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](0, 1, 3)0.50.50.1250.125(I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ...[[1, 1, 1], [0, 1, 1]]0.50.500230
11False[[1, 1, 0]]2[0.5, 0.5, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](0, 3, 4)0.50.50.1250.125(I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ...[[1, 1, 0], [1, 0, 0]]0.50.500230
12False[[0]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](8)1.01.00.5000.500(I 8, X 8, I 8, X 8)[[0], [0]]0.00.500210
13False[[1]]3[1.0, 0.0][1.0, 0.0][0.5, 0.5](2)1.01.00.5000.500(I 2, X 2, I 2, I 2)[[1], [1]]0.00.500210
14False[[0, 0]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 5)1.01.00.2500.250(I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ...[[0, 0], [0, 0]]0.00.750220
15False[[0, 0]]3[1.0, 0.0, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25](4, 7)1.01.00.2500.250(I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ...[[0, 0], [0, 0]]0.00.750220
16False[[1, 0, 0]]3[0.0, 1.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](1, 3, 4)0.00.00.1250.125(I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ...[[0, 0, 0], [0, 0, 0]]1.00.625230
17False[[0, 1, 1]]3[1.0, 0.0, 0.0, 0.0][1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125](3, 4, 5)1.01.00.1250.125(I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ...[[0, 1, 1], [0, 1, 1]]0.00.875230
\n", - "
" - ], - "text/plain": [ - " Active Reset Answer Depth Hamming dist. data \\\n", - "0 False [[0]] 1 [1.0, 0.0] \n", - "1 False [[1]] 1 [1.0, 0.0] \n", - "2 False [[0, 1]] 1 [1.0, 0.0, 0.0] \n", - "3 False [[0, 0]] 1 [1.0, 0.0, 0.0] \n", - "4 False [[0, 1, 0]] 1 [0.5, 0.5, 0.0, 0.0] \n", - "5 False [[0, 1, 1]] 1 [0.5, 0.5, 0.0, 0.0] \n", - "6 False [[0]] 2 [1.0, 0.0] \n", - "7 False [[0]] 2 [1.0, 0.0] \n", - "8 False [[0, 0]] 2 [0.5, 0.5, 0.0] \n", - "9 False [[1, 1]] 2 [0.5, 0.5, 0.0] \n", - "10 False [[1, 1, 1]] 2 [0.5, 0.5, 0.0, 0.0] \n", - "11 False [[1, 1, 0]] 2 [0.5, 0.5, 0.0, 0.0] \n", - "12 False [[0]] 3 [1.0, 0.0] \n", - "13 False [[1]] 3 [1.0, 0.0] \n", - "14 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", - "15 False [[0, 0]] 3 [1.0, 0.0, 0.0] \n", - "16 False [[1, 0, 0]] 3 [0.0, 1.0, 0.0, 0.0] \n", - "17 False [[0, 1, 1]] 3 [1.0, 0.0, 0.0, 0.0] \n", - "\n", - " Hamming dist. ideal Hamming dist. rand Lattice \\\n", - "0 [1.0, 0.0] [0.5, 0.5] (3) \n", - "1 [1.0, 0.0] [0.5, 0.5] (6) \n", - "2 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (6, 7) \n", - "3 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (7, 8) \n", - "4 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", - "5 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", - "6 [1.0, 0.0] [0.5, 0.5] (1) \n", - "7 [1.0, 0.0] [0.5, 0.5] (5) \n", - "8 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", - "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (2, 5) \n", - "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (0, 1, 3) \n", - "11 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (0, 3, 4) \n", - "12 [1.0, 0.0] [0.5, 0.5] (8) \n", - "13 [1.0, 0.0] [0.5, 0.5] (2) \n", - "14 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 5) \n", - "15 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] (4, 7) \n", - "16 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (1, 3, 4) \n", - "17 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] (3, 4, 5) \n", - "\n", - " Pr. success data Pr. success loge data Pr. success loge rand \\\n", - "0 1.0 1.0 0.500 \n", - "1 1.0 1.0 0.500 \n", - "2 1.0 1.0 0.250 \n", - "3 1.0 1.0 0.250 \n", - "4 0.5 0.5 0.125 \n", - "5 0.5 0.5 0.125 \n", - "6 1.0 1.0 0.500 \n", - "7 1.0 1.0 0.500 \n", - "8 0.5 0.5 0.250 \n", - "9 0.5 0.5 0.250 \n", - "10 0.5 0.5 0.125 \n", - "11 0.5 0.5 0.125 \n", - "12 1.0 1.0 0.500 \n", - "13 1.0 1.0 0.500 \n", - "14 1.0 1.0 0.250 \n", - "15 1.0 1.0 0.250 \n", - "16 0.0 0.0 0.125 \n", - "17 1.0 1.0 0.125 \n", - "\n", - " Pr. success rand Program \\\n", - "0 0.500 (I 3, I 3) \n", - "1 0.500 (I 6, X 6) \n", - "2 0.250 (I 6, I 7, I 6, X 7, I 6, I 7) \n", - "3 0.250 (I 7, I 8, I 7, I 8, I 7, I 8) \n", - "4 0.125 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, CNOT ... \n", - "5 0.125 (I 1, I 3, I 4, I 1, X 3, X 4, I 1, I 4, I 3, ... \n", - "6 0.500 (I 1, I 1, I 1) \n", - "7 0.500 (I 5, X 5, X 5) \n", - "8 0.250 (I 4, I 7, I 4, X 7, CNOT 4 7, I 4, X 7, I 4, ... \n", - "9 0.250 (I 2, I 5, I 2, X 5, CNOT 2 5, X 2, I 5, I 2, ... \n", - "10 0.125 (I 0, I 1, I 3, I 0, I 1, X 3, CNOT 0 3, CNOT ... \n", - "11 0.125 (I 0, I 3, I 4, I 0, I 3, X 4, CNOT 0 3, CNOT ... \n", - "12 0.500 (I 8, X 8, I 8, X 8) \n", - "13 0.500 (I 2, X 2, I 2, I 2) \n", - "14 0.250 (I 4, I 5, I 4, I 5, I 4, I 5, I 4, X 5, CNOT ... \n", - "15 0.250 (I 4, I 7, I 4, I 7, CNOT 4 7, X 4, I 7, I 4, ... \n", - "16 0.125 (I 1, I 3, I 4, I 1, X 3, I 4, I 1, I 4, CNOT ... \n", - "17 0.125 (I 3, I 4, I 5, I 3, I 4, X 5, CNOT 3 4, I 4, ... \n", - "\n", - " Samples TVD(data, ideal) TVD(data, rand) Trials Width \\\n", - "0 [[0], [0]] 0.0 0.500 2 1 \n", - "1 [[1], [1]] 0.0 0.500 2 1 \n", - "2 [[0, 1], [0, 1]] 0.0 0.750 2 2 \n", - "3 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "4 [[0, 0, 0], [0, 1, 0]] 0.5 0.500 2 3 \n", - "5 [[0, 1, 0], [0, 1, 1]] 0.5 0.500 2 3 \n", - "6 [[0], [0]] 0.0 0.500 2 1 \n", - "7 [[0], [0]] 0.0 0.500 2 1 \n", - "8 [[0, 1], [0, 0]] 0.5 0.250 2 2 \n", - "9 [[0, 1], [1, 1]] 0.5 0.250 2 2 \n", - "10 [[1, 1, 1], [0, 1, 1]] 0.5 0.500 2 3 \n", - "11 [[1, 1, 0], [1, 0, 0]] 0.5 0.500 2 3 \n", - "12 [[0], [0]] 0.0 0.500 2 1 \n", - "13 [[1], [1]] 0.0 0.500 2 1 \n", - "14 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "15 [[0, 0], [0, 0]] 0.0 0.750 2 2 \n", - "16 [[0, 0, 0], [0, 0, 0]] 1.0 0.625 2 3 \n", - "17 [[0, 1, 1], [0, 1, 1]] 0.0 0.875 2 3 \n", - "\n", - " loge = basement[log_2(Width)-1] \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 \n", - "12 0 \n", - "13 0 \n", - "14 0 \n", - "15 0 \n", - "16 0 \n", - "17 0 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimate_random_classical_circuit_errors(qc_perfect,daty)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot the distribution of sublattice widths" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9, 12, 22, 36, 49, 48, 32, 9, 1]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = qc_perfect.qubit_topology()\n", - "len(qc_perfect.qubit_topology())\n", - "# distribution of graph lengths\n", - "disty = []\n", - "for gdx in range(1,len(G.nodes)+1):\n", - " listg = generate_connected_subgraphs(G,gdx)\n", - " disty.append(len(listg))\n", - "\n", - "cir_wid = list(range(1,len(G.nodes)+1))\n", - "plt.bar(cir_wid, disty, width=0.61, align='center')\n", - "plt.xticks(cir_wid)\n", - "plt.xlabel('sublattice / circuit width')\n", - "plt.ylabel('Frequency of Occurence')\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.title('Distribution of sublattice widths')\n", - "disty" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Acquire data in Z basis" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# # with these parameters the cell below takes about 1 hour 40 minutes\n", - "# num_shots_per_circuit = 400\n", - "# num_rand_subgraphs = 16\n", - "# circuit_depth = 18\n", - "# circuit_width = 15 #max = len(G.nodes)\n", - "# x_basis = False\n", - "# active_reset = True\n", - "# total == 6077" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# with these parameters the cell below takes about 5 minutes\n", - "num_shots_per_circuit = 1000\n", - "num_rand_subgraphs = 20\n", - "circuit_depth = 6\n", - "circuit_width = 4 #max = len(G.nodes)\n", - "x_basis = False\n", - "active_reset = False" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetDepthIn X basisLatticeProgramTrialsWidth
0False1False(13)(I 13)10001
1False1False(1)(I 1, X 1)10001
2False1False(7)(I 7)10001
3False1False(7)(I 7, X 7)10001
4False1False(2)(I 2, X 2)10001
5False1False(10)(I 10, X 10)10001
6False1False(7)(I 7)10001
7False1False(4)(I 4)10001
8False1False(13)(I 13)10001
9False1False(11)(I 11)10001
10False1False(10)(I 10, X 10)10001
11False1False(14)(I 14)10001
12False1False(11)(I 11)10001
13False1False(2)(I 2)10001
14False1False(12)(I 12)10001
15False1False(10)(I 10)10001
16False1False(2)(I 2, X 2)10001
17False1False(16)(I 16)10001
18False1False(15)(I 15, X 15)10001
19False1False(11)(I 11, X 11)10001
20False1False(13, 14)(I 13, I 14, X 13)10002
21False1False(17, 10)(I 17, I 10, X 17)10002
22False1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)10002
23False1False(16, 17)(I 16, I 17, X 16)10002
24False1False(1, 2)(I 1, I 2, CNOT 1 2)10002
25False1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)10002
26False1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)10002
27False1False(17, 10)(I 17, I 10, CNOT 17 10)10002
28False1False(16, 15)(I 16, I 15, X 16)10002
29False1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)10002
........................
450False6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...10003
451False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...10003
452False6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...10003
453False6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...10003
454False6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...10003
455False6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...10003
456False6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...10003
457False6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...10003
458False6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...10003
459False6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...10003
460False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...10004
461False6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...10004
462False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...10004
463False6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...10004
464False6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...10004
465False6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...10004
466False6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...10004
467False6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...10004
468False6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...10004
469False6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...10004
470False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...10004
471False6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...10004
472False6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...10004
473False6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...10004
474False6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...10004
475False6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...10004
476False6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...10004
477False6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...10004
478False6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...10004
479False6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...10004
\n", - "

480 rows × 7 columns

\n", - "
" - ], - "text/plain": [ - " Active Reset Depth In X basis Lattice \\\n", - "0 False 1 False (13) \n", - "1 False 1 False (1) \n", - "2 False 1 False (7) \n", - "3 False 1 False (7) \n", - "4 False 1 False (2) \n", - "5 False 1 False (10) \n", - "6 False 1 False (7) \n", - "7 False 1 False (4) \n", - "8 False 1 False (13) \n", - "9 False 1 False (11) \n", - "10 False 1 False (10) \n", - "11 False 1 False (14) \n", - "12 False 1 False (11) \n", - "13 False 1 False (2) \n", - "14 False 1 False (12) \n", - "15 False 1 False (10) \n", - "16 False 1 False (2) \n", - "17 False 1 False (16) \n", - "18 False 1 False (15) \n", - "19 False 1 False (11) \n", - "20 False 1 False (13, 14) \n", - "21 False 1 False (17, 10) \n", - "22 False 1 False (4, 5) \n", - "23 False 1 False (16, 17) \n", - "24 False 1 False (1, 2) \n", - "25 False 1 False (3, 4) \n", - "26 False 1 False (0, 7) \n", - "27 False 1 False (17, 10) \n", - "28 False 1 False (16, 15) \n", - "29 False 1 False (17, 10) \n", - ".. ... ... ... ... \n", - "450 False 6 False (17, 10, 11) \n", - "451 False 6 False (4, 5, 6) \n", - "452 False 6 False (16, 14, 15) \n", - "453 False 6 False (13, 14, 15) \n", - "454 False 6 False (16, 14, 15) \n", - "455 False 6 False (16, 14, 15) \n", - "456 False 6 False (4, 5, 6) \n", - "457 False 6 False (0, 1, 2) \n", - "458 False 6 False (0, 6, 7) \n", - "459 False 6 False (16, 2, 15) \n", - "460 False 6 False (0, 1, 2, 15) \n", - "461 False 6 False (4, 5, 6, 7) \n", - "462 False 6 False (16, 1, 14, 15) \n", - "463 False 6 False (16, 1, 10, 17) \n", - "464 False 6 False (2, 3, 4, 15) \n", - "465 False 6 False (16, 1, 14, 15) \n", - "466 False 6 False (2, 13, 14, 15) \n", - "467 False 6 False (11, 12, 13, 14) \n", - "468 False 6 False (16, 17, 2, 15) \n", - "469 False 6 False (0, 1, 6, 7) \n", - "470 False 6 False (10, 11, 12, 13) \n", - "471 False 6 False (0, 1, 16, 15) \n", - "472 False 6 False (10, 11, 12, 13) \n", - "473 False 6 False (0, 1, 2, 15) \n", - "474 False 6 False (16, 1, 2, 3) \n", - "475 False 6 False (17, 10, 11, 12) \n", - "476 False 6 False (16, 17, 14, 15) \n", - "477 False 6 False (16, 17, 10, 15) \n", - "478 False 6 False (16, 13, 14, 15) \n", - "479 False 6 False (2, 3, 4, 5) \n", - "\n", - " Program Trials Width \n", - "0 (I 13) 1000 1 \n", - "1 (I 1, X 1) 1000 1 \n", - "2 (I 7) 1000 1 \n", - "3 (I 7, X 7) 1000 1 \n", - "4 (I 2, X 2) 1000 1 \n", - "5 (I 10, X 10) 1000 1 \n", - "6 (I 7) 1000 1 \n", - "7 (I 4) 1000 1 \n", - "8 (I 13) 1000 1 \n", - "9 (I 11) 1000 1 \n", - "10 (I 10, X 10) 1000 1 \n", - "11 (I 14) 1000 1 \n", - "12 (I 11) 1000 1 \n", - "13 (I 2) 1000 1 \n", - "14 (I 12) 1000 1 \n", - "15 (I 10) 1000 1 \n", - "16 (I 2, X 2) 1000 1 \n", - "17 (I 16) 1000 1 \n", - "18 (I 15, X 15) 1000 1 \n", - "19 (I 11, X 11) 1000 1 \n", - "20 (I 13, I 14, X 13) 1000 2 \n", - "21 (I 17, I 10, X 17) 1000 2 \n", - "22 (I 4, I 5, X 4, X 5, CNOT 4 5) 1000 2 \n", - "23 (I 16, I 17, X 16) 1000 2 \n", - "24 (I 1, I 2, CNOT 1 2) 1000 2 \n", - "25 (I 3, I 4, X 3, CNOT 3 4) 1000 2 \n", - "26 (I 0, I 7, X 7, CNOT 0 7) 1000 2 \n", - "27 (I 17, I 10, CNOT 17 10) 1000 2 \n", - "28 (I 16, I 15, X 16) 1000 2 \n", - "29 (I 17, I 10, X 10, CNOT 17 10) 1000 2 \n", - ".. ... ... ... \n", - "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... 1000 3 \n", - "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... 1000 3 \n", - "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... 1000 3 \n", - "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... 1000 3 \n", - "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... 1000 3 \n", - "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... 1000 3 \n", - "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... 1000 3 \n", - "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... 1000 3 \n", - "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... 1000 3 \n", - "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... 1000 3 \n", - "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... 1000 4 \n", - "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... 1000 4 \n", - "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... 1000 4 \n", - "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... 1000 4 \n", - "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... 1000 4 \n", - "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... 1000 4 \n", - "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... 1000 4 \n", - "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... 1000 4 \n", - "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... 1000 4 \n", - "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... 1000 4 \n", - "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... 1000 4 \n", - "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... 1000 4 \n", - "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... 1000 4 \n", - "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... 1000 4 \n", - "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... 1000 4 \n", - "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... 1000 4 \n", - "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... 1000 4 \n", - "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... 1000 4 \n", - "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... 1000 4 \n", - "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... 1000 4 \n", - "\n", - "[480 rows x 7 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "exp =generate_rand_cir_for_rand_lattices_experiments(qc_noisy, \n", - " circuit_depth, \n", - " circuit_width,\n", - " num_rand_subgraphs, \n", - " num_shots_per_circuit, \n", - " in_x_basis=x_basis, \n", - " use_active_reset=active_reset)\n", - "exp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Collect data." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "257.87861728668213\n" - ] - } - ], - "source": [ - "t0 = time.time()\n", - "data_zbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp)\n", - "t1 = time.time()\n", - "total = t1-t0\n", - "print(total)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetAnswerDepthIn X basisLatticeProgramSamplesTrialsWidth
0False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
1False[[1]]1False(1)(I 1, X 1)[[1], [1], [1], [1], [1], [1], [1], [0], [0], ...10001
2False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
3False[[1]]1False(7)(I 7, X 7)[[1], [1], [0], [1], [1], [1], [0], [1], [1], ...10001
4False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
5False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [1], [1], [1], ...10001
6False[[0]]1False(7)(I 7)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
7False[[0]]1False(4)(I 4)[[0], [0], [0], [1], [0], [0], [0], [0], [0], ...10001
8False[[0]]1False(13)(I 13)[[0], [0], [0], [0], [1], [0], [0], [0], [0], ...10001
9False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
10False[[1]]1False(10)(I 10, X 10)[[0], [1], [1], [1], [1], [1], [0], [1], [1], ...10001
11False[[0]]1False(14)(I 14)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
12False[[0]]1False(11)(I 11)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
13False[[0]]1False(2)(I 2)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
14False[[0]]1False(12)(I 12)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
15False[[0]]1False(10)(I 10)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
16False[[1]]1False(2)(I 2, X 2)[[1], [1], [1], [1], [1], [1], [0], [0], [1], ...10001
17False[[0]]1False(16)(I 16)[[0], [0], [0], [0], [0], [0], [0], [0], [0], ...10001
18False[[1]]1False(15)(I 15, X 15)[[1], [1], [1], [1], [0], [1], [1], [1], [1], ...10001
19False[[1]]1False(11)(I 11, X 11)[[1], [1], [0], [1], [1], [1], [1], [1], [1], ...10001
20False[[1, 0]]1False(13, 14)(I 13, I 14, X 13)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
21False[[1, 0]]1False(17, 10)(I 17, I 10, X 17)[[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0...10002
22False[[1, 0]]1False(4, 5)(I 4, I 5, X 4, X 5, CNOT 4 5)[[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0...10002
23False[[1, 0]]1False(16, 17)(I 16, I 17, X 16)[[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0...10002
24False[[0, 0]]1False(1, 2)(I 1, I 2, CNOT 1 2)[[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0...10002
25False[[1, 1]]1False(3, 4)(I 3, I 4, X 3, CNOT 3 4)[[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0...10002
26False[[0, 1]]1False(0, 7)(I 0, I 7, X 7, CNOT 0 7)[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
27False[[0, 0]]1False(17, 10)(I 17, I 10, CNOT 17 10)[[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0...10002
28False[[1, 0]]1False(16, 15)(I 16, I 15, X 16)[[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0...10002
29False[[0, 1]]1False(17, 10)(I 17, I 10, X 10, CNOT 17 10)[[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1...10002
..............................
450False[[0, 0, 0]]6False(17, 10, 11)(I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1...[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [...10003
451False[[1, 1, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ...[[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [...10003
452False[[0, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
453False[[0, 1, 0]]6False(13, 14, 15)(I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1...[[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [...10003
454False[[1, 0, 1]]6False(16, 14, 15)(I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1...[[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
455False[[1, 0, 0]]6False(16, 14, 15)(I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1...[[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [...10003
456False[[0, 0, 1]]6False(4, 5, 6)(I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ...[[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [...10003
457False[[0, 1, 1]]6False(0, 1, 2)(I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ...[[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [...10003
458False[[1, 0, 1]]6False(0, 6, 7)(I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ...[[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [...10003
459False[[0, 0, 1]]6False(16, 2, 15)(I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,...[[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [...10003
460False[[1, 0, 1, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,...[[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,...10004
461False[[0, 1, 1, 1]]6False(4, 5, 6, 7)(I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ...[[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,...10004
462False[[1, 0, 0, 1]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,...[[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,...10004
463False[[1, 0, 0, 0]]6False(16, 1, 10, 17)(I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,...[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,...10004
464False[[1, 1, 1, 1]]6False(2, 3, 4, 15)(I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT...[[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,...10004
465False[[0, 1, 0, 0]]6False(16, 1, 14, 15)(I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ...[[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,...10004
466False[[1, 1, 1, 0]]6False(2, 13, 14, 15)(I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ...[[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,...10004
467False[[0, 0, 1, 0]]6False(11, 12, 13, 14)(I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1...[[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,...10004
468False[[0, 1, 0, 0]]6False(16, 17, 2, 15)(I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ...[[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
469False[[0, 1, 1, 1]]6False(0, 1, 6, 7)(I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ...[[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,...10004
470False[[1, 0, 0, 0]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO...[[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,...10004
471False[[0, 0, 1, 1]]6False(0, 1, 16, 15)(I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ...[[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,...10004
472False[[0, 1, 1, 1]]6False(10, 11, 12, 13)(I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO...[[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,...10004
473False[[1, 1, 0, 1]]6False(0, 1, 2, 15)(I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1...[[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,...10004
474False[[0, 0, 0, 0]]6False(16, 1, 2, 3)(I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
475False[[0, 0, 1, 1]]6False(17, 10, 11, 12)(I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO...[[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,...10004
476False[[1, 0, 0, 1]]6False(16, 17, 14, 15)(I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO...[[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,...10004
477False[[0, 0, 0, 0]]6False(16, 17, 10, 15)(I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1...[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,...10004
478False[[1, 1, 1, 0]]6False(16, 13, 14, 15)(I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1...[[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,...10004
479False[[0, 0, 0, 1]]6False(2, 3, 4, 5)(I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ...[[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,...10004
\n", - "

480 rows × 9 columns

\n", - "
" - ], - "text/plain": [ - " Active Reset Answer Depth In X basis Lattice \\\n", - "0 False [[0]] 1 False (13) \n", - "1 False [[1]] 1 False (1) \n", - "2 False [[0]] 1 False (7) \n", - "3 False [[1]] 1 False (7) \n", - "4 False [[1]] 1 False (2) \n", - "5 False [[1]] 1 False (10) \n", - "6 False [[0]] 1 False (7) \n", - "7 False [[0]] 1 False (4) \n", - "8 False [[0]] 1 False (13) \n", - "9 False [[0]] 1 False (11) \n", - "10 False [[1]] 1 False (10) \n", - "11 False [[0]] 1 False (14) \n", - "12 False [[0]] 1 False (11) \n", - "13 False [[0]] 1 False (2) \n", - "14 False [[0]] 1 False (12) \n", - "15 False [[0]] 1 False (10) \n", - "16 False [[1]] 1 False (2) \n", - "17 False [[0]] 1 False (16) \n", - "18 False [[1]] 1 False (15) \n", - "19 False [[1]] 1 False (11) \n", - "20 False [[1, 0]] 1 False (13, 14) \n", - "21 False [[1, 0]] 1 False (17, 10) \n", - "22 False [[1, 0]] 1 False (4, 5) \n", - "23 False [[1, 0]] 1 False (16, 17) \n", - "24 False [[0, 0]] 1 False (1, 2) \n", - "25 False [[1, 1]] 1 False (3, 4) \n", - "26 False [[0, 1]] 1 False (0, 7) \n", - "27 False [[0, 0]] 1 False (17, 10) \n", - "28 False [[1, 0]] 1 False (16, 15) \n", - "29 False [[0, 1]] 1 False (17, 10) \n", - ".. ... ... ... ... ... \n", - "450 False [[0, 0, 0]] 6 False (17, 10, 11) \n", - "451 False [[1, 1, 1]] 6 False (4, 5, 6) \n", - "452 False [[0, 0, 1]] 6 False (16, 14, 15) \n", - "453 False [[0, 1, 0]] 6 False (13, 14, 15) \n", - "454 False [[1, 0, 1]] 6 False (16, 14, 15) \n", - "455 False [[1, 0, 0]] 6 False (16, 14, 15) \n", - "456 False [[0, 0, 1]] 6 False (4, 5, 6) \n", - "457 False [[0, 1, 1]] 6 False (0, 1, 2) \n", - "458 False [[1, 0, 1]] 6 False (0, 6, 7) \n", - "459 False [[0, 0, 1]] 6 False (16, 2, 15) \n", - "460 False [[1, 0, 1, 1]] 6 False (0, 1, 2, 15) \n", - "461 False [[0, 1, 1, 1]] 6 False (4, 5, 6, 7) \n", - "462 False [[1, 0, 0, 1]] 6 False (16, 1, 14, 15) \n", - "463 False [[1, 0, 0, 0]] 6 False (16, 1, 10, 17) \n", - "464 False [[1, 1, 1, 1]] 6 False (2, 3, 4, 15) \n", - "465 False [[0, 1, 0, 0]] 6 False (16, 1, 14, 15) \n", - "466 False [[1, 1, 1, 0]] 6 False (2, 13, 14, 15) \n", - "467 False [[0, 0, 1, 0]] 6 False (11, 12, 13, 14) \n", - "468 False [[0, 1, 0, 0]] 6 False (16, 17, 2, 15) \n", - "469 False [[0, 1, 1, 1]] 6 False (0, 1, 6, 7) \n", - "470 False [[1, 0, 0, 0]] 6 False (10, 11, 12, 13) \n", - "471 False [[0, 0, 1, 1]] 6 False (0, 1, 16, 15) \n", - "472 False [[0, 1, 1, 1]] 6 False (10, 11, 12, 13) \n", - "473 False [[1, 1, 0, 1]] 6 False (0, 1, 2, 15) \n", - "474 False [[0, 0, 0, 0]] 6 False (16, 1, 2, 3) \n", - "475 False [[0, 0, 1, 1]] 6 False (17, 10, 11, 12) \n", - "476 False [[1, 0, 0, 1]] 6 False (16, 17, 14, 15) \n", - "477 False [[0, 0, 0, 0]] 6 False (16, 17, 10, 15) \n", - "478 False [[1, 1, 1, 0]] 6 False (16, 13, 14, 15) \n", - "479 False [[0, 0, 0, 1]] 6 False (2, 3, 4, 5) \n", - "\n", - " Program \\\n", - "0 (I 13) \n", - "1 (I 1, X 1) \n", - "2 (I 7) \n", - "3 (I 7, X 7) \n", - "4 (I 2, X 2) \n", - "5 (I 10, X 10) \n", - "6 (I 7) \n", - "7 (I 4) \n", - "8 (I 13) \n", - "9 (I 11) \n", - "10 (I 10, X 10) \n", - "11 (I 14) \n", - "12 (I 11) \n", - "13 (I 2) \n", - "14 (I 12) \n", - "15 (I 10) \n", - "16 (I 2, X 2) \n", - "17 (I 16) \n", - "18 (I 15, X 15) \n", - "19 (I 11, X 11) \n", - "20 (I 13, I 14, X 13) \n", - "21 (I 17, I 10, X 17) \n", - "22 (I 4, I 5, X 4, X 5, CNOT 4 5) \n", - "23 (I 16, I 17, X 16) \n", - "24 (I 1, I 2, CNOT 1 2) \n", - "25 (I 3, I 4, X 3, CNOT 3 4) \n", - "26 (I 0, I 7, X 7, CNOT 0 7) \n", - "27 (I 17, I 10, CNOT 17 10) \n", - "28 (I 16, I 15, X 16) \n", - "29 (I 17, I 10, X 10, CNOT 17 10) \n", - ".. ... \n", - "450 (I 17, I 10, I 11, X 10, X 11, X 17, CNOT 10 1... \n", - "451 (I 4, I 5, I 6, X 4, X 5, CNOT 5 6, X 6, CNOT ... \n", - "452 (I 16, I 14, I 15, X 15, CNOT 14 15, X 14, X 1... \n", - "453 (I 13, I 14, I 15, X 13, X 15, CNOT 13 14, X 1... \n", - "454 (I 16, I 14, I 15, CNOT 16 15, CNOT 14 15, X 1... \n", - "455 (I 16, I 14, I 15, X 16, X 14, X 15, CNOT 16 1... \n", - "456 (I 4, I 5, I 6, X 4, X 5, CNOT 4 5, X 4, X 6, ... \n", - "457 (I 0, I 1, I 2, X 1, CNOT 0 1, X 1, X 2, CNOT ... \n", - "458 (I 0, I 6, I 7, X 6, X 7, CNOT 6 7, X 0, X 6, ... \n", - "459 (I 16, I 2, I 15, X 16, X 15, CNOT 16 15, X 2,... \n", - "460 (I 0, I 1, I 2, I 15, X 0, X 1, X 2, CNOT 1 2,... \n", - "461 (I 4, I 5, I 6, I 7, X 4, X 6, CNOT 4 5, CNOT ... \n", - "462 (I 16, I 1, I 14, I 15, X 16, X 15, CNOT 16 1,... \n", - "463 (I 16, I 1, I 10, I 17, X 16, X 1, X 17, X 16,... \n", - "464 (I 2, I 3, I 4, I 15, X 2, X 4, CNOT 2 3, CNOT... \n", - "465 (I 16, I 1, I 14, I 15, X 16, X 1, X 15, CNOT ... \n", - "466 (I 2, I 13, I 14, I 15, X 2, X 14, X 15, CNOT ... \n", - "467 (I 11, I 12, I 13, I 14, X 11, X 12, X 13, X 1... \n", - "468 (I 16, I 17, I 2, I 15, X 16, X 17, X 2, CNOT ... \n", - "469 (I 0, I 1, I 6, I 7, X 0, X 1, CNOT 0 1, CNOT ... \n", - "470 (I 10, I 11, I 12, I 13, X 11, CNOT 11 12, CNO... \n", - "471 (I 0, I 1, I 16, I 15, X 0, CNOT 0 1, CNOT 16 ... \n", - "472 (I 10, I 11, I 12, I 13, X 12, CNOT 10 11, CNO... \n", - "473 (I 0, I 1, I 2, I 15, X 1, X 2, X 15, CNOT 0 1... \n", - "474 (I 16, I 1, I 2, I 3, X 3, CNOT 16 1, CNOT 1 2... \n", - "475 (I 17, I 10, I 11, I 12, X 17, X 10, X 11, CNO... \n", - "476 (I 16, I 17, I 14, I 15, X 16, X 17, X 15, CNO... \n", - "477 (I 16, I 17, I 10, I 15, X 16, X 15, CNOT 17 1... \n", - "478 (I 16, I 13, I 14, I 15, X 13, X 14, CNOT 16 1... \n", - "479 (I 2, I 3, I 4, I 5, X 3, X 5, CNOT 3 4, CNOT ... \n", - "\n", - " Samples Trials Width \n", - "0 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", - "1 [[1], [1], [1], [1], [1], [1], [1], [0], [0], ... 1000 1 \n", - "2 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "3 [[1], [1], [0], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", - "4 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", - "5 [[0], [1], [1], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", - "6 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "7 [[0], [0], [0], [1], [0], [0], [0], [0], [0], ... 1000 1 \n", - "8 [[0], [0], [0], [0], [1], [0], [0], [0], [0], ... 1000 1 \n", - "9 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "10 [[0], [1], [1], [1], [1], [1], [0], [1], [1], ... 1000 1 \n", - "11 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "12 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "13 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "14 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "15 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "16 [[1], [1], [1], [1], [1], [1], [0], [0], [1], ... 1000 1 \n", - "17 [[0], [0], [0], [0], [0], [0], [0], [0], [0], ... 1000 1 \n", - "18 [[1], [1], [1], [1], [0], [1], [1], [1], [1], ... 1000 1 \n", - "19 [[1], [1], [0], [1], [1], [1], [1], [1], [1], ... 1000 1 \n", - "20 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "21 [[1, 0], [0, 0], [1, 0], [1, 0], [1, 1], [1, 0... 1000 2 \n", - "22 [[1, 0], [0, 0], [0, 0], [1, 0], [0, 0], [1, 0... 1000 2 \n", - "23 [[1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "24 [[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [0, 0... 1000 2 \n", - "25 [[1, 0], [1, 0], [1, 1], [1, 1], [1, 1], [0, 0... 1000 2 \n", - "26 [[0, 1], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", - "27 [[0, 0], [0, 0], [1, 0], [0, 0], [0, 0], [0, 0... 1000 2 \n", - "28 [[1, 0], [1, 0], [0, 0], [1, 0], [1, 0], [1, 0... 1000 2 \n", - "29 [[1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [0, 1... 1000 2 \n", - ".. ... ... ... \n", - "450 [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [... 1000 3 \n", - "451 [[1, 1, 0], [1, 1, 1], [0, 1, 0], [1, 1, 1], [... 1000 3 \n", - "452 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", - "453 [[1, 1, 0], [0, 1, 0], [0, 1, 0], [0, 1, 0], [... 1000 3 \n", - "454 [[1, 1, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", - "455 [[1, 1, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [... 1000 3 \n", - "456 [[0, 0, 0], [1, 0, 1], [0, 0, 0], [0, 0, 1], [... 1000 3 \n", - "457 [[0, 1, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [... 1000 3 \n", - "458 [[1, 0, 1], [1, 0, 1], [1, 0, 1], [1, 0, 1], [... 1000 3 \n", - "459 [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [... 1000 3 \n", - "460 [[1, 0, 0, 0], [1, 0, 1, 1], [0, 1, 1, 1], [1,... 1000 4 \n", - "461 [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0,... 1000 4 \n", - "462 [[1, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1,... 1000 4 \n", - "463 [[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1,... 1000 4 \n", - "464 [[1, 1, 0, 0], [0, 1, 1, 1], [1, 1, 0, 0], [1,... 1000 4 \n", - "465 [[0, 1, 0, 0], [0, 1, 1, 0], [0, 0, 0, 1], [0,... 1000 4 \n", - "466 [[1, 1, 0, 0], [1, 1, 1, 0], [1, 1, 1, 0], [1,... 1000 4 \n", - "467 [[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 1, 0], [0,... 1000 4 \n", - "468 [[0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", - "469 [[0, 1, 1, 1], [0, 1, 0, 0], [0, 1, 1, 1], [0,... 1000 4 \n", - "470 [[1, 0, 1, 0], [1, 0, 0, 0], [0, 1, 1, 0], [1,... 1000 4 \n", - "471 [[1, 1, 1, 0], [0, 1, 0, 1], [0, 1, 0, 0], [0,... 1000 4 \n", - "472 [[0, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0,... 1000 4 \n", - "473 [[1, 1, 1, 0], [1, 0, 1, 1], [0, 1, 1, 0], [1,... 1000 4 \n", - "474 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", - "475 [[0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 1, 1], [0,... 1000 4 \n", - "476 [[1, 0, 1, 1], [1, 0, 1, 1], [1, 0, 1, 1], [1,... 1000 4 \n", - "477 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,... 1000 4 \n", - "478 [[1, 1, 1, 0], [1, 0, 0, 0], [1, 1, 1, 0], [1,... 1000 4 \n", - "479 [[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0,... 1000 4 \n", - "\n", - "[480 rows x 9 columns]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_zbasis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#data_zbasis.to_pickle(\"data_z_Aspen-1-16Q-A_2019_02_16.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data_zbasis = pd.read_pickle('data_z_Aspen-1-16Q-A_2019_02_16.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# circuit_width = df['Width'].max()\n", - "# circuit_depth = df['Depth'].max()\n", - "# for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", - "# print(depth,subgraph_size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfz = pd.DataFrame(data_zbasis)\n", - "dfz.to_pickle(\"data_z_Aspen_1_15Q_A_2019_02_09.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_pickle('data_z_Aspen_1_15Q_A_2019_02_09.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Acquire data in X basis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp_xbasis = exp.copy()\n", - "exp_xbasis['In X basis']=True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t0x = time.time()\n", - "data_xbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp_xbasis)\n", - "t1x = time.time()\n", - "totalx = t1x-t0x\n", - "print(totalx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfx = pd.DataFrame(data_xbasis)\n", - "dfx.to_pickle(\"data_x_Aspen_1_15Q_A_2019_02_09.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now put the data into a dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#data_xbasis.to_pickle(\"data_x_Aspen-1-16Q-A_2019_02_16.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "#data_xbasis = pd.read_pickle('data_x_Aspen-1-16Q-A_2019_02_16.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data processing and estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "res_df = estimate_random_classical_circuit_errors(data_zbasis)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "circuit_width = res_df['Width'].max()\n", - "\n", - "for subgraph_size in range(1, circuit_width+1):\n", - " wdx = data_zbasis['Width']==subgraph_size\n", - " res_df[wdx]\n", - " \n", - " df.append(df2, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "circuit_width = res_df['Width'].max()\n", - "circuit_depth = res_df['Depth'].max()\n", - "results = []\n", - "for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", - " wdx = data_zbasis['Width']==subgraph_size\n", - " ddx = data_zbasis['Depth']==depth\n", - " ndf= res_df[wdx&ddx].copy()\n", - " results.append({'Depth': depth,\n", - " 'Width': subgraph_size,\n", - " 'In X basis': ndf['In X basis'].iloc[0],\n", - " 'Active Reset': ndf['Active Reset'].iloc[0],\n", - " 'Trials': ndf['Trials'].iloc[0],\n", - " 'Hamming dist. data': ndf['Hamming dist. data'].mean(),\n", - " 'Hamming dist. rand': ndf['Hamming dist. rand'].mean(),\n", - " 'Hamming dist. ideal': ndf['Hamming dist. ideal'].mean(),\n", - " 'TVD(data, ideal)': ndf['TVD(data, ideal)'].mean(),\n", - " 'TVD(data, rand)': ndf['TVD(data, rand)'].mean(),\n", - " 'Pr. success data': ndf['Pr. success data'].mean(),\n", - " 'Pr. success rand': ndf['Pr. success rand'].mean(),\n", - " 'loge = basement[log_2(Width)-1]': ndf['loge = basement[log_2(Width)-1]'].mean(),\n", - " 'Pr. success loge data': ndf['Pr. success loge data'].mean(),\n", - " 'Pr. success loge rand': ndf['Pr. success loge rand'].mean(),\n", - " }) \n", - "munged = pd.DataFrame(results)" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Active ResetDepthHamming dist. dataHamming dist. idealHamming dist. randIn X basisPr. success dataPr. success loge dataPr. success loge randPr. success randTVD(data, ideal)TVD(data, rand)TrialsWidthloge = basement[log_2(Width)-1]
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7False2[0.67555, 0.24490000000000003, 0.0602499999999...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.675550.920450.31250.06250.3220000.645700100041.0
8False3[0.9037499999999999, 0.0][1.0, 0.0][0.5, 0.5]False0.903750.903750.50000.50000.0481250.451875100010.0
9False3[0.8446999999999999, 0.14550000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.844700.844700.25000.25000.1504000.599600100020.0
10False3[0.75855, 0.20669999999999997, 0.0327500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.758550.758550.12500.12500.2404500.634550100030.0
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14False4[0.76205, 0.19485000000000002, 0.0389500000000...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.762050.762050.12500.12500.2358750.639125100030.0
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16False5[0.9231000000000001, 0.0][1.0, 0.0][0.5, 0.5]False0.923100.923100.50000.50000.0384500.461550100010.0
17False5[0.85725, 0.13285000000000002, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.857250.857250.25000.25000.1378000.612200100020.0
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21False6[0.8439, 0.14684999999999998, 0.0][1.0, 0.0, 0.0][0.25, 0.5, 0.25]False0.843900.843900.25000.25000.1514750.598525100020.0
22False6[0.7076000000000001, 0.23464999999999997, 0.05...[1.0, 0.0, 0.0, 0.0][0.125, 0.375, 0.375, 0.125]False0.707600.707600.12500.12500.2908500.592950100030.0
23False6[0.54185, 0.28845, 0.12315000000000001, 0.0422...[1.0, 0.0, 0.0, 0.0, 0.0][0.0625, 0.25, 0.375, 0.25, 0.0625]False0.541850.830300.31250.06250.4560000.537950100041.0
\n", - "
" - ], - "text/plain": [ - " Active Reset Depth Hamming dist. data \\\n", - "0 False 1 [0.9251000000000001, 0.0] \n", - "1 False 1 [0.8674, 0.12184999999999999, 0.0] \n", - "2 False 1 [0.73105, 0.21615, 0.046950000000000006, 0.0] \n", - "3 False 1 [0.7171500000000001, 0.23810000000000003, 0.03... \n", - "4 False 2 [0.9201, 0.0] \n", - "5 False 2 [0.8482000000000003, 0.1441, 0.0] \n", - "6 False 2 [0.7371000000000001, 0.21269999999999997, 0.04... \n", - "7 False 2 [0.67555, 0.24490000000000003, 0.0602499999999... \n", - "8 False 3 [0.9037499999999999, 0.0] \n", - "9 False 3 [0.8446999999999999, 0.14550000000000002, 0.0] \n", - "10 False 3 [0.75855, 0.20669999999999997, 0.0327500000000... \n", - "11 False 3 [0.6030999999999999, 0.2619, 0.103649999999999... \n", - "12 False 4 [0.9255500000000001, 0.0] \n", - "13 False 4 [0.8305999999999999, 0.15719999999999998, 0.0] \n", - "14 False 4 [0.76205, 0.19485000000000002, 0.0389500000000... \n", - "15 False 4 [0.5921999999999998, 0.26195, 0.10720000000000... \n", - "16 False 5 [0.9231000000000001, 0.0] \n", - "17 False 5 [0.85725, 0.13285000000000002, 0.0] \n", - "18 False 5 [0.7151500000000002, 0.23395000000000002, 0.04... \n", - "19 False 5 [0.5072000000000001, 0.29245, 0.14304999999999... \n", - "20 False 6 [0.9045, 0.0] \n", - "21 False 6 [0.8439, 0.14684999999999998, 0.0] \n", - "22 False 6 [0.7076000000000001, 0.23464999999999997, 0.05... \n", - "23 False 6 [0.54185, 0.28845, 0.12315000000000001, 0.0422... \n", - "\n", - " Hamming dist. ideal Hamming dist. rand \\\n", - "0 [1.0, 0.0] [0.5, 0.5] \n", - "1 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "2 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "3 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "4 [1.0, 0.0] [0.5, 0.5] \n", - "5 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "6 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "7 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "8 [1.0, 0.0] [0.5, 0.5] \n", - "9 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "10 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "11 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "12 [1.0, 0.0] [0.5, 0.5] \n", - "13 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "14 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "15 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "16 [1.0, 0.0] [0.5, 0.5] \n", - "17 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "18 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "19 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "20 [1.0, 0.0] [0.5, 0.5] \n", - "21 [1.0, 0.0, 0.0] [0.25, 0.5, 0.25] \n", - "22 [1.0, 0.0, 0.0, 0.0] [0.125, 0.375, 0.375, 0.125] \n", - "23 [1.0, 0.0, 0.0, 0.0, 0.0] [0.0625, 0.25, 0.375, 0.25, 0.0625] \n", - "\n", - " In X basis Pr. success data Pr. success loge data \\\n", - "0 False 0.92510 0.92510 \n", - "1 False 0.86740 0.86740 \n", - "2 False 0.73105 0.73105 \n", - "3 False 0.71715 0.95525 \n", - "4 False 0.92010 0.92010 \n", - "5 False 0.84820 0.84820 \n", - "6 False 0.73710 0.73710 \n", - "7 False 0.67555 0.92045 \n", - "8 False 0.90375 0.90375 \n", - "9 False 0.84470 0.84470 \n", - "10 False 0.75855 0.75855 \n", - "11 False 0.60310 0.86500 \n", - "12 False 0.92555 0.92555 \n", - "13 False 0.83060 0.83060 \n", - "14 False 0.76205 0.76205 \n", - "15 False 0.59220 0.85415 \n", - "16 False 0.92310 0.92310 \n", - "17 False 0.85725 0.85725 \n", - "18 False 0.71515 0.71515 \n", - "19 False 0.50720 0.79965 \n", - "20 False 0.90450 0.90450 \n", - "21 False 0.84390 0.84390 \n", - "22 False 0.70760 0.70760 \n", - "23 False 0.54185 0.83030 \n", - "\n", - " Pr. success loge rand Pr. success rand TVD(data, ideal) \\\n", - "0 0.5000 0.5000 0.037450 \n", - "1 0.2500 0.2500 0.127225 \n", - "2 0.1250 0.1250 0.266025 \n", - "3 0.3125 0.0625 0.282675 \n", - "4 0.5000 0.5000 0.039950 \n", - "5 0.2500 0.2500 0.147950 \n", - "6 0.1250 0.1250 0.259675 \n", - "7 0.3125 0.0625 0.322000 \n", - "8 0.5000 0.5000 0.048125 \n", - "9 0.2500 0.2500 0.150400 \n", - "10 0.1250 0.1250 0.240450 \n", - "11 0.3125 0.0625 0.394700 \n", - "12 0.5000 0.5000 0.037225 \n", - "13 0.2500 0.2500 0.163300 \n", - "14 0.1250 0.1250 0.235875 \n", - "15 0.3125 0.0625 0.405900 \n", - "16 0.5000 0.5000 0.038450 \n", - "17 0.2500 0.2500 0.137800 \n", - "18 0.1250 0.1250 0.283125 \n", - "19 0.3125 0.0625 0.488225 \n", - "20 0.5000 0.5000 0.047750 \n", - "21 0.2500 0.2500 0.151475 \n", - "22 0.1250 0.1250 0.290850 \n", - "23 0.3125 0.0625 0.456000 \n", - "\n", - " TVD(data, rand) Trials Width loge = basement[log_2(Width)-1] \n", - "0 0.462550 1000 1 0.0 \n", - "1 0.622775 1000 2 0.0 \n", - "2 0.608975 1000 3 0.0 \n", - "3 0.675675 1000 4 1.0 \n", - "4 0.460050 1000 1 0.0 \n", - "5 0.602050 1000 2 0.0 \n", - "6 0.615325 1000 3 0.0 \n", - "7 0.645700 1000 4 1.0 \n", - "8 0.451875 1000 1 0.0 \n", - "9 0.599600 1000 2 0.0 \n", - "10 0.634550 1000 3 0.0 \n", - "11 0.581100 1000 4 1.0 \n", - "12 0.462775 1000 1 0.0 \n", - "13 0.586700 1000 2 0.0 \n", - "14 0.639125 1000 3 0.0 \n", - "15 0.565650 1000 4 1.0 \n", - "16 0.461550 1000 1 0.0 \n", - "17 0.612200 1000 2 0.0 \n", - "18 0.592275 1000 3 0.0 \n", - "19 0.505625 1000 4 1.0 \n", - "20 0.452250 1000 1 0.0 \n", - "21 0.598525 1000 2 0.0 \n", - "22 0.592950 1000 3 0.0 \n", - "23 0.537950 1000 4 1.0 " - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "munged" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.54185, 0.28845, 0.12315, 0.04225, 0. ])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_df[wdx&ddx]['Hamming dist. data'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.0625, 0.25 , 0.375 , 0.25 , 0.0625])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_df[wdx&ddx]['Hamming dist. rand'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot a particular depth and width" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "dep = 6\n", - "wid = 4\n", - "\n", - "distz = get_hamming_dist(res_df, dep, wid)\n", - "\n", - "\n", - "# combine data from different subgraphs\n", - "avg_dist = distz['Hamming dist. data'].mean()\n", - "\n", - "# rand data\n", - "rand_dist = distz['Hamming dist. rand'][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_labels = np.arange(0, len(avg_dist))\n", - "plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - "plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", - "plt.xticks(x_labels)\n", - "plt.xlabel('Hamming Weight of Error')\n", - "plt.ylabel('Relative Frequency of Occurence')\n", - "plt.ylim([0,1])\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.legend(['data','random'])\n", - "plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# For a particular width plot all depths" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "wid = 4\n", - "df_fn_depth = get_hamming_dists_fn_depth(res_df, wid)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for dep in range(1, df_fn_depth.Depth.max()+1):\n", - " idx = df_fn_depth['Depth']== dep\n", - " avg_dist = df_fn_depth[idx]['Hamming dist. data'].mean() \n", - " rand_dist = df_fn_depth[idx]['Hamming dist. rand'].mean() \n", - " x_labels = np.arange(0, len(avg_dist))\n", - " plt.subplot(1,df_fn_depth.Depth.max(),dep)\n", - " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", - " plt.xticks(x_labels)\n", - " plt.xlabel('Hamming Weight of Error')\n", - " plt.ylabel('Relative Frequency of Occurence')\n", - " plt.ylim([0,1])\n", - " plt.grid(axis='y', alpha=0.75)\n", - " plt.legend(['data','random'])\n", - " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", - "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data fron the data frame." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "depth_vec = []\n", - "pcheck = []\n", - "pcheck_rand = []\n", - "pcheck_log_errors = []\n", - "pcheck_log_errors_rand = []\n", - "tvd_rand = []\n", - "tvd_ideal = []\n", - "\n", - "for dep in range(1, df_fn_depth.Depth.max()+1):\n", - " idx = df_fn_depth['Depth']== dep\n", - " depth_vec.append(dep)\n", - " pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", - " pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", - " pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", - " pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", - " tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", - " tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Success probablity and success probablity including a small number of errors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we will plot the success probablity of a circuit with a certain width as a function of depth. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", - "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", - "plt.ylim([-0.05,1.05])\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Pr(success)')\n", - "plt.title('Pr(success) vs Depth for Width = {}'.format(wid))\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Sucess if we allow for a small number of errors**\n", - "\n", - "Some near term algorithms have robustness to noise. In light of that we might want to consider answers that are only a little wrong successes.\n", - "\n", - "To make this notion formal we allow a logarithmic number of bits to flip from the correct answer and call all such instances \"success\".\n", - "\n", - "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", - "\n", - "$\\log_2 ({\\rm number\\ of\\ bits}) -1$\n", - "\n", - "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", - "\n", - "\n", - "Supose we have a circuit of width 4, this means correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4)-1 = 1$.\n", - "\n", - "So any string with hamming weight zero or one counts as a success.\n", - "\n", - "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", - "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", - "plt.scatter(depth_vec,pcheck_log_errors,label='Sucess Probablity + log errors')\n", - "plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", - "plt.ylim([-0.05,1.05])\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Pr(success+log errors)')\n", - "plt.title('Pr(success+log errors) vs Depth for Width = {}'.format(wid))\n", - "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Total variation distance from ideal answer and random distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(depth_vec,tvd_ideal,label='TVD(data, ideal)')\n", - "plt.scatter(depth_vec,tvd_rand,label='TVD(data, rand)')\n", - "plt.scatter(depth_vec,1-np.asarray(pcheck),label='1-Sucess Probablity',alpha=0.33,marker='^',s=80)\n", - "#plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", - "plt.ylim([-0.05,1.05])\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Total variation distance')\n", - "plt.title('Width = {}'.format(wid))\n", - "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot depth = width" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "max_idx = min([max(res_df['Depth']),max(res_df['Width'])])\n", - "\n", - "for idx in range(1,max_idx+1):\n", - " distz = get_hamming_dist(res_df, idx, idx)\n", - " # combine data from different subgraphs\n", - " avg_dist = distz['Hamming dist. data'].mean()\n", - " # rand data\n", - " rand_dist = distz['Hamming dist. rand'][0]\n", - " dep = idx\n", - " wid = idx\n", - " x_labels = np.arange(0, len(avg_dist))\n", - " plt.subplot(1,max_idx,idx)\n", - " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", - " plt.xticks(x_labels)\n", - " plt.xlabel('Hamming Weight of Error')\n", - " plt.ylabel('Relative Frequency of Occurence')\n", - " plt.ylim([0,1])\n", - " plt.grid(axis='y', alpha=0.75)\n", - " plt.legend(['data','random'])\n", - " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", - "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot success probablity landscape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just the success probablity as a function of depth and width." - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.9251 , 0.8674 , 0.73105, 0.71715, 0.9201 , 0.8482 , 0.7371 ,\n", - " 0.67555, 0.90375, 0.8447 , 0.75855, 0.6031 , 0.92555, 0.8306 ,\n", - " 0.76205, 0.5922 , 0.9231 , 0.85725, 0.71515, 0.5072 , 0.9045 ,\n", - " 0.8439 , 0.7076 , 0.54185])" - ] - }, - "execution_count": 326, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values = np.asarray([munged['Pr. success data'][idx] for idx in munged.index])\n", - "values" - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", - " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625,\n", - " 0.5 , 0.25 , 0.125 , 0.0625, 0.5 , 0.25 , 0.125 , 0.0625])" - ] - }, - "execution_count": 327, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "values_rand = np.asarray([munged['Pr. success rand'][idx] for idx in munged.index])\n", - "values_rand" - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(min(res_df['Depth']), max(res_df['Depth'])+1)\n", - "\n", - "y = np.arange(min(res_df['Width']), max(res_df['Width'])+1)\n", - "\n", - "X, Y = np.meshgrid(x, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": {}, - "outputs": [], - "source": [ - "(x1,x2) = X.shape\n", - "Zdata = np.reshape(values,(x2,x1)).T\n", - "Zrand = np.reshape(values_rand,(x2,x1)).T" - ] - }, - { - "cell_type": "code", - "execution_count": 331, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.9251 , 0.9201 , 0.90375, 0.92555, 0.9231 , 0.9045 ],\n", - " [0.8674 , 0.8482 , 0.8447 , 0.8306 , 0.85725, 0.8439 ],\n", - " [0.73105, 0.7371 , 0.75855, 0.76205, 0.71515, 0.7076 ],\n", - " [0.71715, 0.67555, 0.6031 , 0.5922 , 0.5072 , 0.54185]])" - ] - }, - "execution_count": 331, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Zdata" - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 355, - "metadata": {}, - "outputs": [], - "source": [ - "tvd_rand_values = np.asarray([munged['TVD(data, rand)'][idx] for idx in munged.index])\n", - "tvd_ideal_values = np.asarray([munged['TVD(data, ideal)'][idx] for idx in munged.index])\n", - "Ztvd_rand = np.reshape(tvd_rand_values,(x2,x1)).T\n", - "Ztvd_ideal = np.reshape(tvd_ideal_values,(x2,x1)).T" - ] - }, - { - "cell_type": "code", - "execution_count": 357, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.46255 , 0.622775, 0.608975, 0.675675, 0.46005 , 0.60205 ,\n", - " 0.615325, 0.6457 , 0.451875, 0.5996 , 0.63455 , 0.5811 ,\n", - " 0.462775, 0.5867 , 0.639125, 0.56565 , 0.46155 , 0.6122 ,\n", - " 0.592275, 0.505625, 0.45225 , 0.598525, 0.59295 , 0.53795 ])" - ] - }, - "execution_count": 357, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tvd_ideal_values\n", - "tvd_rand_values" - ] - }, - { - "cell_type": "code", - "execution_count": 358, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 359, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 362, - "metadata": {}, - "outputs": [], - "source": [ - "loge_rand_values = np.asarray([munged['Pr. success loge rand'][idx] for idx in munged.index])\n", - "loge_data_values = np.asarray([munged['Pr. success loge data'][idx] for idx in munged.index])\n", - "Zlge_rand = np.reshape(loge_rand_values,(x2,x1)).T\n", - "Zlge_data = np.reshape(loge_data_values,(x2,x1)).T" - ] - }, - { - "cell_type": "code", - "execution_count": 363, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Zlge_data, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 365, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Zlge_rand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data exploration" - ] - }, - { - "cell_type": "code", - "execution_count": 432, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.optimize import curve_fit" - ] - }, - { - "cell_type": "code", - "execution_count": 433, - "metadata": {}, - "outputs": [], - "source": [ - "size = Y.shape\n", - "width_1d = Y.reshape((1,np.prod(size)))\n", - "depth_1d = X.reshape((1,np.prod(size)))" - ] - }, - { - "cell_type": "code", - "execution_count": 441, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 24)" - ] - }, - "execution_count": 441, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_1d = Zdata.reshape((1,np.prod(size)))\n", - "data_1d.shape\n", - "width_1d.shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 435, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[4, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0],\n", - " [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,\n", - " 4, 4],\n", - " [1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4,\n", - " 5, 6]])" - ] - }, - "execution_count": 435, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dims = np.zeros_like(width_1d)\n", - "dims[0,0] = size[0]\n", - "dims[0,1] = size[1]\n", - "\n", - "xdata = np.vstack((dims,width_1d, depth_1d))\n", - "\n", - "\n", - "\n", - "xdata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Two parameter model \n", - "\n", - "\n", - "$f(W,D,p_W,p_D) = (1-p_W)^W * (1-p_D)^D $\n", - "\n", - "The fidelity is proporional to $1 - p$" - ] - }, - { - "cell_type": "code", - "execution_count": 455, - "metadata": {}, - "outputs": [], - "source": [ - "def two_param(x,pw,pd):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = (1-pw)**(width) * (1-pd)**depth\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One parameter model\n", - "\n", - "$f(W,D,p) = (1-p)^{W * D} $" - ] - }, - { - "cell_type": "code", - "execution_count": 447, - "metadata": {}, - "outputs": [], - "source": [ - "def one_param(x,p):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = (1-p)**(width*depth)\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From my prior work a better model to fit to is\n", - "\n", - "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" - ] - }, - { - "cell_type": "code", - "execution_count": 510, - "metadata": {}, - "outputs": [], - "source": [ - "def two_param_exp(x,p,a,b):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = np.exp(-(a*p + b) * width * depth)\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Start with one paramter model**" - ] - }, - { - "cell_type": "code", - "execution_count": 531, - "metadata": {}, - "outputs": [], - "source": [ - "pguess = 0.1\n", - "popt, pcov = curve_fit(one_param, xdata, data_1d.ravel(), p0=pguess, bounds=(0, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 532, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The estimated error is p = 0.0276\n", - "The estimated product of the one and two qubit fidelity is F = 0.9724\n" - ] - } - ], - "source": [ - "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", - "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", - "#print('The one standard deviation on the estimate is ', str(np.round(np.sqrt(np.diag(pcov)[0]),5)))" - ] - }, - { - "cell_type": "code", - "execution_count": 533, - "metadata": {}, - "outputs": [], - "source": [ - "zfit = one_param(xdata,popt)\n", - "Z_fit = zfit.reshape(size)" - ] - }, - { - "cell_type": "code", - "execution_count": 534, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.pcolormesh(X,Y, Z_fit)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 535, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.pcolormesh(X,Y,Zdata)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Two parameter model**" - ] - }, - { - "cell_type": "code", - "execution_count": 541, - "metadata": {}, - "outputs": [], - "source": [ - "pguess2d = [0.0276, 0.01, 0.4]" - ] - }, - { - "cell_type": "code", - "execution_count": 542, - "metadata": {}, - "outputs": [], - "source": [ - "popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d , bounds=(0., 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 543, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00193651, 0.00070045, 0.02802694])" - ] - }, - "execution_count": 543, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "popt2d" - ] - }, - { - "cell_type": "code", - "execution_count": 544, - "metadata": {}, - "outputs": [], - "source": [ - "zfit2d = two_param(xdata,popt2d[0],popt2d[1])\n", - "Z_fit2d = zfit2d.reshape(size)" - ] - }, - { - "cell_type": "code", - "execution_count": 545, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.pcolormesh(X,Y, Z_fit2d)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 486, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.897680214" - ] - }, - "execution_count": 486, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1-1.02319786e-01" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/circuit_testing_kyle.ipynb b/examples/volumetrics.ipynb similarity index 62% rename from examples/circuit_testing_kyle.ipynb rename to examples/volumetrics.ipynb index 48a55a04..8bed18eb 100644 --- a/examples/circuit_testing_kyle.ipynb +++ b/examples/volumetrics.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Circuit testing\n", + "# Volumetrics\n", "\n", "\n", "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", @@ -14,16 +14,9 @@ "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +34,7 @@ "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", "from pyquil.quilbase import Pragma\n", "\n", - "from forest.benchmarking.circuit_testing import *" + "from forest.benchmarking.volumetrics import *" ] }, { @@ -53,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -63,58 +56,40 @@ "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", "\n", - "qc_perfect = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", - "qc_noisy = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" + "noisy_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", + "perfect_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#qc_perfect.device.get_specs()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw(qc_perfect.qubit_topology(),with_labels=True)" + "nx.draw(perfect_qc.qubit_topology(),with_labels=True)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "G = qc_perfect.qubit_topology()" + "G = perfect_qc.qubit_topology()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# gate sets" + "## Gate sets\n", + "\n", + "### Classical" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -122,77 +97,42 @@ " prog = Program()\n", " prog +=I(qb1)\n", " prog +=I(qb2)\n", - " return prog" + " return prog\n", + "\n", + "one_c_gates = [X,I]\n", + "two_c_gates = [two_q_id,CNOT]\n", + "two_c_toffoli = [two_q_id, CNOT, CCNOT]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Some quantum" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "one_q_gates = [X,Z,I]\n", - "two_q_gates = [two_q_id,CZ]\n", - "\n", - "one_c_gates = [X,I]\n", - "two_c_gates = [two_q_id,CNOT]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Z 0\n", - "Z 1\n", - "Z 2\n", - "I 3\n", - "I 4\n", - "I 5\n", - "X 6\n", - "I 7\n", - "X 8\n", - "CZ 0 3\n", - "I 0\n", - "I 1\n", - "CZ 1 4\n", - "CZ 1 2\n", - "CZ 2 5\n", - "CZ 3 6\n", - "CZ 3 4\n", - "I 4\n", - "I 7\n", - "I 4\n", - "I 5\n", - "CZ 5 8\n", - "I 6\n", - "I 7\n", - "I 7\n", - "I 8\n", - "\n" - ] - } - ], - "source": [ - "prog1 = random_single_qubit_gates(G, one_q_gates)\n", - "prog2 = random_two_qubit_gates(G, two_q_gates)\n", - "print(prog1+prog2)" + "two_q_gates = [two_q_id,CZ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# random cliffords" + "### Random Cliffords\n", + "\n", + "We use a benchmarker for this. Typically we use the native gates from `get_rb_gateset` to implement each clifford." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -201,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -211,65 +151,38 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tcp://127.0.0.1:5555'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "bm.client.endpoint" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get random gates on a graph" + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "gateset_1q, q_placeholders1 = get_rb_gateset(rb_type='1q')\n", - "gateset_2q, q_placeholders2 = get_rb_gateset(rb_type='2q')" + "prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "print(prog1+prog2)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RZ(pi/2) 0\n", - "RX(-pi) 0\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi/2) 2\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(-pi) 4\n", - "RX(-pi/2) 5\n", - "RZ(-pi/2) 5\n", - "RX(-pi/2) 6\n", - "RZ(-pi) 6\n", - "RX(pi/2) 7\n", - "RZ(-pi/2) 8\n", - "RX(-pi) 8\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "progy = random_single_qubit_cliffords(G, bm)\n", + "progy = random_single_qubit_cliffords(bm, G)\n", "print(progy)" ] }, @@ -277,103 +190,92 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Make a Template" + "## Make some circuit templates and sample programs from them\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def oneq_twoq_rand_layer(qc, graph, width, depth, sequence, index):\n", - " prog1 = random_single_qubit_gates(graph, one_q_gates)\n", - " prog2 = random_two_qubit_gates(graph, two_q_gates)\n", - " return prog1 + prog2, index+1\n", - "\n", - "rand_gate_sandwich_circuit = CircuitTemplate((oneq_twoq_rand_layer, ))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X 0\n", - "X 1\n", - "Z 2\n", - "I 3\n", - "I 4\n", - "X 5\n", - "Z 6\n", - "Z 7\n", - "X 8\n", - "I 0\n", - "I 3\n", - "CZ 0 1\n", - "I 1\n", - "I 4\n", - "I 1\n", - "I 2\n", - "CZ 2 5\n", - "CZ 3 6\n", - "I 3\n", - "I 4\n", - "CZ 4 7\n", - "I 4\n", - "I 5\n", - "I 5\n", - "I 8\n", - "CZ 6 7\n", - "CZ 7 8\n", - "\n" - ] - } - ], - "source": [ - "print(rand_gate_sandwich_circuit.sample(qc_noisy, G, 2, 2)[0])" + "classical_1q_layer = get_rand_1q_template(one_c_gates)\n", + "print(classical_1q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "classical_2q_layer = get_rand_2q_template(two_c_gates)\n", + "print(classical_2q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clifford_1q_layer = get_rand_1q_cliff_template(bm)\n", + "clifford_2q_layer = get_rand_2q_cliff_template(bm)\n", + "print(clifford_2q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rand_perm_layer = get_rand_qubit_perm_template()\n", + "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "rand_su4_layer = get_rand_su4_template()\n", + "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Quantum Volume" + "## Compose templates" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def quantum_volume_layer(qc, graph, width, depth, sequence, index):\n", - " prog1 = random_permutation(graph, width)\n", - " prog2 = random_su2_pairs(graph, width)\n", - " return prog1 + prog2, index+1\n", - "\n", - "qv_template = CircuitTemplate((quantum_volume_layer, ))" + "classical_1q_2q = classical_1q_layer + classical_2q_layer\n", + "print(classical_1q_2q.sample_program(G, repetitions=2, width=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quantum Volume (unoptimized)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, ]\n" - ] - } - ], + "outputs": [], "source": [ - "print(qv_template.sample(qc_noisy, G, 2, 2))" + "qv_template = rand_perm_layer + rand_su4_layer\n", + "print(qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=5))" ] }, { @@ -1601,22 +1503,9 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" + "pygments_lexer": "ipython3" } }, "nbformat": 4, diff --git a/forest/benchmarking/circuit_testing.py b/forest/benchmarking/volumetrics.py similarity index 73% rename from forest/benchmarking/circuit_testing.py rename to forest/benchmarking/volumetrics.py index baa1e5d4..fb644375 100644 --- a/forest/benchmarking/circuit_testing.py +++ b/forest/benchmarking/volumetrics.py @@ -10,29 +10,17 @@ from dataclasses import dataclass from functools import partial -from pyquil.quilbase import Pragma, Gate, DefGate +from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate from pyquil.quilatom import QubitPlaceholder from pyquil.quil import Program, address_qubits, merge_programs from pyquil.api import QuantumComputer, BenchmarkConnection from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET -from pyquil.unitary_tools import permutation_arbitrary from rpcq.messages import TargetDevice from rpcq._utils import RPCErrorError from forest.benchmarking.randomized_benchmarking import get_rb_gateset from forest.benchmarking.distance_measures import total_variation_distance as tvd -from forest.benchmarking.random_operators import haar_rand_unitary - - -# @dataclass -# class Circuit: -# layers: Tuple[Layer] -# graph: nx.Graph -# needs_compilation: bool = True -# name: str = None - - # def __str__(self): - # return '\n'.join([str(lyr) for lyr in self.layers]) + '\n' +from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary @dataclass @@ -41,7 +29,6 @@ class CircuitTemplate: #TODO: could allow CircuitTemplates, allow definition of depth, subunits... #TODO: add compilation? - # def create_unit(self): # return lambda qc, graph, width, depth, sequence: sum(gen(qc, graph, width, depth, # sequence) for gen in @@ -69,15 +56,22 @@ def __iadd__(self, other): self.append(other) return self - def sample(self, qc, graph, width, repetitions, sequence = None): + def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None): if sequence is None: sequence = [] + + if width is not None: + graph = random.choice(generate_connected_subgraphs(graph, width)) + for _ in range(repetitions): for generator in self.generators: - prog, index = generator(qc, graph, width, sequence) + prog = generator(graph, qc, width, sequence) sequence.append(prog) return sequence + def sample_program(self, graph, repetitions, qc=None, width=None, sequence = None): + return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) + # repetitions = [([1, 1, ([2,1], 2), 3], 4), 1] # For four times do: # the first gen, second gen, @@ -86,8 +80,6 @@ def sample(self, qc, graph, width, repetitions, sequence = None): # the fifth gen 3 times # do the final 6th gen once - # def __str__(self): - # return f'Depth {self.depth}:\n' + '\n'.join([str(comp) for comp in self.components]) + '\n' # ================================================================================================== # Gate Sets @@ -177,44 +169,43 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog -def random_permutation(graph: nx.Graph, width): - #TODO: find another way; this is too slow +def _qubit_perm_to_bitstring_perm(qubit_permutation: List[int]): + bitstring_permutation = [] + for bitstring in range(2**len(qubit_permutation)): + permuted_bitstring = 0 + for idx, q in enumerate(qubit_permutation): + permuted_bitstring |= ((bitstring >> q) & 1) << idx + bitstring_permutation.append(permuted_bitstring) + return bitstring_permutation + + +def random_qubit_permutation(graph: nx.Graph): qubits = list(graph.nodes) - measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes - # TODO: use native permutations - permutation = np.random.permutation(range(len(measure_qubits))) - matrix = permutation_arbitrary(permutation, len(measure_qubits))[0] + permutation = list(np.random.permutation(range(len(qubits)))) - gate_definition = DefGate("Perm" + "".join([str(measure_qubits[idx]) for idx in permutation]), matrix) + gate_definition = DefPermutationGate("Perm" + "".join([str(q) for q in permutation]), + _qubit_perm_to_bitstring_perm(permutation)) PERMUTE = gate_definition.get_constructor() p = Program() p += gate_definition - p += PERMUTE(*measure_qubits) + p += PERMUTE(*qubits) return p -def random_su2_pairs(graph: nx.Graph, width): - qubits = list(graph.nodes)[:width] # arbitrarily pick the first width-many nodes - gates = [] +def random_su4_pairs(graph: nx.Graph): + qubits = list(graph.nodes) + prog = Program() for q1, q2 in zip(qubits[::2], qubits[1::2]): matrix = haar_rand_unitary(4) - gate_definition = DefGate(f"RSU2({q1},{q2})", matrix) - RSU2 = gate_definition.get_constructor() - p = Program() - p += gate_definition - p += RSU2(q1, q2) - gates.append(p) - return gates + gate_definition = DefGate(f"RSU4_{q1}_{q2}", matrix) + RSU4 = gate_definition.get_constructor() + prog += gate_definition + prog += RSU4(q1, q2) + return prog -def quantum_volume_compilation(qc, graph, width, sequence): - prog = merge_programs(sequence) +def graph_restricted_compilation(qc, graph, program): qubits = list(graph.nodes) - measure_qubits = qubits[:width] # arbitrarily pick the first width-many nodes - - ro = prog.declare("ro", "BIT", len(measure_qubits)) - for idx, qubit in enumerate(measure_qubits): - prog.measure(qubit, ro[idx]) # restrict compilation to chosen qubits isa_dict = qc.device.get_isa().to_dict() @@ -237,13 +228,11 @@ def quantum_volume_compilation(qc, graph, width, sequence): new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) # try to compile with the restricted qubit topology try: - native_quil = new_compiler.quil_to_native_quil(prog) + native_quil = new_compiler.quil_to_native_quil(program) except RPCErrorError as e: if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ in str(e): - raise ValueError("naive_program_generator could not generate a program using only the " - "qubits supplied; expand the set of allowed qubits or supply " - "a custom program_generator.") + raise ValueError("The program could not be compiled onto the given subgraph.") raise return native_quil @@ -254,162 +243,50 @@ def quantum_volume_compilation(qc, graph, width, sequence): ### def get_rand_1q_template(gates: Sequence[Gate]): - def func(qc, graph, width, sequence): - partial_func = partial(random_single_qubit_gates, gates = gates) + def func(graph, qc=None, width=None, sequence=None): + partial_func = partial(random_single_qubit_gates, gates=gates) return partial_func(graph) return CircuitTemplate([func]) + def get_rand_2q_template(gates: Sequence[Gate]): - def func(qc, graph, width, sequence): - partial_func = partial(random_two_qubit_gates, gates = gates) + def func(graph, qc=None, width=None, sequence=None): + partial_func = partial(random_two_qubit_gates, gates=gates) return partial_func(graph) return CircuitTemplate([func]) + def get_rand_1q_cliff_template(bm: BenchmarkConnection): - def func(qc, graph, width, sequence): - partial_func = partial(random_single_qubit_cliffords, bm =bm) - return partial_func(graph) + def func(graph, qc=None, width=None, sequence=None): + partial_func = partial(random_single_qubit_cliffords, bm=bm) + return partial_func(graph=graph) return CircuitTemplate([func]) + def get_rand_2q_cliff_template(bm: BenchmarkConnection): - def func(qc, graph, width, sequence): - partial_func = partial(random_two_qubit_cliffords, bm =bm) - return partial_func(graph) + def func(graph, qc=None, width=None, sequence=None): + partial_func = partial(random_two_qubit_cliffords, bm=bm) + return partial_func(graph=graph) return CircuitTemplate([func]) -def get_rand_perm_template(bm: BenchmarkConnection): - def func(qc, graph, width, sequence): - prog = random_permutation(graph, width) + +def get_rand_qubit_perm_template(): + def func(graph, qc, width=None, sequence=None): + prog = random_qubit_permutation(graph) native_quil = qc.compiler.quil_to_native_quil(prog) - return partial_func(graph) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) return CircuitTemplate([func]) -# =========================================== -# Layer tools -# ================================================================================================== -# -# -# def layer_1q_and_2q_rand_cliff(bm: BenchmarkConnection, -# graph: nx.Graph, -# layer_dagger: bool = False): -# """ -# Creates a layer of random one qubit Cliffords followed by random two qubit Cliffords. -# -# :param bm: A benchmark connection that will do the grunt work of generating the Cliffords -# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. -# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer -# effectively the identity -# :return: program -# """ -# prog = Program() -# prog += random_single_qubit_cliffords(bm, graph) -# prog += random_two_qubit_cliffords(bm, graph) -# if layer_dagger: -# prog += prog.dagger() -# return prog -# -# -# def layer_1q_and_2q_rand_gates(graph: nx.Graph, -# one_q_gates, -# two_q_gates, -# layer_dagger: bool = False): -# """ -# You pass in two lists of one and two qubit gates. This function creates a layer of random one -# qubit gates followed by random two qubit gates -# -# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. -# :param one_q_gates: list of one qubit gates -# :param two_q_gates: list of two qubit gates e.g. [CZ, ID] -# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer -# effectively the identity -# :return: program -# """ -# prog = Program() -# prog += random_single_qubit_gates(graph, one_q_gates) -# prog += random_two_qubit_gates(graph, two_q_gates) -# if layer_dagger: -# prog += prog.dagger() -# return prog - - -# ================================================================================================== -# Sandwich tools -# ================================================================================================== -# def circuit_sandwich_rand_gates(graph: nx.Graph, -# circuit_depth: int, -# one_q_gates: list, -# two_q_gates: list, -# layer_dagger: bool = False, -# sandwich_dagger: bool = False): -# """ -# Create a sandwich circuit by adding layers. -# -# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. -# :param circuit_depth: maximum depth of quantum circuit -# :param one_q_gates: list of one qubit gates -# :param two_q_gates: list of two qubit gates e.g. [CZ, ID] -# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer -# :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of -# the first. -# :return: program -# """ -# total_prog = Program() -# total_prog += pre_trival(graph) -# -# if sandwich_dagger: -# circuit_depth = int(np.floor(circuit_depth / 2)) -# -# layer_progs = Program() -# for _ in range(circuit_depth): -# layer_progs += layer_1q_and_2q_rand_gates(graph, -# one_q_gates, -# two_q_gates, -# layer_dagger) -# if sandwich_dagger: -# layer_progs += layer_progs.dagger() -# -# total_prog += layer_progs -# total_prog += post_trival() -# return total_prog -# -# -# def circuit_sandwich_clifford(bm: BenchmarkConnection, -# graph: nx.Graph, -# circuit_depth: int, -# layer_dagger: bool = False, -# sandwich_dagger: bool = False): -# """ -# -# :param bm: A benchmark connection that will do the grunt work of generating the Cliffords -# :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. -# :param circuit_depth: maximum depth of quantum circuit -# :param layer_dagger: Bool if true will add the dagger to the layer, making the layer -# :param sandwich_dagger: Bool if true the second half of the circuit will be the inverse of -# the first. -# :return: program -# """ -# total_prog = Program() -# -# total_prog += pre_trival(graph) -# -# if sandwich_dagger: -# circuit_depth = int(np.floor(circuit_depth / 2)) -# -# layer_progs = Program() -# for _ in range(circuit_depth): -# layer_progs += layer_1q_and_2q_rand_cliff(bm, graph, layer_dagger) -# if sandwich_dagger: -# layer_progs += layer_progs.dagger() -# -# total_prog += layer_progs -# total_prog += post_trival() -# return total_prog -# +def get_rand_su4_template(): + def func(graph, qc, width=None, sequence=None): + prog = random_su4_pairs(graph) + native_quil = graph_restricted_compilation(qc, graph, prog) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) -# ================================================================================================== -# Generate and Acquire functions -# ================================================================================================== def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, @@ -603,7 +480,8 @@ def get_error_hamming_distance_from_results(perfect_bit_string, results): num_shots, n_bits = results.shape _, pn_bits = perfect_bit_string.shape if n_bits != pn_bits: - raise ValueError("Bit strings are not equal length, check you are runing on the same graph") + raise ValueError("Bit strings are not equal length, check you are running on the same " + "graph") wt = [] # loop over all results for shot in results: From 4f8812bd72e8f0230290dfbc3f94aec6e8ffaa2f Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 18 Jul 2019 17:57:40 -0400 Subject: [PATCH 17/49] Add dagger and data acquisition. --- examples/volumetrics.ipynb | 1248 +++++++++++++++++++++++++--- forest/benchmarking/volumetrics.py | 628 -------------- 2 files changed, 1148 insertions(+), 728 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 8bed18eb..5df2dc54 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,32 +46,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# if you want to run on a \"real lattice\"\n", "from pyquil import *\n", "#list_quantum_computers()\n", - "#qc_perfect = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", - "#qc_noisy = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", + "#perfect_qc = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", + "#noisy_qc = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", "\n", - "noisy_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)\n", - "perfect_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "noisy_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)\n", + "perfect_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:518: MatplotlibDeprecationWarning: \n", + "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n", + " if not cb.iterable(width):\n", + "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:565: MatplotlibDeprecationWarning: \n", + "The is_numlike function was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use isinstance(..., numbers.Number) instead.\n", + " if cb.is_numlike(alpha):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "nx.draw(perfect_qc.qubit_topology(),with_labels=True)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -151,9 +174,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'tcp://127.0.0.1:5555'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bm.client.endpoint" ] @@ -167,9 +201,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 0\n", + "Z 1\n", + "Z 2\n", + "Z 3\n", + "X 4\n", + "Z 5\n", + "X 6\n", + "I 7\n", + "Z 8\n", + "CZ 0 3\n", + "I 0\n", + "I 1\n", + "CZ 1 4\n", + "I 1\n", + "I 2\n", + "I 2\n", + "I 5\n", + "CZ 3 6\n", + "CZ 3 4\n", + "CZ 4 7\n", + "I 4\n", + "I 5\n", + "I 5\n", + "I 8\n", + "CZ 6 7\n", + "I 7\n", + "I 8\n", + "\n" + ] + } + ], "source": [ "prog1 = random_single_qubit_gates(G, one_q_gates)\n", "prog2 = random_two_qubit_gates(G, two_q_gates)\n", @@ -178,9 +247,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-pi) 0\n", + "RX(-pi) 0\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RX(pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(-pi/2) 4\n", + "RZ(-pi) 5\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "\n" + ] + } + ], "source": [ "progy = random_single_qubit_cliffords(bm, G)\n", "print(progy)" @@ -195,9 +290,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 7\n", + "I 8\n", + "X 7\n", + "I 8\n", + "\n" + ] + } + ], "source": [ "classical_1q_layer = get_rand_1q_template(one_c_gates)\n", "print(classical_1q_layer.sample_program(G, repetitions=2, width=2))" @@ -205,9 +312,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 0\n", + "I 1\n", + "I 0\n", + "I 1\n", + "\n" + ] + } + ], "source": [ "classical_2q_layer = get_rand_2q_template(two_c_gates)\n", "print(classical_2q_layer.sample_program(G, repetitions=2, width=2))" @@ -215,9 +334,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CZ 3 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 3\n", + "\n" + ] + } + ], "source": [ "clifford_1q_layer = get_rand_1q_cliff_template(bm)\n", "clifford_2q_layer = get_rand_2q_cliff_template(bm)\n", @@ -226,9 +367,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "rand_perm_layer = get_rand_qubit_perm_template()\n", "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=2))" @@ -236,9 +385,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-1.329632786433312) 3\n", + "RX(pi/2) 3\n", + "RZ(0.7604982719369086) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.8156310165752079) 6\n", + "RX(pi/2) 6\n", + "RZ(1.4711114998123926) 6\n", + "RX(-pi/2) 6\n", + "CZ 6 3\n", + "RZ(-3.1241399925819855) 3\n", + "RX(pi/2) 3\n", + "RZ(2.2746571087217635) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.5532719149246832) 6\n", + "RX(-pi/2) 6\n", + "CZ 6 3\n", + "RX(pi/2) 3\n", + "RZ(-1.6114573653372144) 3\n", + "RX(-pi/2) 3\n", + "RZ(2.1015884527806747) 6\n", + "RX(pi/2) 6\n", + "CZ 6 3\n", + "RZ(0.7800514644372821) 3\n", + "RX(pi/2) 3\n", + "RZ(1.5561120572506353) 3\n", + "RX(-pi/2) 3\n", + "RZ(-2.303571544938592) 3\n", + "RZ(2.0461487572707764) 6\n", + "RX(pi/2) 6\n", + "RZ(1.4885403070641674) 6\n", + "RX(-pi/2) 6\n", + "RZ(-1.7188983925062606) 6\n", + "\n" + ] + } + ], "source": [ "rand_su4_layer = get_rand_su4_template()\n", "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))" @@ -253,14 +442,100 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X 3\n", + "I 4\n", + "X 5\n", + "X 7\n", + "CNOT 3 4\n", + "I 4\n", + "I 7\n", + "CNOT 4 5\n", + "I 3\n", + "I 4\n", + "I 5\n", + "X 7\n", + "I 3\n", + "I 4\n", + "CNOT 4 7\n", + "CNOT 4 5\n", + "\n" + ] + } + ], "source": [ "classical_1q_2q = classical_1q_layer + classical_2q_layer\n", "print(classical_1q_2q.sample_program(G, repetitions=2, width=4))" ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RX(-pi) 2\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "CZ 2 5\n", + "RX(pi/2) 5\n", + "RX(-pi/2) 2\n", + "CZ 2 5\n", + "RZ(-pi/2) 5\n", + "RZ(-pi/2) 2\n", + "RX(-pi/2) 2\n", + "RX(-pi/2) 2\n", + "RX(-pi/2) 5\n", + "RX(-pi/2) 2\n", + "CZ 2 5\n", + "RZ(-pi/2) 5\n", + "RZ(-pi) 2\n", + "RZ(-pi/2) 2\n", + "RX(-pi/2) 5\n", + "RZ(-pi/2) 5\n", + "CZ 2 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RX(-pi/2) 2\n", + "CZ 2 5\n", + "RZ(-pi/2) 2\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 2 5\n", + "RZ(pi) 2\n", + "RX(pi) 2\n", + "RX(pi/2) 5\n", + "RZ(pi/2) 5\n", + "\n", + "This program compiles away to nothing: \n", + "HALT\n", + "\n" + ] + } + ], + "source": [ + "clifford_sandwhich = clifford_1q_layer + clifford_2q_layer + get_dagger_all_template()\n", + "# here we demonstrate a simple use of a pattern. We want to do some Clifford layers reps\n", + "# number of times and then dagger the result of all those reps. \n", + "reps = 3\n", + "prog = clifford_sandwhich.sample_program(G, repetitions=1, width=2, pattern=[([0, 1], reps), 2], qc=noisy_qc)\n", + "print(prog)\n", + "\n", + "# We can check that this is the identity by compiling it fully\n", + "print(\"This program compiles away to nothing: \")\n", + "print(noisy_qc.compiler.quil_to_native_quil(prog))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -270,90 +545,796 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(1.8017191111484605) 3\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(1.276579062544581) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(2.486017802099031) 2\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(2.246131166553576) 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RX(pi/2) 2\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "CZ 2 5\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(-0.4283666432126832) 8\n", + "RX(pi/2) 8\n", + "RZ(1.1996621933787561) 8\n", + "RX(-pi/2) 8\n", + "RZ(1.0076215164016324) 8\n", + "RZ(2.9106698692362283) 4\n", + "RX(pi/2) 4\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "CZ 4 5\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 4\n", + "CZ 7 4\n", + "RZ(0.40643443383085465) 5\n", + "RX(pi/2) 5\n", + "RZ(1.6021735778205666) 5\n", + "RX(-pi/2) 5\n", + "RZ(0.7596599838570817) 8\n", + "RX(pi/2) 8\n", + "RZ(2.6414924282541383) 8\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RZ(-2.7242905019992167) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.2362573886500776) 8\n", + "RX(pi/2) 8\n", + "CZ 5 8\n", + "RX(pi/2) 5\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RX(pi/2) 7\n", + "RZ(-2.2179297941114995) 8\n", + "RX(pi/2) 8\n", + "RZ(0.6563946674935734) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "CZ 7 8\n", + "RZ(-2.8884988700249052) 2\n", + "RX(pi/2) 2\n", + "RZ(0.5711301113067844) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.9821717354393478) 5\n", + "RX(pi/2) 5\n", + "RZ(2.996916689268449) 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RZ(0.6168454016858838) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.831802684296635) 5\n", + "RX(pi/2) 5\n", + "CZ 2 5\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RZ(1.3147766336014757) 4\n", + "RX(pi/2) 4\n", + "RZ(0.6132172846209873) 4\n", + "RX(-pi/2) 4\n", + "RZ(2.8071869495514195) 7\n", + "RX(pi/2) 7\n", + "RZ(2.9381092126110544) 7\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RZ(1.1858040262494316) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.0282758603541193) 7\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RZ(-1.0318311998921215) 4\n", + "RX(pi/2) 4\n", + "RZ(2.917298994701893) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.9587061257951355) 5\n", + "RX(pi/2) 5\n", + "RZ(0.8394641621209838) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RZ(2.2966897861387614) 8\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RZ(1.144803718228606) 4\n", + "RX(pi/2) 4\n", + "RZ(1.8992957948764966) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.108086487127095) 7\n", + "RX(pi/2) 7\n", + "RZ(1.052421435298725) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RZ(-1.7214580509840065) 4\n", + "RX(pi/2) 4\n", + "RZ(2.0013939785015324) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "CZ 5 8\n", + "RZ(-3.046892179135674) 4\n", + "RX(pi/2) 4\n", + "RZ(1.246384782476549) 4\n", + "RX(-pi/2) 4\n", + "RZ(2.8998637381266246) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(2.185702808358501) 7\n", + "RX(pi/2) 7\n", + "RZ(1.7177683146657783) 7\n", + "RX(-pi/2) 7\n", + "RZ(2.2314104608517087) 8\n", + "RX(pi/2) 8\n", + "RZ(1.8472097515484471) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(-0.9711724505215837) 7\n", + "RX(-pi/2) 7\n", + "RZ(-1.477854260654719) 8\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 7\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(1.178225994995772) 4\n", + "RX(pi/2) 4\n", + "RZ(2.6799021109782855) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.56979198155268) 7\n", + "RX(pi/2) 7\n", + "RZ(2.1064409602287237) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RZ(-2.8532637822250297) 4\n", + "RX(pi/2) 4\n", + "RZ(0.8904684827129481) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "RZ(-2.7453373167901463) 8\n", + "RX(pi/2) 8\n", + "RZ(0.9621545031244332) 8\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RZ(0.44230386481319917) 4\n", + "RX(pi/2) 4\n", + "RZ(0.6357193163687375) 4\n", + "RX(-pi/2) 4\n", + "CZ 5 4\n", + "RZ(-0.16026621662804685) 7\n", + "RX(pi/2) 7\n", + "RZ(1.1061665431569154) 7\n", + "RX(-pi/2) 7\n", + "RZ(-1.4063606326756255) 8\n", + "RX(pi/2) 8\n", + "RZ(0.22605186185314077) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 7\n", + "RZ(-0.17573465962336776) 7\n", + "RX(pi/2) 7\n", + "RZ(-2.051255968756359) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 7\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 8\n", + "CZ 8 7\n", + "RX(pi/2) 3\n", + "RZ(1.514919026440782) 3\n", + "RX(-pi/2) 3\n", + "RZ(-0.05275673257512681) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 4\n", + "RZ(1.8091362906910984) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.9106332308705216) 7\n", + "RX(pi/2) 7\n", + "RZ(0.20553687567580603) 7\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(-1.3416084196814992) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(-0.28186335021412506) 8\n", + "RX(pi/2) 8\n", + "RZ(1.691627905954305) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RZ(-1.0118379391373078) 2\n", + "RX(pi/2) 2\n", + "RZ(1.3008879401383326) 2\n", + "RX(-pi/2) 2\n", + "RZ(-2.414905059296985) 2\n", + "RZ(2.2195332374487338) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "RZ(-2.974917157799604) 8\n", + "RX(-pi/2) 8\n", + "RZ(-2.4846793549996664) 2\n", + "RX(pi/2) 2\n", + "RZ(1.9697699987194803) 2\n", + "RX(-pi/2) 2\n", + "RZ(0.18769731536138468) 2\n", + "RZ(0.17001930149437125) 4\n", + "RX(pi/2) 4\n", + "RZ(1.712869504477766) 4\n", + "RX(-pi/2) 4\n", + "RZ(2.6079478580879867) 5\n", + "RX(pi/2) 5\n", + "RZ(0.39606389161604927) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RZ(0.8518803309152059) 4\n", + "RX(pi/2) 4\n", + "RZ(2.9195304920905008) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.7846097082010637) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RX(pi/2) 4\n", + "RZ(-1.6711119150132987) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.6043005142669777) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(3.1384004255412314) 3\n", + "RX(pi/2) 3\n", + "RZ(1.7995831175414572) 3\n", + "RX(-pi/2) 3\n", + "RZ(3.0223526285094295) 4\n", + "RX(pi/2) 4\n", + "RZ(2.2797208963568516) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(1.5119945229630982) 3\n", + "RX(-pi/2) 3\n", + "RZ(-0.6749117095193151) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(2.218795197120107) 5\n", + "RX(pi/2) 5\n", + "RZ(1.7294041908479145) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 2\n", + "RZ(-1.51421478604823) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(-0.5261825857961759) 4\n", + "RX(pi/2) 4\n", + "RZ(2.293645641879581) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.2309594227192742) 5\n", + "RX(pi/2) 5\n", + "RZ(0.16831823600878473) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(-2.5651151837593584) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.8379284488721312) 5\n", + "RX(pi/2) 5\n", + "RZ(2.4792546988595814) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(1.4847395054946104) 4\n", + "RX(pi/2) 4\n", + "RX(pi/2) 5\n", + "RZ(-1.6585864185122152) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(0.1478087433714213) 2\n", + "RX(pi/2) 2\n", + "RZ(2.1137479650249293) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.1090549740545101) 2\n", + "RZ(-1.7640422667268632) 3\n", + "RX(pi/2) 3\n", + "RZ(1.6644451088523784) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.3209101043315539) 3\n", + "RZ(0.6229968093952749) 4\n", + "RX(pi/2) 4\n", + "RZ(1.5292935778679262) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.15138470971322932) 4\n", + "RZ(-1.5826953740879837) 5\n", + "RX(pi/2) 5\n", + "RZ(2.760201374187681) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.5390560334734964) 5\n", + "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RZ(0.6104267724806893) 7\n", + "RX(pi/2) 7\n", + "RZ(0.6228398548563105) 7\n", + "RX(-pi/2) 7\n", + "RZ(2.884905346302303) 7\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(-0.5622638162116549) 3\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.580153183042519) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RZ(2.194027215765365) 0\n", + "RX(pi/2) 0\n", + "RZ(0.8571227571766867) 0\n", + 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+ "CZ 1 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RZ(-2.009290286078003) 0\n", + "RX(pi/2) 0\n", + "RZ(1.2031913752704844) 0\n", + "RX(-pi/2) 0\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", + "RZ(0.12044413515577679) 5\n", + "RX(pi/2) 5\n", + "RZ(1.5682338079760971) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.6132896620316137) 5\n", + "RZ(0.8004100630167017) 0\n", + "RX(pi) 0\n", + "RX(pi/2) 3\n", + "CZ 3 4\n", + "CZ 3 0\n", + "RZ(0.5380274043194792) 1\n", + "RX(pi/2) 1\n", + "RZ(1.910559318237438) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.6862490016793266) 2\n", + "RX(pi/2) 2\n", + "RZ(0.6558090478527918) 2\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RZ(-1.4029094245010207) 1\n", + "RX(-pi/2) 1\n", + "RZ(-0.3597421425477565) 2\n", + "RX(pi/2) 2\n", + "CZ 1 2\n", + "RX(pi/2) 1\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RZ(1.7852837368908632) 0\n", + "RX(pi/2) 0\n", + "RZ(2.0377451443910384) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.6847075382554333) 1\n", + "RX(pi/2) 1\n", + "RZ(1.5620362855533372) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RZ(0.16624803271941935) 0\n", + "RX(pi/2) 0\n", + "RZ(2.474525260502766) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(3.021148518434016) 4\n", + "RX(pi/2) 4\n", + "RZ(1.573358845613696) 4\n", + "RX(-pi/2) 4\n", + "CZ 5 4\n", + "RZ(-3.0990993183530753) 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(3.1390278194901975) 4\n", + "RX(pi/2) 4\n", + "RZ(1.613289522431356) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(1.9106332308705163) 5\n", + "RX(pi/2) 5\n", + "RZ(3.013803911964227) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.8017557495141734) 5\n", + "RZ(pi/2) 3\n", + "RZ(1.910633230870521) 1\n", + "RX(pi/2) 1\n", + "RZ(2.938612054858844) 1\n", + "RX(-pi/2) 1\n", + "CZ 4 1\n", + "RZ(2.3935359015380775) 1\n", + "RZ(-0.12055309059496899) 4\n", + "RX(pi/2) 4\n", + "CZ 4 5\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(pi) 5\n", + "CZ 4 1\n", + "RZ(-0.7594665746334813) 6\n", + "RX(pi/2) 6\n", + "RZ(0.9682997039837928) 6\n", + "RX(-pi/2) 6\n", + "RZ(1.130125978255595) 7\n", + "RX(pi/2) 7\n", + "RZ(1.3219978897151594) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 6\n", + "RZ(1.2920975873308231) 6\n", + "RX(pi/2) 6\n", + "RZ(-2.699687752513954) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 6\n", + "RX(-pi/2) 6\n", + "RX(pi/2) 7\n", + "CZ 7 6\n", + "RZ(pi) 1\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.3488048279691702) 4\n", + "RX(pi/2) 4\n", + "RZ(2.080812815899925) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.261936631782986) 7\n", + "RX(pi/2) 7\n", + "RZ(2.512220355359802) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RZ(-2.001770175388918) 4\n", + "RX(pi/2) 4\n", + "RZ(2.3381674154646035) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(-0.36348630478461597) 4\n", + "RX(pi/2) 4\n", + "RZ(2.1462243409518855) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 1\n", + "RZ(2.783380637392832) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RX(pi/2) 3\n", + "CZ 3 4\n", + "RZ(1.2309594227192715) 0\n", + "RX(pi/2) 0\n", + "RZ(2.9386120548588437) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.307771658879897) 0\n", + "RZ(0.8517649965788463) 2\n", + "RX(pi/2) 2\n", + "RZ(0.9601493325239959) 2\n", + "RX(-pi/2) 2\n", + "RZ(-2.9228083460026224) 2\n", + "RZ(-pi/2) 3\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "RZ(0.602055998551568) 6\n", + "RX(pi/2) 6\n", + "RZ(1.6779923954926161) 6\n", + "RX(-pi/2) 6\n", + "RZ(3.057847254569218) 6\n", + "RZ(-2.778106348805177) 7\n", + "RX(pi/2) 7\n", + "RZ(2.146224340951885) 7\n", + "RX(-pi/2) 7\n", + "RZ(-0.3327780583056048) 7\n", + "RZ(-1.3481497896518795) 2\n", + "RX(pi/2) 2\n", + "RZ(1.508276980336258) 2\n", + "RX(-pi/2) 2\n", + "RZ(-2.609629627984429) 2\n", + "RZ(2.860395728283365) 4\n", + "RX(pi/2) 4\n", + "RZ(0.25825392940911907) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.8673351209635076) 5\n", + "RX(pi/2) 5\n", + "RZ(2.200344935074086) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RZ(3.002032753782931) 4\n", + "RX(pi/2) 4\n", + "RZ(1.9016390660570242) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.8783395396195068) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RX(pi/2) 4\n", + "RZ(-1.6422339346518626) 4\n", + "RX(-pi/2) 4\n", + "RZ(2.212559159795388) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(0.05125197036432472) 3\n", + "RX(pi/2) 3\n", + "RZ(2.7247627829273027) 3\n", + "RX(-pi/2) 3\n", + "RZ(-2.6244820173591363) 4\n", + "RX(pi/2) 4\n", + "RZ(2.1559791278594287) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(-1.7338171993680849) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.340711539846267) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(-2.3166462879439145) 5\n", + "RX(pi/2) 5\n", + "RZ(1.3766698356009928) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 2\n", + "RZ(1.6403618581493795) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(-1.6502592850302058) 4\n", + "RX(pi/2) 4\n", + "RZ(1.2500092961217049) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.9106332308705214) 5\n", + "RX(pi/2) 5\n", + "RZ(2.9355259305110066) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(0.4279053812707434) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.39923680862121647) 5\n", + "RX(pi/2) 5\n", + "RZ(2.9462153347870217) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(1.5782153937397592) 4\n", + "RX(pi/2) 4\n", + "RX(pi/2) 5\n", + "RZ(-1.779670397487647) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(1.8186206488316365) 2\n", + "RX(pi/2) 2\n", + "RZ(0.8413921730383077) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.4887535048578582) 2\n", + "RZ(1.465517132139001) 3\n", + "RX(pi/2) 3\n", + "RZ(1.196367585025699) 3\n", + "RX(-pi/2) 3\n", + "RZ(2.6341238409018928) 3\n", + "RZ(1.2597217463218704) 4\n", + "RX(pi/2) 4\n", + "RZ(1.3960289387989866) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.4615424069322529) 4\n", + "RZ(-2.3272494229396004) 5\n", + "RX(pi/2) 5\n", + "RZ(1.6616292269360053) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.8584247543614762) 5\n", + "\n" + ] + } + ], "source": [ "qv_template = rand_perm_layer + rand_su4_layer\n", "print(qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=5))" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "\n", - "circuit_depth = 3\n", - "circuit_width = 3\n", - "circuit_sandwich = partial(circuit_sandwich_rand_gates,\n", - " one_q_gates = one_c_gates, \n", - " two_q_gates = two_c_gates)\n", - "layer_dagger = False\n", - "sandwich_dagger = False\n", - "num_rand_subgraphs = 2\n", - "num_shots_per_circuit = 2\n", - "use_active_reset= False" + "## Acquire data for ranges of (width, depth)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" + ] + } + ], "source": [ - "exp = generate_sandwich_circuits_experiments(qc_noisy,circuit_depth,circuit_width, circuit_sandwich, layer_dagger, sandwich_dagger, num_rand_subgraphs, num_shots_per_circuit, use_active_reset)" + "widths = [2, 3, 4]\n", + "depths = [3, 4]\n", + "ckt = classical_1q_2q\n", + "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=10)\n", + "print(prog_array)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "exp" + "noisy_results = acquire_volumetric_data(noisy_qc, prog_array)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {4: [array([[1, 1]]), array([[1, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 1]])]}, 3: {4: [array([[1, 0, 1]]), array([[1, 1, 0]]), array([[1, 1, 0]]), array([[0, 1, 1]]), array([[0, 0, 0]]), array([[0, 1, 0]]), array([[1, 0, 0]]), array([[0, 0, 0]]), array([[0, 1, 1]]), array([[0, 0, 1]])]}, 4: {4: [array([[0, 1, 1, 0]]), array([[0, 0, 1, 1]]), array([[1, 0, 0, 0]]), array([[0, 0, 1, 1]]), array([[0, 1, 0, 0]]), array([[1, 0, 1, 0]]), array([[0, 0, 1, 0]]), array([[1, 1, 1, 0]]), array([[0, 0, 0, 1]]), array([[0, 0, 0, 1]])]}}\n" + ] + } + ], "source": [ - "dat = acquire_circuit_sandwich_data(qc_noisy,exp)" + "ideal_results = acquire_volumetric_data(perfect_qc, prog_array, num_shots=1)\n", + "print(ideal_results)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {4: [array([0.818, 0.162, 0.02 ]), array([0.802, 0.184, 0.014]), array([0.888, 0.104, 0.008]), array([0.802, 0.184, 0.014]), array([0.916, 0.076, 0.008]), array([0.87, 0.13, 0. ]), array([0.948, 0.05 , 0.002]), array([0.836, 0.158, 0.006]), array([0.886, 0.114, 0. ]), array([0.836, 0.148, 0.016])]}, 3: {4: [array([0.78, 0.2 , 0.02, 0. ]), array([0.776, 0.21 , 0.014, 0. ]), array([0.782, 0.196, 0.022, 0. ]), array([0.826, 0.158, 0.016, 0. ]), array([0.928, 0.072, 0. , 0. ]), array([0.862, 0.132, 0.006, 0. ]), array([0.852, 0.136, 0.012, 0. ]), array([0.922, 0.078, 0. , 0. ]), array([0.794, 0.18 , 0.024, 0.002]), array([0.85 , 0.14 , 0.008, 0.002])]}, 4: {4: [array([0.78 , 0.184, 0.03 , 0.004, 0.002]), array([0.784, 0.196, 0.018, 0.002, 0. ]), array([0.852, 0.136, 0.012, 0. , 0. ]), array([0.808, 0.168, 0.018, 0.004, 0.002]), array([0.848, 0.134, 0.018, 0. , 0. ]), array([0.766, 0.22 , 0.012, 0.002, 0. ]), array([0.798, 0.194, 0.008, 0. , 0. ]), array([0.742, 0.214, 0.042, 0.002, 0. ]), array([0.836, 0.152, 0.012, 0. , 0. ]), array([0.84 , 0.148, 0.012, 0. , 0. ])]}}\n" + ] + } + ], "source": [ - "dat" + "err_hamm_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results)\n", + "print(err_hamm_distrs)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {4: array([0.8602, 0.131 , 0.0088])}, 3: {4: array([8.372e-01, 1.502e-01, 1.220e-02, 4.000e-04])}, 4: {4: array([8.054e-01, 1.746e-01, 1.820e-02, 1.400e-03, 4.000e-04])}}\n" + ] + } + ], "source": [ - "estimate_random_classical_circuit_errors(qc_perfect,daty)" + "avg_err_hamm_distrs = {w: {d: sum(distrs)/len(distrs)} for w, d_arr in err_hamm_distrs.items()\n", + " for d, distrs in d_arr.items()}\n", + "print(avg_err_hamm_distrs)" ] }, { @@ -372,12 +1353,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G = qc_perfect.qubit_topology()\n", - "len(qc_perfect.qubit_topology())\n", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 12, 22, 36, 49, 48, 32, 9, 1]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = perfect_qc.qubit_topology()\n", + "len(perfect_qc.qubit_topology())\n", "# distribution of graph lengths\n", "disty = []\n", "for gdx in range(1,len(G.nodes)+1):\n", @@ -394,11 +1396,18 @@ "disty" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Acquire data in Z basis" + "# OUTDATED BELOW" ] }, { @@ -406,6 +1415,20 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Acquire data in Z basis" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], "source": [ "# # with these parameters the cell below takes about 1 hour 40 minutes\n", "# num_shots_per_circuit = 400\n", @@ -419,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -434,11 +1457,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'generate_rand_cir_for_rand_lattices_experiments' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m exp =generate_rand_cir_for_rand_lattices_experiments(noisy_qc, \n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mcircuit_depth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcircuit_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnum_rand_subgraphs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnum_shots_per_circuit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'generate_rand_cir_for_rand_lattices_experiments' is not defined" + ] + } + ], "source": [ - "exp =generate_rand_cir_for_rand_lattices_experiments(qc_noisy, \n", + "exp =generate_rand_cir_for_rand_lattices_experiments(noisy_qc, \n", " circuit_depth, \n", " circuit_width,\n", " num_rand_subgraphs, \n", @@ -462,7 +1497,7 @@ "outputs": [], "source": [ "t0 = time.time()\n", - "data_zbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp)\n", + "data_zbasis = acquire_data_random_classical_circuit(perfect_qc, noisy_qc, exp)\n", "t1 = time.time()\n", "total = t1-t0\n", "print(total)" @@ -564,7 +1599,7 @@ "outputs": [], "source": [ "t0x = time.time()\n", - "data_xbasis = acquire_data_random_classical_circuit(qc_perfect, qc_noisy, exp_xbasis)\n", + "data_xbasis = acquire_data_random_classical_circuit(perfect_qc, noisy_qc, exp_xbasis)\n", "t1x = time.time()\n", "totalx = t1x-t0x\n", "print(totalx)" @@ -1503,9 +2538,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "pygments_lexer": "ipython3" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index fb644375..e69de29b 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -1,628 +0,0 @@ -from typing import Tuple, Sequence, Callable, Any, List -from copy import copy -import networkx as nx -import numpy as np -import random -import itertools -import pandas as pd -from scipy.spatial.distance import hamming -from scipy.special import comb -from dataclasses import dataclass -from functools import partial - -from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate -from pyquil.quilatom import QubitPlaceholder -from pyquil.quil import Program, address_qubits, merge_programs -from pyquil.api import QuantumComputer, BenchmarkConnection -from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET -from rpcq.messages import TargetDevice -from rpcq._utils import RPCErrorError - -from forest.benchmarking.randomized_benchmarking import get_rb_gateset -from forest.benchmarking.distance_measures import total_variation_distance as tvd -from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary - - -@dataclass -class CircuitTemplate: - generators: List[Callable] - #TODO: could allow CircuitTemplates, allow definition of depth, subunits... - #TODO: add compilation? - - # def create_unit(self): - # return lambda qc, graph, width, depth, sequence: sum(gen(qc, graph, width, depth, - # sequence) for gen in - # self.generators) - - def append(self, other): - self.generators += other.generators - - def __add__(self, other): - """ - Concatenate two circuits together, returning a new one. - - :param Circuit other: Another circuit to add to this one. - :return: A newly concatenated circuit. - :rtype: Program - """ - ckt = CircuitTemplate(self.generators) - ckt.append(other) - return ckt - - def __iadd__(self, other): - """ - Concatenate two circuits together using +=, returning a new one. - """ - self.append(other) - return self - - def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None): - if sequence is None: - sequence = [] - - if width is not None: - graph = random.choice(generate_connected_subgraphs(graph, width)) - - for _ in range(repetitions): - for generator in self.generators: - prog = generator(graph, qc, width, sequence) - sequence.append(prog) - return sequence - - def sample_program(self, graph, repetitions, qc=None, width=None, sequence = None): - return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) - - # repetitions = [([1, 1, ([2,1], 2), 3], 4), 1] - # For four times do: - # the first gen, second gen, - # for two times do - # two third gen, the fourth gen - # the fifth gen 3 times - # do the final 6th gen once - - -# ================================================================================================== -# Gate Sets -# ================================================================================================== -def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): - """ - Create a program comprised of single qubit gates randomly placed on the nodes of the - specified graph. The gates are chosen uniformly from the list provided. - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param gates: A list of gates e.g. [I, X, Z] or [I, X]. - :return: A program that randomly places single qubit gates on a graph. - """ - program = Program() - for q in graph.nodes: - gate = random.choice(gates) - program += gate(q) - return program - - -def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): - """ - Write a program to randomly place two qubit gates on edges of the specified graph. - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] - :return: A program that has two qubit gates randomly placed on the graph edges. - """ - program = Program() - # do the two coloring with pragmas? - # no point until fencing is over - for a, b in graph.edges: - gate = random.choice(gates) - program += gate(a, b) - return program - - -def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): - """ - Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of - the specified graph. Each uniformly random choice of Clifford is implemented in the native - gateset. - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :return: A program that randomly places single qubit Clifford gates on a graph. - """ - num_qubits = len(graph.nodes) - - q_placeholder = QubitPlaceholder() - gateset_1q = get_rb_gateset([q_placeholder]) - - # the +1 is because the depth includes the inverse - clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) - rand_cliffords = clif_n_inv[0:num_qubits] - - prog = Program() - for q, clif in zip(graph.nodes, rand_cliffords): - gate = address_qubits(clif, qubit_mapping={q_placeholder: q}) - prog += gate - return prog - - -def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): - """ - Write a program to place random two qubit Cliffords gates on edges of the graph. - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :return: A program that has two qubit gates randomly placed on the graph edges. - """ - num_2q_gates = len(graph.edges) - q_placeholders = QubitPlaceholder.register(n=2) - gateset_2q = get_rb_gateset(q_placeholders) - - # the +1 is because the depth includes the inverse - clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) - rand_cliffords = clif_n_inv[0:num_2q_gates] - - prog = Program() - # do the two coloring with pragmas? - # no point until fencing is over - for edges, clif in zip(graph.edges, rand_cliffords): - gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], - q_placeholders[1]: edges[1]}) - prog += gate - return prog - - -def _qubit_perm_to_bitstring_perm(qubit_permutation: List[int]): - bitstring_permutation = [] - for bitstring in range(2**len(qubit_permutation)): - permuted_bitstring = 0 - for idx, q in enumerate(qubit_permutation): - permuted_bitstring |= ((bitstring >> q) & 1) << idx - bitstring_permutation.append(permuted_bitstring) - return bitstring_permutation - - -def random_qubit_permutation(graph: nx.Graph): - qubits = list(graph.nodes) - permutation = list(np.random.permutation(range(len(qubits)))) - - gate_definition = DefPermutationGate("Perm" + "".join([str(q) for q in permutation]), - _qubit_perm_to_bitstring_perm(permutation)) - PERMUTE = gate_definition.get_constructor() - p = Program() - p += gate_definition - p += PERMUTE(*qubits) - return p - - -def random_su4_pairs(graph: nx.Graph): - qubits = list(graph.nodes) - prog = Program() - for q1, q2 in zip(qubits[::2], qubits[1::2]): - matrix = haar_rand_unitary(4) - gate_definition = DefGate(f"RSU4_{q1}_{q2}", matrix) - RSU4 = gate_definition.get_constructor() - prog += gate_definition - prog += RSU4(q1, q2) - return prog - - -def graph_restricted_compilation(qc, graph, program): - qubits = list(graph.nodes) - - # restrict compilation to chosen qubits - isa_dict = qc.device.get_isa().to_dict() - single_qs = isa_dict['1Q'] - two_qs = isa_dict['2Q'] - - new_1q = {} - for key, val in single_qs.items(): - if int(key) in qubits: - new_1q[key] = val - new_2q = {} - for key, val in two_qs.items(): - q1, q2 = key.split('-') - if int(q1) in qubits and int(q2) in qubits: - new_2q[key] = val - - new_isa = {'1Q': new_1q, '2Q': new_2q} - - new_compiler = copy(qc.compiler) - new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) - # try to compile with the restricted qubit topology - try: - native_quil = new_compiler.quil_to_native_quil(program) - except RPCErrorError as e: - if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ - in str(e): - raise ValueError("The program could not be compiled onto the given subgraph.") - raise - - return native_quil - - -### -# Templates -### - -def get_rand_1q_template(gates: Sequence[Gate]): - def func(graph, qc=None, width=None, sequence=None): - partial_func = partial(random_single_qubit_gates, gates=gates) - return partial_func(graph) - return CircuitTemplate([func]) - - -def get_rand_2q_template(gates: Sequence[Gate]): - def func(graph, qc=None, width=None, sequence=None): - partial_func = partial(random_two_qubit_gates, gates=gates) - return partial_func(graph) - return CircuitTemplate([func]) - - -def get_rand_1q_cliff_template(bm: BenchmarkConnection): - def func(graph, qc=None, width=None, sequence=None): - partial_func = partial(random_single_qubit_cliffords, bm=bm) - return partial_func(graph=graph) - return CircuitTemplate([func]) - - -def get_rand_2q_cliff_template(bm: BenchmarkConnection): - def func(graph, qc=None, width=None, sequence=None): - partial_func = partial(random_two_qubit_cliffords, bm=bm) - return partial_func(graph=graph) - return CircuitTemplate([func]) - - -def get_rand_qubit_perm_template(): - def func(graph, qc, width=None, sequence=None): - prog = random_qubit_permutation(graph) - native_quil = qc.compiler.quil_to_native_quil(prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) - return CircuitTemplate([func]) - - -def get_rand_su4_template(): - def func(graph, qc, width=None, sequence=None): - prog = random_su4_pairs(graph) - native_quil = graph_restricted_compilation(qc, graph, prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) - return CircuitTemplate([func]) - - - -def generate_sandwich_circuits_experiments(qc_noisy: QuantumComputer, - circuit_depth: int, - circuit_width: int, - circuit_sandwich: callable, - layer_dagger: bool = False, - sandwich_dagger: bool = False, - num_rand_subgraphs: int = 10, - # peter claims that no speed diff 800 shots - num_shots_per_circuit: int = 100, - use_active_reset: bool = False) -> pd.DataFrame: - """ - Return a DataFrame where the rows contain all the information needed to run random circuits - of a certain width and depth on a particular lattice. - - :param qc_noisy: the noisy quantum resource (QPU or QVM) - :param circuit_depth: maximum depth of quantum circuit - :param circuit_width: maximum width of quantum circuit - :param circuit_sandwich: callable. Regardless of the original arguments the function here - must only have graph, circuit_depth, layer_dagger, and sandwich_dagger as remainig keywords. - :param num_rand_subgraphs: number of random circuits of circuit_width to be sampled - :param num_shots_per_circuit: number of shots per random circuit - :param use_active_reset: if True uses active reset. Doing so will speed up execution on a QPU. - :return: pandas DataFrame - """ - # get the networkx graph of the lattice - G = qc_noisy.qubit_topology() - - if circuit_width > len(G.nodes): - raise ValueError("You must have circuit widths less than or equal to the number of qubits " - "on a lattice.") - - experiment = [] - # loop over different graph sizes - for subgraph_size in range(1, circuit_width + 1): - list_of_graphs = generate_connected_subgraphs(G, subgraph_size) - - for depth in range(1, circuit_depth + 1): - for _ in range(num_rand_subgraphs): - # randomly choose a lattice from list - lattice = random.choice(list_of_graphs) - prog = circuit_sandwich(graph=lattice, - circuit_depth=depth, - layer_dagger=layer_dagger, - sandwich_dagger=sandwich_dagger) - - experiment.append({'Depth': depth, - 'Width': subgraph_size, - 'Lattice': lattice, - 'Layer Dagger': layer_dagger, - 'Sandwich Dagger': sandwich_dagger, - 'Active Reset': use_active_reset, - 'Program': prog, - 'Trials': num_shots_per_circuit, - }) - return pd.DataFrame(experiment) - - -def acquire_circuit_sandwich_data(qc_noisy: QuantumComputer, - circ_sand_expt: pd.DataFrame) -> pd.DataFrame: - """ - Convenient wrapper for collecting the results of running circuits sandwiches on a - particular lattice. - - It will run a series of random circuits with widths from [1, ...,circuit_width] and depths - from [1, ..., circuit_depth]. - - - :param qc_noisy: the noisy quantum resource (QPU or QVM) to - :param circ_sand_expt: pandas DataFrame where the rows contain experiments - :return: pandas DataFrame - """ - #:param qc_perfect: the "perfect" quantum resource (QVM) to determine the true outcome. - # if qc_perfect.name == qc_noisy.name: - # raise ValueError("The noisy and perfect device can't be the same device.") - - data = [] - for index, row in circ_sand_expt.iterrows(): - prog = row['Program'] - use_active_reset = row['Active Reset'] - num_shots_per_circuit = row['Trials'] - - # run on perfect QVM or Wavefunction simulator - # perfect_bitstring = qc_perfect.run_and_measure(prog, trials=1) - # perfect_bitstring_array = np.vstack(perfect_bitstring[q] for q in prog.get_qubits()).T - - # add active reset - reset_prog = Program() - if use_active_reset: - reset_prog += RESET() - - # run on hardware or noisy QVM - # only need to pre append active reset on something that may run on the hardware - actual_bitstring = qc_noisy.run_and_measure(reset_prog + prog, trials=num_shots_per_circuit) - actual_bitstring_array = np.vstack(actual_bitstring[q] for q in prog.get_qubits()).T - - # list of dicts. - data.append({'Depth': row['Depth'], - 'Width': row['Width'], - 'Lattice': row['Lattice'], - # 'In X basis': row['In X basis'], - 'Active Reset': use_active_reset, - 'Program': prog, - 'Trials': num_shots_per_circuit, - # 'Answer': perfect_bitstring_array, - 'Samples': actual_bitstring_array, - }) - return pd.DataFrame(data) - - -# ================================================================================================== -# Analysis -# ================================================================================================== -def estimate_random_classical_circuit_errors(qc_perfect: QuantumComputer, - df: pd.DataFrame) -> pd.DataFrame: - """ - asdf - - :param df: pandas DataFrame containing experimental results - :return: pandas DataFrame containing estiamted errors and experimental results - """ - - results = [] - for _, row in df.iterrows(): - wt = [] - prog = row['Program'] - # run on perfect QVM or Wavefunction simulator - perfect_bitstring = qc_perfect.run_and_measure(prog, trials=1) - perfect_bitstring_array = np.vstack(perfect_bitstring[q] for q in prog.get_qubits()).T - # perfect_bitstring_array = np.asarray(row['Answer']) - actual_bitstring_array = np.asarray(row['Samples']) - wt.append(get_error_hamming_distance_from_results(perfect_bitstring_array, - actual_bitstring_array)) - wt_flat = flatten_list(wt) - - # Hamming weight distributions - wt_dist_data = np.asarray( - get_error_hamming_distributions_from_list(wt_flat, row['Width'])) # data - wt_dist_rand = np.asarray(hamming_dist_rand(row['Width'])) # random guessing - wt_dist_ideal = np.zeros_like(wt_dist_rand) # perfect - wt_dist_ideal[0] = 1 - - # Total variation distance - tvd_data_ideal = tvd(wt_dist_data, wt_dist_ideal) - tvd_data_rand = tvd(wt_dist_data, wt_dist_rand) - - # Probablity of success - pr_suc_data = wt_dist_data[0] - pr_suc_rand = wt_dist_rand[0] - - # Probablity of success with basement[ log_2(width) - 1 ] errors - # I.e. error when you allow for a logarithmic number of bit flips from the answer - num_bit_flips_allowed_from_answer = int(basement_function(np.log2(row['Width']) - 1)) - pr_suc_log_err_data = sum( - [wt_dist_data[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) - pr_suc_log_err_rand = sum( - [wt_dist_rand[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) - - results.append({'Depth': row['Depth'], - 'Width': row['Width'], - 'Lattice': row['Lattice'], - # 'In X basis': row['In X basis'], - 'Active Reset': row['Active Reset'], - 'Program': row['Program'], - 'Trials': row['Trials'], - 'Answer': perfect_bitstring_array, - 'Samples': actual_bitstring_array, - 'Hamming dist. data': wt_dist_data, - 'Hamming dist. rand': wt_dist_rand, - 'Hamming dist. ideal': wt_dist_ideal, - 'TVD(data, ideal)': tvd_data_ideal, - 'TVD(data, rand)': tvd_data_rand, - 'Pr. success data': pr_suc_data, - 'Pr. success rand': pr_suc_rand, - 'loge = basement[log_2(Width)-1]': num_bit_flips_allowed_from_answer, - 'Pr. success loge data': pr_suc_log_err_data, - 'Pr. success loge rand': pr_suc_log_err_rand, - }) - return pd.DataFrame(results) - - -def get_error_hamming_distance_from_results(perfect_bit_string, results): - """Get the hamming weight of the error vector (number of bits flipped between output and - expected answer). - - :param perfect_bit_string: a np.ndarray with shape (1,number_of_bits) - :param results: a np.ndarray with shape (num_shots,number_of_bits) - :return: a list of length num_shots containing the hamming weight - """ - num_shots, n_bits = results.shape - _, pn_bits = perfect_bit_string.shape - if n_bits != pn_bits: - raise ValueError("Bit strings are not equal length, check you are running on the same " - "graph") - wt = [] - # loop over all results - for shot in results: - wt.append(n_bits * hamming(perfect_bit_string, shot)) - return wt - - -def get_error_hamming_distributions_from_list(wt_list, n_bits): - """ Get the distribution of the hamming weight of the error vector. - - :param wt_list: a list of length num_shots containing the hamming weight. - :param n_bits: the number of bit in the original binary strings. The hamming weight is an - integer between 0 and n_bits. - :return: the relative frequency of observing each hamming weight - """ - num_shots = len(wt_list) - - if n_bits < max(wt_list): - raise ValueError("Hamming weight can't be larger than the number of bits in a string.") - - hamming_wt_distr = [0. for _ in range(n_bits + 1)] - # record the fraction of shots that resulted in an error of the given weight - for wdx in range(n_bits): - hamming_wt_distr[int(wdx)] = wt_list.count(wdx) / num_shots - return hamming_wt_distr - - -def hamming_dist_rand(num_bits: int, pad: int = 0): - """Return a list representing the Hamming distribution of - a particular bit string, of length num_bits, to randomly drawn bits. - - :param num_bits: number of bits in string - :param pad: number of zero elements to pad - returns: list of hamming weights with zero padding - """ - N = 2 ** num_bits - pr = [comb(num_bits, ndx) / (2 ** num_bits) for ndx in range(0, num_bits + 1)] - padding = [0 for _ in range(pad)] - return flatten_list([pr, padding]) - - -def flatten_list(xlist): - """Flattens a list of lists. - - :param xlist: list of lists - :returns: a flattened list - """ - return [item for sublist in xlist for item in sublist] - - -# helper functions to manipulate the dataframes -def get_hamming_dist(df: pd.DataFrame, depth_val: int, width_val: int): - """ - Get Hamming distance from a dataframe for a particular depth and width. - - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param depth_val: depth of quantum circuit - :param width_val: width of quantum circuit - :return: smaller dataframe - """ - idx = df.Depth == depth_val - jdx = df.Width == width_val - return df[idx & jdx].reset_index(drop=True) - - -def get_hamming_dists_fn_width(df: pd.DataFrame, depth_val: int): - """ - Get Hamming distance from a dataframe for a particular depth. - - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param depth_val: depth of quantum circuit - :return: smaller dataframe - """ - idx = df.Depth == depth_val - return df[idx].reset_index(drop=True) - - -def get_hamming_dists_fn_depth(df: pd.DataFrame, width_val: int): - """ - Get Hamming distance from a dataframe for a particular width. - - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param width_val: width of quantum circuit - :return: smaller dataframe - """ - jdx = df.Width == width_val - return df[jdx].reset_index(drop=True) - - -def basement_function(number: float): - """ - Once you are in the basement you can't go lower. Defined as - - basement_function(number) = |floor(number)*heaviside(number,0)|, - - where heaviside(number,0) implies the value of the step function is - zero if number is zero. - - :param number: the basement function is applied to this number. - :returns: basement of the number - """ - basement_of_number = np.abs(np.floor(number) * np.heaviside(number, 0)) - return basement_of_number - - -def CNOT_X_basis(control, target) -> Program: - """ - The CNOT in the X basis, i.e. - - CNOTX = |+X+| * I + |-X-| * Z - - where |+> and |-> are the +/- eigenstate of the Pauli X operator and * denotes a tensor product. - - :param control: qubit label - :param target: qubit label - :return: program - """ - prog = Program() - prog += H(control) - prog += CZ(control, target) - prog += H(control) - return prog - - -# ================================================================================================== -# Graph tools -# ================================================================================================== - - -def generate_connected_subgraphs(G: nx.Graph, n_vert: int): - """ - Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all - subgraphs with n_vert connect vertices. - - :params n_vert: number of vertices of connected subgraph. - :params G: networkx Graph - :returns: list of subgraphs with n_vert connected vertices - """ - subgraph_list = [] - for sub_nodes in itertools.combinations(G.nodes(), n_vert): - subg = G.subgraph(sub_nodes) - if nx.is_connected(subg): - subgraph_list.append(subg) - return subgraph_list From 13ee64735ca17301dda201118ef0f2dade3dac0f Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 19 Jul 2019 12:05:58 -0400 Subject: [PATCH 18/49] Revert after accidental deletion. --- forest/benchmarking/volumetrics.py | 590 +++++++++++++++++++++++++++++ 1 file changed, 590 insertions(+) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index e69de29b..8e1a60e2 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -0,0 +1,590 @@ +from typing import Tuple, Sequence, Callable, Any, List +from copy import copy +import networkx as nx +import numpy as np +import random +import itertools +import pandas as pd +from scipy.spatial.distance import hamming +from scipy.special import comb +from dataclasses import dataclass +from functools import partial + +from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate +from pyquil.quilatom import QubitPlaceholder +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.api import QuantumComputer, BenchmarkConnection +from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET +from rpcq.messages import TargetDevice +from rpcq._utils import RPCErrorError + +from forest.benchmarking.randomized_benchmarking import get_rb_gateset +from forest.benchmarking.distance_measures import total_variation_distance as tvd +from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary + + +@dataclass +class CircuitTemplate: + generators: List[Callable] + + # def create_unit(self): + # # returns a function that can be used as a generator in another template + # return lambda qc, graph, width, depth, sequence: sum(gen(qc, graph, width, depth, + # sequence) for gen in + # self.generators) + + def append(self, other): + self.generators += other.generators + + # TODO: store the pattern? + # TODO: add reps keyword to pattern? + + def __add__(self, other): + """ + Concatenate two circuits together, returning a new one. + + :param Circuit other: Another circuit to add to this one. + :return: A newly concatenated circuit. + :rtype: Program + """ + ckt = CircuitTemplate(self.generators) + ckt.append(other) + return ckt + + def __iadd__(self, other): + """ + Concatenate two circuits together using +=, returning a new one. + """ + self.append(other) + return self + + def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None, pattern=None): + if width is not None: + graph = random.choice(generate_connected_subgraphs(graph, width)) + + if pattern is None: + # by default sweep over each generator in sequence repetitions many times + pattern = range(len(self.generators)) + + if sequence is None: + sequence = [] + + def _do_pattern(patt): + for elem in patt: + if isinstance(elem, int): + # the elem is an index; we use the generator at this index to generate the + # next program in the sequence + sequence.append(self.generators[elem](graph=graph, qc=qc, width=width, + sequence=sequence)) + elif len(elem) == 2: + # elem[0] is a pattern that we will execute elem[1] many times + for _ in range(elem[1]): + _do_pattern(elem[0]) + else: + raise ValueError('Pattern is malformed. A pattern is a list where each element ' + 'can either be a generator index or a (pattern_i, num) tuple, ' + 'where num is an integer indicating how many times to ' + 'repeat the associated pattern_i.') + + for _ in range(repetitions): + _do_pattern(pattern) + + return sequence + + def sample_program(self, graph, repetitions, qc=None, width=None, sequence = None, + pattern = None): + return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence, pattern)) + + +# ================================================================================================== +# Gate Sets +# ================================================================================================== +def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): + """ + Create a program comprised of single qubit gates randomly placed on the nodes of the + specified graph. The gates are chosen uniformly from the list provided. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I, X, Z] or [I, X]. + :return: A program that randomly places single qubit gates on a graph. + """ + program = Program() + for q in graph.nodes: + gate = random.choice(gates) + program += gate(q) + return program + + +def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): + """ + Write a program to randomly place two qubit gates on edges of the specified graph. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + program = Program() + # do the two coloring with pragmas? + # no point until fencing is over + for a, b in graph.edges: + gate = random.choice(gates) + program += gate(a, b) + return program + + +def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): + """ + Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of + the specified graph. Each uniformly random choice of Clifford is implemented in the native + gateset. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that randomly places single qubit Clifford gates on a graph. + """ + num_qubits = len(graph.nodes) + + q_placeholder = QubitPlaceholder() + gateset_1q = get_rb_gateset([q_placeholder]) + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) + rand_cliffords = clif_n_inv[0:num_qubits] + + prog = Program() + for q, clif in zip(graph.nodes, rand_cliffords): + gate = address_qubits(clif, qubit_mapping={q_placeholder: q}) + prog += gate + return prog + + +def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): + """ + Write a program to place random two qubit Cliffords gates on edges of the graph. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + num_2q_gates = len(graph.edges) + q_placeholders = QubitPlaceholder.register(n=2) + gateset_2q = get_rb_gateset(q_placeholders) + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) + rand_cliffords = clif_n_inv[0:num_2q_gates] + + prog = Program() + # do the two coloring with pragmas? + # no point until fencing is over + for edges, clif in zip(graph.edges, rand_cliffords): + gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], + q_placeholders[1]: edges[1]}) + prog += gate + return prog + + +def dagger_all_prior(sequence: List[Program]): + return merge_programs(sequence).dagger() + + +def dagger_previous(sequence: List[Program]): + return sequence[-1].dagger() + + +def _qubit_perm_to_bitstring_perm(qubit_permutation: List[int]): + bitstring_permutation = [] + for bitstring in range(2**len(qubit_permutation)): + permuted_bitstring = 0 + for idx, q in enumerate(qubit_permutation): + permuted_bitstring |= ((bitstring >> q) & 1) << idx + bitstring_permutation.append(permuted_bitstring) + return bitstring_permutation + + +def random_qubit_permutation(graph: nx.Graph): + qubits = list(graph.nodes) + permutation = list(np.random.permutation(range(len(qubits)))) + + gate_definition = DefPermutationGate("Perm" + "".join([str(q) for q in permutation]), + _qubit_perm_to_bitstring_perm(permutation)) + PERMUTE = gate_definition.get_constructor() + p = Program() + p += gate_definition + p += PERMUTE(*qubits) + return p + + +def random_su4_pairs(graph: nx.Graph): + qubits = list(graph.nodes) + prog = Program() + for q1, q2 in zip(qubits[::2], qubits[1::2]): + matrix = haar_rand_unitary(4) + gate_definition = DefGate(f"RSU4_{q1}_{q2}", matrix) + RSU4 = gate_definition.get_constructor() + prog += gate_definition + prog += RSU4(q1, q2) + return prog + + +def graph_restricted_compilation(qc, graph, program): + qubits = list(graph.nodes) + + # restrict compilation to chosen qubits + isa_dict = qc.device.get_isa().to_dict() + single_qs = isa_dict['1Q'] + two_qs = isa_dict['2Q'] + + new_1q = {} + for key, val in single_qs.items(): + if int(key) in qubits: + new_1q[key] = val + new_2q = {} + for key, val in two_qs.items(): + q1, q2 = key.split('-') + if int(q1) in qubits and int(q2) in qubits: + new_2q[key] = val + + new_isa = {'1Q': new_1q, '2Q': new_2q} + + new_compiler = copy(qc.compiler) + new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) + # try to compile with the restricted qubit topology + try: + native_quil = new_compiler.quil_to_native_quil(program) + except RPCErrorError as e: + if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ + in str(e): + raise ValueError("The program could not be compiled onto the given subgraph.") + raise + + return native_quil + + +### +# Templates +### + +def get_rand_1q_template(gates: Sequence[Gate]): + def func(graph, **kwargs): + partial_func = partial(random_single_qubit_gates, gates=gates) + return partial_func(graph) + return CircuitTemplate([func]) + + +def get_rand_2q_template(gates: Sequence[Gate]): + def func(graph, **kwargs): + partial_func = partial(random_two_qubit_gates, gates=gates) + return partial_func(graph) + return CircuitTemplate([func]) + + +def get_rand_1q_cliff_template(bm: BenchmarkConnection): + def func(graph, **kwargs): + partial_func = partial(random_single_qubit_cliffords, bm=bm) + return partial_func(graph=graph) + return CircuitTemplate([func]) + + +def get_rand_2q_cliff_template(bm: BenchmarkConnection): + def func(graph, **kwargs): + partial_func = partial(random_two_qubit_cliffords, bm=bm) + return partial_func(graph=graph) + return CircuitTemplate([func]) + + +def get_dagger_all_template(): + def func(qc, sequence, **kwargs): + prog = dagger_all_prior(sequence) + native_quil = qc.compiler.quil_to_native_quil(prog) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) + + +def get_dagger_previous(): + def func(qc, sequence, **kwargs): + prog = dagger_previous(sequence) + native_quil = qc.compiler.quil_to_native_quil(prog) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) + + +def get_rand_qubit_perm_template(): + def func(graph, qc, **kwargs): + prog = random_qubit_permutation(graph) + native_quil = qc.compiler.quil_to_native_quil(prog) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) + + +def get_rand_su4_template(): + def func(graph, qc, **kwargs): + prog = random_su4_pairs(graph) + native_quil = graph_restricted_compilation(qc, graph, prog) + # remove gate definitions and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) + + +def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], + depths: List[int], num_circuit_samples: int, + graph: nx.Graph = None, pattern = None): + if graph is None: + graph = qc.qubit_topology() + + programs = {width: {depth: []} for width in widths for depth in depths} + + for width, depth_array in programs.items(): + for depth, prog_list in depth_array.items(): + for _ in range(num_circuit_samples): + prog = ckt.sample_program(graph, repetitions=depth, width=width, + qc=qc, pattern=pattern) + prog_list.append(prog) + + return programs + + +def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = 500, + use_active_reset: bool = False, + use_compiler: bool = False): + reset_prog = Program() + if use_active_reset: + reset_prog += RESET() + + results = {width: {depth: []} for width, depth_array in program_array.items() + for depth in depth_array.keys()} + + for width, depth_array in program_array.items(): + for depth, prog_list in depth_array.items(): + for program in prog_list: + prog = program.copy() + + # TODO: provide some way to ensure spectator qubits measured when relevant. + qubits = sorted(list(program.get_qubits())) + + ro = prog.declare('ro', 'BIT', len(qubits)) + for idx, q in enumerate(qubits): + prog += MEASURE(q, ro[idx]) + + prog.wrap_in_numshots_loop(num_shots) + + if use_compiler: + prog = qc.compiler.quil_to_native_quil(prog) + + exe = qc.compiler.native_quil_to_executable(prog) + shots = qc.run(exe) + results[width][depth].append(shots) + + return results + + +# def do_volumetric_measurements(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], +# depths: List[int], +# num_circuit_samples: int, graph: nx.Graph = None, pattern = None, +# num_shots: int = 500, +# use_active_reset: bool = False, +# compile_circuits: bool = False): +# +# +# prog_array = generate_volumetric_program_array(qc, ckt, widths, depths, num_circuit_samples, +# graph, pattern) +# +# return [] + + + +# ================================================================================================== +# Analysis +# ================================================================================================== +def get_error_hamming_weight_distributions(noisy_results, perfect_results): + distrs = {width: {depth: []} for width, depth_array in noisy_results.items() + for depth in depth_array.keys()} + + for width, depth_array in distrs.items(): + for depth, samples in depth_array.items(): + + noisy_ckt_sample_results = noisy_results[width][depth] + perfect_ckt_sample_results = perfect_results[width][depth] + + for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, + perfect_ckt_sample_results): + + hamm_dist_per_shot = [hamming_distance(ideal_result, shot) for shot in noisy_shots] + + # Hamming weight distribution + hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) + samples.append(np.asarray(hamm_wt_distr)) + return distrs + # TODO: separate these out + # wt_dist_rand = np.asarray(hamming_dist_rand(width)) # random guessing + # wt_dist_ideal = np.zeros_like(wt_dist_rand) # perfect + # wt_dist_ideal[0] = 1 + + # Total variation distance + # tvd_data_ideal = tvd(wt_dist_data, wt_dist_ideal) + # tvd_data_rand = tvd(wt_dist_data, wt_dist_rand) + + # Probability of success + # pr_suc_data = hamm_wt_distr[0] + # pr_suc_rand = wt_dist_rand[0] + + # Probability of success with basement[ log_2(width) - 1 ] errors + # I.e. error when you allow for a logarithmic number of bit flips from the answer + # num_bit_flips_allowed_from_answer = int(basement_function(np.log2(width) - 1)) + # pr_suc_log_err_data = sum( + # [wt_dist_data[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) + # pr_suc_log_err_rand = sum( + # [wt_dist_rand[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) + # + # + # sample_stats = { + # 'Hamming dist. data': wt_dist_data, + # 'TVD(data, ideal)': tvd_data_ideal, + # 'TVD(data, rand)': tvd_data_rand, + # 'Pr. success data': pr_suc_data, + # # 'Pr. success rand': pr_suc_rand, + # 'loge = basement[log_2(Width)-1]': num_bit_flips_allowed_from_answer, + # 'Pr. success loge data': pr_suc_log_err_data} + # # 'Pr. success loge rand': pr_suc_log_err_rand} + # + # samples.append(sample_stats) + + # return stats + + +def hamming_distance(arr1, arr2): + """ + Compute the hamming distance between arr1 and arr2, or the total number of indices which + differ between them. + + The hamming distance is equivalently the hamming weight of the 'error vector' between the + two arrays. + + :return: hamming distance between arr1 and arr2 + """ + n_bits = np.asarray(arr1).size + if not n_bits == np.asarray(arr2).size: + raise ValueError('Arrays must be equal size.') + + return hamming(arr1, arr2) * n_bits + + +def get_hamming_wt_distr_from_list(wt_list, n_bits): + """ + Get the distribution of the hamming weight of the error vector. + + :param wt_list: a list of length num_shots containing the hamming weight. + :param n_bits: the number of bit in the original binary strings. The hamming weight is an + integer between 0 and n_bits. + :return: the relative frequency of observing each hamming weight + """ + num_shots = len(wt_list) + + if n_bits < max(wt_list): + raise ValueError("Hamming weight can't be larger than the number of bits in a string.") + + # record the fraction of shots that resulted in an error of the given weight + return [wt_list.count(weight) / num_shots for weight in range(n_bits + 1)] + + +def hamming_dist_rand(num_bits: int, pad: int = 0): + """Return a list representing the Hamming distribution of + a particular bit string, of length num_bits, to randomly drawn bits. + + :param num_bits: number of bits in string + :param pad: number of zero elements to pad + returns: list of hamming weights with zero padding + """ + N = 2 ** num_bits + pr = [comb(num_bits, ndx) / (2 ** num_bits) for ndx in range(0, num_bits + 1)] + padding = [0 for _ in range(pad)] + return flatten_list([pr, padding]) + + +def flatten_list(xlist): + """Flattens a list of lists. + + :param xlist: list of lists + :returns: a flattened list + """ + return [item for sublist in xlist for item in sublist] + + +# helper functions to manipulate the dataframes +def get_hamming_dist(df: pd.DataFrame, depth_val: int, width_val: int): + """ + Get Hamming distance from a dataframe for a particular depth and width. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param depth_val: depth of quantum circuit + :param width_val: width of quantum circuit + :return: smaller dataframe + """ + idx = df.Depth == depth_val + jdx = df.Width == width_val + return df[idx & jdx].reset_index(drop=True) + + +def get_hamming_dists_fn_width(df: pd.DataFrame, depth_val: int): + """ + Get Hamming distance from a dataframe for a particular depth. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param depth_val: depth of quantum circuit + :return: smaller dataframe + """ + idx = df.Depth == depth_val + return df[idx].reset_index(drop=True) + + +def get_hamming_dists_fn_depth(df: pd.DataFrame, width_val: int): + """ + Get Hamming distance from a dataframe for a particular width. + + :param df: dataframe generated from data from 'get_random_classical_circuit_results' + :param width_val: width of quantum circuit + :return: smaller dataframe + """ + jdx = df.Width == width_val + return df[jdx].reset_index(drop=True) + + +def basement_function(number: float): + """ + Once you are in the basement you can't go lower. Defined as + + basement_function(number) = |floor(number)*heaviside(number,0)|, + + where heaviside(number,0) implies the value of the step function is + zero if number is zero. + + :param number: the basement function is applied to this number. + :returns: basement of the number + """ + basement_of_number = np.abs(np.floor(number) * np.heaviside(number, 0)) + return basement_of_number + + +# ================================================================================================== +# Graph tools +# ================================================================================================== + + +def generate_connected_subgraphs(G: nx.Graph, n_vert: int): + """ + Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all + subgraphs with n_vert connect vertices. + + :params n_vert: number of vertices of connected subgraph. + :params G: networkx Graph + :returns: list of subgraphs with n_vert connected vertices + """ + subgraph_list = [] + for sub_nodes in itertools.combinations(G.nodes(), n_vert): + subg = G.subgraph(sub_nodes) + if nx.is_connected(subg): + subgraph_list.append(subg) + return subgraph_list From 082ec09bcdbe633412cb8374ae1a9ab1c50d8e51 Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 25 Jul 2019 12:56:36 -0400 Subject: [PATCH 19/49] Expand support for patterns. --- examples/volumetrics.ipynb | 1496 +++++++++++----------------- forest/benchmarking/volumetrics.py | 57 +- 2 files changed, 642 insertions(+), 911 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 5df2dc54..780ff193 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -79,7 +79,7 @@ }, { "data": { - "image/png": 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lSmiTW4hnxcZC0aIvvM/3Mvrs2bG6dElmdQpAhjrN02u2dbo4YgQuLi7kzZuXvXv3StETxsXeHn29eujf8Mv1Oh2/Ab3HjOHWrVvpmUyYKCl85uaZtk4v9E9bp3zffcfS6tWZN28e1tbWmZNTiFd08+ZNfK5cIfENOwRZ2djgvnEjKSkplC5dmvnz55OSkpLOKYUpkcJnTiIjHxe9J3gC1jzuafjxM19mC1QJDIQDBzIjpRCvLCoqisqVK/OOpyfZZ840tB97Hba2MHUquf/v/5g7dy6bNm1i0aJFVKlShfDw8IwJLYyfEubDy0spnU4pw7Xcf4+aoOY/89xzD51OqWbNtH4HQvxn9erVKn/+/GrZsmWPn5wzRylb21R/zp/7eba1NRz/DL1er5YsWaIKFSqkOnbsqK5du5aJ70oYA7niMxexsYaJLG86V+nJtk5CaEiv1zNq1Cj69evH5s2badu27eMXe/QwNJz28gJra8NErSfZ2Bie9/IyHNejx3Pn1+l0eHt7c+rUKfLly0e5cuWYMWMGSWk0wBbmR2Z1movJk2H06FRnvnkCJwCFYZhz/D/PPcfGBsaMgUGDMi6nEC9w7949vL29uXnzJqtXr6ZAgQJpH3z9umGpTlSUYXF6njyGJQsdO77W7M3o6Gj69OnD1atXmTlzpjRftwCyjs9cvKCt0ySgDJAdWAk0Bo4Az83dlLZOQkNnz56lSZMm1KhRg6CgILJnz/7iL7CzS5cPaaVLl2br1q2sXbuWTp06UblyZaZOnSqNG8yYDHWaixe0daqCYYFvDgyNfKtj2LcsVdLWSWhgy5YtuLu706dPH+bNm/fyopfOdDodzZo14+TJk5QuXZoKFSowfvx44t9i7aAwXlL4zMUL2jo9S4dh2DNV0tZJZCKlFFOmTOGrr74iODiY7t27a5rH1taWMWPGEBkZSWRkJOXKlWPDhg2aZhLpTwqfuXB2NtzUf8YdYAsQDyQDy4GdQP3UziFtnUQmiouLo127dqxcuZLw8HA8PDy0jvSfjz76iJCQEGbNmsWAAQNo2LAhZ8+e1TqWSCdS+MxFx46pPp0EjATsgPxAABAClErlWKVUmucRIj1duHABd3d3wLDfo7HeT6tfvz5RUVHUrFmTqlWrMnz4cB4+fKh1LPGWpPCZC3t7kuvWfa6tkx0QCdzHcPUXDtRN5cv1wNYsWVi7ezcy0VdkpF27duHm5kabNm1YtmwZtq+7KD2TZc+encGDB3P06FHOnz9P6dKlWbVqlfw7MWFS+MzE33//TZc//yQpS5Y3+norW1tyT5rE6NGjqVmzJgeki4vIAPPmzaNFixYsXLiQgQMHotPptI70ygoXLszy5ctZvnw53333HbVr1yZKZkGbJCl8ZuDkyZO4ublR/IsvyB4Q8MZtnar06sXhw4dp3749n3/+Od7e3ly8eDFjQguLkpiYSPfu3Zk5cya7d++mXr16Wkd6Yx4eHhw8eJAWLVpQp04d+vbty507d7SOJV6DFD4T9/vvv+Pp6cnYsWMZPXo0uh49YOpUQzF72adpne6/ovdvh4ssWbLQpUsXTp8+TbFixfjkk0/w9fXl/v37mfBuhDn6+++/qVOnDlevXiU8PJySJUtqHemtZc2alV69enHixAni4uJwdHTk559/Rq9/0z0kRKbSsl+aeDs//fSTsre3V6Ghoc+/GBlp6L1pba2Ujc3TfQxtbAzPN2tmOO4FLly4oLy9vVWhQoXUjz/+qJKTkzPo3QhzFBkZqT744AM1atQolZKSonWcDBMZGanc3NxU5cqV1f79+7WOI15CWpaZIL1ez8iRIwkMDGTjxo18/PGz+y08IZ3aOh04cIABAwZw+/Ztpk2bRt26qU2REeKx5cuX069fP3744QeaNWumdZwMp9frWbJkCcOGDaNhw4Z899132Nvbax1LpEIKn4mJj4+nY8eOXLx4kZCQEOwycUdppRQhISEMHjyYkiVLMnXqVMqUKZNp31+YhpSUFIYOHcqaNWsICQnBycLWht69e5cxY8awdOlSfH196dmzJ1mzSndIYyL3+EzI9evXqVOnDkopfv/990wtemBo6+Tl5cWJEyf49NNP8fT0pEePHsTGxmZqDmG8bt++TcOGDTl8+DAREREWV/QAcuXKxfTp0wkNDSUkJISKFSsSFhamdSzxBCl8JuL06dNUrVqVmjVrsmLFCk13Ss+ePTv9+vXj1KlTWFtbU6ZMGSZNmiR9DS3cyZMnqVy5MmXKlGHz5s3ky5dP60iaKlu2LL///ju+vr54e3vTpk0bLl26pHUsgRQ+k7Bz505q1KjBsGHD+O6777CyMo7/2/LmzYu/vz/79u1j//79ODo6snLlSlnYa4HWrVuHp6cnvr6+TJ8+XYb2/qHT6WjZsiXR0dGUKFGC8uXLM3HiRBISErSOZtk0nFgjXsHSpUuVnZ2d2rZtm9ZRXio0NFRVrFhRubm5qb1792odR2SClJQUNWbMGFWkSBGZzfgKYmJiVOPGjVXJkiXVxo0btY5jsWRyi5FSSjF27FgWLlzIxo0bKVu2rNaRXoler2f58uUMHz6catWqMXHiRIoXL651LJEBHjx4QIcOHbh69SqrV6+mUKFCWkcyGb/99ht9+/aldOnS+Pv7U6LEc7tjigxkHGNm4ikJCQl06NCBjRs3Eh4ebjJFD8DKygpvb29Onz6Nk5MTLi4uDB48WDpbmJlz585RtWpV8uTJw44dO6TovaYGDRpw/PhxqlWrRuXKlfH19eXRo0dax7IYUviMzK1bt6hXrx4PHjwgNDSUggULah3pjdja2jJy5EiOHz/OrVu3cHR0ZPbs2SQlJWkdTbyl7du3U7VqVbp37878+fPJkSOH1pFMUo4cORg6dChHjx4lJiaG0qVLExQUJPfIM4EMdRqRP//8k4YNG9KoUSMmTZpEljdsOG2Mjh49ysCBA7l06RJTpkyhYcOGJtWgWBiG32fMmMHkyZNZsWIFnp6eWkcyK6Ghofj4+GBvb09AQICskc1IGt5fFE/Ys2ePKliwoJozZ47WUTKMXq9XGzduVI6OjqpOnTrqyJEjWkcSryguLk61b99elS9fXv3vf//TOo7ZSkpKUjNnzlT58+dX33zzjbpz547WkcySDHUagcDAQJo2bcpPP/1Ej3+aRZsjnU5HgwYNOHbsGM2aNaNevXp07tyZK1euaB1NvMDly5epUaMG8fHx7Nmzh2LFimkdyWxlzZoVHx8fTpw4wb1793B0dGTx4sXS/DqdSeHTkFKKiRMnMnDgQLZt20aDBg20jpQpsmXLRs+ePTl9+jT58+fHycmJsWPHys7WRmjv3r1UrlwZLy8vVq5cyTvvvKN1JItgb2/PggULWLduHbNnz6Z69eocPHhQ61jmQ+tLTkuVmJiounTpoj755BN16dIlreNo6ty5c6pVq1aqSJEiavHixWbdxd+UzJ8/X9nZ2akNGzZoHcWipaSkqAULFqgCBQqor7/+Wl2/fl3rSCZPrvg0cPfuXRo0aMDVq1fZtWsXhQsX1jqSpooXL87KlSsJDAxk7ty5uLq6EhoaqnUsi5WUlETv3r2ZOnUqu3btomHDhlpHsmhWVlZ07tz5qRaBs2fPJjk5WetoJktmdWay8+fP07BhQzw9PZkxY4a0dnqGUorAwECGDh1K+fLlmTx5MqVKldI6lsW4fv06LVu2JGfOnCxfvpxcuXJpHUk8IyoqCh8fH+7cuUNAQAAeHh5aRzI5csWXiSIjI6lWrRpdu3YlICBAil4qdDodrVq1Ijo6mmrVqlGtWjX69u3LzZs3tY5m9o4cOYKrqyvVq1dn3bp1UvSMlJOTEzt27GDYsGF8+eWXtGvXTiaIvSYpfJkkJCSEBg0aMGfOHPr27Str2F7C2tqawYMHEx0dTXJyMo6Ojvj7+5OYmKh1NLO0atUq6taty+TJkxk/frxZrSE1R09+QPzwww9xdnZmypQp8u/jVWl8j9Hs6fV6NX36dPX++++ryMhIreOYrJMnT6qGDRuqEiVKqNWrVyu9Xq91JLOQnJyshg4dqooVK6YOHz6sdRzxhs6cOaMaNGigPv74Y7V582at4xg9uceXgZKTk+nbty87d+5kw4YNFC1aVOtIJm/btm0MGDCA3LlzM23aNFxdXbWOZLLu3LnDl19+SVxcHIGBgZm+sbFIfxs2bKBfv344OTkxffp0aRCfBhnqzCD379+nSZMmnD17lt27d0vRSyd169bl8OHDtG/fniZNmuDt7c3Fixe1jmVyTp06RZUqVXBwcGDr1q1S9MxEo0aNOH78OC4uLri4uDB69Ghpfp0KKXwZ4NKlS3h4eFC4cGE2btwokwTSWZYsWejSpQtnzpyhePHifPLJJ4wcOZL79+9rHc0kbNiwgRo1ajBkyBBmzpxJtmzZtI4k0pG1tTUjRozg8OHDREdHU6ZMGdasWSPNr5+k8VCr2Tl8+LAqUqSImjRpktyHyiQXLlxQ3t7eqmDBgurHH39UycnJWkcySnq9Xo0fP169//77slGwBfn9999VmTJlVN26dVV0dLTWcYyCFL50tGHDBpU/f34VGBiodRSLFBkZqWrUqKGcnJzUli1btI5jVB48eKC++OILVblyZYvvFGSJEhMTlb+/v8qXL58aMGCAunv3rtaRNCVDnelk9uzZdOnShV9//ZWWLVtqHcciubi4EBoaytixY+nVqxcNGjTgxIkTWsfS3F9//UX16tWxsbEhLCzM4jsFWaJs2bLRr18/Tpw4wc2bNyldujTLli2z3OFPrSuvqUtOTlbffPONcnR0VH/++afWccQ/EhISlL+/v7Kzs1Pdu3dXf//9t9aRNLFjxw5VsGBBNWPGDBl6F//Zt2+fqlSpkqpevbo6dOiQ1nEynVzxvYWHDx/SvHlzDh8+zN69e/noo4+0jiT+kT17dvr16/dUf8OJEycSHx+vdbRMoZQiICCA1q1bs2zZMmmaIJ7i5ubG/v376dChA/Xr16dnz54W1R1JCt8bunbtGp6enuTOnZstW7aQJ08erSOJVOTNmxd/f3/27dtHREQEjo6OrFixwqyHeBISEujSpQvz589n79691KlTR+tIwghlyZKFrl27Eh0djZWVFWXKlGHevHmkpKS82gliY2HyZGjXDho3Nvw5eTJcv56xwdOD1pecpigqKkoVLVpUjR07VoaPTExoaKiqVKmSqlKlitqzZ4/WcdLdlStXlJubm2revLm6f/++1nGECTly5Ijy8PBQFSpUePG/jYgIpby8lLK2Njzg8cPGxvCcl5fhOCMlhe81bdmyRdnZ2ally5ZpHUW8oZSUFLVkyRJVpEgR1bJlS7O5NxseHq6KFCmi/Pz85AOZeCN6vV4tX75cFS5cWHl7e6srV648fcCcOUrZ2iql0z1d8J596HSG4+bM0eaNvIQUvtcwf/58VaBAAbVz506to4h08PDhQ+Xn56fy5s2rBg4cqG7fvq11pDe2cOFCZWdnp9atW6d1FGEG7t27pwYPHqzy5cunpk6dqhITEx8XvRcVvGcfRlr8pPC9gpSUFDV06FDl4OCgTp8+rXUckc6uXLmiOnfurOzt7VVAQIDhH7mJSEpKUn379lUODg7qxIkTWscRZubUqVOqXr16qmWxYio5R45Ui9sKUI6gbEF9BGpnasXPyBr0S5Pql4iLi6NDhw5cuXKFkJAQ8ufPr3UkkUGOHj3KwIEDuXjxIlOnTqVhw4ZGPRPy5s2bfPHFF2TLlo0VK1bIBCuRIZRSXK1alQL79/PsZlXbgC7AKqAycPWf559aKarTgZcXrF6d8WFfkRS+F7h+/TpNmjShWLFi/Pzzz1hbW2sdSWQwpRSbNm1iwIABvP/++0ybNo1PPvlE61jPOXbsGE2bNqVFixZMmDBB9s8TGSc2FooWhVSWAlUDOv/zeCFra7hwAYykGbosZ0jDqVOncHNzo3bt2ixbtkyKnoXQ6XQ0aNCAY8eO0bx5c+rXr0+nTp2Maofr4OBg6tSpw7hx45g8ebIUPZGxFi1K9ekU4ABwHXAAigC9gbjUDtbp0jyPFqTwpSI0NJSaNWsycuRIxo0bh5WV/M9kabJly0bPnj05ffo0dnZ2ODk5MXbsWOTpwcEAACAASURBVB4+fKhZJr1ej6+vLwMGDGDz5s18+eWXmmURFuTYsVSv9v4GkoBgYBdwBDgMjEvtHHFxEBWVgSFfj/xGf8aSJUto1aoVK1as4KuvvtI6jtBYrly5mDRpEgcOHODkyZN8/PHHLF68GL1en6k57t27R9OmTQkLCyMyMpJKlSpl6vcXFuzu3VSftvnnTx+gEJAf6A/8ltZ5bt9O72RvTArfP5RSjB49mm+//ZYdO3ZQu3ZtrSMJI1K8eHFWrlxJUFAQ8+bNw8XFhR07dmTK9z5z5gxubm4UKVKE7du3Y29vnynfVwgA0thPNA+G4c0np3+9cCqYEU2+yqp1AGOQkJBA586dOXv2LPv27aNAgQJaRxJGqmrVquzdu5fAwEA6depE+fLlmTx5MqVKlXq1E8TGGu51HDtm+CSdKxc4O8NXX6V643/z5s20b9+ecePG8fXXX6fvmxHiVTg7G2ZkpjLc+RUQANQHsgH+QKPUzmFjA05OGZny9Wi4lMIo3LhxQ3l4eKjmzZurhw8fah1HmJC4uDg1adIklS9fPtWnTx9148aNtA9+zTZPer1eTZo0SRUqVEjt2rUrk96REKn4++/nf2b/eSSC6gEqF6gCoHxAxaW2kN3aWqnYWK3fyX8seqgzJiaGatWq4ebmRmBgILa2tlpHEibE2tqawYMHEx0dTXJyMo6OjkyfPp2EhISnD5w7Fzw9ISTE8Kn52U/OcXGG50JCwNOThO+/p23btgQGBrJ//37c3d0z7T0J8Rx7e/jsM/SprGnNBswB7gDXgJnAc/PfdTpo0MBoljKABd/j27NnD+7u7vTv35/JkyfLzE3xxuzs7Jg9ezY7d+7kjz/+oEyZMqxevdqwA8TcuTBwIDx6ZPjs+yJKwaNH6L/5hrp//smuXbv44IMPMudNCJEGpRTLPviAN97Qy8YGhg1Lz0hvzSIXsK9cuZI+ffqwdOlS6tWrp3UcYWa2bdvGgAEDqGJlxbzTp8mSxh6AZwEnoAWw7JnXlK0turAwcHHJ4LRCpC05OZk+ffqwe/dudrRqRb7vvjN8iHtVtrYwdSr06JFxId+ARRU+pRQTJkzghx9+YP369Tg7O2sdSZiplJQULrq68sHhw8+1efrXpxgW+xbl+cJnjG2ehGW5f/8+rVu3Jjk5maCgIN57773HIxhxcS8ewdDpDFd6Rlj0wIKGOhMTE+ncuTPBwcHs27dPip7IUFlu3qRYdHSaRW8lkBtIc4tYpeC330xjU09hdi5fvkyNGjUoXLgwGzZsMBQ9MBSxsDDDhzJra0Nxe5KNjeF5Ly/DcUZY9MBCljPcuXOH5s2b884777Bz505y5sypdSRh7l7QnukeMAr4A1jwonP82+Zp0KD0TCbECx09epTGjRvTq1cvBg8e/HyjdhcXw0jE9euGn8+oKMPi9Dx5DEsWOnY0qoksqTH7wvfXX3/RoEED6taty/Tp06WvocgcabR5AvDF0NS3yMvOYWRtnoT527RpEx06dGDWrFl88cUXLz7Yzs5kP5SZ9VBnREQE1apVo3v37nz//fdS9ETmSaPN0xFgO/DNq57HiNo8CfM2b948OnXqREhIyMuLnokz2yu+NWvW0K1bN37++WcaN26sdRxhadJo8xQK/AV8+M/fH2Docn8SOJTaFxhRmydhnvR6PUOGDOHXX39l9+7dlChRQutIGc50Ct8rtnpSSjFt2jRmzJjBli1bqFixonaZheVKo83T10DrJ/4+FUMhnJvaOYytzZMwO3FxcXh7exMbG8vevXvJly+f1pEyhfEvZ4iMhAkTYNMmw9+f/EViY2OY/fbZZzBsGMkVKtC7d2/27t3Lxo0bZfGv0M4LNu980rdADKksZwBUjhzoLl40+okCwjTFxsby+eef4+DgwE8//USOHDm0jpRpjPse32u0elI1azK3fHnOnz/P7t27pegJbb2gzdOTviX1oqcHNltZsXzr1kzfAkmYv+joaNzc3Pj0009ZunSpRRU9MObC95qtnnRxcXx95gwbGjV6vOZECI3o9Xrm5Mr1xm2erGxtKTBjBjNnzsTFxYXff/89XfMJyxUaGoqnpyejRo1i7Nixzy9XsARadcd+oYgIpWxtn+ruHQ+qE6gPQeUEVR7Ub6l1Abe1VSoyUut3ICzYnTt3VMOGDVWNGjXU3UmTnvtZfunD1lapOXOUUoZdGoKCglSJEiVU/fr11dGjRzV+d8KULV68WNnb26vff/9d6yiaMs7C5+WllE731C+DB6BGg/ofqBRQ6/8pgP979peGTqdUs2ZavwNhoaKjo9XHH3+sevXqpRITEw1PzpljKGbP/Ew/99Dpnip6T0pISFABAQGqQIECqmPHjurChQuZ/M6EKdPr9Wr06NGqePHi6uTJk1rH0ZzxFb4X7P307MMJVLAJ7P0kLMP69euVnZ2dmj9//vMvRkYaPpBZWxv230ttP75mzV46WnH37l01YsQIlTdvXjV06FB1586dDHo3wlzEx8crb29vVblyZXXt2jWt4xgF45vVOXkyjB790tlwf2No7nsEcHz2RRsbGDPGZLsKCNOilGL8+PHMnTuX4OBgqlatmvbB6dTm6fLly4waNYoNGzYwfPhwevToQfbs2d/6vQjzcuvWLZo1a0bevHlZtmyZ7Dn6D+MrfO3awfLlLzwkCfgMKAH8kNZB3t6wZEn6ZhPiGQ8ePKBjx45cvHiRNWvWULhw4Uz9/lFRUQwdOpRTp07x3Xff8cUXX1jmZAXxnHPnztGgQQMaNWrEpEmTpHPVE4xvVmcarZ7+pQe8gezArBcdKK2eRAY7d+4c1apV47333iMsLCzTix6Ak5MTGzduZMGCBUyZMoUqVaoQGhqa6TmEcQkPD8fd3Z2+ffsydepUKXrPML7Cl0arJwCFobnv38BqDNvep0laPYkMtH37dqpWrcrXX3/NTz/9hLW1taZ5atWqRUREBP3796dTp040btyYEydOaJpJaCMoKIjPP/+cBQsW0MNItwXSmvEVPmdnw35OqegBRAPrAZtUj/iHtHoSGUQpxfTp0/H29mbVqlX07t3baIYWraysaN26NdHR0dSpU4datWrRtWtXrly5onU0kQmUUkyePJn+/fuzdetWGjRooHUko2V89/jSaPV0HigG5ODpBqM/AG2fPYe1NVy4IK2eRLqKi4vj66+/5vjx44SEhFC0aFGtI73QnTt3mDhxIvPnz6dnz54MGjRImjuYqeTkZHr37k14eDgbNmygSJGXbnpl0Yzviu+fVk888ym6KIahzngMHe3/fTxb9FKAgwUK8Le0eRLp6OLFi3h4eJCcnMyePXuMvugB5M6dm4kTJ3L48GEuXrxIqVKlmD17NklJSVpHE+no3r17NGrUiPPnz7Nr1y4peq/A+AofwLBhz29p/4qsbGzY5e5OuXLl8Pf3l3/k4q3t2rWLKlWq8MUXX/DLL7+Y3JTwDz/8kEWLFrFlyxbWr19P2bJlCQ4OxtgGe8Tr+/cDWfHixVm/fj3vvvuu1pFMg1YLCF/q324Xb9jqKTo6Wn366aeqdOnSatu2bRq/GWGq5s6dq+zs7NSmTZu0jpJutm7dqj755BPl5uamdu3apXUc8YYOHTqkihQpoqZMmaL0er3WcUyK8RY+pd661ZNer1chISGqePHiysvLS507d06jNyJMTXx8vPr6669VmTJl1JkzZ7SOk+5SUlLU0qVL1YcffqiaNGmioqOjtY4kXsOGDRtU/vz5VXBwsNZRTJJxDnX+q0cPCAsDLy/DhJVnhz9tbAzPe3kZjntm6q5Op6NJkyacPHmSihUr4uLiwujRo3n06FEmvglhaq5evUrt2rX5+++/CQ8Pp2TJklpHSndWVla0a9eO06dP4+7ujoeHBz169ODatWtaRxMvMXv2bLp27cr69etp3ry51nFMkvHN6kxLOrR6unjxIoMGDWLfvn1MnTqVFi1aGM1UdGEcIiIiaN68OV26dMHX1xcrK+P+bJhebt26xXfffcfChQvp06cPAwYMIGfOnFrHEk9ISUlh0KBBbNq0iY0bN/LRRx9pHclkmU7hS0dhYWH4+PiQP39+vv/+e5xkzZ8AFi9ezMCBA5k/fz5NmzbVOo4m/vrrL0aOHMkff/zBqFGj6Ny5M9myvbBVhMgEjx49ol27dty+fZs1a9aQRxp0vBXL+Dj7jJo1a3Lo0CGaN29OnTp16NOnD7elxZnFSkpKom/fvowbN46wsDCLLXoAxYoVY9myZaxfv56goCCcnJwICQmRGaAaunbtGp6enuTMmZMtW7ZI0UsHFln4ALJmzUqvXr04efIkSUlJODo68uOPP5KSkqJ1NJGJbty4Qb169Thz5gwRERGUKVNG60hGoVKlSmzfvp0ZM2YwatQoPDw82Ldvn9axLM7JkyepWrUqDRs2ZPHixbIDR3rRdGqNETl06JByd3dXFStWVHv27NE6jsgER44cUcWKFVNDhgxRycnJWscxWsnJyWrhwoWqSJEiqnnz5mY5y9UYbd++Xdnb26ulS5dqHcXsSOF7gl6vV8uXL1eFCxdW7dq1U5cvX9Y6ksggK1euVPnz51crVqzQOorJePTokZowYYLKly+f6tWrl/r777+1jmS2Fi5cqOzt7VVoaKjWUcySxQ51pkan0/Hll19y6tQpihQpgrOzM5MnTyYhIUHraCKdpKSkMGzYMIYMGcLWrVtp3bq11pFMho2NzX97/2XNmpUyZcowbtw4Hj58qHU0s6GUwtfXFz8/P8LCwqhZs6bWkcySFL5U5MyZkwkTJrBv3z527dqFk5MTmzZt0jqWeEt37tyhcePGhIeHExkZSYUKFbSOZJLy58/PjBkz2L9/P8ePH6dUqVIsWLCA5ORkraOZtISEBNq1a8f27dsJDw/H0dFR60hmSwrfC5QsWZL169czY8YM+vbtS+PGjYmJidE6lngD0dHRVK5cGQcHB7Zu3Yqd7Nzx1kqUKMHKlStZu3YtS5cupXz58mzYsEFmgL6BmzdvUrduXRITE/njjz/k5zODSeF7BQ0aNCAqKgoPDw/c3NwYNmwYDx480DqWeEXr1q2jRo0aDBs2jJkzZ8q6tHRWuXJlQkNDmTRpEoMHD6ZWrVpERkZqHctkxMTEUK1aNapWrcqqVauwecMG/eI1aH2T0dRcvnxZtWvXThUuXFgtX75cmsMasZSUFDVmzBhVuHBhFR4ernUci5CUlKTmz5+v3n//fdWqVSsVExOjdSSjtmfPHlWwYEH1ww8/aB3Folhk55b0sGfPHvr06YONjQ0BAQFyv8jI3L9/nw4dOnDt2jVWr15NoUKFtI5kUR4+fIi/vz8zZsygXbt2jBw5kvz582sdy6isWrUKHx8flixZQv369bWOY1FkqPMNVa9enYiICDp06ED9+vXp3r07N27c0DqWwDB0VLVqVfLly8eOHTuk6GngnXfeYeTIkZw8eZLk5GQcHR2ZOHEicXFxWkfTnFKKCRMmMGjQILZv3y5FTwNS+N5ClixZ6Nq1K6dOnSJ79uyUKVOG2bNny+w2DW3dupXq1avTs2dPfvzxR3LkyKF1JItmb2/PrFmz2Lt3LwcOHKBUqVIsWrTIYjskJSUl0bVrV4KCgggPD8fZ2VnrSBZJhjrTUVRUFH369OHWrVvMnDlT1uBkIqUU06ZNY9q0aaxatYoaNWpoHUmkYt++fQwaNIh79+4xefJk6tWrZzE7pNy9e5eWLVuSPXt2Vq5cKbtfaEgKXzpTShEcHMzAgQOpWrUqU6ZM4YMPPtA6lll79OjRf1fea9eu5cMPP9Q6kngBpRTr1q1jyJAhfPDBB0yePJmKFStqHStDXbhwgYYNG1KzZk1mzJhB1qxZtY5k0WSoM53pdDpatmxJdHQ0H3/8MRUqVGD8+PHEx8drHc0sXbhwAXd3dwB27dolRc8E6HQ6mjZtyvHjx2nRogUNGzakXbt2/PXXX1pHyxAHDx6kWrVqdO7cmYCAACl6RkAKXwaxtbVlzJgxREZGcvDgQcqWLcu6detkcW862rlzJ1WqVKFt27YsW7YMW1tbrSOJ15AtWza6d+/O2bNncXBwoFKlSgwcOJBbt25pHS3d/Prrr3z22WfMmjWLfv36WcywrtHTaBmFxdm6dasqXbq0qlevnoqOjtY6jknT6/Vq1qxZyt7eXm3ZskXrOCKdXLlyRXXr1k3lz59fTZkyRcXFxWkd6a18//33qlChQioiIkLrKOIZcsWXSerWrcvRo0epX78+Hh4eDBw4kHv37mkdy+QkJCTQpUsX5s2bx969e/n000+1jiTSSaFChZg3bx47d+5k9+7dfPzxxyxbtgy9Xq91tNeSkpJC3759+eGHH9i7dy+urq5aRxLPkMKXibJly0a/fv04fvw4t2/fxtHRkUWLFpncP2ytXLlyBU9PT+7cucO+ffsoUaKE1pFEBihdujQhISEsW7aMWbNm/bcpril4+PAhzZo148SJE+zZs4dixYppHUmkQmZ1aigiIgIfHx90Oh0BAQHyyfAFwsPDadGiBd27d2f48OFYWclnNkuglGL16tUMGzaMEiVKMGnSJMqXL691rFRdvXqVxo0b4+zszLx582S3dGOm8VCrxUtJSVELFy5UBQsWVJ06dVLXrl3TOpLR+emnn5SdnZ369ddftY4iNJKQkKACAgJUgQIFVPv27dX58+e1jvSUqKgoVbRoUTVu3Djp32sC5GOzxqysrOjYsSOnTp0id+7clCtXjhkzZpCUlKR1NM0lJSXh4+PDpEmTCAsLo3HjxlpHEhrJnj07vXv35syZM3z44YdUqFCBIUOGcOfOHa2jsW3bNmrXrs2ECRMYMWKEzNw0AVL4jESuXLmYNm0aO3fu5LfffqN8+fImc18jI1y/fp26dety7tw59u/fT+nSpbWOJIzAe++9h5+fH8eOHePWrVuUKlUKf39/EhISNMnz008/4e3tzerVq2nTpo0mGcQb0PqSUzxPr9erkJAQVbx4cdWsWTP1v//9T+tImerQoUOqaNGiatiwYSo5OVnrOMKIHT9+XDVq1EgVL15c/fLLLyolJSVTvm9KSooaNmyYcnBwUKdPn86U7ynSj0xuMWLx8fFMnToVf39/evfuzZAhQ8x+kfaKFSvo06cPs2fP5osvvtA6jjARO3bsYPDgwSilmDJlCrVq1cqw7xUfH0/Hjh25ePEi69atk+2WTJAUPhNw8eJFBg0axL59+5g6dSotWrQwu/sIKSkpDB8+nKCgINauXWu0M/eE8dLr9QQGBjJ8+HBKly7NpEmTKFeu3Kt9cWwsLFoEx47B3buQKxc4O8NXX4Gd3X+H3bhxgyZNmvDBBx+waNEirK2tM+bNiIyl6fWmeC2hoaHKyclJ1apVS0VFRWkdJ93cunVL1atXT9WuXVvduHFD6zjCxMXHxyt/f39lZ2enOnXqpC5dupT2wRERSnl5KWVtbXjA44eNjeE5Ly+lIiLUmTNnlIODgxo+fHimDamKjCGFz8QkJSWpWbNmKTs7O+Xj46Nu3bqldaS3cvz4cVWiRAnVr18/lZSUpHUcYUZu376thg4dqvLmzauGDx+u7ty58/QBc+YoZWurlE73dMF79qHTqeQcOdSgd99VCxYs0ObNiHQlszpNTNasWenVqxcnT54kMTGR0qVLM3/+fJPc2DMkJARPT09GjRqFv7+/dK0X6Sp37txMmDCBI0eOcOXKFUqVKkVAQACJiYkwdy4MHAiPHhnKG9AOKAS8B5QCFvx7IqXIkpDAhMREOicmavJeRPqSe3wm7vDhw/j4+BAfH8/MmTOpVq2a1pFeSq/XM3bsWH7++WdWr14tHWtEpjh27BhDhgzBOiqKoOvXyfpMETsBOAA5gFOAJ7ARqPTkQba2EBYGLi6ZE1pkCCl8ZkApxYoVKxg8eDC1a9dm0qRJFCpUSOtYqbp37x7t27fnxo0bBAcHU7BgQa0jCQsT6+5Ovj17yPKCY05jKHzfA0/NLdbpwMsLVq/OwIQio8lQpxnQ6XR8+eWXnDp1isKFC+Pk5MSUKVMMQzpG5OzZs7i5uVGgQAH++OMPKXoi88XGYn/wYJpFrydgCzhiGPZs8OwBSsFvv8H16xkYUmQ0KXxmJGfOnEyYMIF9+/axc+dOnJyc2LRpk9axANi8eTPVq1f/b7sWaeArNLFo0QtfngPcB3YBzTAMez5Hp3vpeYRxk8JnhkqWLMn69evx9/enb9++NG7cmJiYGE2yKKWYNGkSnTp1Ys2aNXTr1k2THEIAhnV68fEvPCQL4A5cAuamdkBcHERFpX82kWmk8JmxBg0aEBUVhYeHB25ubgwbNowHDx5k2vd/9OgRX375JcHBwURERODu7p5p31uIVN29+8qHJgN/pvXi7dvpkUZoRAqfmcuRIweDBw/m2LFjXLp0CUdHR3755Rcyek7TX3/9RfXq1cmWLRs7d+6kSJEiGfr9hHgluXKl+nQssBJ4AKQAW4AVQJ20zpMnTwaEE5lFCp+FeP/991m6dCmrVq1i6tSp1KhRgyNHjrzaF8fGwuTJ0K4dNG5s+HPy5DRv8O/YsYOqVavSoUMHFi9ejI2NTTq+EyHegrMzpNJmTIdhWLMIkAcYCMwAPk/tHDY24OSUgSFFRpPlDBYoJSWFn3/+GV9fX7y8vBg3bhz58uV7/sDISJgwAf6dIPPkvREbG8MMt88+g2HDwNUVpRQBAQF89913LF++nDp10vy8LIQ2YmNRRYuie8l9vheytoYLF57q4SlMi1zxWaAsWbLQtWtXoqOjyZYtG6VLl2bOnDkkJyc/PmjuXPD0hJAQQ8F79hdFXJzhuZAQ8PQkaeZMOnXqxIIFC9i7d68UPWGULiclseudd9C/6Ql0OmjQQIqeiZMrPkFUVBR9+vTh1q1bzJw5k5onTz5u5/SK4qysWFa+PF/u2sU777yTgWmFeDNbtmyhY8eOjG/alK+WLEH3Gj/f/5HOLWZBCp8ADMsOgoODWeLjQ/CNG+R4pvfnLGAREAW0+ee/nzuHrS06+aUgjExycjK+vr4sXbqU5cuXU7Nmzad7db4qW1uYOhV69Mi4sCJTSOETT0n+/HOs1q9/bgx8DYZx8S1AHKkXPmnnJIzNpUuXaNOmDba2tixduhR7e/vHL/5b/OLi/mtUnSqdznBPW4qe2ZB7fOKx2FiybtuW6g9FM6ApkMoUmMeknZMwIps2bcLFxYUGDRqwadOmp4seGIpYWJjhw5q1taG4PcnGxvC8l5fhOCl6ZkP2gRGPpUcbpn/bOQ0a9PbnEuINJCUl4evry/LlywkKCsLDwyPtg11cDCMU168bfm6jogyL0/PkMSxZ6NhRJrKYISl84rFXaOf0UtLOSWjo4sWLtG7dmvfee49Dhw5h96pFy85OPqxZEBnqFI+9RjunF5J2TkIDGzZswMXFhc8//5yNGze+etETFkeu+MRjabRzem3SzklkoqSkJEaMGMHKlStZs2YN1atX1zqSMHJyxSceS6OdExga9sZj6GOY8s9/J6d2oLRzEpnowoUL1KxZkxMnTnDo0CEpeuKVSOETj3XsmOZL4wAbYCKw7J//HpfKccnJySS3a5cR6YR4yvr163F1dcXLy4v169eTP39+rSMJEyGFTzxmb2/ovanTPffSt4B65vHtM8conY69uXJRpmZNVq5ciV7/xo2hhEhTYmIiAwYMoHfv3qxdu5ZBgwZhZSW/ysSrk58W8bRhw55fz/SKdDY2ePz2G3PmzMHf358KFSrw66+/ZvgWSMJynD9/nho1anDmzBkOHTpEtWrVtI4kTJAUPvE0V1dDhwpb29f7un/aOelcXfm///s/wsPD8fPzY+TIkVStWpXff/89Y/IKi7Fu3ToqV65My5Yt+fXXX1PfUUSIVyAty0Tq0qmdk16vZ9WqVYwePZoiRYowfvx4qlatmoHBhblJTExkyJAhrF27lpUrV+Lm5qZ1JGHi5IpPpC6d2jlZWVnRpk0bTp48Sdu2bWndujWNGjV69U1whUX73//+h7u7O+fOnePQoUNS9ES6kCs+8XLp2M4pISGBH374gQkTJlCjRg3GjBmDo6NjhsQWpm3t2rV069aNYcOG0a9fP3SpTLoS4k1I4ROaePjwIQEBAUybNo1GjRoxevRoihUrpnUsYQQSEhIYPHgwv/76KytXrqRKlSpaRxJmRoY6hSbeeecdhg4dytmzZylSpAiVKlWiV69eXL16VetoQkPnzp2jevXqnD9/nkOHDknRExlCCp/QVO7cufHz8+PUqVNYW1tTrlw5Bg8ezM2bN7WOJjLZ6tWrcXNzw9vbm7Vr15JHWt+JDCKFTxgFOzs7pk2bxrFjx7h//z6lSpXi22+/5d69e1pHExksISEBHx8fBg0axMaNG+nbt6/czxMZSgqfMCqFCxdm7ty5REZGcu7cORwcHJgyZQqPHj3SOprIAH/++SfVq1fnypUrHDp0CFdXV60jCQsghU8YpY8++oglS5YQGhrK/v37KVmyJLNnzyYxMVHraCKdBAUF4ebmRocOHQgODiZ37txaRxIWQmZ1CpNw8OBBRo4cSXR0NKNHj8bb25usWWVXLVMUHx/PgAED2Lx5M6tWrcLFxUXrSMLCyBWfMAmVKlVi06ZNLFu2jEWLFlGuXDkCAwOlEbaJiYmJoVq1asTGxnLo0CEpekITUviESXF3dyc0NJSZM2cyZcoUKlasyIYNG6QRtglYtWoVVatWpXPnzgQGBpIrvTY+FuI1yVCnMFlKKUJCQvD19eW9995j/Pjx1KpVS+tY4hnx8fF88803bNu2jcDAQCpWrKh1JGHh5IpPmCydToeXlxdHjx6lV69edO3alf/7v/9j//79WkcT/zhz5gxubm7cunWLgwcPStETRkEKnzB5WbJkoW3btkRHR9OqVStatGjB559/zrFjx7SOZtFWrFhB9erV6d69OytXBwNi9QAAB/9JREFUrpShTWE0ZKhTmJ34+HjmzZvHxIkTqVWrFmPGjKFUqVJax7IYcXFx9OvXjz/++IPAwEAqVKigdSQhniJXfMLsWFtb069fP2JiYnBycqJ69ep07tyZ8+fPax3N7J0+fRo3Nzfu3r3LwYMHpegJoySFT5itnDlzMnz4cM6cOUPBggWpWLEiPj4+XLt2TetoZmn58uW4u7vTs2dPVqxYwXvvvad1JCFSJYVPmL08efIwfvx4oqOjyZo1K2XLlmXo0KHcunVL62hm4dGjR3Tt2pWxY8eyfft2unXrJr02hVGTwicshr29Pf7+/hw5coTbt29TqlQp/Pz8uH//vtbRTNapU6eoUqUKDx8+5MCBA5QvX17rSEK8lBQ+YXE++OADfvjhB8LDwzl9+jQODg5MmzaNuLg4raOZlKVLl+Lh4UGfPn1Yvnw57777rtaRhHglMqtTWLzjx48zatQoIiIiGDlyJJ06dSJ79uxaxzJajx49wsfHhz179hAYGIizs7PWkYR4LXLFJyxeuXLlWLNmDWvXrmXt2rU4OjqyZMkSUlJStI5mdE6ePEnlypVJTEzkwIEDUvSESZLCJ8Q/XF1d2bJlC4sWLWL+/Pk4OTkRHBwsjbD/sXjxYmrWrEn//v1ZsmQJOXPm1DqSEG9EhjqFSIVSii1btjBixAgAxo0bR/369S1ytuLDhw/p3bs34eHhBAUFUa5cOa0jCfFW5IpPiFTodDrq16/PgQMHGD58OAMGDMDDw4OwsDCto2WqEydOULlyZfR6PZGRkVL0hFmQwifEC+h0Opo3b05UVBTdunWjU6dOfPrpp0RGRmodLUMppVi4cCGenp4MGjSIxYsXy9CmMBsy1CnEa0hKSuLnn3/Gz88PV1dX/Pz8zO4q6MGDB/Ts2ZMDBw4QFBRE2bJltY4kRLqSKz4hXkO2bNno1q0bZ8+excPDgzp16tC2bVtiYmK0jpYujh8/jqurK1myZCEyMlKKnjBLUviEeAM2Njb079+fmJgYSpcujZubG19//TUXL17UOtobUUrx008/UatWLYYOHcrChQt55513tI4lRIaQwifEW3j33XcZOXIkZ86cIV++fHzyySf069ePv//+W+tor+zBgwd4e3vj7+9PWFgYHTp00DqSEBlKCp8Q6SBv3rxMmDCBEydOoJSiTJkyDB8+nNu3b2sd7YWOHTuGi4sLOXLkICIigjJlymgdSYgMJ4VPiHRUsGBBvv/+ew4fPsz169f/v707CI3iiuM4/t2aQ3bTNqRtROhBKGSjh6SHGDwJQkQwPaWHQFoK2oKSuyCysBFx0cOC4MVqDOSgHoyB9GKggWAOLUoEm6xFaG5CLxEKm6SJaMh4GGzMZpcqmN1Z5vu5hLyZffzn9OMN/3mPdDpNLpdjZWWl1qVtEQQBw8PD9PT0kMlkGBkZIZVK1bosqSrs6pR20MLCAkNDQ0xPT3PmzBkGBwdpbGysaU3Ly8ucOnWKQqHA2NgY+/btq2k9UrW54pN2UFtbG7dv32ZqaoqZmRna2tq4fv06r169qkk9c3NzdHV10dTUxMOHDw09xZLBJ1VBR0cHExMT3L17l7GxMfbv38/NmzerthF2EARcu3aNI0eOMDQ0xPDwsK82FVu+6pRq4P79+2QyGYrFIufPn6evr+/d9wFdXITRUZifh2IRmpuhsxNOnIDW1m23Ly0tcfLkSZ4+fcqdO3dob2//sA8j1RmDT6qRIAiYnJwkk8nQ0NDAhQsXOHr0aOUAnJ2FixdhcjL8/8WLzWvJJAQBHDsGZ89CdzcAjx8/pr+/n56eHi5fvkwymdzhp5Kiz+CTamxjY4Px8XGy2Sytra3kcjkOHTq09aarV+H0aVhbCwOukkQCkkmCfJ6fgWw2y5UrVxgYGNjRZ5DqicEnRcT6+jq3bt3i3LlztLe3k8vl6Orq2gy91dV3nuvFrl3k9+yhf3qadDq9g1VL9cfgkyLm5cuX3Lhxg1wux/fpNJcePOCjt19rAv8APwG/Al8AF4HvSuYJUikSMzNw4EBV6pbqhcEnRdTq6ip/HzzIV0+esKvk2gCwAYwAfwDfAL8DW7aUTiSgrw/Gx6tSr1QvDD4pqhYXYe/erU0swL9AC/AEePMS8wfgS+BS6RyNjfDsWdluTymu/I5PiqrR0bLDfwENbIYewNfAn+VuTiQqziPFlcEnRdX8/LbVHsAK8GnJWDOwXG6OtTUoFD58bVIdM/ikqCoWyw5/DCyVjC0Bn1SaJ+InREjVZvBJUdXcXHY4DawDC2+NzVHS2PK2lpYPWpZU7ww+Kao6O8PmlBJNwLdAlrDR5TfgF8IGl22SSejo2MEipfpjV6cUVRW6OiH8ju9HYAr4nLCbs/Q7PsCuTqkMV3xSVO3eHe69WWbvzs+ACcIV3zMqhF4iAb29hp5UwhWfFGWzs3D48HttV/afVArcuUXaxhWfFGXd3ZDPhyH2PlKp8HeGnrRNQ60LkPQ/BgfDv+9xOgP5/ObvJG3hq06pXjx6FJ7Hd+9eGHBra5vX3pzH19sbnsfnSk+qyOCT6s3z5+E2ZIVC+HF6S0v4ycLx4zaySO/A4JMkxYrNLZKkWDH4JEmxYvBJkmLF4JMkxYrBJ0mKFYNPkhQrBp8kKVYMPklSrBh8kqRYMfgkSbFi8EmSYsXgkyTFisEnSYoVg0+SFCsGnyQpVgw+SVKsGHySpFgx+CRJsWLwSZJixeCTJMWKwSdJipXXir1Z8FDrxPoAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -123,8 +123,14 @@ " return prog\n", "\n", "one_c_gates = [X,I]\n", - "two_c_gates = [two_q_id,CNOT]\n", - "two_c_toffoli = [two_q_id, CNOT, CCNOT]" + "two_c_gates = [two_q_id, CNOT]\n", + "two_c_toffoli = two_c_gates + [CCNOT]\n", + "\n", + "from forest.benchmarking.classical_logic import CNOT_X_basis, CCNOT_X_basis\n", + "x_basis_one_c_gates = [Z, I]\n", + "two_x_c_gates = [two_q_id, CNOT_X_basis]\n", + "two_x_c_toffoli = two_x_c_gates + [CCNOT_X_basis]\n", + "# if you want to do something in the X basis, add Hadamard layers appropriately; see below." ] }, { @@ -208,31 +214,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", + "Z 0\n", "Z 1\n", - "Z 2\n", - "Z 3\n", - "X 4\n", - "Z 5\n", - "X 6\n", + "I 2\n", + "X 3\n", + "I 4\n", + "X 5\n", + "I 6\n", "I 7\n", - "Z 8\n", + "X 8\n", "CZ 0 3\n", "I 0\n", "I 1\n", - "CZ 1 4\n", + "I 1\n", + "I 4\n", "I 1\n", "I 2\n", "I 2\n", "I 5\n", "CZ 3 6\n", - "CZ 3 4\n", - "CZ 4 7\n", + "I 3\n", "I 4\n", - "I 5\n", - "I 5\n", - "I 8\n", - "CZ 6 7\n", + "CZ 4 7\n", + "CZ 4 5\n", + "CZ 5 8\n", + "I 6\n", + "I 7\n", "I 7\n", "I 8\n", "\n" @@ -254,24 +261,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi) 0\n", - "RX(-pi) 0\n", + "RZ(pi/2) 0\n", "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", - "RX(pi/2) 2\n", - "RZ(pi/2) 2\n", "RX(-pi/2) 2\n", - "RX(pi/2) 3\n", + "RZ(pi/2) 2\n", "RZ(pi/2) 3\n", - "RX(-pi/2) 4\n", - "RZ(-pi) 5\n", - "RX(-pi/2) 6\n", + "RX(-pi) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi) 4\n", + "RX(-pi) 5\n", "RZ(-pi/2) 6\n", "RX(-pi/2) 6\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RZ(-pi) 8\n", + "RZ(-pi) 8\n", "\n" ] } @@ -297,10 +301,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 7\n", - "I 8\n", - "X 7\n", - "I 8\n", + "X 3\n", + "I 4\n", + "I 3\n", + "I 4\n", "\n" ] } @@ -312,17 +316,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "I 1\n", - "I 0\n", "I 1\n", + "I 2\n", + "CNOT 1 2\n", "\n" ] } @@ -341,20 +344,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "CZ 3 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 3\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RX(pi/2) 0\n", + "CZ 0 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 0\n", + "CZ 0 1\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 0\n", "\n" ] } @@ -392,38 +393,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-1.329632786433312) 3\n", + "RZ(0.11785201792915327) 3\n", "RX(pi/2) 3\n", - "RZ(0.7604982719369086) 3\n", + "RZ(1.2581786467023577) 3\n", "RX(-pi/2) 3\n", - "RZ(1.8156310165752079) 6\n", - "RX(pi/2) 6\n", - "RZ(1.4711114998123926) 6\n", - "RX(-pi/2) 6\n", - "CZ 6 3\n", - "RZ(-3.1241399925819855) 3\n", + "RZ(2.6505976415265136) 4\n", + "RX(pi/2) 4\n", + "RZ(2.0200943254789485) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(2.7706770614283514) 3\n", "RX(pi/2) 3\n", - "RZ(2.2746571087217635) 3\n", + "RZ(2.116190488581367) 3\n", "RX(-pi/2) 3\n", - "RZ(1.5532719149246832) 6\n", - "RX(-pi/2) 6\n", - "CZ 6 3\n", + "RZ(-2.0161284266416732) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", "RX(pi/2) 3\n", - "RZ(-1.6114573653372144) 3\n", + "RZ(-1.910267805532091) 3\n", "RX(-pi/2) 3\n", - "RZ(2.1015884527806747) 6\n", - "RX(pi/2) 6\n", - "CZ 6 3\n", - "RZ(0.7800514644372821) 3\n", + "RZ(1.2522862991683366) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(1.508578705534201) 3\n", "RX(pi/2) 3\n", - "RZ(1.5561120572506353) 3\n", + "RZ(1.6765904540675152) 3\n", "RX(-pi/2) 3\n", - "RZ(-2.303571544938592) 3\n", - "RZ(2.0461487572707764) 6\n", - "RX(pi/2) 6\n", - "RZ(1.4885403070641674) 6\n", - "RX(-pi/2) 6\n", - "RZ(-1.7188983925062606) 6\n", + "RZ(2.6543304268984675) 3\n", + "RZ(-0.07923360008273406) 4\n", + "RX(pi/2) 4\n", + "RZ(1.5665896468803189) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.24384831275800067) 4\n", "\n" ] } @@ -442,29 +443,32 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "X 3\n", + "X 1\n", + "I 3\n", + "X 4\n", + "I 7\n", + "I 1\n", "I 4\n", - "X 5\n", - "X 7\n", - "CNOT 3 4\n", + "I 3\n", + "I 4\n", + "CNOT 4 7\n", + "I 1\n", + "X 3\n", "I 4\n", "I 7\n", - "CNOT 4 5\n", - "I 3\n", + "I 1\n", "I 4\n", - "I 5\n", - "X 7\n", "I 3\n", "I 4\n", - "CNOT 4 7\n", - "CNOT 4 5\n", + "I 4\n", + "I 7\n", "\n" ] } @@ -476,46 +480,84 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi) 2\n", - "RZ(-pi/2) 5\n", - "RX(pi/2) 5\n", - "CZ 2 5\n", - "RX(pi/2) 5\n", - "RX(-pi/2) 2\n", - "CZ 2 5\n", - "RZ(-pi/2) 5\n", - "RZ(-pi/2) 2\n", - "RX(-pi/2) 2\n", - "RX(-pi/2) 2\n", - "RX(-pi/2) 5\n", - "RX(-pi/2) 2\n", - "CZ 2 5\n", - "RZ(-pi/2) 5\n", - "RZ(-pi) 2\n", - "RZ(-pi/2) 2\n", - "RX(-pi/2) 5\n", - "RZ(-pi/2) 5\n", - "CZ 2 5\n", - "RX(-pi/2) 5\n", - "CZ 2 5\n", - "RX(-pi/2) 2\n", - "CZ 2 5\n", - "RZ(-pi/2) 2\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", - "RX(pi/2) 5\n", - "CZ 2 5\n", - "RZ(pi) 2\n", - "RX(pi) 2\n", - "RX(pi/2) 5\n", - "RZ(pi/2) 5\n", + "RZ(-pi) 7\n", + "RX(-pi) 7\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(-pi/2) 8\n", + "RX(pi/2) 8\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 7\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(pi) 7\n", + "RX(pi) 7\n", + "RX(-pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 7\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 7\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 7\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 7\n", + "RZ(-pi/2) 7\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi) 7\n", + "RZ(-pi) 7\n", + "RX(-pi/2) 8\n", + "RZ(-pi) 8\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 7\n", + "RZ(pi) 7\n", + "RX(pi/2) 7\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(-pi/2) 7\n", + "RZ(pi) 8\n", + "RX(pi/2) 8\n", + "RZ(-pi/2) 8\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -524,11 +566,11 @@ } ], "source": [ - "clifford_sandwhich = clifford_1q_layer + clifford_2q_layer + get_dagger_all_template()\n", - "# here we demonstrate a simple use of a pattern. We want to do some Clifford layers reps\n", + "clifford_sandwich = clifford_1q_layer + clifford_2q_layer + get_dagger_all_template()\n", + "# here we demonstrate a simple use of a pattern. We want to do some Clifford layers n=reps\n", "# number of times and then dagger the result of all those reps. \n", - "reps = 3\n", - "prog = clifford_sandwhich.sample_program(G, repetitions=1, width=2, pattern=[([0, 1], reps), 2], qc=noisy_qc)\n", + "clifford_sandwich.template = [([0, 1], 'n'), -1]\n", + "prog = clifford_sandwich.sample_program(G, repetitions=3, width=2, qc=noisy_qc)\n", "print(prog)\n", "\n", "# We can check that this is the identity by compiling it fully\n", @@ -552,690 +594,525 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(1.8017191111484605) 3\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(1.951795907900012) 1\n", + "RX(pi/2) 1\n", + "RZ(0.7919116729617787) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.7853607053298983) 1\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 3 0\n", + "RZ(pi/2) 4\n", + "RZ(1.5177465526100262) 0\n", + "RX(pi/2) 0\n", + "RZ(1.1084325856156383) 0\n", + "RX(-pi/2) 0\n", + "RZ(-0.6161242247014961) 1\n", + "RX(pi/2) 1\n", + "RZ(2.234857238002245) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RZ(2.7548278038463287) 0\n", + "RX(pi/2) 0\n", + "RZ(-1.8087058700424092) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "CZ 4 3\n", + "RZ(-3.0230813617482806) 1\n", + "RX(pi/2) 1\n", + "RZ(0.4130278604476975) 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(-2.142349732148442) 1\n", + "RX(pi/2) 1\n", + "CZ 1 4\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RZ(-2.8444744722311635) 0\n", + "RX(pi/2) 0\n", + "RZ(0.4119373131834168) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.05985619862119851) 0\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(pi) 3\n", "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", "RZ(pi/2) 4\n", + "RZ(2.181679007482475) 0\n", + "RX(pi/2) 0\n", + "RZ(2.4168208899321426) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.3561639132775347) 1\n", + "RX(pi/2) 1\n", + "RZ(0.5054358777299659) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RZ(-3.0981258310535864) 0\n", + "RX(pi/2) 0\n", + "RZ(2.5876412414818506) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.5299586458264516) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 0\n", + "RZ(-1.8459094809233645) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.6576704051401236) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(-1.1294230724143155) 3\n", + "RX(pi/2) 3\n", + "RZ(0.22436173749974242) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.926836831398025) 4\n", "RX(pi/2) 4\n", - "RZ(1.276579062544581) 4\n", + "RZ(1.6926187245214688) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(-0.5824264796431509) 3\n", + "RX(pi/2) 3\n", + "RZ(2.2769133712525482) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.2132552473825973) 4\n", "RX(-pi/2) 4\n", "CZ 4 3\n", "RX(pi/2) 3\n", + "RZ(-1.7375310687324212) 3\n", + "RX(-pi/2) 3\n", + "RZ(2.0860557134920192) 4\n", "RX(pi/2) 4\n", "CZ 4 3\n", - "RZ(2.486017802099031) 2\n", - "RX(pi/2) 2\n", - "RZ(-pi/2) 5\n", - "RX(pi/2) 5\n", - "RZ(2.246131166553576) 5\n", - "RX(-pi/2) 5\n", - "CZ 2 5\n", - "RX(pi/2) 2\n", - "RZ(pi) 5\n", - "RX(pi/2) 5\n", - "CZ 2 5\n", + "RZ(1.432997872991327) 0\n", + "RX(pi/2) 0\n", + "RZ(0.5048863714003274) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.236589370291653) 0\n", + "RZ(-2.7692562170320967) 1\n", + "RX(pi/2) 1\n", + "RZ(2.3998923362770515) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.225226394027306) 1\n", + "RZ(0.6792109675957007) 3\n", + "RX(pi/2) 3\n", + "RZ(2.111387716875907) 3\n", + "RX(-pi/2) 3\n", + "RZ(2.1616969755312274) 3\n", + "RZ(0.5842987066334734) 4\n", "RX(pi/2) 4\n", + "RZ(1.0718260636200534) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.9254322669483628) 4\n", + "RZ(pi/2) 0\n", "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", + "RX(-pi/2) 7\n", "CZ 7 4\n", - "RZ(-0.4283666432126832) 8\n", - "RX(pi/2) 8\n", - "RZ(1.1996621933787561) 8\n", - "RX(-pi/2) 8\n", - "RZ(1.0076215164016324) 8\n", - "RZ(2.9106698692362283) 4\n", - "RX(pi/2) 4\n", - "RZ(pi) 5\n", - "RX(pi/2) 5\n", - "CZ 4 5\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(-pi/2) 4\n", "CZ 7 4\n", - "RZ(0.40643443383085465) 5\n", - "RX(pi/2) 5\n", - "RZ(1.6021735778205666) 5\n", - "RX(-pi/2) 5\n", - "RZ(0.7596599838570817) 8\n", - "RX(pi/2) 8\n", - "RZ(2.6414924282541383) 8\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RZ(-2.7242905019992167) 5\n", - "RX(-pi/2) 5\n", - "RZ(-2.2362573886500776) 8\n", - "RX(pi/2) 8\n", - "CZ 5 8\n", - "RX(pi/2) 5\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RX(pi/2) 7\n", - "RZ(-2.2179297941114995) 8\n", - "RX(pi/2) 8\n", - "RZ(0.6563946674935734) 8\n", - "RX(-pi/2) 8\n", - "CZ 8 7\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "CZ 7 8\n", - "RZ(-2.8884988700249052) 2\n", - "RX(pi/2) 2\n", - "RZ(0.5711301113067844) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.9821717354393478) 5\n", - "RX(pi/2) 5\n", - "RZ(2.996916689268449) 5\n", - "RX(-pi/2) 5\n", - "CZ 2 5\n", - "RZ(0.6168454016858838) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.831802684296635) 5\n", - "RX(pi/2) 5\n", - "CZ 2 5\n", - "RX(pi/2) 2\n", - "RX(-pi/2) 5\n", - "CZ 2 5\n", - "RZ(1.3147766336014757) 4\n", - "RX(pi/2) 4\n", - "RZ(0.6132172846209873) 4\n", - "RX(-pi/2) 4\n", - "RZ(2.8071869495514195) 7\n", - "RX(pi/2) 7\n", - "RZ(2.9381092126110544) 7\n", + "CZ 4 3\n", + "RZ(1.8685253975378617) 7\n", "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RZ(1.1858040262494316) 4\n", - "RX(-pi/2) 4\n", - "RZ(-2.0282758603541193) 7\n", - "RX(pi/2) 7\n", - "CZ 4 7\n", - "RX(pi/2) 4\n", + "RZ(2.49350362084352) 7\n", "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RZ(-1.0318311998921215) 4\n", - "RX(pi/2) 4\n", - "RZ(2.917298994701893) 4\n", + "RZ(2.320400158101815) 7\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", + "RZ(-2.1218250792931617) 4\n", "RX(-pi/2) 4\n", - "RZ(-2.9587061257951355) 5\n", - "RX(pi/2) 5\n", - 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"RZ(2.6341238409018928) 3\n", - "RZ(1.2597217463218704) 4\n", + "RZ(-0.6621453330057185) 3\n", + "RZ(0.8786147451018624) 4\n", "RX(pi/2) 4\n", - "RZ(1.3960289387989866) 4\n", + "RZ(1.5367007245233615) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.4615424069322529) 4\n", - "RZ(-2.3272494229396004) 5\n", - "RX(pi/2) 5\n", - "RZ(1.6616292269360053) 5\n", - "RX(-pi/2) 5\n", - "RZ(-2.8584247543614762) 5\n", + "RZ(1.2164717139543244) 4\n", "\n" ] } @@ -1254,14 +1131,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" + "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" ] } ], @@ -1291,7 +1168,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [array([[1, 1]]), array([[1, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 1]])]}, 3: {4: [array([[1, 0, 1]]), array([[1, 1, 0]]), array([[1, 1, 0]]), array([[0, 1, 1]]), array([[0, 0, 0]]), array([[0, 1, 0]]), array([[1, 0, 0]]), array([[0, 0, 0]]), array([[0, 1, 1]]), array([[0, 0, 1]])]}, 4: {4: [array([[0, 1, 1, 0]]), array([[0, 0, 1, 1]]), array([[1, 0, 0, 0]]), array([[0, 0, 1, 1]]), array([[0, 1, 0, 0]]), array([[1, 0, 1, 0]]), array([[0, 0, 1, 0]]), array([[1, 1, 1, 0]]), array([[0, 0, 0, 1]]), array([[0, 0, 0, 1]])]}}\n" + "{2: {4: [array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]])]}, 3: {4: [array([[1, 0, 0]]), array([[1, 1, 1]]), array([[0, 1, 1]]), array([[1, 1, 1]]), array([[1, 1, 0]]), array([[0, 0, 0]]), array([[1, 1, 0]]), array([[1, 0, 1]]), array([[0, 1, 1]]), array([[0, 0, 1]])]}, 4: {4: [array([[0, 1, 0, 0]]), array([[0, 1, 0, 1]]), array([[1, 0, 1, 0]]), array([[0, 0, 1, 0]]), array([[1, 0, 0, 1]]), array([[0, 1, 1, 0]]), array([[0, 1, 1, 0]]), array([[0, 1, 1, 1]]), array([[1, 0, 0, 1]]), array([[0, 0, 1, 1]])]}}\n" ] } ], @@ -1309,7 +1186,7 @@ "name": "stdout", "output_type": "stream", "text": [ - 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"{2: {4: array([0.8602, 0.131 , 0.0088])}, 3: {4: array([8.372e-01, 1.502e-01, 1.220e-02, 4.000e-04])}, 4: {4: array([8.054e-01, 1.746e-01, 1.820e-02, 1.400e-03, 4.000e-04])}}\n" + "{2: {4: array([0.8916, 0.104 , 0.0044])}, 3: {4: array([8.092e-01, 1.732e-01, 1.680e-02, 8.000e-04])}, 4: {4: array([7.780e-01, 2.012e-01, 1.860e-02, 2.000e-03, 2.000e-04])}}\n" ] } ], @@ -1348,21 +1225,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Plot the distribution of sublattice widths" + "## Plot the distribution of sublattice widths" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[9, 12, 22, 36, 49, 48, 32, 9, 1]" + "[9, 12, 22, 36, 49, 48, 32, 9, 1, 9, 12, 22, 36, 49, 48, 32, 9, 1]" ] }, - "execution_count": 25, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, @@ -1381,13 +1258,13 @@ "G = perfect_qc.qubit_topology()\n", "len(perfect_qc.qubit_topology())\n", "# distribution of graph lengths\n", - "disty = []\n", - "for gdx in range(1,len(G.nodes)+1):\n", - " listg = generate_connected_subgraphs(G,gdx)\n", - " disty.append(len(listg))\n", + "distr = []\n", + "for num_nodes in range(1, len(G.nodes) + 1):\n", + " listg = generate_connected_subgraphs(G, num_nodes)\n", + " distr.append(len(listg))\n", "\n", - "cir_wid = list(range(1,len(G.nodes)+1))\n", - "plt.bar(cir_wid, disty, width=0.61, align='center')\n", + "cir_wid = list(range(1, len(G.nodes) + 1))\n", + "plt.bar(cir_wid, distr, width=0.61, align='center')\n", "plt.xticks(cir_wid)\n", "plt.xlabel('sublattice / circuit width')\n", "plt.ylabel('Frequency of Occurence')\n", @@ -1396,185 +1273,6 @@ "disty" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# OUTDATED BELOW" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Acquire data in Z basis" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# # with these parameters the cell below takes about 1 hour 40 minutes\n", - "# num_shots_per_circuit = 400\n", - "# num_rand_subgraphs = 16\n", - "# circuit_depth = 18\n", - "# circuit_width = 15 #max = len(G.nodes)\n", - "# x_basis = False\n", - "# active_reset = True\n", - "# total == 6077" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# with these parameters the cell below takes about 5 minutes\n", - "num_shots_per_circuit = 1000\n", - "num_rand_subgraphs = 20\n", - "circuit_depth = 6\n", - "circuit_width = 4 #max = len(G.nodes)\n", - "x_basis = False\n", - "active_reset = False" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'generate_rand_cir_for_rand_lattices_experiments' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m exp =generate_rand_cir_for_rand_lattices_experiments(noisy_qc, \n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mcircuit_depth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcircuit_width\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnum_rand_subgraphs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnum_shots_per_circuit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'generate_rand_cir_for_rand_lattices_experiments' is not defined" - ] - } - ], - "source": [ - "exp =generate_rand_cir_for_rand_lattices_experiments(noisy_qc, \n", - " circuit_depth, \n", - " circuit_width,\n", - " num_rand_subgraphs, \n", - " num_shots_per_circuit, \n", - " in_x_basis=x_basis, \n", - " use_active_reset=active_reset)\n", - "exp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Collect data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t0 = time.time()\n", - "data_zbasis = acquire_data_random_classical_circuit(perfect_qc, noisy_qc, exp)\n", - "t1 = time.time()\n", - "total = t1-t0\n", - "print(total)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_zbasis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#data_zbasis.to_pickle(\"data_z_Aspen-1-16Q-A_2019_02_16.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_zbasis = pd.read_pickle('data_z_Aspen-1-16Q-A_2019_02_16.pkl')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# circuit_width = df['Width'].max()\n", - "# circuit_depth = df['Depth'].max()\n", - "# for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", - "# print(depth,subgraph_size)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfz = pd.DataFrame(data_zbasis)\n", - "dfz.to_pickle(\"data_z_Aspen_1_15Q_A_2019_02_09.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.read_pickle('data_z_Aspen_1_15Q_A_2019_02_09.pkl')" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 8e1a60e2..e1086cc0 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -1,4 +1,4 @@ -from typing import Tuple, Sequence, Callable, Any, List +from typing import Tuple, Sequence, Callable, Any, List, Union from copy import copy import networkx as nx import numpy as np @@ -7,7 +7,7 @@ import pandas as pd from scipy.spatial.distance import hamming from scipy.special import comb -from dataclasses import dataclass +from dataclasses import dataclass, field from functools import partial from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate @@ -23,9 +23,24 @@ from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary +def make_default_pattern(num_generators): + """ + By default sweep over each generator in sequence n many times + + :param num_generators: + :return: + """ + return [(list(range(num_generators)), 'n')] + +# TODO: perhaps best for pattern to be sample-time specified given ambiguity in append + @dataclass class CircuitTemplate: - generators: List[Callable] + generators: List[Callable] = field(default_factory=lambda : []) + pattern: List[Union[int, Tuple[List, int], Tuple[List, str]]] = field(init=False, repr=False) + + def __post_init__(self): + self.pattern = make_default_pattern(len(self.generators)) # def create_unit(self): # # returns a function that can be used as a generator in another template @@ -34,10 +49,20 @@ class CircuitTemplate: # self.generators) def append(self, other): - self.generators += other.generators + """ + Mutates the CircuitTemplate object by appending new generators - # TODO: store the pattern? - # TODO: add reps keyword to pattern? + :param other: + :return: + """ + if isinstance(other, list): + self.generators += other + elif isinstance(other, CircuitTemplate): + self.generators += other.generators + # make default pattern since it is unclear how to compose general patterns. + self.pattern = make_default_pattern(len(self.generators)) + else: + raise ValueError(f'Cannot append type {type(other)}.') def __add__(self, other): """ @@ -47,7 +72,8 @@ def __add__(self, other): :return: A newly concatenated circuit. :rtype: Program """ - ckt = CircuitTemplate(self.generators) + ckt = CircuitTemplate() + ckt.append(self) ckt.append(other) return ckt @@ -63,8 +89,7 @@ def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None graph = random.choice(generate_connected_subgraphs(graph, width)) if pattern is None: - # by default sweep over each generator in sequence repetitions many times - pattern = range(len(self.generators)) + pattern = self.pattern if sequence is None: sequence = [] @@ -77,8 +102,17 @@ def _do_pattern(patt): sequence.append(self.generators[elem](graph=graph, qc=qc, width=width, sequence=sequence)) elif len(elem) == 2: + # elem[0] is a pattern that we will execute elem[1] many times - for _ in range(elem[1]): + if elem[1] == 'n': + # n indicates `repetitions` number of times + reps = repetitions + elif isinstance(elem[1], int) and elem[1]>=0: + reps = elem[1] + else: + raise ValueError('Repetitions must be specified by int or `n`.') + + for _ in range(reps): _do_pattern(elem[0]) else: raise ValueError('Pattern is malformed. A pattern is a list where each element ' @@ -86,8 +120,7 @@ def _do_pattern(patt): 'where num is an integer indicating how many times to ' 'repeat the associated pattern_i.') - for _ in range(repetitions): - _do_pattern(pattern) + _do_pattern(pattern) return sequence From 9aad2908c1530752f75cd1146b8324cc4899adef Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 25 Jul 2019 13:21:39 -0400 Subject: [PATCH 20/49] Add x basis logic helpers and demo. --- examples/volumetrics.ipynb | 1305 +++++++++++----------------- forest/benchmarking/volumetrics.py | 9 + 2 files changed, 523 insertions(+), 791 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 780ff193..dfc07ffa 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -11,12 +11,12 @@ "\n", "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", "\n", - "The `depth` is defined in an unusual way. We consider a \"depth 1\" circuit to be a round of X gates randomly applied or not to a particular vertex AND a round of CNOTs randomly applied or not to each edge of the graph." + "The `depth` is defined in context-dependent way; to avoid confusion with circuit depth we may use the term 'repetitions'." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -126,8 +126,9 @@ "two_c_gates = [two_q_id, CNOT]\n", "two_c_toffoli = two_c_gates + [CCNOT]\n", "\n", + "# x basis gates\n", "from forest.benchmarking.classical_logic import CNOT_X_basis, CCNOT_X_basis\n", - "x_basis_one_c_gates = [Z, I]\n", + "one_x_c_gates = [Z, I]\n", "two_x_c_gates = [two_q_id, CNOT_X_basis]\n", "two_x_c_toffoli = two_x_c_gates + [CCNOT_X_basis]\n", "# if you want to do something in the X basis, add Hadamard layers appropriately; see below." @@ -142,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -161,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -180,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -189,7 +190,7 @@ "'tcp://127.0.0.1:5555'" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -207,41 +208,39 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Z 0\n", + "X 0\n", "Z 1\n", - "I 2\n", - "X 3\n", + "X 2\n", + "Z 3\n", "I 4\n", "X 5\n", - "I 6\n", - "I 7\n", + "Z 6\n", + "Z 7\n", "X 8\n", "CZ 0 3\n", - "I 0\n", - "I 1\n", - "I 1\n", - "I 4\n", + "CZ 0 1\n", + "CZ 1 4\n", "I 1\n", "I 2\n", "I 2\n", "I 5\n", "CZ 3 6\n", - "I 3\n", + "CZ 3 4\n", + "I 4\n", + "I 7\n", "I 4\n", - "CZ 4 7\n", - "CZ 4 5\n", + "I 5\n", "CZ 5 8\n", "I 6\n", "I 7\n", - "I 7\n", - "I 8\n", + "CZ 7 8\n", "\n" ] } @@ -254,27 +253,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "RX(-pi/2) 0\n", "RZ(pi/2) 0\n", "RZ(-pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RZ(pi/2) 3\n", - "RX(-pi) 3\n", - "RX(pi/2) 4\n", - "RZ(-pi) 4\n", - "RX(-pi) 5\n", - "RZ(-pi/2) 6\n", + "RX(-pi) 1\n", + "RZ(-pi) 2\n", + "RZ(-pi) 2\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RZ(pi/2) 5\n", + "RZ(pi/2) 6\n", "RX(-pi/2) 6\n", "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RZ(-pi) 8\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 8\n", "RZ(-pi) 8\n", "\n" ] @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -302,9 +302,9 @@ "output_type": "stream", "text": [ "X 3\n", - "I 4\n", + "I 6\n", "I 3\n", - "I 4\n", + "I 6\n", "\n" ] } @@ -316,16 +316,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 1\n", - "I 2\n", - "CNOT 1 2\n", + "I 4\n", + "I 7\n", + "CNOT 4 7\n", "\n" ] } @@ -337,25 +337,45 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RX(pi/2) 0\n", - "CZ 0 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 0\n", - "CZ 0 1\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 0\n", + "H 2\n", + "H 5\n", + "H 7\n", + "H 8\n", + "\n" + ] + } + ], + "source": [ + "switch_basis_layer = get_switch_basis_x_z_template()\n", + "print(switch_basis_layer.sample_program(G, repetitions=1, width=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CZ 3 4\n", + "RZ(pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", + "RZ(pi/2) 4\n", + "RX(-pi/2) 4\n", "\n" ] } @@ -368,63 +388,91 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "RZ(-3.11559416083055) 2\n", + "RX(pi/2) 2\n", + "RZ(0.9985930931695964) 2\n", + "RX(-pi/2) 2\n", + "RZ(0.33645965947699885) 5\n", + "RX(pi/2) 5\n", + "RZ(1.1143132339260913) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RZ(1.8502545221336462) 2\n", + "RX(pi/2) 2\n", + "RZ(-1.493206408133509) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(1.7273460464564303) 2\n", + "RX(pi/2) 2\n", + "RZ(0.4625719031986815) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.5110412432518459) 2\n", + "RZ(-1.166166861993211) 5\n", + "RX(pi/2) 5\n", + "RZ(0.6298859814840272) 5\n", + "RX(-pi/2) 5\n", + "RZ(2.6282638093266915) 5\n", "\n" ] } ], "source": [ "rand_perm_layer = get_rand_qubit_perm_template()\n", - "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=2))" + "# sometimes this returns an empty program, i.e. no permutation\n", + "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=3))" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(0.11785201792915327) 3\n", - "RX(pi/2) 3\n", - "RZ(1.2581786467023577) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.6505976415265136) 4\n", + "RZ(-1.2990942230030267) 4\n", "RX(pi/2) 4\n", - "RZ(2.0200943254789485) 4\n", + "RZ(1.5155074169065497) 4\n", "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RZ(2.7706770614283514) 3\n", - "RX(pi/2) 3\n", - "RZ(2.116190488581367) 3\n", - "RX(-pi/2) 3\n", - "RZ(-2.0161284266416732) 4\n", + "RZ(1.7430125039887816) 5\n", + "RX(pi/2) 5\n", + "RZ(1.4191531043790895) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RZ(-1.2516854599739062) 4\n", + "RX(pi/2) 4\n", + "RZ(2.110642864158322) 4\n", "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.910267805532091) 3\n", - "RX(-pi/2) 3\n", - "RZ(1.2522862991683366) 4\n", + "RZ(-2.8531720822941167) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(1.508578705534201) 3\n", - "RX(pi/2) 3\n", - "RZ(1.6765904540675152) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.6543304268984675) 3\n", - "RZ(-0.07923360008273406) 4\n", + "RZ(-2.0269992194907793) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.7707585610544267) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(-2.7752501314248956) 4\n", "RX(pi/2) 4\n", - "RZ(1.5665896468803189) 4\n", + "RZ(0.6563900562878288) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.24384831275800067) 4\n", + "RZ(2.3643866667806357) 4\n", + "RZ(-2.2772139390064376) 5\n", + "RX(pi/2) 5\n", + "RZ(1.8032677495210245) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.022168105894388) 5\n", "\n" ] } @@ -443,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -451,13 +499,12 @@ "output_type": "stream", "text": [ "X 1\n", - "I 3\n", - "X 4\n", - "I 7\n", - "I 1\n", + "X 3\n", "I 4\n", - "I 3\n", + "X 7\n", + "I 1\n", "I 4\n", + "CNOT 3 4\n", "CNOT 4 7\n", "I 1\n", "X 3\n", @@ -478,86 +525,98 @@ "print(classical_1q_2q.sample_program(G, repetitions=2, width=4))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classical Logic in X basis" + ] + }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi) 7\n", - "RX(-pi) 7\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(-pi/2) 8\n", - "RX(pi/2) 8\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RZ(-pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 7\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(pi) 7\n", - "RX(pi) 7\n", - "RX(-pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 7\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 7\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 7\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 7\n", - "RZ(-pi/2) 7\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "RZ(-pi) 7\n", - "RZ(-pi) 7\n", - "RX(-pi/2) 8\n", - "RZ(-pi) 8\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 7\n", - "RZ(pi) 7\n", - "RX(pi/2) 7\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", - "RZ(-pi/2) 7\n", - "RZ(pi) 8\n", - "RX(pi/2) 8\n", - "RZ(-pi/2) 8\n", + "H 5\n", + "H 8\n", + "Z 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "H 5\n", + "CZ 5 8\n", + "H 5\n", + "H 5\n", + "H 8\n", + "\n" + ] + } + ], + "source": [ + "logic_layers = get_rand_1q_template(one_x_c_gates) + get_rand_2q_template(two_x_c_gates)\n", + "classical_x_1q_2q = switch_basis_layer + logic_layers + switch_basis_layer\n", + "# here we demonstrate a simple use of a pattern. We want to do the basis switch at beginning and end \n", + "# while doing the repetitions in between some variable number of times.\n", + "# The pattern says to do the 0 idx generator, do [1,2] idx generators n times, then finish with 3 idx generator\n", + "classical_x_1q_2q.pattern = [0, ([1, 2], 'n'), 3]\n", + "print(classical_x_1q_2q.sample_program(G, repetitions=3, width=2))\n", + "# note that the x basis CNOT(0, 1) is H(0) CZ(0, 1) H(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RX(-pi/2) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi) 4\n", + "RX(pi/2) 3\n", + "CZ 3 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi) 4\n", + "RX(pi/2) 3\n", + "CZ 3 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 4\n", + "RZ(-pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(-pi) 3\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 4\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -567,9 +626,9 @@ ], "source": [ "clifford_sandwich = clifford_1q_layer + clifford_2q_layer + get_dagger_all_template()\n", - "# here we demonstrate a simple use of a pattern. We want to do some Clifford layers n=reps\n", + "# here we demonstrate another simple use of a pattern. We want to do some Clifford layers n=reps\n", "# number of times and then dagger the result of all those reps. \n", - "clifford_sandwich.template = [([0, 1], 'n'), -1]\n", + "clifford_sandwich.pattern = [([0, 1], 'n'), -1]\n", "prog = clifford_sandwich.sample_program(G, repetitions=3, width=2, qc=noisy_qc)\n", "print(prog)\n", "\n", @@ -587,532 +646,358 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(1.951795907900012) 1\n", - "RX(pi/2) 1\n", - "RZ(0.7919116729617787) 1\n", - "RX(-pi/2) 1\n", - "RZ(2.7853607053298983) 1\n", - "RZ(pi/2) 3\n", + "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", - "CZ 3 0\n", - "RZ(pi/2) 4\n", - "RZ(1.5177465526100262) 0\n", + "CZ 0 3\n", + "RZ(0.6669073931670509) 4\n", + "RX(pi/2) 4\n", + "RZ(0.5854615772794022) 4\n", + "RX(-pi/2) 4\n", + "RZ(-3.06066309923684) 4\n", + "RZ(-pi/2) 0\n", "RX(pi/2) 0\n", - "RZ(1.1084325856156383) 0\n", + "RZ(2.4107959821067877) 0\n", "RX(-pi/2) 0\n", - "RZ(-0.6161242247014961) 1\n", + "RZ(-0.5585293438296013) 1\n", "RX(pi/2) 1\n", - "RZ(2.234857238002245) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RZ(2.7548278038463287) 0\n", + "CZ 0 1\n", "RX(pi/2) 0\n", - "RZ(-1.8087058700424092) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(-pi/2) 0\n", + "RZ(pi) 1\n", "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "CZ 4 3\n", - "RZ(-3.0230813617482806) 1\n", + "CZ 0 1\n", + "RZ(pi/2) 3\n", + "RZ(pi) 0\n", + "RX(pi/2) 0\n", + "CZ 3 0\n", + "RZ(-2.4305289034535735) 1\n", "RX(pi/2) 1\n", - "RZ(0.4130278604476975) 1\n", + "RZ(0.4608188356599866) 1\n", "RX(-pi/2) 1\n", + "RZ(2.740126082031923) 4\n", "RX(pi/2) 4\n", + "RZ(1.1064691618894351) 4\n", + "RX(-pi/2) 4\n", "CZ 4 1\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(-2.142349732148442) 1\n", + "RZ(1.3016838095222525) 1\n", "RX(pi/2) 1\n", - "CZ 1 4\n", - "RZ(pi) 3\n", + "RZ(-1.5895747244078953) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", "RX(pi/2) 3\n", + "RZ(-0.31660508427036405) 4\n", "RX(pi/2) 4\n", + "RZ(1.9412910023569236) 4\n", + "RX(-pi/2) 4\n", "CZ 3 4\n", - "RZ(-2.8444744722311635) 0\n", - "RX(pi/2) 0\n", - "RZ(0.4119373131834168) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.05985619862119851) 0\n", - "RZ(pi/2) 1\n", + "RX(-pi/2) 3\n", + "CZ 3 0\n", + "RZ(0.25293664903812485) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(-2.583063309760192) 0\n", + "RZ(-0.46129210523277475) 1\n", "RX(pi/2) 1\n", - "RZ(pi) 3\n", + "RZ(0.2166354441029604) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.6427579756968358) 1\n", "RX(pi/2) 3\n", - "RZ(pi/2) 3\n", - "RZ(pi/2) 4\n", - "RZ(2.181679007482475) 0\n", + "RZ(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.7757916810497658) 0\n", "RX(pi/2) 0\n", - "RZ(2.4168208899321426) 0\n", + "RZ(2.031126351623488) 0\n", "RX(-pi/2) 0\n", - "RZ(1.3561639132775347) 1\n", + "RZ(1.8194447794701258) 1\n", "RX(pi/2) 1\n", - "RZ(0.5054358777299659) 1\n", + "RZ(1.014314986197423) 1\n", "RX(-pi/2) 1\n", "CZ 1 0\n", - "RZ(-3.0981258310535864) 0\n", + "RZ(-2.0269578329482236) 0\n", "RX(pi/2) 0\n", - "RZ(2.5876412414818506) 0\n", + "RZ(2.24195814306528) 0\n", "RX(-pi/2) 0\n", - "RZ(2.5299586458264516) 1\n", + "RZ(-0.8884838731302743) 1\n", "RX(-pi/2) 1\n", "CZ 1 0\n", "RX(pi/2) 0\n", - "RZ(-1.8459094809233645) 0\n", + "RZ(-1.8885086700579947) 0\n", "RX(-pi/2) 0\n", - "RZ(1.6576704051401236) 1\n", + "RZ(1.2380507840193058) 1\n", "RX(pi/2) 1\n", "CZ 1 0\n", - "RZ(-1.1294230724143155) 3\n", + "RZ(-0.5388224609174566) 3\n", "RX(pi/2) 3\n", - "RZ(0.22436173749974242) 3\n", + "RZ(2.7984452402871334) 3\n", "RX(-pi/2) 3\n", - "RZ(-1.926836831398025) 4\n", + "RZ(2.479910807613208) 4\n", "RX(pi/2) 4\n", - "RZ(1.6926187245214688) 4\n", + "RZ(0.37049816162871296) 4\n", "RX(-pi/2) 4\n", "CZ 4 3\n", - "RZ(-0.5824264796431509) 3\n", + "RZ(-1.5354346359628757) 3\n", "RX(pi/2) 3\n", - "RZ(2.2769133712525482) 3\n", + 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4\n", + "RZ(-1.7493642198525963) 3\n", + "RZ(-0.7124392035325942) 4\n", "RX(pi/2) 4\n", - "RZ(1.0718260636200534) 4\n", + "RZ(0.6644954490767097) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.9254322669483628) 4\n", - "RZ(pi/2) 0\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(-pi/2) 4\n", - "CZ 7 4\n", - "CZ 4 3\n", - "RZ(1.8685253975378617) 7\n", - "RX(-pi/2) 7\n", - "RZ(2.49350362084352) 7\n", - "RX(-pi/2) 7\n", - "RZ(2.320400158101815) 7\n", + "RZ(0.4701152601719749) 4\n", + "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", "CZ 0 3\n", - "RZ(-2.1218250792931617) 4\n", - "RX(-pi/2) 4\n", - "RZ(2.5750710552018274) 4\n", - "RX(-pi/2) 4\n", - "CZ 7 4\n", - "RZ(-2.9937689827524547) 4\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RX(-pi/2) 4\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", + "CZ 1 4\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 2\n", + "CZ 1 2\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", "CZ 0 1\n", - 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"RZ(1.3071530036896222) 0\n", "RX(-pi/2) 0\n", - "RZ(-0.3913625233735054) 0\n", - "RZ(-1.055503703949598) 1\n", + "RZ(-0.7860124239672821) 0\n", + "RZ(-2.5610770607411144) 1\n", "RX(pi/2) 1\n", - "RZ(1.2377305279076454) 1\n", + "RZ(1.0388517327800924) 1\n", "RX(-pi/2) 1\n", - "RZ(0.026076573890575716) 1\n", - "RZ(2.8184044971909157) 3\n", + "RZ(3.098199823290461) 1\n", + "RZ(0.7146722390552813) 3\n", "RX(pi/2) 3\n", - "RZ(1.6544189953105404) 3\n", + "RZ(1.4352692603518304) 3\n", "RX(-pi/2) 3\n", - "RZ(-0.6621453330057185) 3\n", - "RZ(0.8786147451018624) 4\n", + "RZ(2.5835346399507184) 3\n", + "RZ(-0.9543567877261507) 4\n", "RX(pi/2) 4\n", - "RZ(1.5367007245233615) 4\n", + "RZ(2.511147801544665) 4\n", "RX(-pi/2) 4\n", - "RZ(1.2164717139543244) 4\n", + "RZ(0.10327658195147739) 4\n", "\n" ] } @@ -1131,14 +1016,14 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" + "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" ] } ], @@ -1152,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1161,14 +1046,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]])]}, 3: {4: [array([[1, 0, 0]]), array([[1, 1, 1]]), array([[0, 1, 1]]), array([[1, 1, 1]]), array([[1, 1, 0]]), array([[0, 0, 0]]), array([[1, 1, 0]]), array([[1, 0, 1]]), array([[0, 1, 1]]), array([[0, 0, 1]])]}, 4: {4: [array([[0, 1, 0, 0]]), array([[0, 1, 0, 1]]), array([[1, 0, 1, 0]]), array([[0, 0, 1, 0]]), array([[1, 0, 0, 1]]), array([[0, 1, 1, 0]]), array([[0, 1, 1, 0]]), array([[0, 1, 1, 1]]), array([[1, 0, 0, 1]]), array([[0, 0, 1, 1]])]}}\n" + "{2: {4: [array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 0]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 0]])]}, 3: {4: [array([[1, 1, 0]]), array([[0, 0, 0]]), array([[1, 1, 1]]), array([[1, 0, 1]]), array([[1, 0, 0]]), array([[0, 1, 0]]), array([[1, 0, 1]]), array([[1, 1, 0]]), array([[1, 0, 0]]), array([[0, 1, 0]])]}, 4: {4: [array([[1, 0, 0, 0]]), array([[0, 0, 1, 1]]), array([[0, 0, 1, 0]]), array([[0, 1, 1, 0]]), array([[0, 0, 1, 1]]), array([[0, 1, 1, 1]]), array([[0, 0, 1, 0]]), array([[1, 0, 0, 0]]), array([[1, 1, 0, 0]]), array([[1, 0, 1, 1]])]}}\n" ] } ], @@ -1179,14 +1064,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [array([0.884, 0.112, 0.004]), array([0.804, 0.178, 0.018]), array([0.95, 0.05, 0. ]), array([0.884, 0.116, 0. ]), array([0.902, 0.096, 0.002]), array([0.892, 0.106, 0.002]), array([0.942, 0.058, 0. ]), array([0.896, 0.102, 0.002]), array([0.886, 0.108, 0.006]), array([0.876, 0.114, 0.01 ])]}, 3: {4: [array([0.856, 0.138, 0.006, 0. ]), array([0.746, 0.214, 0.04 , 0. ]), array([0.778, 0.198, 0.022, 0.002]), array([0.748, 0.232, 0.02 , 0. ]), array([0.804, 0.188, 0.008, 0. ]), array([0.93 , 0.054, 0.016, 0. ]), array([0.79 , 0.186, 0.02 , 0.004]), array([0.79 , 0.196, 0.014, 0. ]), array([0.818, 0.172, 0.01 , 0. ]), array([0.832, 0.154, 0.012, 0.002])]}, 4: {4: [array([0.872, 0.118, 0.01 , 0. , 0. ]), array([0.76 , 0.21 , 0.026, 0.004, 0. ]), array([0.756, 0.226, 0.018, 0. , 0. ]), array([0.794, 0.19 , 0.016, 0. , 0. ]), array([0.77 , 0.206, 0.022, 0.002, 0. ]), array([0.762, 0.228, 0.01 , 0. , 0. ]), array([0.77 , 0.206, 0.016, 0.008, 0. ]), array([0.7 , 0.266, 0.03 , 0.002, 0.002]), array([0.814, 0.166, 0.016, 0.004, 0. ]), array([0.782, 0.196, 0.022, 0. , 0. ])]}}\n" + "{2: {4: [array([0.888, 0.112, 0. ]), array([0.852, 0.148, 0. ]), array([0.882, 0.112, 0.006]), array([0.94, 0.06, 0. ]), array([0.904, 0.094, 0.002]), array([0.828, 0.162, 0.01 ]), array([0.946, 0.054, 0. ]), array([0.934, 0.066, 0. ]), array([0.862, 0.138, 0. ]), array([0.958, 0.042, 0. ])]}, 3: {4: [array([0.828, 0.154, 0.018, 0. ]), array([0.91, 0.08, 0.01, 0. ]), array([0.738, 0.23 , 0.032, 0. ]), array([0.776, 0.202, 0.02 , 0.002]), array([0.868, 0.114, 0.018, 0. ]), array([0.858, 0.136, 0.006, 0. ]), array([0.802, 0.182, 0.016, 0. ]), array([0.79 , 0.194, 0.014, 0.002]), array([0.848, 0.128, 0.018, 0.006]), array([0.828, 0.164, 0.006, 0.002])]}, 4: {4: [array([0.88 , 0.112, 0.008, 0. , 0. ]), array([0.772, 0.21 , 0.014, 0.004, 0. ]), array([0.832, 0.158, 0.01 , 0. , 0. ]), array([0.778, 0.196, 0.022, 0.002, 0.002]), array([0.782, 0.196, 0.02 , 0.002, 0. ]), array([0.708, 0.244, 0.034, 0.014, 0. ]), array([0.802, 0.182, 0.014, 0.002, 0. ]), array([0.862, 0.12 , 0.018, 0. , 0. ]), array([0.768, 0.202, 0.026, 0.004, 0. ]), array([0.736, 0.23 , 0.032, 0.002, 0. ])]}}\n" ] } ], @@ -1197,14 +1082,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: array([0.8916, 0.104 , 0.0044])}, 3: {4: array([8.092e-01, 1.732e-01, 1.680e-02, 8.000e-04])}, 4: {4: array([7.780e-01, 2.012e-01, 1.860e-02, 2.000e-03, 2.000e-04])}}\n" + "{2: {4: array([0.8994, 0.0988, 0.0018])}, 3: {4: array([0.8246, 0.1584, 0.0158, 0.0012])}, 4: {4: array([7.92e-01, 1.85e-01, 1.98e-02, 3.00e-03, 2.00e-04])}}\n" ] } ], @@ -1225,221 +1110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the distribution of sublattice widths" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9, 12, 22, 36, 49, 48, 32, 9, 1, 9, 12, 22, 36, 49, 48, 32, 9, 1]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = perfect_qc.qubit_topology()\n", - "len(perfect_qc.qubit_topology())\n", - "# distribution of graph lengths\n", - "distr = []\n", - "for num_nodes in range(1, len(G.nodes) + 1):\n", - " listg = generate_connected_subgraphs(G, num_nodes)\n", - " distr.append(len(listg))\n", - "\n", - "cir_wid = list(range(1, len(G.nodes) + 1))\n", - "plt.bar(cir_wid, distr, width=0.61, align='center')\n", - "plt.xticks(cir_wid)\n", - "plt.xlabel('sublattice / circuit width')\n", - "plt.ylabel('Frequency of Occurence')\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.title('Distribution of sublattice widths')\n", - "disty" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Acquire data in X basis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "exp_xbasis = exp.copy()\n", - "exp_xbasis['In X basis']=True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t0x = time.time()\n", - "data_xbasis = acquire_data_random_classical_circuit(perfect_qc, noisy_qc, exp_xbasis)\n", - "t1x = time.time()\n", - "totalx = t1x-t0x\n", - "print(totalx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dfx = pd.DataFrame(data_xbasis)\n", - "dfx.to_pickle(\"data_x_Aspen_1_15Q_A_2019_02_09.pkl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now put the data into a dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#data_xbasis.to_pickle(\"data_x_Aspen-1-16Q-A_2019_02_16.pkl\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#data_xbasis = pd.read_pickle('data_x_Aspen-1-16Q-A_2019_02_16.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data processing and estimation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res_df = estimate_random_classical_circuit_errors(data_zbasis)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "circuit_width = res_df['Width'].max()\n", - "\n", - "for subgraph_size in range(1, circuit_width+1):\n", - " wdx = data_zbasis['Width']==subgraph_size\n", - " res_df[wdx]\n", - " \n", - " df.append(df2, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "circuit_width = res_df['Width'].max()\n", - "circuit_depth = res_df['Depth'].max()\n", - "results = []\n", - "for depth, subgraph_size in itertools.product(range(1, circuit_depth+1), range(1, circuit_width+1)):\n", - " wdx = data_zbasis['Width']==subgraph_size\n", - " ddx = data_zbasis['Depth']==depth\n", - " ndf= res_df[wdx&ddx].copy()\n", - " results.append({'Depth': depth,\n", - " 'Width': subgraph_size,\n", - " 'In X basis': ndf['In X basis'].iloc[0],\n", - " 'Active Reset': ndf['Active Reset'].iloc[0],\n", - " 'Trials': ndf['Trials'].iloc[0],\n", - " 'Hamming dist. data': ndf['Hamming dist. data'].mean(),\n", - " 'Hamming dist. rand': ndf['Hamming dist. rand'].mean(),\n", - " 'Hamming dist. ideal': ndf['Hamming dist. ideal'].mean(),\n", - " 'TVD(data, ideal)': ndf['TVD(data, ideal)'].mean(),\n", - " 'TVD(data, rand)': ndf['TVD(data, rand)'].mean(),\n", - " 'Pr. success data': ndf['Pr. success data'].mean(),\n", - " 'Pr. success rand': ndf['Pr. success rand'].mean(),\n", - " 'loge = basement[log_2(Width)-1]': ndf['loge = basement[log_2(Width)-1]'].mean(),\n", - " 'Pr. success loge data': ndf['Pr. success loge data'].mean(),\n", - " 'Pr. success loge rand': ndf['Pr. success loge rand'].mean(),\n", - " }) \n", - "munged = pd.DataFrame(results)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "munged" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res_df[wdx&ddx]['Hamming dist. data'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res_df[wdx&ddx]['Hamming dist. rand'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot a particular depth and width" + "## Plot a particular depth and width" ] }, { @@ -1691,6 +1362,58 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the distribution of sublattice widths" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[9, 12, 22, 36, 49, 48, 32, 9, 1, 9, 12, 22, 36, 49, 48, 32, 9, 1]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = perfect_qc.qubit_topology()\n", + "len(perfect_qc.qubit_topology())\n", + "# distribution of graph lengths\n", + "distr = []\n", + "for num_nodes in range(1, len(G.nodes) + 1):\n", + " listg = generate_connected_subgraphs(G, num_nodes)\n", + " distr.append(len(listg))\n", + "\n", + "cir_wid = list(range(1, len(G.nodes) + 1))\n", + "plt.bar(cir_wid, distr, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "disty" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index e1086cc0..d29abf29 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -362,6 +362,15 @@ def func(graph, qc, **kwargs): return CircuitTemplate([func]) +def get_switch_basis_x_z_template(): + def func(graph, **kwargs): + prog = Program() + for node in graph.nodes: + prog.inst(H(node)) + return prog + return CircuitTemplate([func]) + + def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, graph: nx.Graph = None, pattern = None): From 33d2fee440bd5c03a2a6fe8030e4e7978b0b28e4 Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 25 Jul 2019 16:14:26 -0400 Subject: [PATCH 21/49] Add some helper method support, including for plotting. --- examples/volumetrics.ipynb | 1326 ++++++++++++++++------------ forest/benchmarking/volumetrics.py | 143 +-- 2 files changed, 861 insertions(+), 608 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index dfc07ffa..a9336f36 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -79,7 +79,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -190,7 +190,7 @@ "'tcp://127.0.0.1:5555'" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -208,38 +208,37 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "X 0\n", - "Z 1\n", - "X 2\n", - "Z 3\n", - "I 4\n", - "X 5\n", - "Z 6\n", + "I 0\n", + "I 1\n", + "I 2\n", + "X 3\n", + "Z 4\n", + "Z 5\n", + "X 6\n", "Z 7\n", - "X 8\n", + "Z 8\n", "CZ 0 3\n", "CZ 0 1\n", "CZ 1 4\n", - "I 1\n", - "I 2\n", + "CZ 1 2\n", "I 2\n", "I 5\n", "CZ 3 6\n", - "CZ 3 4\n", + "I 3\n", + "I 4\n", "I 4\n", "I 7\n", "I 4\n", "I 5\n", "CZ 5 8\n", - "I 6\n", - "I 7\n", + "CZ 6 7\n", "CZ 7 8\n", "\n" ] @@ -253,29 +252,28 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "RX(-pi/2) 1\n", "RZ(-pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi) 2\n", - "RZ(-pi) 2\n", - "RX(-pi/2) 3\n", + "RZ(-pi/2) 2\n", "RZ(-pi/2) 3\n", + "RX(pi/2) 4\n", "RZ(-pi/2) 4\n", - "RZ(pi/2) 5\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RX(pi/2) 7\n", + "RZ(-pi) 5\n", + "RZ(-pi) 5\n", + "RX(pi/2) 6\n", + "RZ(-pi) 6\n", "RZ(-pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(-pi) 8\n", + "RX(-pi/2) 7\n", + "RX(-pi/2) 8\n", "\n" ] } @@ -294,17 +292,17 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "X 3\n", - "I 6\n", - "I 3\n", - "I 6\n", + "I 4\n", + "X 7\n", + "X 4\n", + "X 7\n", "\n" ] } @@ -316,16 +314,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "I 7\n", - "CNOT 4 7\n", + "I 0\n", + "I 3\n", + "CNOT 0 3\n", "\n" ] } @@ -337,15 +335,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H 2\n", - "H 5\n", + "H 1\n", + "H 4\n", "H 7\n", "H 8\n", "\n" @@ -359,23 +357,24 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CZ 3 4\n", + "CZ 1 4\n", "RZ(pi/2) 4\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", "RX(pi/2) 4\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", + "RX(pi/2) 1\n", + "RZ(-pi) 1\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", + "CZ 1 4\n", "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 1\n", "\n" ] } @@ -388,40 +387,74 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-3.11559416083055) 2\n", - "RX(pi/2) 2\n", - "RZ(0.9985930931695964) 2\n", - "RX(-pi/2) 2\n", - "RZ(0.33645965947699885) 5\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", + "RZ(-0.5581195580028985) 8\n", + "RX(pi/2) 8\n", + "RZ(1.2736807691803615) 8\n", + "RX(-pi/2) 8\n", + "RZ(0.8654548211806258) 8\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(1.683714870976314) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.164556904728772) 5\n", "RX(pi/2) 5\n", - "RZ(1.1143132339260913) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RZ(1.8502545221336462) 2\n", - "RX(pi/2) 2\n", - "RZ(-1.493206408133509) 5\n", + "CZ 4 5\n", + "RX(pi/2) 4\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "CZ 4 5\n", + "RZ(pi/2) 7\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "CZ 7 4\n", + "RZ(-2.5423448512921096) 5\n", + "RX(pi/2) 5\n", + "RZ(1.0298565022999515) 5\n", "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RX(-pi/2) 2\n", + "RZ(-1.4107150935903878) 8\n", + "RX(pi/2) 8\n", + "RZ(0.9343859424350573) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RZ(1.8455338844194458) 5\n", "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(1.7273460464564303) 2\n", - "RX(pi/2) 2\n", - "RZ(0.4625719031986815) 2\n", - "RX(-pi/2) 2\n", - "RZ(-0.5110412432518459) 2\n", - "RZ(-1.166166861993211) 5\n", + "RZ(1.2152422654059585) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RX(-pi/2) 5\n", + "RX(pi/2) 8\n", + "CZ 8 5\n", + "RX(pi/2) 7\n", + "RZ(-0.5939372112013284) 8\n", + "RX(pi/2) 8\n", + "RZ(2.0985221442302406) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RZ(0.18821978522872662) 8\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RZ(1.9770357488610202) 4\n", + "RZ(-2.5476554423884643) 5\n", "RX(pi/2) 5\n", - "RZ(0.6298859814840272) 5\n", + "RZ(2.098522144230241) 5\n", "RX(-pi/2) 5\n", - "RZ(2.6282638093266915) 5\n", + "RZ(-0.1882197852287284) 5\n", + "RX(pi/2) 7\n", + "RZ(-pi/2) 7\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", "\n" ] } @@ -434,45 +467,45 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-1.2990942230030267) 4\n", - "RX(pi/2) 4\n", - "RZ(1.5155074169065497) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.7430125039887816) 5\n", + "RZ(-0.7313267215276299) 2\n", + "RX(pi/2) 2\n", + "RZ(0.8738083564575054) 2\n", + "RX(-pi/2) 2\n", + "RZ(1.1108387720144826) 5\n", "RX(pi/2) 5\n", - "RZ(1.4191531043790895) 5\n", + "RZ(1.9239464510235134) 5\n", "RX(-pi/2) 5\n", - "CZ 5 4\n", - "RZ(-1.2516854599739062) 4\n", - "RX(pi/2) 4\n", - "RZ(2.110642864158322) 4\n", - "RX(-pi/2) 4\n", - "RZ(-2.8531720822941167) 5\n", + "CZ 5 2\n", + "RZ(-0.7988576166116559) 2\n", + "RX(pi/2) 2\n", + "RZ(2.0611770298297554) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.16257974334726644) 5\n", "RX(-pi/2) 5\n", - "CZ 5 4\n", - "RX(pi/2) 4\n", - "RZ(-2.0269992194907793) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.7707585610544267) 5\n", + "CZ 5 2\n", + "RX(pi/2) 2\n", + "RZ(-1.7986523134027053) 2\n", + "RX(-pi/2) 2\n", + "RZ(1.4365683405145715) 5\n", "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(-2.7752501314248956) 4\n", - "RX(pi/2) 4\n", - "RZ(0.6563900562878288) 4\n", - "RX(-pi/2) 4\n", - "RZ(2.3643866667806357) 4\n", - "RZ(-2.2772139390064376) 5\n", + "CZ 5 2\n", + "RZ(2.573992416316516) 2\n", + "RX(pi/2) 2\n", + "RZ(0.5011562609035493) 2\n", + "RX(-pi/2) 2\n", + "RZ(1.2391707193285333) 2\n", + "RZ(-1.5517677060657928) 5\n", "RX(pi/2) 5\n", - "RZ(1.8032677495210245) 5\n", + "RZ(1.557823493357372) 5\n", "RX(-pi/2) 5\n", - "RZ(-2.022168105894388) 5\n", + "RZ(-0.9421500589018095) 5\n", "\n" ] } @@ -491,31 +524,32 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "X 1\n", - "X 3\n", - "I 4\n", - "X 7\n", - "I 1\n", - "I 4\n", - "CNOT 3 4\n", - "CNOT 4 7\n", - "I 1\n", - "X 3\n", - "I 4\n", + "I 3\n", + "X 4\n", + "X 6\n", "I 7\n", - "I 1\n", - "I 4\n", + "CNOT 3 6\n", "I 3\n", "I 4\n", "I 4\n", "I 7\n", + "I 6\n", + "I 7\n", + "I 3\n", + "X 4\n", + "I 6\n", + "I 7\n", + "CNOT 3 6\n", + "CNOT 3 4\n", + "CNOT 4 7\n", + "CNOT 6 7\n", "\n" ] } @@ -534,30 +568,32 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H 5\n", - "H 8\n", - "Z 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "H 5\n", - "CZ 5 8\n", - "H 5\n", - "H 5\n", - "H 8\n", + "H 0\n", + "H 1\n", + "Z 0\n", + "Z 1\n", + "H 0\n", + "CZ 0 1\n", + "H 0\n", + "I 0\n", + "Z 1\n", + "H 0\n", + "CZ 0 1\n", + "H 0\n", + "Z 0\n", + "I 1\n", + "H 0\n", + "CZ 0 1\n", + "H 0\n", + "H 0\n", + "H 1\n", "\n" ] } @@ -575,48 +611,62 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 3\n", - "RX(pi/2) 4\n", - "RZ(-pi) 4\n", - "RX(pi/2) 3\n", - "CZ 3 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", "RZ(-pi/2) 3\n", "RX(-pi/2) 3\n", - "RX(pi/2) 4\n", - "RZ(-pi) 4\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 0\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi) 0\n", "RX(pi/2) 3\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RZ(-pi/2) 4\n", + "RZ(-pi) 3\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "CZ 0 3\n", "RZ(-pi/2) 3\n", - "RZ(pi/2) 3\n", - "RX(-pi) 3\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RZ(-pi) 0\n", "RX(-pi/2) 3\n", - "CZ 3 4\n", - "RX(-pi/2) 4\n", + "RZ(-pi) 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", "RX(-pi/2) 3\n", - "RZ(pi/2) 3\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(0.19942343037115873) 0\n", + "RX(-pi/2) 0\n", + "RZ(-0.778788619183817) 3\n", "RX(pi/2) 3\n", - "RZ(pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", + "CZ 0 3\n", + "RX(pi/2) 0\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", + "RX(pi/2) 0\n", + "RZ(2.3628040344059755) 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 3\n", + "RZ(0.19942343037115845) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 3\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -646,358 +696,494 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RZ(0.6669073931670509) 4\n", - "RX(pi/2) 4\n", - "RZ(0.5854615772794022) 4\n", - "RX(-pi/2) 4\n", - "RZ(-3.06066309923684) 4\n", - "RZ(-pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.4107959821067877) 0\n", - "RX(-pi/2) 0\n", - "RZ(-0.5585293438296013) 1\n", - "RX(pi/2) 1\n", - "CZ 0 1\n", - "RX(pi/2) 0\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "CZ 0 1\n", - "RZ(pi/2) 3\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "CZ 3 0\n", - "RZ(-2.4305289034535735) 1\n", - "RX(pi/2) 1\n", - "RZ(0.4608188356599866) 1\n", - "RX(-pi/2) 1\n", - "RZ(2.740126082031923) 4\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", + "RZ(-2.3181435067958773) 8\n", + "RX(pi/2) 8\n", + "RZ(1.7684965768495358) 8\n", + "RX(-pi/2) 8\n", + "RZ(0.07088205710553819) 8\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(1.1064691618894351) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RZ(1.3016838095222525) 1\n", - "RX(pi/2) 1\n", - "RZ(-1.5895747244078953) 4\n", + "RZ(0.9726985271045892) 4\n", "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RX(-pi/2) 1\n", - "RX(pi/2) 4\n", - "CZ 4 1\n", - "RX(pi/2) 3\n", - "RZ(-0.31660508427036405) 4\n", + "RZ(1.7400726697677005) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", "RX(pi/2) 4\n", - "RZ(1.9412910023569236) 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(-pi/2) 3\n", - "CZ 3 0\n", - "RZ(0.25293664903812485) 4\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(pi/2) 7\n", + "RX(pi) 7\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(-2.583063309760192) 0\n", - "RZ(-0.46129210523277475) 1\n", - "RX(pi/2) 1\n", - "RZ(0.2166354441029604) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.6427579756968358) 1\n", + "CZ 7 4\n", + "RZ(0.45826797068731434) 5\n", + "RX(pi/2) 5\n", + "RZ(2.468284038036106) 5\n", + "RX(-pi/2) 5\n", + "RZ(-1.4428100443331238) 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"RZ(0.9012599436802047) 3\n", + "RZ(0.22387022860890937) 3\n", "RX(pi/2) 3\n", - "RZ(2.4889923518664103) 3\n", + "RZ(2.520106548466318) 3\n", "RX(-pi/2) 3\n", - "RZ(-2.4272854794008447) 4\n", + "RZ(0.6832453432356136) 4\n", "RX(-pi/2) 4\n", "CZ 4 3\n", "RX(pi/2) 3\n", - "RZ(-1.6186824319994155) 3\n", + "RZ(-1.591068095352492) 3\n", "RX(-pi/2) 3\n", - "RZ(1.5703275860611186) 4\n", + "RZ(1.5316351849624512) 4\n", "RX(pi/2) 4\n", "CZ 4 3\n", - "RZ(-1.2431397202040146) 0\n", - "RX(pi/2) 0\n", - "RZ(1.3071530036896222) 0\n", - "RX(-pi/2) 0\n", - "RZ(-0.7860124239672821) 0\n", - "RZ(-2.5610770607411144) 1\n", - "RX(pi/2) 1\n", - "RZ(1.0388517327800924) 1\n", - "RX(-pi/2) 1\n", - "RZ(3.098199823290461) 1\n", - "RZ(0.7146722390552813) 3\n", + "RZ(-1.821359594811281) 5\n", + "RX(pi/2) 5\n", + "RZ(2.8027191581286446) 5\n", + "RX(-pi/2) 5\n", + "RZ(2.373458701758155) 5\n", + "RZ(1.3116028920617289) 3\n", + "RX(pi/2) 3\n", + "RZ(0.354665791601617) 3\n", + "RX(-pi/2) 3\n", + "RZ(-2.477980471436438) 6\n", + "RX(pi/2) 6\n", + "RZ(2.200761150993738) 6\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RZ(0.16427583758354403) 3\n", + "RX(-pi/2) 3\n", + "RZ(-0.07250573872746546) 6\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RZ(1.3793219877966427) 4\n", + "RX(pi/2) 4\n", + "RZ(0.7605654592910209) 4\n", + "RX(-pi/2) 4\n", + "CZ 5 4\n", + "RZ(-1.5986420098470244) 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(2.3370440201008487) 3\n", + "RX(pi/2) 3\n", + "RZ(1.6048729491619764) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.6873227029058877) 4\n", + "RX(pi/2) 4\n", + "RZ(2.3060399089324894) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(1.294547445744064) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.2653966587785472) 4\n", + "RX(pi/2) 4\n", + "RZ(2.056248402854976) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(1.6307388202172834) 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 4\n", + "RZ(-1.6500474085614147) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(0.9850349000271519) 3\n", "RX(pi/2) 3\n", - "RZ(1.4352692603518304) 3\n", + "RZ(0.7234011926298821) 3\n", "RX(-pi/2) 3\n", - "RZ(2.5835346399507184) 3\n", - "RZ(-0.9543567877261507) 4\n", + "RZ(0.4372310052066348) 3\n", + "RZ(-2.2707193744507577) 4\n", "RX(pi/2) 4\n", - "RZ(2.511147801544665) 4\n", + "RZ(2.607476272820057) 4\n", "RX(-pi/2) 4\n", - "RZ(0.10327658195147739) 4\n", + "RZ(0.014198795741173775) 4\n", + "RZ(0.18253925555781378) 5\n", + "RX(pi/2) 5\n", + "RZ(2.0543534919795854) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.5016261059035454) 5\n", + "RZ(1.7603979343383471) 6\n", + "RX(pi/2) 6\n", + "RZ(1.3911815184306133) 6\n", + "RX(-pi/2) 6\n", + "RZ(0.8940746753816429) 6\n", "\n" ] } @@ -1016,20 +1202,20 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [, , , , , , , , , ]}, 3: {4: [, , , , , , , , , ]}, 4: {4: [, , , , , , , , , ]}}\n" + "{2: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 3: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 4: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}}\n" ] } ], "source": [ "widths = [2, 3, 4]\n", - "depths = [3, 4]\n", + "depths = [3, 4, 5]\n", "ckt = classical_1q_2q\n", "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=10)\n", "print(prog_array)" @@ -1037,7 +1223,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1046,14 +1232,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 0]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 0]])]}, 3: {4: [array([[1, 1, 0]]), array([[0, 0, 0]]), array([[1, 1, 1]]), array([[1, 0, 1]]), array([[1, 0, 0]]), array([[0, 1, 0]]), array([[1, 0, 1]]), array([[1, 1, 0]]), array([[1, 0, 0]]), array([[0, 1, 0]])]}, 4: {4: [array([[1, 0, 0, 0]]), array([[0, 0, 1, 1]]), array([[0, 0, 1, 0]]), array([[0, 1, 1, 0]]), array([[0, 0, 1, 1]]), array([[0, 1, 1, 1]]), array([[0, 0, 1, 0]]), array([[1, 0, 0, 0]]), array([[1, 1, 0, 0]]), array([[1, 0, 1, 1]])]}}\n" + "{2: {3: [array([[1, 1]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 0]]), array([[1, 0]]), array([[1, 1]]), array([[1, 0]]), array([[0, 1]])], 4: [array([[1, 0]]), array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 1]]), array([[1, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]])], 5: [array([[0, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 1]]), array([[1, 1]]), array([[0, 1]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[1, 1]])]}, 3: {3: [array([[0, 0, 1]]), array([[1, 1, 0]]), array([[1, 0, 0]]), array([[0, 0, 1]]), array([[0, 1, 0]]), array([[1, 0, 1]]), array([[0, 1, 0]]), array([[1, 1, 0]]), array([[0, 1, 0]]), array([[1, 1, 0]])], 4: [array([[0, 1, 1]]), array([[0, 1, 0]]), array([[0, 1, 1]]), array([[0, 1, 0]]), array([[0, 1, 0]]), array([[0, 0, 1]]), array([[0, 0, 0]]), array([[1, 1, 0]]), array([[0, 1, 0]]), array([[0, 1, 0]])], 5: [array([[1, 1, 1]]), array([[1, 0, 0]]), array([[1, 1, 0]]), array([[0, 1, 1]]), array([[1, 0, 1]]), array([[0, 0, 1]]), array([[0, 1, 0]]), array([[1, 1, 1]]), array([[0, 0, 1]]), array([[1, 1, 1]])]}, 4: {3: [array([[0, 1, 1, 1]]), array([[1, 0, 0, 0]]), array([[1, 0, 0, 1]]), array([[1, 0, 0, 0]]), array([[0, 1, 1, 0]]), array([[0, 0, 0, 1]]), array([[1, 1, 0, 1]]), array([[1, 1, 0, 0]]), array([[1, 0, 1, 1]]), array([[1, 0, 1, 1]])], 4: [array([[0, 1, 0, 0]]), array([[1, 0, 0, 1]]), array([[0, 1, 0, 1]]), array([[1, 0, 0, 0]]), array([[1, 0, 1, 1]]), array([[1, 1, 0, 1]]), array([[1, 0, 1, 0]]), array([[1, 1, 1, 1]]), array([[1, 0, 1, 0]]), array([[0, 1, 1, 0]])], 5: [array([[0, 1, 1, 0]]), array([[0, 0, 0, 0]]), array([[0, 0, 1, 0]]), array([[1, 1, 0, 1]]), array([[1, 0, 1, 1]]), array([[0, 0, 1, 0]]), array([[0, 0, 1, 0]]), array([[0, 0, 1, 1]]), array([[1, 0, 0, 0]]), array([[1, 1, 0, 1]])]}}\n" ] } ], @@ -1064,14 +1250,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: [array([0.888, 0.112, 0. ]), array([0.852, 0.148, 0. ]), array([0.882, 0.112, 0.006]), array([0.94, 0.06, 0. ]), array([0.904, 0.094, 0.002]), array([0.828, 0.162, 0.01 ]), array([0.946, 0.054, 0. ]), array([0.934, 0.066, 0. ]), array([0.862, 0.138, 0. ]), array([0.958, 0.042, 0. ])]}, 3: {4: [array([0.828, 0.154, 0.018, 0. ]), array([0.91, 0.08, 0.01, 0. ]), array([0.738, 0.23 , 0.032, 0. ]), array([0.776, 0.202, 0.02 , 0.002]), array([0.868, 0.114, 0.018, 0. ]), array([0.858, 0.136, 0.006, 0. ]), array([0.802, 0.182, 0.016, 0. ]), array([0.79 , 0.194, 0.014, 0.002]), array([0.848, 0.128, 0.018, 0.006]), array([0.828, 0.164, 0.006, 0.002])]}, 4: {4: [array([0.88 , 0.112, 0.008, 0. , 0. ]), array([0.772, 0.21 , 0.014, 0.004, 0. ]), array([0.832, 0.158, 0.01 , 0. , 0. ]), array([0.778, 0.196, 0.022, 0.002, 0.002]), array([0.782, 0.196, 0.02 , 0.002, 0. ]), array([0.708, 0.244, 0.034, 0.014, 0. ]), array([0.802, 0.182, 0.014, 0.002, 0. ]), array([0.862, 0.12 , 0.018, 0. , 0. ]), array([0.768, 0.202, 0.026, 0.004, 0. ]), array([0.736, 0.23 , 0.032, 0.002, 0. ])]}}\n" + "{2: {3: [array([0.788, 0.206, 0.006]), array([0.836, 0.152, 0.012]), array([0.874, 0.124, 0.002]), array([0.952, 0.048, 0. ]), array([0.958, 0.04 , 0.002]), array([0.882, 0.118, 0. ]), array([0.886, 0.11 , 0.004]), array([0.84 , 0.152, 0.008]), array([0.842, 0.154, 0.004]), array([0.888, 0.102, 0.01 ])], 4: [array([0.862, 0.132, 0.006]), array([0.96, 0.04, 0. ]), array([0.846, 0.138, 0.016]), array([0.89 , 0.108, 0.002]), array([0.876, 0.122, 0.002]), array([0.874, 0.124, 0.002]), array([0.822, 0.174, 0.004]), array([0.896, 0.102, 0.002]), array([0.878, 0.122, 0. ]), array([0.888, 0.108, 0.004])], 5: [array([0.954, 0.046, 0. ]), array([0.876, 0.118, 0.006]), array([0.868, 0.128, 0.004]), array([0.834, 0.158, 0.008]), array([0.784, 0.202, 0.014]), array([0.864, 0.124, 0.012]), array([0.832, 0.16 , 0.008]), array([0.89 , 0.108, 0.002]), array([0.904, 0.092, 0.004]), array([0.832, 0.158, 0.01 ])]}, 3: {3: [array([0.874, 0.122, 0.004, 0. ]), array([0.804, 0.174, 0.018, 0.004]), array([0.836, 0.156, 0.008, 0. ]), array([0.84, 0.15, 0.01, 0. ]), array([0.874, 0.124, 0.002, 0. ]), array([0.814, 0.176, 0.008, 0.002]), array([0.84 , 0.152, 0.006, 0.002]), array([0.784, 0.196, 0.02 , 0. ]), array([0.892, 0.092, 0.016, 0. ]), array([0.808, 0.18 , 0.012, 0. ])], 4: [array([0.856, 0.142, 0. , 0.002]), array([0.888, 0.102, 0.01 , 0. ]), array([0.77 , 0.194, 0.034, 0.002]), array([0.852, 0.144, 0.004, 0. ]), array([0.872, 0.12 , 0.008, 0. ]), array([0.862, 0.13 , 0.008, 0. ]), array([0.93 , 0.068, 0.002, 0. ]), array([0.816, 0.174, 0.008, 0.002]), array([0.846, 0.146, 0.008, 0. ]), array([0.866, 0.13 , 0.004, 0. ])], 5: [array([0.738, 0.234, 0.024, 0.004]), array([0.86 , 0.136, 0.004, 0. ]), array([0.806, 0.18 , 0.01 , 0.004]), array([0.798, 0.176, 0.026, 0. ]), array([0.778, 0.21 , 0.01 , 0.002]), array([0.84 , 0.154, 0.006, 0. ]), array([0.846, 0.142, 0.012, 0. ]), array([0.744, 0.23 , 0.026, 0. ]), array([0.852, 0.132, 0.016, 0. ]), array([0.756, 0.22 , 0.022, 0.002])]}, 4: {3: [array([0.708, 0.26 , 0.032, 0. , 0. ]), array([0.838, 0.158, 0.004, 0. , 0. ]), array([0.756, 0.224, 0.02 , 0. , 0. ]), array([0.882, 0.11 , 0.008, 0. , 0. ]), array([0.786, 0.194, 0.02 , 0. , 0. ]), array([0.852, 0.138, 0.01 , 0. , 0. ]), array([0.732, 0.228, 0.03 , 0.01 , 0. ]), array([0.788, 0.188, 0.022, 0.002, 0. ]), array([0.75 , 0.23 , 0.018, 0. , 0.002]), array([0.7 , 0.27 , 0.028, 0.002, 0. ])], 4: [array([0.812, 0.162, 0.026, 0. , 0. ]), array([0.764, 0.202, 0.022, 0.01 , 0.002]), array([0.75 , 0.23 , 0.014, 0.006, 0. ]), array([0.83 , 0.148, 0.022, 0. , 0. ]), array([0.736, 0.238, 0.026, 0. , 0. ]), array([0.718, 0.244, 0.038, 0. , 0. ]), array([0.78 , 0.202, 0.018, 0. , 0. ]), array([0.684, 0.27 , 0.038, 0.008, 0. ]), array([0.822, 0.162, 0.014, 0.002, 0. ]), array([0.784, 0.21 , 0.006, 0. , 0. ])], 5: [array([0.778, 0.206, 0.012, 0.004, 0. ]), array([0.876, 0.118, 0.006, 0. , 0. ]), array([0.83 , 0.156, 0.014, 0. , 0. ]), array([0.724, 0.23 , 0.042, 0.004, 0. ]), array([0.756, 0.214, 0.03 , 0. , 0. ]), array([0.834, 0.146, 0.02 , 0. , 0. ]), array([0.822, 0.16 , 0.018, 0. , 0. ]), array([0.748, 0.23 , 0.02 , 0.002, 0. ]), array([0.83 , 0.166, 0.004, 0. , 0. ]), array([0.72 , 0.252, 0.028, 0. , 0. ])]}}\n" ] } ], @@ -1082,30 +1268,22 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {4: array([0.8994, 0.0988, 0.0018])}, 3: {4: array([0.8246, 0.1584, 0.0158, 0.0012])}, 4: {4: array([7.92e-01, 1.85e-01, 1.98e-02, 3.00e-03, 2.00e-04])}}\n" + "{2: {3: array([0.8746, 0.1206, 0.0048]), 4: array([0.8792, 0.117 , 0.0038]), 5: array([0.8638, 0.1294, 0.0068])}, 3: {3: array([8.366e-01, 1.522e-01, 1.040e-02, 8.000e-04]), 4: array([8.558e-01, 1.350e-01, 8.600e-03, 6.000e-04]), 5: array([0.8018, 0.1814, 0.0156, 0.0012])}, 4: {3: array([7.792e-01, 2.000e-01, 1.920e-02, 1.400e-03, 2.000e-04]), 4: array([7.680e-01, 2.068e-01, 2.240e-02, 2.600e-03, 2.000e-04]), 5: array([0.7918, 0.1878, 0.0194, 0.001 , 0. ])}}\n" ] } ], "source": [ - "avg_err_hamm_distrs = {w: {d: sum(distrs)/len(distrs)} for w, d_arr in err_hamm_distrs.items()\n", - " for d, distrs in d_arr.items()}\n", + "avg_err_hamm_distrs = get_average_of_distributions(err_hamm_distrs)\n", "print(avg_err_hamm_distrs)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1115,39 +1293,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "dep = 6\n", - "wid = 4\n", - "\n", - "distz = get_hamming_dist(res_df, dep, wid)\n", - "\n", + "w = 3 # width\n", + "d = 4 # depth\n", "\n", - "# combine data from different subgraphs\n", - "avg_dist = distz['Hamming dist. data'].mean()\n", + "avg_distr = avg_err_hamm_distrs[3][4]\n", "\n", "# rand data\n", - "rand_dist = distz['Hamming dist. rand'][0]" + "rand_distr = get_random_hamming_wt_distr(w)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "x_labels = np.arange(0, len(avg_dist))\n", - "plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - "plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", + "x_labels = np.arange(0, len(avg_distr))\n", + "plt.bar(x_labels, avg_distr, width=0.61, align='center')\n", + "plt.bar(x_labels, rand_distr, width=0.31, align='center')\n", "plt.xticks(x_labels)\n", "plt.xlabel('Hamming Weight of Error')\n", - "plt.ylabel('Relative Frequency of Occurence')\n", - "plt.ylim([0,1])\n", + "plt.ylabel('Relative Frequency of Occurrence')\n", + "plt.ylim([0, 1])\n", "plt.grid(axis='y', alpha=0.75)\n", "plt.legend(['data','random'])\n", - "plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", + "plt.title(f'Width = {w}, Depth = {d}')\n", "plt.show()" ] }, @@ -1155,74 +1340,142 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# For a particular width plot all depths" + "Using our helper function" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "wid = 4\n", - "df_fn_depth = get_hamming_dists_fn_depth(res_df, wid)" + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], depths=[d], plot_rand_distr=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For a particular width, plot all depths" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+oKq2JtmaJBs3bsy11167j6XPxksfffesS2CNOlB/ZwAAAGCseQ/S9lYluX+S/7O7358kVXVlkpuT/EySX118QXdfkOSCJNmyZUtv3rx5/1W7jF5y4a2zLoE16qytB+bvDAAAAIw170s7dyQ5fIn2DZO+3V3XST64s2Gyz9o1SZ6wjPUBAAAAsEbMe5B2YxbthVZVRyS5bxbtnbbIDRlmpdWi9kpyz3IWCAAAAMDaMO9B2sVJjquqQxe0nZzkq0mu2M117508P3dnQ1UdnuSpST6x3EUCAAAAsPrNe5D2piRfT/Kuqnr+5ECA05KcM1mqmSSpqpuq6s07X3f31Un+JMmbq+onquqEJO9O8o0k/2l/fgMAAAAArA5zHaR1944kxyY5OMl7kpye5Nwkr1s0dN1kzEIvS3JRknOS/FGGEO15k3sCAAAAwFTm/tTO7r4+yfP2MGbTEm1fSfKqyQMAAAAA9slcz0gDAAAAgHkhSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYYe6DtKp6QlVtq6o7q+q2qjqjqg6e4vqDqurqquqqevFK1goAAADA6rVu1gXsTlVtSHJZkuuTnJjkMUnOzhAAnjryNq9M8sgVKRAAAACANWPeZ6SdkmR9kpO6+9LuflOS05P8fFUdtqeLJ0Hc65P8ysqWCQAAAMBqN+9B2vFJLunuOxa0XZghXDtmxPX/PslHkmxbgdoAAAAAWEPmPUg7MsmNCxu6+5Ykd076dqmqvifJTyV5zYpVBwAAAMCaMe9B2oYkty/RvmPStzv/b5Lzu/umZa8KAAAAgDVnrg8b2FtV9SNJHp/kB6a4ZmuSrUmycePGXHvttStU3cp66aPvnnUJrFEH6u8MAAAAjDXvQdqOJIcv0b5h0vdtqupeSf5DkjOTHFRVD0iy82CC+1XVod395cXXdfcFSS5Iki1btvTmzZuXofz97yUX3jrrElijztp6YP7OAAAAwFjzvrTzxizaC62qjkhy3yzaO22B+yV5ZJJzMoRtO5J8YtJ3YZK/WpFKAQAAAFjV5n1G2sVJXrtoFtnJSb6a5IpdXPOVJM9d1PawJH+Y5JeTXL4ShQIAAACwus17kPamJK9O8q6qOjPJo5OcluSc7r5j56CquinJFd39iu7+ZpIPLrxJVW2afPnfuvvjK182AAAAAKvNXAdp3b2jqo5Ncn6S92Q4wfPcDGHaQuuSHLx/qwMAAABgLZnrIC1Juvv6JM/bw5hNe+jfnqSWryoAAAAA1pq5D9IAWKVOW+pQZqZy2pdmXQEsH58J+85nAgCsuHk/tRMAAAAA5oIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAI8x9kFZVT6iqbVV1Z1XdVlVnVNXBe7jme6vqv1TVTZPrPllVr6uq++yvugEAAABYXdbNuoDdqaoNSS5Lcn2SE5M8JsnZGQLAU3dz6cmTsWcm+dsk35Pk30+e/48VLBkAAACAVWqug7QkpyRZn+Sk7r4jyaVVdViS06rqrEnbUt7Y3V9c8PqDVfW1JP9fVT2qu29e4boBAAAAWGXmfWnn8UkuWRSYXZghXDtmVxctCtF2+qvJ88OXrzwAAAAA1op5D9KOTHLjwobuviXJnZO+aRyV5J4kf7c8pQEAAACwlsx7kLYhye1LtO+Y9I1SVQ/LsKfa27r788tUGwAAAABryLzvkbbPquqQJG9P8pUk//duxm1NsjVJNm7cmGuvvXb/FLjMXvrou2ddAmvUgfo7wwwd8fJZV3Dg83vHauIzYd/5TACAFTfvQdqOJIcv0b5h0rdbVVVJ3prkiUme2d27vKa7L0hyQZJs2bKlN2/evFcFz9pLLrx11iWwRp219cD8nWGGLnrLrCs48L3iP866Alg+PhP2nc8EAFhx8x6k3ZhFe6FV1RFJ7ptFe6ftwnlJTkzygu4eMx4AAAAAljTve6RdnOS4qjp0QdvJSb6a5IrdXVhVv5TkZ5K8rLs/vHIlAgAAALAWzHuQ9qYkX0/yrqp6/mQfs9OSnNPdd+wcVFU3VdWbF7z+0SRvyLCs89aqesaCx4P377cAAAAAwGow10s7u3tHVR2b5Pwk78lwgue5GcK0hdYlOXjB6++fPL988ljoJ5O8ZXkrBQAAAGC1m+sgLUm6+/okz9vDmE2LXr883x6gAQAAAMBem/elnQAAAAAwFwRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYIR1sy4AgJWz6RffN+sSdmn7fWZdwYFvrv/7vvGEWZcAAADLzow0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGWDfrAlg7tt/nR2ddwgFv09f+YNYlAADAyjvt8FlXcOA77UuzrgBWJTPSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYYe6DtKp6QlVtq6o7q+q2qjqjqg4ecd3hVfVfqmpHVX2pqv5rVT1of9QMAAAAwOqzbtYF7E5VbUhyWZLrk5yY5DFJzs4QAJ66h8vfnuRxSV6Z5J4kZya5KMmzV6peAAAAAFavuQ7SkpySZH2Sk7r7jiSXVtVhSU6rqrMmbd+mqo5K8v1JjunuP5+03Zrk41X1/O6+bD/VDwAwNzb94vtmXcIubb/PrCs48M31f983njDrEgBgWcz70s7jk1yyKDC7MEO4dswervvczhAtSbr7qiR/P+kDAAAAgKnMe5B2ZJIbFzZ09y1J7pz0jb5u4oY9XAcAAAAAS5r3pZ0bkty+RPuOSd/eXPfopS6oqq1Jtk5efqWqPjlFnYxQsy5gz74jyRdnXcTuvXjWBexSnTnrCjjQ+ExYDj4TWD18JiwHnwmwn83358LpB8An64HpUbMugNma9yBtv+nuC5JcMOs6mJ2qurq7t8y6DmA++EwAFvKZACzmcwHWpnlf2rkjyeFLtG+Y9C33dQAAAACwpHkP0m7Moj3NquqIJPfN0nug7fK6iV3tnQYAAAAAuzXvQdrFSY6rqkMXtJ2c5KtJrtjDdQ+rqmftbKiqLRn2R7t4JQplVbC0F1jIZwKwkM8EYDGfC7AGVXfPuoZdqqoNSa5P8t+TnJkhCDsnyXndfeqCcTcluaK7X7Gg7ZIkj03ymiT3TK7/fHc/e/99BwAAAACsFnM9I627dyQ5NsnBSd6T5PQk5yZ53aKh6yZjFjo5w6y1303y1iTXJPmhlawXAAAAgNVrrmekAQAAAMC8mOsZaQAAAAAwLwRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwBYYVV1WlV1VT1n1rUAALD3BGkAwKpWVYdV1XlV9aGquq2qvlZVn6+qq6rq56rqfrOucZZqcOkk6OuqWjfrmgAA5pUgDQBY7R6YZGuSu5O8L8k5Sd6R5NAk5ya5qqoOm115M/czSZ6b5GuzLgQAYN75F0cAYLX7dJLDu/sbizuq6veT/FiSU5Kctb8Lm7WqenySM5P8ZpIfSfKo2VYEADDfpp6RVlUPqqpXVtXZVfWmRe1Pqar7LG+JAMCsVdX9q+quqvrIovb1k6WSXVU/vqjvVZP2n9q/1f5z3X33UiHaxDsmz49djveqqqdW1fur6stVdUdVXVZVRy3HvZfbZAnn25J8KsnrZlwOAMABYaoZaVX1E0nOT3LfJJWkM/wLbpI8IslfJPm/kvzuMtYIAMxYd3+lqq5K8vSqOrS7vzzpemaSe0++PjZDMJMFr5Nk234qc2/8wOT5r/f1RlV1dJLLkhyS5F1JbkqyOckHk1y+r/dfAacmeXKSo7r761U163oAAObe6CCtqo7NEJBdl+S0JC/IsN9IkqS7/7qqbkjykgjSAGA1ujxDcPZ9GfYaS4aw7O4kV+RbwVmq6qAM+259qrtv3tONq+oBSX5uynou6u5rxw6ezMA6dfLygUmenSHo+rMkvz3ley++d2X4+2d9kpd0958s6Ps3Sc6b8n6bM/xNNY3zuvv2kff/3iS/kuSN3X31lO8DALBmTTMj7d8l+Yckz+7uL1XVk5YYc22SZyxLZQDAvNmW5FczBGYLg7RrMszAOr+qHtfdf5MhoHpgkneOvPcDMv3ywu0Z/vYYa90S7/G2JP+6u/d1o/2jkzw+yZ8vDNEmzk/ys0keM8X9Nmf6n8dbkuwxSKuq9Rm+7+uSnDHlewAArGnT7JH2vUne291f2s2YzyR52L6VBADMqY8m+WomM8+q6vAkT8kQsO1curhzVtrzJs+jljR29/burikfb5mm+O7+WndXhr9/Hpnk5Umen+Tqqto0zb2W8JTJ8xVLvO/dST48zc26+y178fPYPvL2ZyV5dJKf2M3ecQAALGGaIO0+Sb68hzEPSHLP3pcDAMyr7r4rQyD0pKp6cJLnJDk4ybbuviHJZ/OtIO3YDHupzt3eYD24tbt/L8lJGWaSnb+Ptz188vy5XfT/wz7ef1lU1TFJfjrJr3f3J2ZdDwDAgWaapZ3bkzx1D2OeluRv9roaAGDeXZ5hn9RjMyxn/FqSjyzoO76q7p1h/7HruvvzY266P/ZIW0p3f6yqbs8QCu6LnTP2H7qL/qlm7K/gHmlPznBg1OlVdfouxnxjcvDAk/f15wsAsNpME6S9O8lrquqk7n7X4s6q+pdJ/kWGvVMAgNVp5wmcxyY5KsmVC/YX25bkx5K8Ksn9Mt1pnftjj7RvU1WHJjkse551vyd/OXk+Zon3ODjJs6a830rtkfbfk7x5F30nJ7l/hkMTOsk/Tvn+AACrXnX3uIFVD8zwR+Ijk/z/STYkOS7D8oBnJ3lpkk8leWp3f2VFqgUAZmoSCv1jkruSPDjJr3T3GyZ9j8oQbn0+yUOSnNjd755Rqf/L5ICkv118oEqoAEYAACAASURBVEBVHZLhtM5/meQPuvvHFvV3kkz2VdvTe1SSGzIsE93dqZ3P7e4P7v13s3KqanuSRyW5V3d/c8blAADMpdFBWpJMNuL9/QxLORb7aJIf6e5PL0tlAMBcqqqLkpw4efmM7v74gr6bMpxOeXeSB+3hkKL9oqrOS/KTGZag3pxh1tbDk3x/hiWXn8wQcH12wTUHZfge7u7uUTP4q+qZSS5NckiGU0xvyjCz7NgMy15fGEEaAMABbZqlnZmcBvWsqnpKhuUcD8qwJ8jHFv4RDQCsatsyBGl3JLl6ib7HJLlmHkK0iXdkWLJ41ORxaIbar09ydpLf6u47F13zpMnzhWPfpLs/UlXPTvL6JMdPmj+eYf+14zIEaQAAHMCmmpG2v1XVdyV5bYY/ep+Y5EPd/ZwR1x2eYQnFSzKcTPreJK/ubnt9AAB7VFWvzvC3xJO6+7pZ1wMAwHw4aOzAqrp3VT28qu61i/5DJv33Xr7y8sQkL8qw5GKa00DfnuFff1+Z5OVJvjfJRctYFwCwuh2T5N1CNAAAFprmsIHXJ/n5JI9camZXVT0oyWeSnNXd054ytav3PKi775l8/UdJvmNPM9Kq6qgkVyY5prv/fNL2tAxLK17Q3ZctR20AAAAArC2jZ6RlmBm2bVfLIyftlyZ58XIUNrnnPXtx2fFJPrczRJvc56okf59v7VcCAAAAAFOZJkj7zgxLLHfnb5Js2utqlseRSW5cov2GSR8AAAAATG2aIO1eGY6B3517kqzf+3KWxYYMx9ovtmPSBwAAAABTWzfF2L/PsPHu7hyT5Ja9L2d2qmprkq1Jsn79+qdu2rRptgUBAAAAc+WGG274Ync/eNZ1MDvTBGnvTvLvqurnu/ucxZ1V9ZokW5L85nIVt5d2JFnqf+oNk74ldfcFSS5Iki1btvTVV1+9MtUBAAAAB6SqunnWNTBb0wRpv5nkZUn+Q1W9NMkHktya5BFJjssQon0myVnLXeSUbkzy7CXaj0xy0X6uBQAAAIBVYnSQ1t3/VFXPSfKHSZ42eXSSmgy5KsmP7upUz/3o4iS/WlXP6u4PJ0lVbUny6EkfAAAAAExtmhlp6e5PJXl6VT0tyTOSPCDDxv4f6+6rlru4qrpvkhdNXj4iyWFV9cOT13/a3XdW1U1JrujuV0xq/GhVfSDJWyfLTe9JcmaSD3f3ZctdIwAAAABrw1RB2k6T0GzZg7MlPCTJOxa17Xz9nUm2Z/geDl405uQk5yb53Qwnk743yatXrEoAAAAAVr29CtL2l+7enm8tHd3VmE1LtN2e5CcnDwAAAADYZ1MFaVW1LsmLM+yPtiHfPhMsSbq7/9Uy1AYAAAAAc2N0kFZVD0tyaZInZPezxDqJIA0AAACAVWWaGWlnJ3lihj3KfjvJp5N8cyWKAgAAAIB5M02QdlyGky9PXqliAAAAAGBeHTTF2PVJPrpShQAAAADAPJsmSLsuyf+2UoUAAAAAwDybJkg7O8kPVtWRK1UMAAAAAMyrafZI+3SS9yb5aFWdk+SaJLcvNbC7r1yG2gAAAABgbkwTpH04SSepJKftYezBe1sQAAAAAMyjaYK0N2QI0gAAAABgzRkdpHX3qStZCAAAAADMs2kOGwAAAACANWuapZ1Jkqpal+Q5Sf73JPfv7t+YtB+S5P5JdnS3JaAAAAAArCpTzUirqucn+VSSS5L8xyS/vqD7qUm+kOTkZasOAAAAAObE6CCtqp6S5L0ZZrG9NsmFC/u7+6NJtif5oWWsDwAAAADmwjQz0n4tyVeTbOnuc5J8cokxf5Fk83IUBgAAAADzZJog7VlJ/ri7b9vNmFuSbNy3kgAAAABg/kwTpN0/wx5ou7N+ynsCAAAAwAFhmtDr1iRP3MOYzUn+fu/LAQAAAID5NE2QdkmSF1bVUUt1VtX3J3lmhgMJAAAAAGBVmSZIe0OSLyW5rKpen+TIJKmq4yav35nkc0nOWfYqAQAAAGDG1o0d2N2fqarjkrw9yS8l6SSV5E8nz9uTnNTde9pHDQAAAAAOOKODtCTp7qur6nFJTkzyjCQPyjBL7WMZTvS8a/lLBAAAAIDZGx2kVdXDk3xjMuPsnZMHAAAAAKwJ0+yR9ukkZ61UIQAAAAAwz6YJ0m5P8vmVKgQAAAAA5tk0QdrHkzx5pQoBAAAAgHk2TZB2epJjqurlK1QLAAAAAMytaU7tPDbJ5UneXFWnJPmLJP+QpBeN6+7+jWWqDwAAAADmwjRB2q8v+Pppk8dSOokgDQAAAIBVZZog7QUrVgUAAAAAzLnRQVp3b1vJQgAAAABgno0+bKCqPlBVp61gLQAAAAAwt6Y5tfNZSQ5ZqUIAAAAAYJ5NE6TdlOSIlSoEAAAAAObZNIcNvDnJr1XVI7v7MytVEPtm0y++b9YlsEZtf+MJsy4BAAAAVtQ0Qdo7kxyb5CNV9RtJ/iLJPyTpxQO7+7blKQ8AAAAA5sM0QdotGUKzSvKfdjOup7wvAAAAAMy9aQKvP8gSs88AAAAAYC0YHaR198tWshAAAAAAmGfTnNoJAAAAAGuWIA0AAAAARhi9tLOqLhg5tLv7X+1lPQAAAAAwl6Y5bOCVe+jfeaJnJxGkAQAAALCqTBOkPXYX7Q9I8r1JTk3yockzAAAAAKwq05za+Xe76b6mqi5O8tdJLkmyu7EAAAAAcMBZtsMGuvvmJH+S5OeW655JUlVPqKptVXVnVd1WVWdU1cEjrttSVR+oqn+aPC6rqqcvZ20AAAAArB3LfWrn55I8brluVlUbklyWYd+1E5OckeQXkpy+h+uOmFy3LsmPTx7rklxaVY9arvoAAAAAWDum2SNtt6rqoCTPTXLHct0zySlJ1ic5qbvvyBCEHZbktKo6a9K2lBOSHJrkh7r7S5P6rkzyxSQvSvKfl7FGAAAAANaA0UFaVR29m3sckeSnkjw5yZuXoa6djk9yyaLA7MIkZyY5Jsl7dnHdvZJ8M8n/WND2lUlbLWN9AAAAAKwR08xI+3CGJZa7UkmuTPJv96mif+7IJJcvbOjuW6rqzknfroK0d2ZYBnp2Vb1+0vZrSXYkeccy1gcAAADAGjFNkPaGLB2k3ZMhoLqqu69clqq+ZUOS25do3zHpW1J331ZVz03y3iSvnjR/Nslx3f2FZa4RAAAAgDVgdJDW3aeuZCHLqao2Zph5dk2SV06afzrJ+6rq6O6+ZYlrtibZmiQbN27Mtddeu7/KXVYvffTdsy6BNepA/Z0BAACAsZbtsIEVsiPJ4Uu0b5j07cprM+yT9sPd/Y0kqarLk/xtktfkW7PU/pfuviDJBUmyZcuW3rx5875VPiMvufDWWZfAGnXW1gPzdwYAAADGOmjswKp6clX9clU9dBf9D530f8/ylZcbM+yFtvB9jkhy30nfrhyZ5LqdIVqSdPddSa5L8phlrA8AAACANWJ0kJZhJterknx+F/1fSHJKkp/f16IWuDjJcVV16IK2k5N8NckVu7nu5iTfXVWH7Gyoqnsn+e4k25exPgAAAADWiGmCtKOT/Fl3L3lyZ3ffk+GEzWctR2ETb0ry9STvqqrnT/YxOy3JOd19x85BVXVTVb15wXW/k+ThSf64qk6oqhcnuSjJxkyWbwIAAADANKYJ0h6W5NN7GHNrhrBqWXT3jiTHJjk4yXuSnJ7k3CSvWzR03WTMzuuuSfLCJIcmeVuSt2ZYDvqC7v7EctUHAAAAwNoxzWEDdyZ58B7GPDjJXXtfzrfr7uuTPG8PYzYt0bYtybblrAUAAACAtWuaGWmfSPKDVXW/pTon+5j94GQcAAAAAKwq0wRpv53kIUkuqaonLuyoqu9O8v4MM9J+Z/nKAwAAAID5MHppZ3f/YVWdkORHk3yiqm7LsCfaIzJs7H9Qkv/a3b+/IpUCAAAAwAxNs0dauvtlVXVlkp9N8vgkj5x03Zjk/+nuNy1zfQAAAAAwF6YK0pKku38ryW9V1WFJHpDk9u6+Y9krAwAAAIA5MnWQttMkPBOgAQAAALAmjD5soKo2V9UvV9VDd9H/0En/9yxfeQAAAAAwH6Y5tfO1SV6V5PO76P9CklOS/Py+FgUAAAAA82aaIO3oJH/W3b1UZ3ffk+TyJM9ajsIAAAAAYJ5ME6Q9LMmn9zDm1iQb976c/8nenUfJVdb5H39/k5CkE8hiAlnYmhD2PcQgKrtsooPsGkRRmCgzLKPjxgyQIMimyCI/ZFGBKIIjRBhgANk3UZZIANnBgJAQBEMCZIGkv78/qhqLptNd1alOVXe/X+fUqdS9z7396TCp4/nMc59HkiRJkiRJqk+VFGkLgFXbGbMq8G7H40iSJEmSJEn1qZIibQbwLxExsLWTEbEK8C/FcZIkSZIkSVK3UkmRdjGwGnBzRGxSeiIiNgVuojAj7WfViydJkiRJkiTVhz7lDszMKyJiL2AiMCMiZlFYE211YDSFUu7yzPxVpySVJEmSJEmSaqjsIg0gM78YEX8AjgI2ANYonnoKODczL6hyPkmSJEmSJKkuVFSkAWTm+cD5ETEIGAK8mZnzq55MkiRJkiRJqiMVF2nNiuWZBZokSZIkSZJ6hIqKtIj4BPAJCmuiAcwC7svM+6odTJIkSZIkSaonZRVpEfFJ4KfAxs2Hiu9ZPP8X4AgLNUmSJEmSJHVX7RZpEbEPcCWwEjAHuAv4W/H0msAOwKbA7RFxYGZe20lZJUmSJEmSpJpps0iLiFHAVKCJwk6dF2bmkhZj+gD/CpwJ/DIiNsjM2Z2UV5IkSZIkSaqJXu2c/w9gIHBIZv6/liUaQGYuycyfAocAKwPHVD+mJEmSJEmSVFvtFWl7AA9m5lXt3SgzrwYeAPasRjBJkiRJkiSpnrRXpDUC91Zwv/uK10iSJEmSJEndSntF2krAuxXc793iNZIkSZIkSVK30l6RNpvCjpzl2gR4teNxJEmSJEmSpPrUXpF2D7BrRKzf3o0iYgNgd+DuagSTJEmSJEmS6kl7Rdr/A/oC1xeLslYVi7brgD7A+dWLJ0mSJEmSJNWHPm2dzMwHI+LHwDeBRyLit8BtwN+KQ9YEPgXsD/QDzs7MBzoxryRJkiRJklQTbRZpRd8GFgDHAl8EDm5xPoAm4FTguKqmkyRJkiRJkupEu0VaZiZwQkRcChwGfAIYVTz9KnAvcElmPtdZISVJkiRJkqRaK2dGGgCZ+QLw352YRZIkSZIkSapb7W02IEmSJEmSJAmLNEmSJEmSJKksFmmSJEmSJElSGSzSJEmSJEmSpDJYpEmSJEmSJEllsEiTJEmSJEmSyrDMIi0iXouIb5V8/q+I+OSKiSVJkiRJkiTVl7ZmpA0HBpR8PhnYuXPjSJIkSZIkSfWprSJtDrD6igoiSZIkSZIk1bM+bZx7ADgkIt4FZhePbR8R/9XOPTMzT61KOkmSJEmSJKlOtFWkfRu4Fvj3kmM70/7jnQlYpEmSJEmSJKlbWWaRlpnPRMSmwFgKj3jeCkwFfrmCskmSJEmSJEl1o60ZaWTmUuBp4OmIAHghM29bEcEkSZIkSZKketJmkdbCSkBTZwWRJEmSJEmS6lnZRVpxdhoAETEK2BIYAswD/pyZs5d1rSRJkiRJktTV9apkcESsERHXAy8D1wO/Aq4DXo6I6yNirWoHjIiNI+K2iFgQEbMi4vsR0bvMa/eNiAcjYmFEvBERN0XEwGpnlCRJkiRJUvdX9oy0iBgB3AesCfwNuAeYDYwCPgF8Grg3Ij6amXOqES4ihlLY5OAJYG9gXeBMCgXgce1cezhwHnAGhR1Ih1LYcbSSx1klSZIkSZIkoLJS6TgKJdp/Az/MzCXNJyKiD/At4JTiuKOqlO/rQAOwb2bOB26JiEHAlIg4o3jsQyJiOHAWcFRmXlxy6ndVyiVJkiRJkqQeppJHOz8D3JqZp5aWaACZuSQzTwNuKY6rlj2Bm1sUZldSKNd2aOO6A4vvl1UxiyRJkiRJknqwSoq0UcCD7Yx5qDiuWjYEnio9kJkvAQuK55ZlG+Bp4LCIeDki3ouIP0XEx6uYTZIkSZIkST1IJY92zgfa20xgzeK4ahkKvNnK8bnFc8syEtiAwmOm3wHeKL7fFBHrtbaGW0RMAiYBjBo1ikceeWQ5o9fGgWOWtj9I6gRd9d+MJEmSJEnlqqRIuw/YPyLOy8w/tTwZEeOBA4AbqxVuOQSwMnBAZt4EEBF/AF4EjgSOb3lBZl4EXAQwfvz43HLLLVdc2ir63JWv1DqCeqgzJnXNfzOSJEmSJJWrkiLtBxR25rwnIi4H7qCwa+dIYEfgi8Vxp1Yx31xgcCvHhxbPtXVdAnc2H8jM+RHxMLBxFfNJkiRJkiSphyi7SMvMhyLiIOAS4MvAl0pOB4VHMA/LzPbWUavEU7RYCy0i1gQG0GLttBaeLGaKFscDaKpiPkmSJEmSJPUQlWw2QGZeQ2GdtEOBnwBTi+9fAdbOzN9VOd+NwO4RsUrJsYOAhcBdbVx3ffF9p+YDETEY2BqYUeWMkiRJkiRJ6gEqebQTgMx8i0KBNrX6cT7kAuBoYFpEnA6MAaYAP87M9zc1iIjngLsy87Bixoci4lrg5xHxPeB1CpsNvAf8vxWQW5IkSZIkSd1MRTPSVrTMnAvsAvQGrgNOBM4CJrcY2qc4ptQXgWuAHwNXUSjRdi7eU5IkSZIkSapIxTPSVrTMfALYuZ0xja0cexs4oviSJEmSJEmSlktdz0iTJEmSJEmS6oVFmiRJkiRJklQGizRJkiRJkiSpDBZpkiRJkiRJUhnKLtIiYnhnBpEkSZIkSZLqWSUz0v4WEZdHxPadlkaSJEmSJEmqU5UUaX8FvgDcERFPRMQxETG0k3JJkiRJkiRJdaXsIi0zNwZ2BK4A1gHOAl6JiMsi4uOdE0+SJEmSJEmqDxVtNpCZd2fmF4HRwH8CM4FDgHsi4rGI+PeIGFT9mJIkSZIkSVJtdWjXzsycm5lnlcxS+zUwFjgXmBURP4uIraoXU5IkSZIkSaqtDhVpLbwCzAbeBgJoAL4KPBQRV0XEkCr8DEmSJEmSJKmmOlSkRUTviNg/Im4Bnga+BcwDvgOsBuwG3ArsC5xfpaySJEmSJElSzfSpZHBErAP8K/AVCoVZAjcA52fmzSVDbwVujYhpwB5VyipJkiRJkiTVTNlFWkTcDOxCYRbbHOBU4MLM/Fsblz0I7L1cCSVJkiRJkqQ6UMmMtF2Beyg8qjktM98r45rrgdc6EkySJEmSJEmqJ5UUaZtl5l8quXlmPgY8VlkkSZIkSZIkqf6UvdlApSWaJEmSJEmS1J2UXaRFxH4R8fuIWH0Z50cXz7smmiRJkiRJkrqdsos0Crt1rpqZr7R2MjNnAcOASdUIJkmSJEmSJNWTSoq0zSjswtmWB4EtOh5HkiRJkiRJqk+VFGnDaX8HzjeK4yRJkiRJkqRupZIi7XVgbDtj1gXe7HgcSZIkSZIkqT5VUqTdB/xLRKzf2smI2ADYuzhOkiRJkiRJ6lYqKdJ+DPQF7o2If4uIMRHRr/j+78C9QB/gR50RVJIkSZIkSaqlPuUOzMw/RsSRwE+Kr5aagKMy8/5qhZMkSZIkSZLqRdlFGkBmXhAR9wH/BmwDDKGwJtofgfMz8/HqR5QkSZIkSZJqr6IiDSAzHwOO6IQskiRJkiRJUt2qZI00SZIkSZIkqceqeEZaRASwHjAU6N3amMz8w3LmkiRJkiRJkupKRUVaRBwL/CeFEq0trRZskiRJkiRJUldVdpEWEf8J/AB4C7gC+BuwpJNySZIkSZIkSXWlkhlpXwNmAVtn5pxOyiNJkiRJkiTVpUo2G1gL+J0lmiRJkiRJknqiSoq0Obj2mSRJkiRJknqoSoq0q4BdI6JfZ4WRJEmSJEmS6lUlRdrxwN+B30TEmp2UR5IkSZIkSapLlWw28AjQF9gG+GxEvAG82cq4zMwNqhFOkiRJkiRJqheVFGkDgKSwc2ezhurGkSRJkiRJkupT2UVaZq7RmUEkSZIkSZKkelbJGmmSJEmSJElSj1XJo50fEBGrACtn5uwq5pEkSZIkSerypk+fvnufPn0mZ+ZInMjUFTRFxKtLliw5cdy4cTcva1BFRVpEDAAmAwcDoyismdaneG4CcBxwQmY+0uHYkiRJkiRJXdj06dN379ev33mNjY3vNjQ0zO3Vq1fWOpPa1tTUFAsXLhw8c+bM86ZPn37kssq0shvR4gy0PwDfBv4BPA1EyZC/ADsDEzseW5IkSZIkqWvr06fP5MbGxncHDhy40BKta+jVq1cOHDhwYWNj47t9+vSZvMxxFdzzOGBz4PDM3Bz4n9KTmfkOcBewS0cCS5IkSZIkdQeZObKhoWFRrXOocg0NDYuKj+O2qpIibT/g95n5i+Ln1hrVmYC7e0qSJEmSpJ6slzPRuqbif7dl9mWVFGlrADPaGfM2MLiCe0qSJEmSJEldQiVF2tvAqu2MWQd4veNxPiwiNo6I2yJiQUTMiojvR0TvCq7vFREPRURGxGeqmU2SJEmSJEk9RyW7dj4IfCYiVs7Mt1uejIiRwJ7AjdUKFxFDgVuBJ4C9gXWBMykUgMeVeZvD8XFTSZIkSZJUY43fu2HrWvzcmaft9XA17vPggw/2nzBhwibXXXfdM5/5zGfeKueaH/3oR8NHjBix5JBDDnmzGhlqrZIZaecCw4HrI2K90hPFz78BGorjquXrxXvum5m3ZOYFwInANyNiUHsXF4u4HwD/XcVMkiRJkiRJKsOll1666jXXXDOk1jmqpewiLTNvBE4GtgeeAr4LEBGvFj9vBxyfmfdWMd+ewM2ZOb/k2JUUyrUdyrj+JOA+4LYqZpIkSZIkSVIPVMmMNDLzBGB34P+Ad4qH+wG/B3bPzFOrG48NKZR0pRleAhYUzy1TRGwOfBX4VpUzSZIkSZIkdXunnXbaqiNHjty8oaFhq5133nnsyy+/3Lf0/OTJk0dsuummG62yyipbDhs2bIudd9557OOPP96v+fyECRM2+Mtf/jJg2rRpwyJi64jY+txzzx0GcN555w3beuutNxg8ePCWgwYN2nKbbbZZ/+677x6won/HSlWyRhoAmXkLcEsnZGnNUKC1Z2jnFs+15SfAeZn5XEQ0VjmXJEmSJElSt/WrX/1qyLHHHrvWxIkT/77vvvu+eccdd6xyxBFHNJaOefnll/t+7Wtfe22dddZ5d968eb0uuuiiVbfffvsNn3322ceHDRu29Kc//emLBxxwwLprrbXW4uOPP342wEYbbbQYYObMmX2/8IUvvLHeeustXrx4cVxxxRUf2W233TacPn364xtvvPG7NfiVy1JxkdYVRMTngQ2Az1ZwzSRgEsCoUaN45JFHOild5zpwzNJaR1AP1VX/zUiSJEmSPuz0008ftd12282//PLLXwLYb7/95r/++ut9fvOb3wxvHvPzn//8b81/XrJkCXvvvff8ESNGbHnFFVcMOfLII9/YeuutFw0YMKBp2LBhS3bZZZd3Su//ox/9aHbzn5cuXco+++wzf/311x/4i1/8YljpuXpT70XaXGBwK8eHFs99SESsBPwQOB3oFRFDgOaNCQZGxCqZ+aGdJTLzIuAigPHjx+eWW25Zhfgr3ueufKXWEdRDnTGpa/6bkSRJkiR90HvvvceTTz454JRTTnmp9Pi+++47t7RIu+222wYef/zxo5944omB8+bN6918/JlnnulHO6ZPn97/u9/97urTp09f+R//+Mf7/dSzzz7bv1q/R2cou0iLiPeALGNoZma7f2FleooWa6FFxJrAAFqsnVZiILAG8OPiq9SVwPPA2CrlkyRJkiRJ6lZmz57dZ+nSpYwYMeK90uOjRo1a0vznZ599tu/ee++9/uabb/7OWWed9eIaa6zxbr9+/XKfffZZb9GiRW2uyT937txen/70p9cfPnz4eyeffPLfxowZ825DQ0PTpEmTGhcvXhyd9XtVQyUz0v5E60XaEArFVD/gMWB+K2M66kbg2y1mkR0ELATuWsY1bwM7tTg2ErgC+C/g9irmkyRJkiRJ6lZGjRq1pHfv3syZM2el0uOzZ89+v0e69tprBy1atKjXTTfd9NygQYOaoDCTrXRm2rLccccdK8+ZM2elG2+88ZmtttpqUfPxt956q91ra63sXTsz85OZuV0rr82AEcBUoDcVrEtWhguAxcC0iPhUcR2zKcCPM/P9wi4inouInxdzLsnMO0tfwB+LQx/LzD9VMZ8kSZIkSVK3stJKK7HhhhsuuP7664eUHp82bdr7Gz8uXLiwV0TkSiut9P6kq5///OcfWbp0abS4Vy5evPgD/dOCBQt6ATQ0NDQ1H7vlllsGzpo16wO7gtajsou0thRLrcMozFj7QTXuWbzvXGAXCgXddcCJwFnA5BZD+xTHSJIkSZIkaTl95zvfmX3PPfcMOvjgg9eaNm3aoKOOOmr1O++88/117Hffffe3mpqa4sADD2y89tprVzn55JNXO/HEE1dfZZVVPrAL4tixYxc98MADK1999dWD7r777gGvvvpq7x122OHtAQMGNH31q19tnDZt2qCzzz572Je+9KUxq6222nsfTlJfqrbZQGYujYg7gP2Bf6/ifZ8Adm5nTGM752cCdf2MrSRJkiRJ6t5mnrbXw7XOUK4vfelLb7788ssvnXPOOaOmTZs2bMKECW+df/75M/fbb7/1ACZMmLDw3HPP/etpp502+qCDDhq6wQYbLLj88stfOOSQQ8aU3ufEE0+cdfjhh/c99NBDx7z99tu9zznnnJlHH330G5dddtnzxx577JoTJ04cu9Zaay06++yzXzrzzDNH1ua3LV9klrN/QJk3i7gA+HJmNlTtpjUwfvz4fOihh2odo0Mav3dDrSOoh5p52l61jqCuZkprmzKrIlPm1TqBVD1+Jyw/vxMkqdNFxMOZOb69cTNmzJi5xRZbvL4iMqn6ZsyYMXyLLbZobO1cVR7tBIiICMvQLgAAIABJREFU9YADKOyKKUmSJEmSJHUrZT/aGREXtXGPNYHti3/+bhVySZIkSZIkSXWlkjXSDm/n/HPADzPzZ8uRR5IkSZIkSapLlRRp6y3jeBMwNzPfrEIeSZIkSZIkqS6VXaRlpmufSZIkSZIkqceq2mYDkiRJkiRJUndWyWYDH+/oD8nMP3T0WkmSJEmSJKkeVLJG2r1AdvDn9O7gdZIkSZIkSVJdqKRIOwXYGtgdmAncB7wKjAQ+ATQCNwEPVzWhJEmSJEmSVAcqKdL+F/jP4uvczFzafCIiegP/AZwETM7MB6uaUpIkSZIkSd3avHnzeg0ZMmSrc845Z+bRRx/9Rq3ztKaSIu1k4PbMPKvliWKpdmZE7EKhTNujSvkkSZIkSZK6hymDt67Nz53n04NVUsmunROAP7cz5s/AxzoeR5IkSZIkSfVmyZIlLFq0KGqdo9YqKdJ6AWPaGTOmwntKkiRJkiSpzuy3336Nm2666Ua//OUvh4wdO3aT/v37j7vzzjsHHnDAAY1rrLHGZv379x/X2Ni46dFHHz26tGB7+umn+0bE1j/72c+GTpw4ce1VVlllyxEjRmz+jW98Y/TSpUs/8DMuvfTSIY2NjZv2799/3Pjx4zeYMWNG/5Y5lixZwje/+c3Ro0aN2qxv377jxo4du8kFF1zwkdayXnnllYPXXXfdTRoaGrbacccdx86ZM6f3448/3m+bbbZZv6GhYatNN910oz/96U8Ny/P3UknpdT+wf0S0+thmRHwa2B/4w/IEkiRJkiRJUu298sorfY8//vg1vvnNb86+6qqrngUYOnToklNPPfVvV1999TNHHXXUq1deeeXwr371q2u1vHby5MlrDBw4cOnUqVNf2G+//d44++yzR11yySVDm8/fe++9Aw4//PB1N9poowVTp059bs8993xz4sSJ67a8zze+8Y3Vzz333JGHHHLI61dcccVzH/3oR98+4ogj1rnwwgs/UKbNmjWr70knnTT6hBNOeOXMM898cfr06St/+ctfXvvzn//8mP333/8fl1122fNLliyJiRMnjmlqaurw30kla6QdB9wF3BARtwF3A3OAEcAOwM7AYuC/O5xGkiRJkiRJdeHNN9/sc8MNNzzz8Y9/fGHzsT322OPt5j/vtttubw8cOLDpmGOOaVy0aNFL/fv3z+ZzEyZMeOviiy9+GWCfffaZf/vttw++5pprhh5++OFzAU455ZSRa6+99qIbbrjhhV69enHggQfOf/fdd+OMM85Yvfkec+bM6f2zn/1stWOOOWb2GWecMRtgv/32mz9r1qyVTj311NFf+9rX/tE8dv78+X3uueeepzbZZJPFAI8++uiACy+8cMRPfvKTmUceeeQbAJn5yuc///mxjzzySP9x48Yt6sjfSdkz0oo7ce4OvAB8Cvg+cEHxfZfi8d0z0wXsJEmSJEmSurjVVlvtvdISrampie9///urrbvuupv0799/XN++fbc+4ogj1nn33Xfjueee61t67a677jq/9PN66623cPbs2Ss1f54xY8bA3Xff/c1evf5ZTR100EFvll4zffr0hkWLFvWaOHHi3NLj+++//9wXX3yx36xZs96fIDZ69OjFzSUawNixYxcB7Lnnnu/n2GijjRYBvPTSSyvRQZXMSCMz74mI9YHtgHHAYGAeMB24JzOzreslSZIkSZLUNQwfPvy90s8nnXTSaieddNKaRxxxxKs77bTTW8OGDVty//33Dzz22GPXWrhw4Qc2Ihg6dOgHFkTr27dvLl68+P3W7PXXX19ptdVWW1I6ZvTo0R/4eS+//PJKAKuvvvoHjo8aNeo9gL///e+9R48evQRg0KBBH/p5xd/h/eP9+vVLgIULF3Z4ff+KijSAYll2d/ElSZIkSZKkbijig5t0XnPNNR/ZY4895v7kJz95pfnYo48+2qHF+4cPH/7ea6+99oFeatasWR+YKbbGGmu813x85MiR7xdizTPbVl111Q/uXrACdKiBi4iGiNgsIratdiBJkiRJkiTVn0WLFvXq27fvB1bqv/LKKz+yrPFt2Xzzzd+5+eabh5Qu/P+b3/xmSOmYcePGLezfv3/Tr3/966Glx6+++uqha6+99uLm2WgrUkUz0iJiFHA28Lnitdl8j4j4BPBT4MjMdLaaJEmSJElSN7LDDjvMv+SSS1Y77bTT3llvvfUW/+pXv/rIiy++2L8j9zr22GNf3WmnnTbaa6+9xhx22GGvP/roow2XX375qqVjRowYsfTwww9/7ZxzzhnVp0+fnDBhwoKrrrpqyF133TX4wgsvfKE6v1Vlyi7SImIk8AAwCvg/YDiwTcmQB4DVgQPxsU9JkiRJkqQPmjKvS2/QePrpp896/fXX+5x66qmrA+yxxx5zf/jDH740ceLEsZXea/vtt19w8cUXvzBlypTVDz744LGbbrrpO5dffvnzO+6440al484666xX+vTpk5deeulqZ555Zp+11lpr8fnnn//XSZMmzV3WvTtTlLs/QET8FPhXYI/MvDUiJgMnZGbvkjHXAGMyc/NOSbuCjB8/Ph966KFax+iQxu/dUOsI6qFmnrZXrSOoq5kyuNYJur4p82qdQKoevxOWn98JktTpIuLhzBzf3rgZM2bM3GKLLV5fEZlUfTNmzBi+xRZbNLZ2rpI10vYC/jczb21jzEvA6AruKUmSJEmSJHUJlRRpI4Bn2hmzGBjY8TiSJEmSJElSfaqkSJsLrNHOmPWAVzseR5IkSZIkSapPlRRp9wH/EhGrtXYyItYF9gTurEIuSZIkSZIkqa5UUqT9CBgA3BkRuwL9ASKiX/HzdUACP656SkmSJEmSpK6jqampKWodQpUr/ndrWtb5PuXeKDPvj4gjgPOAm0pOLSi+LwUOy8zHOhJUkiRJkiSpO4iIVxcuXDh44MCBC2udRZVZuHBh/4hY5rJllcxIIzMvBrYAzgemAy8CjwIXAVtm5i+XI6skSZIkSVKXt2TJkhNnzpzZ95133mlwZlrX0NTUFO+8807DzJkz+y5ZsuTEZY0re0Zas8x8CjhqudJJkiRJkiR1U+PGjbt5+vTpRz7//POTM3MkFU5kUk00RcSrS5YsOXHcuHE3L2tQ2UVaRDwD3JSZR1clniRJkiRJUjdVLGOWWcioa6qkER0FvN1ZQSRJkiRJkqR6VkmR9gQwprOCSJIkSZIkSfWskiLtPOCzEbFpZ4WRJEmSJEmS6lUlmw08D9wG/CEizgceBF4FsuXAzPxDdeJJkiRJkiRJ9aGSIu1eCqVZAN+hlQKtRO/lCSVJkiRJkiTVm0qKtFNouzyTJEmSJEmSuq2yi7TMPK4zg0iSJEmSJEn1rJLNBiRJkiRJkqQeq80iLSJOiIjtV1QYSZIkSZIkqV61NyNtCrBj6YGIOCYiXuisQJIkSZIkSVI96sijnUOAtasdRJIkSZIkSapnrpEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUhnKKtCERsVbzi8IaaUTEmqXHW4ypmojYOCJui4gFETErIr4fEb3bueajEXFJRDxXvO7piJgcEf2rmU2SJEmSJEk9R58yxhxTfLU0cxnjs8z7tisihgK3Ak8AewPrAmdSKACPa+PSg4pjTweeBTYHTiq+71eNbJIkSZIkSepZ2iu8XqJQjNXK14EGYN/MnA/cEhGDgCkRcUbxWGtOy8zXSz7fGRGLgAsjYu3MfLGTc0uSJEmSJKmbabNIy8zGFZRjWfYEbm5RmF1JYabZDsB1rV3UokRr9ufi+2jAIk2SJEmSJEkVqffNBjYEnio9kJkvAQuK5yqxLdAEPF+daJIkSZIkSepJ6r1IGwq82crxucVzZYmIkRTWVPtlZr5WpWySJEmSJEnqQaqyKUA9i4i+wP8AbwPfaGPcJGASwKhRo3jkkUdWTMAqO3DM0lpHUA/VVf/NqIbWPLTWCbo+/92pO/E7Yfn5nSBJUqer9yJtLjC4leNDi+faFBEBTAU2AT6Rmcu8JjMvAi4CGD9+fG655ZYdClxrn7vylVpHUA91xqSu+W9GNXTNpbVO0PUddk6tE0jV43fC8vM7QZKkTlfvRdpTtFgLLSLWBAbQYu20ZTgb2BvYNTPLGS9JkiRJkiS1qt7XSLsR2D0iVik5dhCwELirrQsj4ljgSOCLmXlv50WUJEmSJElST1DvRdoFwGJgWkR8qriO2RTgx5k5v3lQRDwXET8v+TwROIXCY52vRMTHSl6rrthfQZIkSZIkSd1BXT/amZlzI2IX4DzgOgo7eJ5FoUwr1QfoXfJ5t+L7ocVXqa8Al1Y3qSRJkiRJkrq7iou04oyu/YCNgIGZeXjJ8XWAxzJzYbUCZuYTwM7tjGls8flQPlygSZIkSZIkSR1WUZEWEYcB5wL9gQASOLx4egRwPzAJ+HmrN5AkSZIkSZK6qLLXSIuIXYGLgGeAfYCflp7PzMeBvwCfq2ZASZIkSZIkqR5UMiPtu8BsYIfMnB8RW7Uy5lFg26okkyRJkiRJkupIJbt2jgeuL90tsxUvAyOXL5IkSZIkSZJUfyop0voC77QzZgiwtONxJEmSJEmSpPpUSZE2E9i6nTHbAE93OI0kSZIkSZJUpyop0q4FtouIA1o7GRFfATYHrq5GMEmSJEmSJKmeVLLZwBnA54ErImJ/YDBARBwJbAfsCzwL/KTaISVJkiRJkqRaK7tIy8y5EbEDMBUonZV2bvH9HmBiZra3jpokSZIkSZLU5VQyI43MfAnYMSI2B7YFhgHzgD9m5sOdkE+SJEmSJEmqCxUVac0y81Hg0SpnkSRJkiRJkupW2ZsNRMQZEbFRZ4aRJEmSJEmS6lUlM9K+BfxnRDwMXAZckZn/6JxYkqRqaPzeDbWOsEwz+9c6QddX1/99T9ur1hEkSZKkqit7RhrwBeBmYCsKGwzMioirIuKzEdG7U9JJkiRJkiRJdaLsIi0zf5OZnwbWAL4LPAvsC1xDoVT7cURs2TkxJUmSJEmSpNqqeLOBzJwD/Aj4UURsBRxKYbbafwDHRMRjmWmhpg+Z2X9irSN0eY2Lfl3rCJIkSVLnmzK41gm6vinzap1A6pYqebTzQzLzz5l5DDAa+DawBNisGsEkSZIkSZKkelLxjLRSETEYOAj4MvAxIABrb0mSJEmSJHU7FRdpEdEL2J1CefYvQD8ggdso7OY5rZoBJUmSJEmSpHpQdpEWEZsBXwIOBkZQmH32DDAVmJqZL3dKQkmSJEmSJKkOVDIjbUbxfR7wM+DSzLy/+pEkSZIkSZKk+lNJkfZ74FLgd5m5uHPiSJIkSZIkSfWp7CItM/fozCCSJEmSJElSPetV6wCSJEmSJElSV7DMGWkR8QsKu3H+V2bOKX4uR2bmYVVJJ0mSJEmSJNWJth7tPJRCkXY6MKf4uRwJWKRJkiRJkiSpW2mrSFun+P5Ki8+SJEmSJElSj7PMIi0zX2zrsyRJkiRJktSTlL3ZQEScEBHbtzNmu4g4YfljSZIkSZIkSfWlkl07pwA7tjNme2ByR8NIkiRJkiRJ9aqSIq0cKwFNVb6nJEmSJEmSVHPVLtLGAa9X+Z6SJEmSJElSzbW1aycRcXuLQ4dGxI6tDO0NrAmsDVxRnWiSJEmSJElS/WizSOODa6Il0Fh8tdQEvAH8BvhGFXJJkiRJkiRJdaXNIi0z33/0MyKagCmZ+f1OTyVJkiRJkiTVmfZmpJX6CvDnzgoiSZIkSZIk1bOyi7TMvKwzg0iSJEmSJEn1rJIZae+LiDWA1YF+rZ3PzLuXJ5QkSZIkSZJUbyoq0iJiN+AsYMN2hvbucCJJkiRJkiSpDvVqf0hBRHwMuB4YApwHBHA3cDHwVPHzdYCbEUiSJEmSJKnbKbtIA44FFgEfzcxjisfuyMyvA5sCJwOfAq6qbkRJkiRJkiSp9iop0rYF/jczZ7W8PgtOAJ4ETqxiPkmSJEmSJKkuVFKkDQZeKvn8LjCwxZj7gO2XN5QkSZIkSZJUbyop0l4Dhrb4vG6LMSsBDcsbSpIkSZIkSao3lRRpz/DB4uyPwK4RsT5ARIwE9gOerV48SZIkSZIkqT5UUqTdBOwQER8pfj6HwuyzP0fEgxR27lwVOLu6ESVJkiRJkqTaq6RIu5DC+mfvAWTmfcABwF8p7No5GzgiM6dWO6QkSZIkSZJUa2UXaZk5PzP/lJlvlRz7XWZumpkNmblRZl5U7YARsXFE3BYRCyJiVkR8PyJ6l3Hd4Ii4JCLmRsS8iLg8IoZVO58kSZIkSZJ6hj61DtCWiBgK3Ao8AexNYY22MykUgMe1c/n/AOsDhwNNwOnANcB2nZVXkiRJkiRJ3VddF2nA1ymsw7ZvZs4HbomIQcCUiDijeOxDImJbYDdgh8y8u3jsFeBPEfGpzLx1BeWXJEmSJElSN7HMIi0iXujgPTMz121/WFn2BG5uUZhdSWF22Q7AdW1cN6e5RCuGeiAi/lo8Z5EmSZJ6nMbv3VDrCMs0s3+tE3R9df3f97S9ah1BkqSqaGuNtF5AdOBVyQYG7dmQwm6g78vMl4AFxXNlX1f0ZDvXSZIkSZIkSa2KzKx1hmWKiPeAb2fm2S2OvwxMzcz/WsZ1twDvZObnWhz/FTAmMz/eyjWTgEnFjxsAT1fhV1DXMhx4vdYhJNUNvxMklfI7QVJLfi/0TGtn5qq1DqHaqfc10laY4o6jVd91VF1HRDyUmeNrnUNSffA7QVIpvxMkteT3gtQzdfgxzIgYGhFrVjNMK+YCg1s5PrR4rtrXSZIkSZIkSa2qqEiLiJUj4syIeJXCFNa/lpzbJiL+LyLGVTHfU7RY06xY3g2g9TXQlnld0bLWTpMkSZIkSZLaVHaRFhGDgfuBbwCzKCzcHyVDHgO2A75QxXw3ArtHxColxw4CFgJ3tXPdyIj4ZPOBiBgPjCmek1rjo72SSvmdIKmU3wmSWvJ7QeqByt5sICLOAL4FHJqZUyNiMnBCZvYuGXM9MDozqzIrLSKGAk8AjwOnUyjCfgycnZnHlYx7DrgrMw8rOXYzsF4xc1Px+tcyc7tqZJMkSZIkSVLPUsmjnfsCN2fm1DbGvAisvnyR/ikz5wK7AL2B64ATgbOAyS2G9imOKXUQhVlrvwCmAg8D+1QrmyRJkiRJknqWSnbtXAO4up0xb9P6Iv8dlplPADu3M6axlWNvAl8pviRJkiRJkqTlUsmMtLeA1doZsw6FTQgkSZIkSZKkbqWSIu1B4DMtFv5/X0SMAj4N3FuNYJIkSZIkSVI9qaRIOwcYBvxfRGxUeqL4+bdAf+Dc6sWTJEmSJEmS6kPZu3YCFHfqnAwk8B6wEjAXGAoE8N3M/GEn5JQkSZIkSZJqqqIiDSAidgKOBj5GYYbaPOCPwFmZeXvVE0qSJEmSJEl1oOIiTZIkSZIkSeqJKlkjrSwRsWq17ylJkiRJkiTVWtWKtIgYHBGnAM9X656SJEmSJElSvehTzqCIWBvYmsIGAw9k5pySc/2BbwDforDpwIJOyClJkiRJkiTVVLsz0iLiXAqzzH4LXAPMjIh/K57bEXgaOBkYAJwDjOmssJIkSZIkSVKttLnZQER8GbgEaAKeKh7esPh+GHAh0Bu4GDg5M2d1XlRJkiRJkiSpdtor0u4AtgV2ysz7i8e2B26hUKC9DHw2Mx9bAVklSZIkSZKkmmnv0c7Ngd81l2gAmXk3hUc8A/iqJZokSZIkSZJ6gvaKtMHAc60cf7b4fn8r5yRJkiRJkqRup70irReFnTpbeg8gMxdWPZEkSZIkSZJUh9rdtRNY9iJqkiRJkiRJUg/R3mYDTVRepGVm9lmuVJIkSd1IREwBJlPYwOnO2qaRJElSR5UzIy0qfJVzT0mSpBUiIgZFxNkRcU9EzIqIRRHxWkQ8EBH/EREDa51xRYqIHSMi23idVuuMkiRJ9arNmWOZaSkmSZK6uo8Ak4AHgBuAv1PYUGln4CzgXyNi28ycX7uINXEXcGcrx+9dwTkkSZK6DB/BlCRJ3d3fgMGZ+aENlCLiV8DBwNeBM1Z0sBq7MzOn1DqEJElSV+KMM0mS1K6IWDki3o2I+1ocbyg+KpkRcUiLc0cUj391xab9oMxc2lqJVvTb4vt61fhZEbF1RNwUEW9FxPyIuDUitq3GvSVJklR7zkiTJEntysy3I+IBYJuIWCUz3yqe+gTQr/jnXYBflly2S/H9thUUsyM+W3x/dHlvFBEfB24F+gLTgOeALSk8Pnn78t6/E4yNiCOBQcCrwD2Z+WyNM0mSJNU1izRJklSu2ykUZ9tTWGsMCmXZUgrrbTUXZ0REL2An4IXMfLG9G0fEEOA/KsxzTWY+Uu7giOgDHFf8+BFgOwpF1x3AxRX+7Jb3DuAXQAPwucy8tuTcMcDZFd5vS+BzFcY4OzPfrGD8wcVX6c+9GvjXzJxb4c+WJEnqESIza51BkiR1ARGxA4XZVWdl5jeLxx4AEpgKnAdskJnPRMQ44GHg4sycVMa9G4G/VhjpK5l5aQX5+wMLWxz+JfBvmfl2hT+75b0/QWGR/rszc4cW53oDTwPrAjtl5p1l3O9Q4JIKY6yTmTPLuPcmwGcolKEzgf7AeOAUYCvgPmD7zGyq8OdLkiR1e66RJkmSynU/hSJqF4CIGAyMo/DoZvOji82z0nYuvpf1SGNmzszMqPB1aSXhM3NRZgaF//2zBnAo8CngoWKRtzzGFd/vauXnLqXCnTAz89IO/H3MLPPef8nM0zPz8cx8OzNfz8ybgB0plJmf4J+PvEqSJKmERZokSSpLZr5LoRDaLCJWpVC89AZuy8wngdn8s0jbhcJMtbpbGywLXsnMy4B9gQ0ozKZbHoOL73OWcf7V5bx/p8vM+cCvix+3r2UWSZKkeuUaaZIkqRK3A7tSKMo+Diyi8Chg87k9I6IfhfXH/pKZr5Vz0xWxRlprMvOPEfEmhVJwecwrvo9YxvmRldxsBa2R1pq/F98HLud9JEmSuiWLNEmSVInmHTh3AbYF/pCZi0rOHQwcQaGIqWS3ziHA5AqzzASWq0iLiFUo7Fr5Vntj2zG9+L5DyxPFNdI+WeH9tqTyv49LgeUt0j5WfH9hOe8jSZLULflopyRJqsR0CrOv9gY24YNlWfNjnMe2+NyuzlwjLSI2K2400PJ4XwqPdPbin7uQlp7PiCh3V6Y/UNhQYPuI2LvFuSMpbDRQts5cIy0ixi/j+BeBg4B3gf+pJK8kSVJP4Yw0SZJUtsxcGhF3UijSoKRIy8wXI+J5CqXRUlpZeL9GDgO+EhH3AS9SmLU1GtiNwiOXTwPfKr0gIpr/n41Ly/kBmZkRcRhwC3B1REwDnqMws2wX4CZgj+X/VariqohYAjwEvExh186PAhOAJcDXyi3lJEmSehqLNEmSVKnbKBRp8ymUMS3PrQs8nJnzWl5YI78FVqbwKOq2wCoUsj8BnAmcn5kLWlyzWfH9ynJ/SGbeFxHbAT8A9iwe/hOF9dd2p36KtJ9S2K30E8BwIIBXKDwaenZmzqhdNEmSpPoWmeU+sbDiRcRY4NsU/kfvJsA9mbljGdcNBs6msEhvL+B64OjMfKPz0kqSpO4iIo6m8L8lNsvMv9Q6jyRJkupDvc9I2wT4NPBHYKUKrvsfYH3gcKAJOB24hsIOYpIkSe3ZAfhfSzRJkiSVqvcZab0ys6n456uA4e3NSIuIbSks+LtDZt5dPDaBwqMVu2bmrZ2bWpIkSZIkSd1RXe/a2VyiVWhPYE5ziVa8zwPAX/nneiWSJEmSJElSReq6SOugDYGnWjn+ZPGcJEmSJEmSVLHuWKQNpbCtfUtzi+ckSZIkSZKkitX7ZgMrTERMAiYBNDQ0bN3Y2FjbQJIkSZIkqa48+eSTr2fmqrXOodrpjkXaXKC1/6MeWjzXqsy8CLgIYPz48fnQQw91TjpJkiRJktQlRcSLtc6g2uqOj3Y+RetroS1r7TRJkiRJkiSpXd2xSLsRGBkRn2w+EBHjgTHFc5IkSZIkSVLF6vrRzogYAHy6+HF1YFBE7F/8/H+ZuSAingPuyszDADLz/oj4PTA1Ir4FNAGnA/dm5q0r+FeQJEmSJElSN1HXRRqwGvDbFseaP68DzKTwO/RuMeYg4CzgFxRm3V0PHN1pKSVJkiRJktTt1XWRlpkzgWhnTGMrx94EvlJ8SZIkSZIkScutO66RJkmSJEmSJFWdRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWo+yItIjaOiNsiYkFEzIqI70dE7zKuGx8Rv4+IfxRft0b8//buPty2qq4X+PcnxxdIwONbHBNBySupt+hGJSqXAM18KdRSsvKqyUNapqXSVaPrAR99hAItLZFEjcroxXcNSUBR82qpoDeRFAtJSE08SAgqL+P+MecWOQPoAAAgAElEQVTO5Wbts8c57L3XOnt/Ps+zn3X2GHPM+Vtzn72AL2OMWT++FjUDAAAAsP7MdZBWVZuTnJukJTkqyYlJnpfkhGXG7TuO25TkyePXpiTvrar9VrNmAAAAANanTbMuYBnPSLJ7kse31q7JEITtlWRrVZ08tk3z6CR7Jnlca+3rSVJVH07y1SSPSvKa1S8dAAAAgPVkrmekJXlkknMWBWZnZQjXDtvOuNsmuTHJNybarh3baqWLBAAAAGD9m/cg7cAkl0w2tNYuT3Ld2LeUN4/HnFJVd6+quyd5RZJtSf56lWoFAAAAYB2b96Wdm5NcPaV929g3VWvtyqo6PMm7kjx7bP73JI9orf3Hilc5R/Z/wbtnXQIb1GUvf/SsSwAAAIBVNe9B2k6pqi0ZZp59PMkxY/OvJXl3VT14nNW2eMyxSY5Nki1btuSiiy5aq3JX1BPvc9OsS2CD2lV/ZwAAAKDXvAdp25LsPaV989i3lOMy7JP2c621G5Kkqs5P8rkkz893Zqn9l9ba6UlOT5KDDz64HXTQQbeu8hl57FlXzLoENqiTj901f2cAAACg17zvkXZJFu2FVlX7Jtkji/ZOW+TAJJ9eCNGSpLX27SSfTnLAKtQJAAAAwDo370Ha2UkeUVV7TrQdneT6JBdsZ9wXkjywqm630FBVt0/ywCSXrUKdAAAAAKxz8x6knZbkW0neUlUPG/cx25rk1NbaNQsHVdWlVXXGxLjXJblHkrdW1aOr6jFJ3pZkS8blmwAAAACwI+Y6SGutbUtyZJLdkrwzyQlJXpHkxYsO3TQeszDu40l+KsmeSf40yZkZloM+vLX2ydWvHAAAAID1Zt4fNpDW2sVJjljmmP2ntJ2X5LxVKgsAAACADWauZ6QBAAAAwLwQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHSY+yCtqu5fVedV1XVVdWVVnVhVu3WOfXxV/WNVXV9VV1XVe6rqe1a7ZgAAAADWn7kO0qpqc5Jzk7QkRyU5McnzkpzQMfaYJG9KcnaSRyY5JsnnkmxarXoBAAAAWL/mPVR6RpLdkzy+tXZNkvdW1V5JtlbVyWPbLVTVXZO8Ismvt9b+eKLrrateMQAAAADr0lzPSMswk+ycRYHZWRnCtcO2M+6J4+ufrFZhAAAAAGws8x6kHZjkksmG1trlSa4b+5by40n+OcnTq+qLVXVDVX20qh68eqUCAAAAsJ7N+9LOzUmuntK+bexbyj5J7pfk+CS/leSq8fU9VXXf1tqXFw+oqmOTHJskW7ZsyUUXXXQrS5+NJ97nplmXwAa1q/7OAAAAQK95D9J2ViW5Y5IntNbekyRV9eEkX0jyrCS/s3hAa+30JKcnycEHH9wOOuigtat2BT32rCtmXQIb1MnH7pq/MwAAANBr3pd2bkuy95T2zWPf9sa1JO9faBj3Wft4kvuvYH0AAAAAbBDzHqRdkkV7oVXVvkn2yKK90xb5TIZZabWovZLcvJIFAgAAALAxzHuQdnaSR1TVnhNtRye5PskF2xn3rvH18IWGqto7yY8k+eRKFwkAAADA+jfvQdppSb6V5C1V9bDxgQBbk5w6LtVMklTVpVV1xsL3rbWPJXl7kjOq6ilV9egk70hyQ5I/XMs3AAAAAMD6MNdBWmttW5Ijk+yW5J1JTkjyiiQvXnTopvGYSb+U5G1JTk3yNxlCtCPGcwIAAADADpn7p3a21i5OcsQyx+w/pe3aJM8cvwAAAADgVpnrGWkAAAAAMC8EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQYe6DtKq6f1WdV1XXVdWVVXViVe22A+NvU1Ufq6pWVY9ZzVoBAAAAWL82zbqA7amqzUnOTXJxkqOSHJDklAwB4PGdpzkmyT1XpUAAAAAANox5n5H2jCS7J3l8a+29rbXTkpyQ5LlVtddyg8cg7qVJfnt1ywQAAABgvZv3IO2RSc5prV0z0XZWhnDtsI7xL0ny90nOW4XaAAAAANhA5j1IOzDJJZMNrbXLk1w39i2pqn4wyS8nef6qVQcAAADAhjHXe6Ql2Zzk6int28a+7XlVkle31i6tqv2Xu1BVHZvk2CTZsmVLLrrooh2rdE488T43zboENqhd9XcGAAAAes17kLZTqurnk9wvyU/3jmmtnZ7k9CQ5+OCD20EHHbRK1a2ux551xaxLYIM6+dhd83cGAAAAes370s5tSfae0r557LuFqrptkt9NclKS21TVnZIsPJjge6pqz9UoFAAAAID1bd6DtEuyaC+0qto3yR5ZtHfahO9Jcs8kp2YI27Yl+eTYd1aSC1elUgAAAADWtXlf2nl2kuOqas/W2n+ObUcnuT7JBUuMuTbJ4Yva9knyF0lelOT81SgUAAAAgPVt3oO005I8O8lbquqkJPdJsjXJqa21axYOqqpLk1zQWnt6a+3GJO+fPMnEwwb+X2vto6tfNgAAAADrzVwHaa21bVV1ZJJXJ3lnhid4viJDmDZpU5Ld1rY6AAAAADaSuQ7SkqS1dnGSI5Y5Zv9l+i9LUitXFQAAAAAbzdwHaQCsU1unPZSZHbL167OuAFaOz4Rbz2cCAKy6eX9qJwAAAADMBUEaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAh7kP0qrq/lV1XlVdV1VXVtWJVbXbMmN+tKreUFWXjuP+uapeXFV3WKu6AQAAAFhfNs26gO2pqs1Jzk1ycZKjkhyQ5JQMAeDx2xl69HjsSUk+l+QHk7xkfP3ZVSwZAAAAgHVqroO0JM9IsnuSx7fWrkny3qraK8nWqjp5bJvm5a21r058//6q+maS11bVfq21L6xy3QAAAACsM/O+tPORSc5ZFJidlSFcO2ypQYtCtAUXjq/3WLnyAAAAANgo5j1IOzDJJZMNrbXLk1w39u2IQ5LcnOTzK1MaAAAAABvJvAdpm5NcPaV929jXpar2ybCn2p+21r6yQrUBAAAAsIHM+x5pt1pV3S7JXyW5Nslvbue4Y5McmyRbtmzJRRddtDYFrrAn3uemWZfABrWr/s4wQ/s+ddYV7Pr83rGe+Ey49XwmAMCqm/cgbVuSvae0bx77tquqKsmZSR6Q5CGttSXHtNZOT3J6khx88MHtoIMO2qmCZ+2xZ10x6xLYoE4+dtf8nWGG3vbGWVew63v678+6Alg5PhNuPZ8JALDq5j1IuySL9kKrqn2T7JFFe6ct4ZVJjkry8NZaz/EAAAAAMNW875F2dpJHVNWeE21HJ7k+yQXbG1hVL0zyrCS/1Fr70OqVCAAAAMBGMO9B2mlJvpXkLVX1sHEfs61JTm2tXbNwUFVdWlVnTHz/C0lelmFZ5xVV9aCJr7ut7VsAAAAAYD2Y66WdrbVtVXVkklcneWeGJ3i+IkOYNmlTkt0mvv/J8fWp49ekpyV548pWCjCf9n/Bu2ddwpIuu8OsK9j1zfXP9+WPnnUJAACw4uY6SEuS1trFSY5Y5pj9F33/1NwyQAMAAACAnTbvSzsBAAAAYC4I0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADpsmnUBbByX3eEXZl3CLm//b75p1iUAAMDq27r3rCvY9W39+qwrgHXJjDQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6LBp1gUsp6run+RVSQ5JcnWS1yU5obV20zLj9k7yyiSPzRAYvivJs1trV61uxQAA82n/F7x71iUs6bI7zLqCXd9c/3xf/uhZlwAAK2Kug7Sq2pzk3CQXJzkqyQFJTskQjB2/zPC/SvLfkhyT5OYkJyV5W5JDV6teAAAAANavuQ7Skjwjye5JHt9auybJe6tqryRbq+rkse0WquqQJD+Z5LDW2gfGtiuSfLSqHtZaO3eN6gcAAABgnZj3PdIemeScRYHZWRnCtcOWGfflhRAtSVpr/5DkX8c+AAAAANgh8x6kHZjkksmG1trlSa4b+7rHjT6zzDgAAAAAmGrel3ZuzvCAgcW2jX07M+4+0wZU1bFJjh2/vbaq/nkH6qRDzbqA5d01yVdnXcT2PWbWBSypTpp1BexqfCasBJ8JrB8+E1aCzwRYY/P9uXDCLvDJumvab9YFMFvzHqStmdba6UlOn3UdzE5Vfay1dvCs6wDmg88EYJLPBGAxnwuwMc370s5tSfae0r557FvpcQAAAAAw1bwHaZdk0Z5mVbVvkj0yfQ+0JceNlto7DQAAAAC2a96DtLOTPKKq9pxoOzrJ9UkuWGbcPlX10IWGqjo4w/5oZ69GoawLlvYCk3wmAJN8JgCL+VyADahaa7OuYUlVtTnJxUn+KclJGYKwU5O8srV2/MRxlya5oLX29Im2c5LcN8nzk9w8jv9Ka+3QtXsHAAAAAKwXcz0jrbW2LcmRSXZL8s4kJyR5RZIXLzp003jMpKMzzFp7fZIzk3w8yeNWs14AAAAA1q+5npEGAAAAAPNirmekwVqoqvtX1XlVdV1VXVlVJ1bV4hmOwAZQVd9fVa+tqk9V1U1V9f5Z1wTMTlU9oareUVVXVNW1VfXxqnrSrOsCZqOqfq6qPlxVV1XVN6vqn6vq+Kq63axrA9bOplkXALM07sN3boa9+I5KckCSUzKEzMdvZyiwPj0gyaOSfCTJbWdcCzB7z03yr0l+M8lXM3w+vKmq7tpae9VMKwNm4S5Jzk/yu0muTvJjSbYm2SfJs2ZXFrCWLO1kQ6uqFyb5rST7tdauGdt+K+M/EBfagI2hqm7TWrt5/PPfJLlra+0nZlsVMCtjYPbVRW1vSnJIa+3eMyoLmCNV9dIkv5Zkc/Mf17AhWNrJRvfIJOcsCszOSrJ7ksNmUxIwKwshGkCSLA7RRhcmucda1wLMrauSWNoJG4ggjY3uwCSXTDa01i5Pct3YBwAw6ZAkn511EcDsVNVuVbVHVT00ybOTvMZsNNg47JHGRrc5w/4Gi20b+wAAkiRVdWSSxyb55VnXAszUN5LcfvzzmUmOm2EtwBozIw0AAJZRVfsneVOSt7fW3jjTYoBZe3CSQ5M8L8MDy14923KAtWRGGhvdtiR7T2nfPPYBABtcVd05ydlJvpDkF2dcDjBjrbVPjH/8UFV9NcmfVNUprbXPz7IuYG2YkcZGd0kW7YVWVfsm2SOL9k4DADaeqtojybsybCb+mNbadTMuCZgvC6GaJ/nCBiFIY6M7O8kjqmrPibajk1yf5ILZlAQAzIOq2pTkr5PcN8lPtda+MuOSgPnzkPH1X2daBbBmLO1kozstw5N23lJVJyW5T5KtSU5trV0zy8KAtTfOPHnU+O33Jdmrqn5u/P5vzUSBDeePMnwmPCfJXarqLhN9F7bWvjWbsoBZqKr3JDk3yaeT3JQhRHtekr+0rBM2jvKUXja6qrp/hg1CD8nwBM/XJdnaWrtppoUBa27cTHyp/6N879baZWtWDDBzVXVZkv2W6PaZABtMVb0kyeOS7J/kxiT/kuQNSU5rrd0ww9KANSRIAwAAAIAO9kgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNACgW1U9tapaVT111rXMk6r6YlVdugLn+bPx/t5zJepaaVW1d1W9uqouq6obx1ofOOu6AADWiiANADqMgUFb5pjLxuP2X5uqqKq7VtXNVfWlJfoPWfjZVdXhSxzzhbH/Xqtb7epYqRCv0ylJfi3JJ5O8LMkJSb6yvQFV9aGJn8FSX8evQe0AALfaplkXAADsUt6a5CNJ/n3WhSRJa+2rVfWpJD9UVQ9orX160SFHLhya5Igk75vsrKrvT3KvJJ9rrV1+K0o5bLzGeveYJBe31o7aibFvSLLUPf7AzpcEALB2BGkAQLfW2teTfH3WdSxyfpIfyhCULQ7Sjkjy+STXjH/+nSn9SXLerSmgtfb5WzN+V1BVuyX53iT/tJOneH1r7UMrWBIAwJqztBMAVllVPXbc++qzVfWN8evjVfXsqrrFP4ur6o3jcrd7V9WzquriqvrmuHT0RVVV43FPqKp/GM/3lXHvqt2nnK9V1fur6nur6vVV9eVxzIer6tDxmO+pqt8dlzl+q6o+XVVPmHKuqXukjbVdNnGey8fzXFpV/3uh5kVjqqqeM/H+rhjfw94L5+u8xQsh2BGTjVV1hySHZJiF9r4kP1pVd1w0dskgraoeWVVnV9VV43v5fFWdXFV7TTl26vLKqrpTVf3B+N6+WVWfqarfqKr7jvfxdUu8p6qqX62qfxrHfamqTpu8dlU9bFxu/H1JDli0VHKp8y6+yD2q6jUTP/evVNWbq+qHFx33oSQ3jt8eOXGdc3uusyMW3ldVHV9VD6qqv62qr9XE3nEL93v8u/LKsf4bamKJ6HjvT6qqz4338GtV9Z6qOmJnrgkAkJiRBgBr4eVJbk7y0SRXJNk7Q4Dz+0l+NMmTlxj3e0l+Isk7k/xdkp9J8tIkt6uqr43nfVuSDyZ5eIa9q3ZL8swp57pTkr9P8p9J/iLJnZP8fJJzquqQJK8d296V5LZJnpTkL6vq31prH+l8n7dNck6SeyQ5O0Pw8tixzjtk2E9r0h+OtV6Z5PQk3x7f44+N57qh87ofGK/1E1V1m9bazWP7Q8brnj++7+cm+Z9J/jYZkqokh2dYkrl4yeeJGWavXZXh/v9HhllvxyX5qap6cGvt2u0VVVV7jOc9KMknkvxpks1JXpxhKej2nJLhZ/quDPf0yCS/kuSAsT1J/iXDPX3u+P7/YGL8J5Y5f6rqgCQfSrJPknOTvCnDMtcnJHl0VT2utXb2ePjrM9zH30nyr0nOnKhhtTw0yf/J8PM9I8nd891/J+6Q5P1J9kryngw/48uSpKrunOHv+4FJ/iHJm5PcLckTk5xbVce21qaFjctdEwDY4Kq1jbCdBwDcOvWdBw0sDoMm/UaGkOzerbXLJsYesHjpXw0z0d6Q5H8leVBr7aMTfW9M8pQkX0jykNbaFWP7nZJcmmT3JNcl+Z+ttc+MfbdPcmGGoGXf1tpXJs63UPtrk/zqQtBUVU/OEIhsyxA6PKG19s2x79AMYcLbWmuPmzjXU8e6n9Zae+NE+2VJ9ssQoP1sa+36sf3uST47Hna31toNi87/2SQ/3lq7emy/XYZQ59AkX2it7b/07f6u+/nhDLPPfrS19rGx7aVJXpRky3i/vpbkla2154/9/z3Jp5Jc2Fr7HxPneniG4PJDSR4zLmdd6DsmyR8n+b3W2nET7V9M8s3W2vdPtJ2QIZT58yRPbuO/dFXVfhmCrjsnOaO1dszEmD9L8osZAqFDW2tfHNtvm+SC8T3+SGvtExNjbnHtznt2XoZA9wWttZMm2g/NEFB9Lcl+rbXrxvZNGUKl81prD9uB63woQ6i5vT3S/mjh72xVPSzJe8f2Y1prZ0w55xczzMQ7J8njF2qc6D8jyS8neU1r7Vcn2g9M8o8Zgtr7ttb+rfeaAACJpZ0AsKNevJ2vvacNmLZ/1hhm/f747SOWuNZLFkK0cczVSd6RZI8MAcFnJvq+leQvk9wuyQ9MOdd1SY6bmK2VDDOQbswwS+o5CyHaeL4PZghzDlqitqU8eyFEG8/zlSRvz3Bv7jdx3FPG15cuhGjj8d9O8sIdvGYyfXnnEUk+01r7Umvtmgzh1eL+ybH/9R7G12MmQ7Sxvtdl2CPsFztqekqSm5K8cCFEG8/xhXz37LFpTlgI0cYxN2QIopJhxt6tUsOTZY/IMLvslMm+8Wf/V0nummFG4Up5Wpb+3bn7lOM/1hFoPW9KiHb7JL+QYV+8F032tdYuSfLqJLfP9JmgPdcEADYwQRoA7IDWWi31lWEG2S1U1V2q6uVV9amqunZhf6kkHx8P+b4lLvexKW1Xjq8fn9K3ELpN29Pps621/1z0Xm5K8uUkV7fWpi3Ru2KJcy3l6621W+wTluTfxtfNE20Le3BN23z+I/nOfly9zh9fj0iSqtozycH57iWb78vwdM87Tx6bWwZphyT5VpInVdXWxV8ZtsbYUlVTg9Px+pszzNC7fGHW0yLLbbo/7Wc/7T7urIX7/4HW2rR7ff6i41bCodv5/Zn2AIN/WOZ835jylNYkuX+GZZ8XToa0E7b33pa7JgCwwdkjDQBW0bgc8x+T3DvDf6SfmWHJ3I0Z9i17TobZMdNMezrmjR19t+0818KY7fXtyL8rTAstJuvabaJtIYT68uKDW2s3VdVVO3DdJPlwkuuTHDougzwsQ+3nTxzz/iS/leTwqnrbeMy3MywxnXTnJJVhptT23DFL37sl398y7Qum3ctp93FnLdT370v0L7TfaQWutbO+tEz/Uvfw1ry35a4JAGxwgjQAWF3HZAjRTmitbZ3sGDf5f84sipoD14yv35tFG9ZX1W5J7pLvzLBbVmvtW+M+aUcmeVCG2WYtQ3i24IMZwqgjMszu2jvDjKzrvvtsuSbJt1tr05Yb9pp8f9Ms1b5WFgLAfZbo37LouFlYbiPfpfpvzXuzeTAAsF2WdgLA6lrYAP7NU/qWe3Ljenbh+PrQKX0Pys79z77JfdKOSPKp1tp/zWwbn7L5sYn+yTGTPpLkblV1vyl9XVprX8uwsf69qmrfKYdMe98766bs+Cy1hft/6BhcLnb4+Lrs0z/n0GcyLM394araa0r/rvzeAIAZE6QBwOq6bHz9icnGqvrh7Nym+uvFmePrb0/uNTY+tfNlO3nOhWWcT0jyg/nu/dEWvC/JgfnOwwKmBWmnju5dFusAAAMSSURBVK+vq6otizur6o5V9eMd9ZyZIeB6WVXVxPh75TsPNFgJVyW5+7jJfpfxqbLvy/CU11+f7KuqhyQ5ejzv21euzLUxPjTjTRlmHJ442VdV903yrAxLev9s7asDAHZ1lnYCwOo6M8lxSV5ZVYcn+VyS+yZ5TJK3ZAgsNpzW2gVVdXqSY5N8uqrenOSGJD+dYcndlUlu3s4ppvnYOPYB4/fnTznmfRkCzAcmuTZTNpdvrf1dVR2f5CVJPldVZ2d4uuUdk+yfYSbh+zL8DLfn5UmOSvJLSX6gqs7NsC/XE5NckOGJmDv6Hqc5L8PG+e+pqg9mCIkubK29e5lxv5LhoQevqKpHZniAxb0yBJE3Jnlqa+0bK1Dfgl+uqoct0feJ1to7VvBax2WY9fecqvqxDPf7bhnu/R2TPLO1dvkKXg8A2CAEaQCwilprV1bVoRlClYcmeUSSS5L8apJzs0GDtNEzM9yLX0nyjAwzoN6a5EVJvpjk8ztysvEhBRck+ZkMyx0XP0QgSf4+Q9B0uwz7o92wxLleOoZSz07ykAyB2NfHuk5L8ucd9Xyjqg7LEMg9PslvZtgP7sQkH80QpF2z9Bm6nZBkrwzB3qEZZsGdkWS7QVpr7XNV9SNJjk/yqAxLHq8Zx72stTbtyaG3xtO203dGkhUL0lprV42zBl+U5HFJnpvkuiT/N8nvttbOXalrAQAbS7VmT1UAYH6My+8+m+Ss1tqTZl3PaqiqZyb5oyTHtNbOmHU9AAD0sUcaADATVbVPVd1mUdseSV45fvvWta9qZVXVPaa07ZfktzMsZV1u+SUAAHPE0k4AYFZ+I8mTqur9Sf49yT5JjkxyzyRnJ/nr2ZW2Yt4+PmfgE0muTnLvDEswd09yXGvtSzOsDQCAHWRpJwAwE1V1ZJLnJzkoyZ0zbHD/2QxPXHzlUvuX7Uqq6tczPCH0vhn2Mbs2Q6j2qtba22ZZGwAAO06QBgAAAAAd7JEGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQ4f8D2HriwdRw2HAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "for dep in range(1, df_fn_depth.Depth.max()+1):\n", - " idx = df_fn_depth['Depth']== dep\n", - " avg_dist = df_fn_depth[idx]['Hamming dist. data'].mean() \n", - " rand_dist = df_fn_depth[idx]['Hamming dist. rand'].mean() \n", - " x_labels = np.arange(0, len(avg_dist))\n", - " plt.subplot(1,df_fn_depth.Depth.max(),dep)\n", - " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", - " plt.xticks(x_labels)\n", - " plt.xlabel('Hamming Weight of Error')\n", - " plt.ylabel('Relative Frequency of Occurence')\n", - " plt.ylim([0,1])\n", - " plt.grid(axis='y', alpha=0.75)\n", - " plt.legend(['data','random'])\n", - " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", - "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", - "plt.show()" + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], plot_rand_distr=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data fron the data frame." + "### Plot all of the distributions" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=None, depths=None, plot_rand_distr=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {3: 0.6246, 4: 0.6292, 5: 0.6138}, 3: {3: 0.7116, 4: 0.7308000000000001, 5: 0.6768}, 4: {3: 0.7167, 4: 0.7055000000000001, 5: 0.7293000000000001}}\n" + ] + } + ], "source": [ - "depth_vec = []\n", - "pcheck = []\n", - "pcheck_rand = []\n", - "pcheck_log_errors = []\n", - "pcheck_log_errors_rand = []\n", - "tvd_rand = []\n", - "tvd_ideal = []\n", + "# extract data from avg_err_hamm_distrs\n", + "widths = list(avg_err_hamm_distrs.keys())\n", + "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", + "\n", + "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "# this is equivalently wrapped up in the following\n", + "# assert pr_succ_arr == get_success_probabilites(noisy_results, ideal_results)\n", + "pr_succ_rand = [1/2**w for w in widths]\n", + "\n", + "ideal_distrs = {w: np.asarray([[1] + [0 for _ in range(w)]]).T for w in widths}\n", + "rand_distrs = {w: np.asarray([get_random_hamming_wt_distr(w)]).T for w in widths}\n", + "\n", + "# total variation distance\n", + "tvd_noisy_ideal = {w: {d: tvd(np.asarray([distr]).T, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "np.testing.assert_allclose([pr for d_vals in pr_succ_arr.values() for pr in d_vals.values()], \n", + " [1 - val for d_vals in tvd_noisy_ideal.values() for val in d_vals.values()])\n", + "\n", + "tvd_noisy_rand = {w: {d: tvd(np.asarray([distr]).T, rand_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "print(tvd_noisy_rand)\n", "\n", - "for dep in range(1, df_fn_depth.Depth.max()+1):\n", - " idx = df_fn_depth['Depth']== dep\n", - " depth_vec.append(dep)\n", - " pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", - " pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", - " pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", - " pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", - " tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", - " tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" + "# pcheck_log_errors = []\n", + "# pcheck_log_errors_rand = []\n", + "# tvd_rand = []\n", + "# tvd_ideal = []\n", + "\n", + "# for dep in range(1, df_fn_depth.Depth.max()+1):\n", + "# idx = df_fn_depth['Depth']== dep\n", + "# depth_vec.append(dep)\n", + "# pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", + "# pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", + "# pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", + "# pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", + "# tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", + "# tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" ] }, { @@ -1371,30 +1624,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[9, 12, 22, 36, 49, 48, 32, 9, 1, 9, 12, 22, 36, 49, 48, 32, 9, 1]" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "G = perfect_qc.qubit_topology()\n", "len(perfect_qc.qubit_topology())\n", diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index d29abf29..28033bb2 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -1,4 +1,4 @@ -from typing import Tuple, Sequence, Callable, Any, List, Union +from typing import Tuple, Sequence, Callable, Dict, List, Union from copy import copy import networkx as nx import numpy as np @@ -9,6 +9,7 @@ from scipy.special import comb from dataclasses import dataclass, field from functools import partial +import matplotlib.pyplot as plt from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate from pyquil.quilatom import QubitPlaceholder @@ -377,7 +378,7 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, if graph is None: graph = qc.qubit_topology() - programs = {width: {depth: []} for width in widths for depth in depths} + programs = {width: {depth: [] for depth in depths} for width in widths} for width, depth_array in programs.items(): for depth, prog_list in depth_array.items(): @@ -396,8 +397,8 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = if use_active_reset: reset_prog += RESET() - results = {width: {depth: []} for width, depth_array in program_array.items() - for depth in depth_array.keys()} + results = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in program_array.items()} for width, depth_array in program_array.items(): for depth, prog_list in depth_array.items(): @@ -442,8 +443,14 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = # Analysis # ================================================================================================== def get_error_hamming_weight_distributions(noisy_results, perfect_results): - distrs = {width: {depth: []} for width, depth_array in noisy_results.items() - for depth in depth_array.keys()} + + # allow for perfect result to depend only on width (pass in a list) + if not isinstance(perfect_results, dict): + perfect_results = {width: {depth: perfect_results[width] for depth in depth_array.keys()} + for width, depth_array in noisy_results.items()} + + distrs = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in noisy_results.items()} for width, depth_array in distrs.items(): for depth, samples in depth_array.items(): @@ -460,18 +467,23 @@ def get_error_hamming_weight_distributions(noisy_results, perfect_results): hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) samples.append(np.asarray(hamm_wt_distr)) return distrs - # TODO: separate these out - # wt_dist_rand = np.asarray(hamming_dist_rand(width)) # random guessing - # wt_dist_ideal = np.zeros_like(wt_dist_rand) # perfect - # wt_dist_ideal[0] = 1 - # Total variation distance - # tvd_data_ideal = tvd(wt_dist_data, wt_dist_ideal) - # tvd_data_rand = tvd(wt_dist_data, wt_dist_rand) - # Probability of success - # pr_suc_data = hamm_wt_distr[0] - # pr_suc_rand = wt_dist_rand[0] +def get_average_of_distributions(distrs): + # take in output of `get_error_hamming_weight_distributions` + return {w: {d: sum(distr_list) / len(distr_list) for d, distr_list in d_arr.items()} + for w, d_arr in distrs.items()} + + +def get_success_probabilites(noisy_results, perfect_results): + avg_distrs = get_average_of_distributions(get_error_hamming_weight_distributions( + noisy_results, perfect_results)) + return {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_distrs.items()} + + +# def get_total_variation_dist(distrs1, distrs2): + + # TODO: separate these out # Probability of success with basement[ log_2(width) - 1 ] errors # I.e. error when you allow for a logarithmic number of bit flips from the answer @@ -532,66 +544,75 @@ def get_hamming_wt_distr_from_list(wt_list, n_bits): return [wt_list.count(weight) / num_shots for weight in range(n_bits + 1)] -def hamming_dist_rand(num_bits: int, pad: int = 0): - """Return a list representing the Hamming distribution of - a particular bit string, of length num_bits, to randomly drawn bits. +def get_random_hamming_wt_distr(num_bits: int): + """ + Return the distribution of Hamming weight for randomly drawn bitstrings of length num_bits. + + This is equivalent to the error distribution, e.g. from + :func:`get_error_hamming_weight_distributions` where the `noisy_results` are entirely random. + Comparing real data against this distribution may be a useful benchmark in determining + whether the real data contains any actual information. :param num_bits: number of bits in string - :param pad: number of zero elements to pad - returns: list of hamming weights with zero padding + returns: list of hamming weights """ - N = 2 ** num_bits - pr = [comb(num_bits, ndx) / (2 ** num_bits) for ndx in range(0, num_bits + 1)] - padding = [0 for _ in range(pad)] - return flatten_list([pr, padding]) + # comb(N, k) = N choose k + return [comb(num_bits, num_ones) / (2 ** num_bits) for num_ones in range(0, num_bits + 1)] -def flatten_list(xlist): - """Flattens a list of lists. +def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], widths=None, + depths=None, plot_rand_distr=False): + if widths is None: + widths = distr_arr.keys() - :param xlist: list of lists - :returns: a flattened list - """ - return [item for sublist in xlist for item in sublist] + if depths is None: + depths = list(distr_arr.values())[0].keys() + legend = ['data'] + if plot_rand_distr: + legend.append('random') -# helper functions to manipulate the dataframes -def get_hamming_dist(df: pd.DataFrame, depth_val: int, width_val: int): - """ - Get Hamming distance from a dataframe for a particular depth and width. + fig = plt.figure(figsize=(18, 6 * len(depths))) + axs = fig.subplots(len(depths), len(widths), sharex='col', sharey=True) - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param depth_val: depth of quantum circuit - :param width_val: width of quantum circuit - :return: smaller dataframe - """ - idx = df.Depth == depth_val - jdx = df.Width == width_val - return df[idx & jdx].reset_index(drop=True) + for w_idx, w in enumerate(widths): + x_labels = np.arange(0, w + 1) + depth_distrs = distr_arr[w] + if plot_rand_distr: + rand_distr = get_random_hamming_wt_distr(w) -def get_hamming_dists_fn_width(df: pd.DataFrame, depth_val: int): - """ - Get Hamming distance from a dataframe for a particular depth. + for d_idx, d in enumerate(depths): + distr = depth_distrs[d] - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param depth_val: depth of quantum circuit - :return: smaller dataframe - """ - idx = df.Depth == depth_val - return df[idx].reset_index(drop=True) + idx = d_idx * len(widths) + w_idx + if len(widths) == len(depths) == 1: + ax = axs + else: + ax = axs.flatten()[idx] + ax.bar(x_labels, distr, width=0.61, align='center') + if plot_rand_distr: + ax.bar(x_labels, rand_distr, width=0.31, align='center') -def get_hamming_dists_fn_depth(df: pd.DataFrame, width_val: int): - """ - Get Hamming distance from a dataframe for a particular width. + ax.set_xticks(x_labels) + ax.grid(axis='y', alpha=0.75) + ax.set_title(f'w = {w}, d = {d}', size=20) - :param df: dataframe generated from data from 'get_random_classical_circuit_results' - :param width_val: width of quantum circuit - :return: smaller dataframe - """ - jdx = df.Width == width_val - return df[jdx].reset_index(drop=True) + for tick in ax.xaxis.get_major_ticks(): + tick.label.set_fontsize(15) + + for tick in ax.yaxis.get_major_ticks(): + tick.label.set_fontsize(15) + + fig.legend(legend, loc='right', fontsize=15) + plt.ylim(0, 1) + fig.text(0.5, 0.05, 'Hamming Weight of Error', ha='center', va='center', fontsize=20) + fig.text(0.06, 0.5, 'Relative Frequency of Occurrence', ha='center', va='center', + rotation='vertical', fontsize=20) + plt.subplots_adjust(wspace=0, hspace=.15, left=.1) + + return fig, axs def basement_function(number: float): From 32138bbf5f638de5836e792ed1dab45cb227de8b Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 25 Jul 2019 16:17:00 -0400 Subject: [PATCH 22/49] Demonstrate helpers in notebook. --- examples/volumetrics.ipynb | 1121 ++++++++++++++++-------------------- 1 file changed, 496 insertions(+), 625 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index a9336f36..7e38044d 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -79,7 +79,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -216,30 +216,33 @@ "output_type": "stream", "text": [ "I 0\n", - "I 1\n", - "I 2\n", - "X 3\n", + "Z 1\n", + "X 2\n", + "I 3\n", "Z 4\n", - "Z 5\n", + "X 5\n", "X 6\n", - "Z 7\n", + "X 7\n", "Z 8\n", - "CZ 0 3\n", + "I 0\n", + "I 3\n", "CZ 0 1\n", "CZ 1 4\n", - "CZ 1 2\n", + "I 1\n", + "I 2\n", "I 2\n", "I 5\n", - "CZ 3 6\n", + "I 3\n", + "I 6\n", "I 3\n", "I 4\n", "I 4\n", "I 7\n", - "I 4\n", - "I 5\n", + "CZ 4 5\n", "CZ 5 8\n", "CZ 6 7\n", - "CZ 7 8\n", + "I 7\n", + "I 8\n", "\n" ] } @@ -259,20 +262,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 1\n", + "RZ(-pi) 0\n", "RZ(-pi/2) 1\n", - "RZ(-pi/2) 2\n", + "RX(-pi/2) 1\n", + "RZ(-pi) 2\n", + "RX(-pi) 2\n", "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", - "RZ(-pi) 5\n", - "RZ(-pi) 5\n", - "RX(pi/2) 6\n", + "RX(-pi/2) 5\n", + "RZ(pi/2) 5\n", "RZ(-pi) 6\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RZ(-pi/2) 8\n", "RX(-pi/2) 8\n", "\n" ] @@ -299,10 +302,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "X 7\n", + "I 1\n", + "X 4\n", + "I 1\n", "X 4\n", - "X 7\n", "\n" ] } @@ -321,9 +324,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "I 3\n", - "CNOT 0 3\n", + "CNOT 7 8\n", + "CNOT 7 8\n", "\n" ] } @@ -343,9 +345,9 @@ "output_type": "stream", "text": [ "H 1\n", + "H 2\n", "H 4\n", "H 7\n", - "H 8\n", "\n" ] } @@ -364,17 +366,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "CZ 1 4\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", "RX(pi/2) 1\n", - "RZ(-pi) 1\n", + "CZ 0 1\n", + "RX(-pi/2) 1\n", + "RX(-pi/2) 0\n", + "CZ 0 1\n", "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "CZ 1 4\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "CZ 0 1\n", + "RX(-pi/2) 0\n", "\n" ] } @@ -394,67 +399,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 4 7\n", - "RZ(-0.5581195580028985) 8\n", - "RX(pi/2) 8\n", - "RZ(1.2736807691803615) 8\n", - "RX(-pi/2) 8\n", - "RZ(0.8654548211806258) 8\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(1.683714870976314) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.164556904728772) 5\n", - "RX(pi/2) 5\n", - "CZ 4 5\n", - "RX(pi/2) 4\n", - "RZ(pi) 5\n", - "RX(pi/2) 5\n", - "CZ 4 5\n", - "RZ(pi/2) 7\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "CZ 7 4\n", - "RZ(-2.5423448512921096) 5\n", - "RX(pi/2) 5\n", - "RZ(1.0298565022999515) 5\n", - "RX(-pi/2) 5\n", - "RZ(-1.4107150935903878) 8\n", - "RX(pi/2) 8\n", - "RZ(0.9343859424350573) 8\n", - "RX(-pi/2) 8\n", - "CZ 8 5\n", - "RZ(1.8455338844194458) 5\n", - "RX(pi/2) 5\n", - "RZ(1.2152422654059585) 8\n", - "RX(-pi/2) 8\n", - "CZ 8 5\n", - "RX(-pi/2) 5\n", - "RX(pi/2) 8\n", - "CZ 8 5\n", - "RX(pi/2) 7\n", - "RZ(-0.5939372112013284) 8\n", - "RX(pi/2) 8\n", - "RZ(2.0985221442302406) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RZ(0.18821978522872662) 8\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RZ(1.9770357488610202) 4\n", - "RZ(-2.5476554423884643) 5\n", - "RX(pi/2) 5\n", - "RZ(2.098522144230241) 5\n", - "RX(-pi/2) 5\n", - "RZ(-0.1882197852287284) 5\n", - "RX(pi/2) 7\n", - "RZ(-pi/2) 7\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", + "RZ(-2.0251704131299655) 1\n", + "RX(pi/2) 1\n", + "RZ(1.671564714694243) 1\n", + "RX(-pi/2) 1\n", + "RZ(-1.3830973810959275) 2\n", + "RX(pi/2) 2\n", + "RZ(1.2413369689433633) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", + "RZ(-2.8836019621636186) 1\n", + "RX(pi/2) 1\n", + "RZ(0.9759904846486989) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(2.593711329555103) 1\n", + "RX(pi/2) 1\n", + "RZ(2.471415229947945) 1\n", + "RX(-pi/2) 1\n", + "RZ(-0.6337679516296877) 1\n", + "RZ(0.10419300890719785) 2\n", + "RX(pi/2) 2\n", + "RZ(1.8274486780321837) 2\n", + "RX(-pi/2) 2\n", + "RZ(0.4809127509292681) 2\n", "\n" ] } @@ -474,38 +445,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-0.7313267215276299) 2\n", - "RX(pi/2) 2\n", - "RZ(0.8738083564575054) 2\n", - "RX(-pi/2) 2\n", - "RZ(1.1108387720144826) 5\n", - "RX(pi/2) 5\n", - "RZ(1.9239464510235134) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RZ(-0.7988576166116559) 2\n", - "RX(pi/2) 2\n", - "RZ(2.0611770298297554) 2\n", - "RX(-pi/2) 2\n", - "RZ(-0.16257974334726644) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RX(pi/2) 2\n", - "RZ(-1.7986523134027053) 2\n", - "RX(-pi/2) 2\n", - "RZ(1.4365683405145715) 5\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(2.573992416316516) 2\n", - "RX(pi/2) 2\n", - "RZ(0.5011562609035493) 2\n", - "RX(-pi/2) 2\n", - "RZ(1.2391707193285333) 2\n", - "RZ(-1.5517677060657928) 5\n", - "RX(pi/2) 5\n", - "RZ(1.557823493357372) 5\n", - "RX(-pi/2) 5\n", - "RZ(-0.9421500589018095) 5\n", + "RZ(1.229760623484783) 3\n", + "RX(pi/2) 3\n", + "RZ(1.021574308763614) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.8486661215139757) 4\n", + "RX(pi/2) 4\n", + "RZ(1.1613694661492238) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RZ(-2.107290395324421) 3\n", + "RX(pi/2) 3\n", + "RZ(2.482724125564382) 3\n", + "RX(-pi/2) 3\n", + "RZ(-0.14029325543178395) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 3\n", + "RZ(-1.9416631561063133) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.8868409863344517) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(1.722512065613851) 3\n", + "RX(pi/2) 3\n", + "RZ(1.1538066968468772) 3\n", + "RX(-pi/2) 3\n", + "RZ(-3.0483439973439523) 3\n", + "RZ(-0.3273187044737542) 4\n", + "RX(pi/2) 4\n", + "RZ(1.3899233354854028) 4\n", + "RX(-pi/2) 4\n", + "RZ(-3.0375946996294916) 4\n", "\n" ] } @@ -531,25 +502,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 3\n", - "X 4\n", + "I 1\n", + "I 4\n", "X 6\n", "I 7\n", - "CNOT 3 6\n", - "I 3\n", - "I 4\n", + "I 1\n", "I 4\n", - "I 7\n", + "CNOT 4 7\n", "I 6\n", "I 7\n", - "I 3\n", + "I 1\n", "X 4\n", "I 6\n", "I 7\n", - "CNOT 3 6\n", - "CNOT 3 4\n", + "I 1\n", + "I 4\n", "CNOT 4 7\n", - "CNOT 6 7\n", + "I 6\n", + "I 7\n", "\n" ] } @@ -575,25 +545,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 0\n", - "H 1\n", - "Z 0\n", - "Z 1\n", - "H 0\n", - "CZ 0 1\n", - "H 0\n", - "I 0\n", - "Z 1\n", - "H 0\n", - "CZ 0 1\n", - "H 0\n", - "Z 0\n", - "I 1\n", - "H 0\n", - "CZ 0 1\n", - "H 0\n", - "H 0\n", - "H 1\n", + "H 5\n", + "H 8\n", + "I 5\n", + "Z 8\n", + "I 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "Z 5\n", + "I 8\n", + "I 5\n", + "I 8\n", + "H 5\n", + "H 8\n", "\n" ] } @@ -618,55 +585,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi) 0\n", - "RX(pi/2) 3\n", - "RZ(-pi) 3\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "CZ 0 3\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", - "RZ(-pi) 0\n", - "RX(-pi/2) 3\n", - "RZ(-pi) 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(pi/2) 3\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(0.19942343037115873) 0\n", - "RX(-pi/2) 0\n", - "RZ(-0.778788619183817) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RX(pi/2) 0\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RX(pi/2) 0\n", - "RZ(2.3628040344059755) 0\n", - "RX(-pi/2) 0\n", - "RX(pi/2) 3\n", - "RZ(0.19942343037115845) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 3\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 8\n", + "RZ(-pi) 8\n", + "CZ 7 8\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi) 8\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(-pi) 7\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(-pi) 8\n", + "RX(-pi) 8\n", + "RX(pi/2) 7\n", + "RZ(-pi) 7\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RX(pi/2) 8\n", + "CZ 7 8\n", + "RZ(pi/2) 8\n", + "RZ(-pi) 7\n", + "CZ 7 8\n", + "RZ(-pi/2) 7\n", + "RX(pi) 7\n", + "RZ(pi/2) 8\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -703,487 +657,392 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 4 7\n", - "RZ(-2.3181435067958773) 8\n", - "RX(pi/2) 8\n", - "RZ(1.7684965768495358) 8\n", - "RX(-pi/2) 8\n", - "RZ(0.07088205710553819) 8\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(0.9726985271045892) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.7400726697677005) 5\n", - "RX(pi/2) 5\n", - "CZ 5 4\n", - "RX(pi/2) 4\n", - "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(pi/2) 7\n", - "RX(pi) 7\n", - "RX(pi/2) 4\n", - "CZ 7 4\n", - "RZ(0.45826797068731434) 5\n", - "RX(pi/2) 5\n", - "RZ(2.468284038036106) 5\n", - "RX(-pi/2) 5\n", - "RZ(-1.4428100443331238) 8\n", - "RX(pi/2) 8\n", - "RZ(0.7807142006412494) 8\n", - "RX(-pi/2) 8\n", - "CZ 8 5\n", - "RZ(-2.594297231677391) 5\n", - "RX(pi/2) 5\n", - 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2.068e-01, 2.240e-02, 2.600e-03, 2.000e-04]), 5: array([0.7918, 0.1878, 0.0194, 0.001 , 0. ])}}\n" + "{2: {3: array([0.903 , 0.0946, 0.0024]), 4: array([0.882 , 0.1152, 0.0028]), 5: array([0.8798, 0.1148, 0.0054])}, 3: {3: array([0.8006, 0.185 , 0.0134, 0.001 ]), 4: array([0.8078, 0.1736, 0.0168, 0.0018]), 5: array([8.262e-01, 1.586e-01, 1.440e-02, 8.000e-04])}, 4: {3: array([0.783 , 0.1976, 0.0168, 0.0016, 0.001 ]), 4: array([8.046e-01, 1.786e-01, 1.600e-02, 6.000e-04, 2.000e-04]), 5: array([8.112e-01, 1.680e-01, 1.920e-02, 1.400e-03, 2.000e-04])}}\n" ] } ], @@ -1313,7 +1172,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1345,12 +1204,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1372,12 +1231,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+oKq2JtmaJBs3bsy11167j6XPxksfffesS2CNOlB/ZwAAAGCseQ/S9lYluX+S/7O7358kVXVlkpuT/EySX118QXdfkOSCJNmyZUtv3rx5/1W7jF5y4a2zLoE16qytB+bvDAAAAIw170s7dyQ5fIn2DZO+3V3XST64s2Gyz9o1SZ6wjPUBAAAAsEbMe5B2YxbthVZVRyS5bxbtnbbIDRlmpdWi9kpyz3IWCAAAAMDaMO9B2sVJjquqQxe0nZzkq0mu2M117508P3dnQ1UdnuSpST6x3EUCAAAAsPrNe5D2piRfT/Kuqnr+5ECA05KcM1mqmSSpqpuq6s07X3f31Un+JMmbq+onquqEJO9O8o0k/2l/fgMAAAAArA5zHaR1944kxyY5OMl7kpye5Nwkr1s0dN1kzEIvS3JRknOS/FGGEO15k3sCAAAAwFTm/tTO7r4+yfP2MGbTEm1fSfKqyQMAAAAA9slcz0gDAAAAgHkhSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYYe6DtKp6QlVtq6o7q+q2qjqjqg6e4vqDqurqquqqevFK1goAAADA6rVu1gXsTlVtSHJZkuuTnJjkMUnOzhAAnjryNq9M8sgVKRAAAACANWPeZ6SdkmR9kpO6+9LuflOS05P8fFUdtqeLJ0Hc65P8ysqWCQAAAMBqN+9B2vFJLunuOxa0XZghXDtmxPX/PslHkmxbgdoAAAAAWEPmPUg7MsmNCxu6+5Ykd076dqmqvifJTyV5zYpVBwAAAMCaMe9B2oYkty/RvmPStzv/b5Lzu/umZa8KAAAAgDVnrg8b2FtV9SNJHp/kB6a4ZmuSrUmycePGXHvttStU3cp66aPvnnUJrFEH6u8MAAAAjDXvQdqOJIcv0b5h0vdtqupeSf5DkjOTHFRVD0iy82CC+1XVod395cXXdfcFSS5Iki1btvTmzZuXofz97yUX3jrrElijztp6YP7OAAAAwFjzvrTzxizaC62qjkhy3yzaO22B+yV5ZJJzMoRtO5J8YtJ3YZK/WpFKAQAAAFjV5n1G2sVJXrtoFtnJSb6a5IpdXPOVJM9d1PawJH+Y5JeTXL4ShQIAAACwus17kPamJK9O8q6qOjPJo5OcluSc7r5j56CquinJFd39iu7+ZpIPLrxJVW2afPnfuvvjK182AAAAAKvNXAdp3b2jqo5Ncn6S92Q4wfPcDGHaQuuSHLx/qwMAAABgLZnrIC1Juvv6JM/bw5hNe+jfnqSWryoAAAAA1pq5D9IAWKVOW+pQZqZy2pdmXQEsH58J+85nAgCsuHk/tRMAAAAA5oIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAI8x9kFZVT6iqbVV1Z1XdVlVnVNXBe7jme6vqv1TVTZPrPllVr6uq++yvugEAAABYXdbNuoDdqaoNSS5Lcn2SE5M8JsnZGQLAU3dz6cmTsWcm+dsk35Pk30+e/48VLBkAAACAVWqug7QkpyRZn+Sk7r4jyaVVdViS06rqrEnbUt7Y3V9c8PqDVfW1JP9fVT2qu29e4boBAAAAWGXmfWnn8UkuWRSYXZghXDtmVxctCtF2+qvJ88OXrzwAAAAA1op5D9KOTHLjwobuviXJnZO+aRyV5J4kf7c8pQEAAACwlsx7kLYhye1LtO+Y9I1SVQ/LsKfa27r788tUGwAAAABryLzvkbbPquqQJG9P8pUk//duxm1NsjVJNm7cmGuvvXb/FLjMXvrou2ddAmvUgfo7wwwd8fJZV3Dg83vHauIzYd/5TACAFTfvQdqOJIcv0b5h0rdbVVVJ3prkiUme2d27vKa7L0hyQZJs2bKlN2/evFcFz9pLLrx11iWwRp219cD8nWGGLnrLrCs48L3iP866Alg+PhP2nc8EAFhx8x6k3ZhFe6FV1RFJ7ptFe6ftwnlJTkzygu4eMx4AAAAAljTve6RdnOS4qjp0QdvJSb6a5IrdXVhVv5TkZ5K8rLs/vHIlAgAAALAWzHuQ9qYkX0/yrqp6/mQfs9OSnNPdd+wcVFU3VdWbF7z+0SRvyLCs89aqesaCx4P377cAAAAAwGow10s7u3tHVR2b5Pwk78lwgue5GcK0hdYlOXjB6++fPL988ljoJ5O8ZXkrBQAAAGC1m+sgLUm6+/okz9vDmE2LXr883x6gAQAAAMBem/elnQAAAAAwFwRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYIR1sy4AgJWz6RffN+sSdmn7fWZdwYFvrv/7vvGEWZcAAADLzow0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGWDfrAlg7tt/nR2ddwgFv09f+YNYlAADAyjvt8FlXcOA77UuzrgBWJTPSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYQZAGAAAAACMI0gAAAABgBEEaAAAAAIwgSAMAAACAEQRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwAAAIARBGkAAAAAMIIgDQAAAABGEKQBAAAAwAiCNAAAAAAYYe6DtKp6QlVtq6o7q+q2qjqjqg4ecd3hVfVfqmpHVX2pqv5rVT1of9QMAAAAwOqzbtYF7E5VbUhyWZLrk5yY5DFJzs4QAJ66h8vfnuRxSV6Z5J4kZya5KMmzV6peAAAAAFavuQ7SkpySZH2Sk7r7jiSXVtVhSU6rqrMmbd+mqo5K8v1JjunuP5+03Zrk41X1/O6+bD/VDwAwNzb94vtmXcIubb/PrCs48M31f983njDrEgBgWcz70s7jk1yyKDC7MEO4dswervvczhAtSbr7qiR/P+kDAAAAgKnMe5B2ZJIbFzZ09y1J7pz0jb5u4oY9XAcAAAAAS5r3pZ0bkty+RPuOSd/eXPfopS6oqq1Jtk5efqWqPjlFnYxQsy5gz74jyRdnXcTuvXjWBexSnTnrCjjQ+ExYDj4TWD18JiwHnwmwn83358LpB8An64HpUbMugNma9yBtv+nuC5JcMOs6mJ2qurq7t8y6DmA++EwAFvKZACzmcwHWpnlf2rkjyeFLtG+Y9C33dQAAAACwpHkP0m7Moj3NquqIJPfN0nug7fK6iV3tnQYAAAAAuzXvQdrFSY6rqkMXtJ2c5KtJrtjDdQ+rqmftbKiqLRn2R7t4JQplVbC0F1jIZwKwkM8EYDGfC7AGVXfPuoZdqqoNSa5P8t+TnJkhCDsnyXndfeqCcTcluaK7X7Gg7ZIkj03ymiT3TK7/fHc/e/99BwAAAACsFnM9I627dyQ5NsnBSd6T5PQk5yZ53aKh6yZjFjo5w6y1303y1iTXJPmhlawXAAAAgNVrrmekAQAAAMC8mOsZaQAAAAAwLwRpAAAAADCCIA0AAAAARhCkAQAAAMAIgjQAAAAAGEGQBgAAAAAjCNIAAAAAYARBGgAAAACMIEgDAAAAgBEEaQAAAAAwgiANAAAAAEYQpAEAAADACII0AAAAABhBkAYAAAAAIwjSAAAAAGAEQRoAAAAAjCBIAwBYYVV1WlV1VT1n1rUAALD3BGkAwKpWVYdV1XlV9aGquq2qvlZVn6+qq6rq56rqfrOucZZqcOkk6OuqWjfrmgAA5pUgDQBY7R6YZGuSu5O8L8k5Sd6R5NAk5ya5qqoOm115M/czSZ6b5GuzLgQAYN75F0cAYLX7dJLDu/sbizuq6veT/FiSU5Kctb8Lm7WqenySM5P8ZpIfSfKo2VYEADDfpp6RVlUPqqpXVtXZVfWmRe1Pqar7LG+JAMCsVdX9q+quqvrIovb1k6WSXVU/vqjvVZP2n9q/1f5z3X33UiHaxDsmz49djveqqqdW1fur6stVdUdVXVZVRy3HvZfbZAnn25J8KsnrZlwOAMABYaoZaVX1E0nOT3LfJJWkM/wLbpI8IslfJPm/kvzuMtYIAMxYd3+lqq5K8vSqOrS7vzzpemaSe0++PjZDMJMFr5Nk234qc2/8wOT5r/f1RlV1dJLLkhyS5F1JbkqyOckHk1y+r/dfAacmeXKSo7r761U163oAAObe6CCtqo7NEJBdl+S0JC/IsN9IkqS7/7qqbkjykgjSAGA1ujxDcPZ9GfYaS4aw7O4kV+RbwVmq6qAM+259qrtv3tONq+oBSX5uynou6u5rxw6ezMA6dfLygUmenSHo+rMkvz3ley++d2X4+2d9kpd0958s6Ps3Sc6b8n6bM/xNNY3zuvv2kff/3iS/kuSN3X31lO8DALBmTTMj7d8l+Yckz+7uL1XVk5YYc22SZyxLZQDAvNmW5FczBGYLg7RrMszAOr+qHtfdf5MhoHpgkneOvPcDMv3ywu0Z/vYYa90S7/G2JP+6u/d1o/2jkzw+yZ8vDNEmzk/ys0keM8X9Nmf6n8dbkuwxSKuq9Rm+7+uSnDHlewAArGnT7JH2vUne291f2s2YzyR52L6VBADMqY8m+WomM8+q6vAkT8kQsO1curhzVtrzJs+jljR29/burikfb5mm+O7+WndXhr9/Hpnk5Umen+Tqqto0zb2W8JTJ8xVLvO/dST48zc26+y178fPYPvL2ZyV5dJKf2M3ecQAALGGaIO0+Sb68hzEPSHLP3pcDAMyr7r4rQyD0pKp6cJLnJDk4ybbuviHJZ/OtIO3YDHupzt3eYD24tbt/L8lJGWaSnb+Ptz188vy5XfT/wz7ef1lU1TFJfjrJr3f3J2ZdDwDAgWaapZ3bkzx1D2OeluRv9roaAGDeXZ5hn9RjMyxn/FqSjyzoO76q7p1h/7HruvvzY266P/ZIW0p3f6yqbs8QCu6LnTP2H7qL/qlm7K/gHmlPznBg1OlVdfouxnxjcvDAk/f15wsAsNpME6S9O8lrquqk7n7X4s6q+pdJ/kWGvVMAgNVp5wmcxyY5KsmVC/YX25bkx5K8Ksn9Mt1pnftjj7RvU1WHJjkse551vyd/OXk+Zon3ODjJs6a830rtkfbfk7x5F30nJ7l/hkMTOsk/Tvn+AACrXnX3uIFVD8zwR+Ijk/z/STYkOS7D8oBnJ3lpkk8leWp3f2VFqgUAZmoSCv1jkruSPDjJr3T3GyZ9j8oQbn0+yUOSnNjd755Rqf/L5ICkv118oEqoAEYAACAASURBVEBVHZLhtM5/meQPuvvHFvV3kkz2VdvTe1SSGzIsE93dqZ3P7e4P7v13s3KqanuSRyW5V3d/c8blAADMpdFBWpJMNuL9/QxLORb7aJIf6e5PL0tlAMBcqqqLkpw4efmM7v74gr6bMpxOeXeSB+3hkKL9oqrOS/KTGZag3pxh1tbDk3x/hiWXn8wQcH12wTUHZfge7u7uUTP4q+qZSS5NckiGU0xvyjCz7NgMy15fGEEaAMABbZqlnZmcBvWsqnpKhuUcD8qwJ8jHFv4RDQCsatsyBGl3JLl6ib7HJLlmHkK0iXdkWLJ41ORxaIbar09ydpLf6u47F13zpMnzhWPfpLs/UlXPTvL6JMdPmj+eYf+14zIEaQAAHMCmmpG2v1XVdyV5bYY/ep+Y5EPd/ZwR1x2eYQnFSzKcTPreJK/ubnt9AAB7VFWvzvC3xJO6+7pZ1wMAwHw4aOzAqrp3VT28qu61i/5DJv33Xr7y8sQkL8qw5GKa00DfnuFff1+Z5OVJvjfJRctYFwCwuh2T5N1CNAAAFprmsIHXJ/n5JI9camZXVT0oyWeSnNXd054ytav3PKi775l8/UdJvmNPM9Kq6qgkVyY5prv/fNL2tAxLK17Q3ZctR20AAAAArC2jZ6RlmBm2bVfLIyftlyZ58XIUNrnnPXtx2fFJPrczRJvc56okf59v7VcCAAAAAFOZJkj7zgxLLHfnb5Js2utqlseRSW5cov2GSR8AAAAATG2aIO1eGY6B3517kqzf+3KWxYYMx9ovtmPSBwAAAABTWzfF2L/PsPHu7hyT5Ja9L2d2qmprkq1Jsn79+qdu2rRptgUBAAAAc+WGG274Ync/eNZ1MDvTBGnvTvLvqurnu/ucxZ1V9ZokW5L85nIVt5d2JFnqf+oNk74ldfcFSS5Iki1btvTVV1+9MtUBAAAAB6SqunnWNTBb0wRpv5nkZUn+Q1W9NMkHktya5BFJjssQon0myVnLXeSUbkzy7CXaj0xy0X6uBQAAAIBVYnSQ1t3/VFXPSfKHSZ42eXSSmgy5KsmP7upUz/3o4iS/WlXP6u4PJ0lVbUny6EkfAAAAAExtmhlp6e5PJXl6VT0tyTOSPCDDxv4f6+6rlru4qrpvkhdNXj4iyWFV9cOT13/a3XdW1U1JrujuV0xq/GhVfSDJWyfLTe9JcmaSD3f3ZctdIwAAAABrw1RB2k6T0GzZg7MlPCTJOxa17Xz9nUm2Z/geDl405uQk5yb53Qwnk743yatXrEoAAAAAVr29CtL2l+7enm8tHd3VmE1LtN2e5CcnDwAAAADYZ1MFaVW1LsmLM+yPtiHfPhMsSbq7/9Uy1AYAAAAAc2N0kFZVD0tyaZInZPezxDqJIA0AAACAVWWaGWlnJ3lihj3KfjvJp5N8cyWKAgAAAIB5M02QdlyGky9PXqliAAAAAGBeHTTF2PVJPrpShQAAAADAPJsmSLsuyf+2UoUAAAAAwDybJkg7O8kPVtWRK1UMAAAAAMyrafZI+3SS9yb5aFWdk+SaJLcvNbC7r1yG2gAAAABgbkwTpH04SSepJKftYezBe1sQAAAAAMyjaYK0N2QI0gAAAABgzRkdpHX3qStZCAAAAADMs2kOGwAAAACANWuapZ1Jkqpal+Q5Sf73JPfv7t+YtB+S5P5JdnS3JaAAAAAArCpTzUirqucn+VSSS5L8xyS/vqD7qUm+kOTkZasOAAAAAObE6CCtqp6S5L0ZZrG9NsmFC/u7+6NJtif5oWWsDwAAAADmwjQz0n4tyVeTbOnuc5J8cokxf5Fk83IUBgAAAADzZJog7VlJ/ri7b9vNmFuSbNy3kgAAAABg/kwTpN0/wx5ou7N+ynsCAAAAwAFhmtDr1iRP3MOYzUn+fu/LAQAAAID5NE2QdkmSF1bVUUt1VtX3J3lmhgMJAAAAAGBVmSZIe0OSLyW5rKpen+TIJKmq4yav35nkc0nOWfYqAQAAAGDG1o0d2N2fqarjkrw9yS8l6SSV5E8nz9uTnNTde9pHDQAAAAAOOKODtCTp7qur6nFJTkzyjCQPyjBL7WMZTvS8a/lLBAAAAIDZGx2kVdXDk3xjMuPsnZMHAAAAAKwJ0+yR9ukkZ61UIQAAAAAwz6YJ0m5P8vmVKgQAAAAA5tk0QdrHkzx5pQoBAAAAgHk2TZB2epJjqurlK1QLAAAAAMytaU7tPDbJ5UneXFWnJPmLJP+QpBeN6+7+jWWqDwAAAADmwjRB2q8v+Pppk8dSOokgDQAAAIBVZZog7QUrVgUAAAAAzLnRQVp3b1vJQgAAAABgno0+bKCqPlBVp61gLQAAAAAwt6Y5tfNZSQ5ZqUIAAAAAYJ5NE6TdlOSIlSoEAAAAAObZNIcNvDnJr1XVI7v7MytVEPtm0y++b9YlsEZtf+MJsy4BAAAAVtQ0Qdo7kxyb5CNV9RtJ/iLJPyTpxQO7+7blKQ8AAAAA5sM0QdotGUKzSvKfdjOup7wvAAAAAMy9aQKvP8gSs88AAAAAYC0YHaR198tWshAAAAAAmGfTnNoJAAAAAGuWIA0AAAAARhi9tLOqLhg5tLv7X+1lPQAAAAAwl6Y5bOCVe+jfeaJnJxGkAQAAALCqTBOkPXYX7Q9I8r1JTk3yockzAAAAAKwq05za+Xe76b6mqi5O8tdJLkmyu7EAAAAAcMBZtsMGuvvmJH+S5OeW655JUlVPqKptVXVnVd1WVWdU1cEjrttSVR+oqn+aPC6rqqcvZ20AAAAArB3LfWrn55I8brluVlUbklyWYd+1E5OckeQXkpy+h+uOmFy3LsmPTx7rklxaVY9arvoAAAAAWDum2SNtt6rqoCTPTXLHct0zySlJ1ic5qbvvyBCEHZbktKo6a9K2lBOSHJrkh7r7S5P6rkzyxSQvSvKfl7FGAAAAANaA0UFaVR29m3sckeSnkjw5yZuXoa6djk9yyaLA7MIkZyY5Jsl7dnHdvZJ8M8n/WND2lUlbLWN9AAAAAKwR08xI+3CGJZa7UkmuTPJv96mif+7IJJcvbOjuW6rqzknfroK0d2ZYBnp2Vb1+0vZrSXYkeccy1gcAAADAGjFNkPaGLB2k3ZMhoLqqu69clqq+ZUOS25do3zHpW1J331ZVz03y3iSvnjR/Nslx3f2FZa4RAAAAgDVgdJDW3aeuZCHLqao2Zph5dk2SV06afzrJ+6rq6O6+ZYlrtibZmiQbN27Mtddeu7/KXVYvffTdsy6BNepA/Z0BAACAsZbtsIEVsiPJ4Uu0b5j07cprM+yT9sPd/Y0kqarLk/xtktfkW7PU/pfuviDJBUmyZcuW3rx5875VPiMvufDWWZfAGnXW1gPzdwYAAADGOmjswKp6clX9clU9dBf9D530f8/ylZcbM+yFtvB9jkhy30nfrhyZ5LqdIVqSdPddSa5L8phlrA8AAACANWJ0kJZhJterknx+F/1fSHJKkp/f16IWuDjJcVV16IK2k5N8NckVu7nu5iTfXVWH7Gyoqnsn+e4k25exPgAAAADWiGmCtKOT/Fl3L3lyZ3ffk+GEzWctR2ETb0ry9STvqqrnT/YxOy3JOd19x85BVXVTVb15wXW/k+ThSf64qk6oqhcnuSjJxkyWbwIAAADANKYJ0h6W5NN7GHNrhrBqWXT3jiTHJjk4yXuSnJ7k3CSvWzR03WTMzuuuSfLCJIcmeVuSt2ZYDvqC7v7EctUHAAAAwNoxzWEDdyZ58B7GPDjJXXtfzrfr7uuTPG8PYzYt0bYtybblrAUAAACAtWuaGWmfSPKDVXW/pTon+5j94GQcAAAAAKwq0wRpv53kIUkuqaonLuyoqu9O8v4MM9J+Z/nKAwAAAID5MHppZ3f/YVWdkORHk3yiqm7LsCfaIzJs7H9Qkv/a3b+/IpUCAAAAwAxNs0dauvtlVXVlkp9N8vgkj5x03Zjk/+nuNy1zfQAAAAAwF6YK0pKku38ryW9V1WFJHpDk9u6+Y9krAwAAAIA5MnWQttMkPBOgAQAAALAmjD5soKo2V9UvV9VDd9H/0En/9yxfeQAAAAAwH6Y5tfO1SV6V5PO76P9CklOS/Py+FgUAAAAA82aaIO3oJH/W3b1UZ3ffk+TyJM9ajsIAAAAAYJ5ME6Q9LMmn9zDm1iQb976c/8nenUfJVdb5H39/k5CkE8hiAlnYmhD2PcQgKrtsooPsGkRRmCgzLKPjxgyQIMimyCI/ZFGBKIIjRBhgANk3UZZIANnBgJAQBEMCZIGkv78/qhqLptNd1alOVXe/X+fUqdS9z7396TCp4/nMc59HkiRJkiRJqk+VFGkLgFXbGbMq8G7H40iSJEmSJEn1qZIibQbwLxExsLWTEbEK8C/FcZIkSZIkSVK3UkmRdjGwGnBzRGxSeiIiNgVuojAj7WfViydJkiRJkiTVhz7lDszMKyJiL2AiMCMiZlFYE211YDSFUu7yzPxVpySVJEmSJEmSaqjsIg0gM78YEX8AjgI2ANYonnoKODczL6hyPkmSJEmSJKkuVFSkAWTm+cD5ETEIGAK8mZnzq55MkiRJkiRJqiMVF2nNiuWZBZokSZIkSZJ6hIqKtIj4BPAJCmuiAcwC7svM+6odTJIkSZIkSaonZRVpEfFJ4KfAxs2Hiu9ZPP8X4AgLNUmSJEmSJHVX7RZpEbEPcCWwEjAHuAv4W/H0msAOwKbA7RFxYGZe20lZJUmSJEmSpJpps0iLiFHAVKCJwk6dF2bmkhZj+gD/CpwJ/DIiNsjM2Z2UV5IkSZIkSaqJXu2c/w9gIHBIZv6/liUaQGYuycyfAocAKwPHVD+mJEmSJEmSVFvtFWl7AA9m5lXt3SgzrwYeAPasRjBJkiRJkiSpnrRXpDUC91Zwv/uK10iSJEmSJEndSntF2krAuxXc793iNZIkSZIkSVK30l6RNpvCjpzl2gR4teNxJEmSJEmSpPrUXpF2D7BrRKzf3o0iYgNgd+DuagSTJEmSJEmS6kl7Rdr/A/oC1xeLslYVi7brgD7A+dWLJ0mSJEmSJNWHPm2dzMwHI+LHwDeBRyLit8BtwN+KQ9YEPgXsD/QDzs7MBzoxryRJkiRJklQTbRZpRd8GFgDHAl8EDm5xPoAm4FTguKqmkyRJkiRJkupEu0VaZiZwQkRcChwGfAIYVTz9KnAvcElmPtdZISVJkiRJkqRaK2dGGgCZ+QLw352YRZIkSZIkSapb7W02IEmSJEmSJAmLNEmSJEmSJKksFmmSJEmSJElSGSzSJEmSJEmSpDJYpEmSJEmSJEllsEiTJEmSJEmSyrDMIi0iXouIb5V8/q+I+OSKiSVJkiRJkiTVl7ZmpA0HBpR8PhnYuXPjSJIkSZIkSfWprSJtDrD6igoiSZIkSZIk1bM+bZx7ADgkIt4FZhePbR8R/9XOPTMzT61KOkmSJEmSJKlOtFWkfRu4Fvj3kmM70/7jnQlYpEmSJEmSJKlbWWaRlpnPRMSmwFgKj3jeCkwFfrmCskmSJEmSJEl1o60ZaWTmUuBp4OmIAHghM29bEcEkSZIkSZKketJmkdbCSkBTZwWRJEmSJEmS6lnZRVpxdhoAETEK2BIYAswD/pyZs5d1rSRJkiRJktTV9apkcESsERHXAy8D1wO/Aq4DXo6I6yNirWoHjIiNI+K2iFgQEbMi4vsR0bvMa/eNiAcjYmFEvBERN0XEwGpnlCRJkiRJUvdX9oy0iBgB3AesCfwNuAeYDYwCPgF8Grg3Ij6amXOqES4ihlLY5OAJYG9gXeBMCgXgce1cezhwHnAGhR1Ih1LYcbSSx1klSZIkSZIkoLJS6TgKJdp/Az/MzCXNJyKiD/At4JTiuKOqlO/rQAOwb2bOB26JiEHAlIg4o3jsQyJiOHAWcFRmXlxy6ndVyiVJkiRJkqQeppJHOz8D3JqZp5aWaACZuSQzTwNuKY6rlj2Bm1sUZldSKNd2aOO6A4vvl1UxiyRJkiRJknqwSoq0UcCD7Yx5qDiuWjYEnio9kJkvAQuK55ZlG+Bp4LCIeDki3ouIP0XEx6uYTZIkSZIkST1IJY92zgfa20xgzeK4ahkKvNnK8bnFc8syEtiAwmOm3wHeKL7fFBHrtbaGW0RMAiYBjBo1ikceeWQ5o9fGgWOWtj9I6gRd9d+MJEmSJEnlqqRIuw/YPyLOy8w/tTwZEeOBA4AbqxVuOQSwMnBAZt4EEBF/AF4EjgSOb3lBZl4EXAQwfvz43HLLLVdc2ir63JWv1DqCeqgzJnXNfzOSJEmSJJWrkiLtBxR25rwnIi4H7qCwa+dIYEfgi8Vxp1Yx31xgcCvHhxbPtXVdAnc2H8jM+RHxMLBxFfNJkiRJkiSphyi7SMvMhyLiIOAS4MvAl0pOB4VHMA/LzPbWUavEU7RYCy0i1gQG0GLttBaeLGaKFscDaKpiPkmSJEmSJPUQlWw2QGZeQ2GdtEOBnwBTi+9fAdbOzN9VOd+NwO4RsUrJsYOAhcBdbVx3ffF9p+YDETEY2BqYUeWMkiRJkiRJ6gEqebQTgMx8i0KBNrX6cT7kAuBoYFpEnA6MAaYAP87M9zc1iIjngLsy87Bixoci4lrg5xHxPeB1CpsNvAf8vxWQW5IkSZIkSd1MRTPSVrTMnAvsAvQGrgNOBM4CJrcY2qc4ptQXgWuAHwNXUSjRdi7eU5IkSZIkSapIxTPSVrTMfALYuZ0xja0cexs4oviSJEmSJEmSlktdz0iTJEmSJEmS6oVFmiRJkiRJklQGizRJkiRJkiSpDBZpkiRJkiRJUhnKLtIiYnhnBpEkSZIkSZLqWSUz0v4WEZdHxPadlkaSJEmSJEmqU5UUaX8FvgDcERFPRMQxETG0k3JJkiRJkiRJdaXsIi0zNwZ2BK4A1gHOAl6JiMsi4uOdE0+SJEmSJEmqDxVtNpCZd2fmF4HRwH8CM4FDgHsi4rGI+PeIGFT9mJIkSZIkSVJtdWjXzsycm5lnlcxS+zUwFjgXmBURP4uIraoXU5IkSZIkSaqtDhVpLbwCzAbeBgJoAL4KPBQRV0XEkCr8DEmSJEmSJKmmOlSkRUTviNg/Im4Bnga+BcwDvgOsBuwG3ArsC5xfpaySJEmSJElSzfSpZHBErAP8K/AVCoVZAjcA52fmzSVDbwVujYhpwB5VyipJkiRJkiTVTNlFWkTcDOxCYRbbHOBU4MLM/Fsblz0I7L1cCSVJkiRJkqQ6UMmMtF2Beyg8qjktM98r45rrgdc6EkySJEmSJEmqJ5UUaZtl5l8quXlmPgY8VlkkSZIkSZIkqf6UvdlApSWaJEmSJEmS1J2UXaRFxH4R8fuIWH0Z50cXz7smmiRJkiRJkrqdsos0Crt1rpqZr7R2MjNnAcOASdUIJkmSJEmSJNWTSoq0zSjswtmWB4EtOh5HkiRJkiRJqk+VFGnDaX8HzjeK4yRJkiRJkqRupZIi7XVgbDtj1gXe7HgcSZIkSZIkqT5VUqTdB/xLRKzf2smI2ADYuzhOkiRJkiRJ6lYqKdJ+DPQF7o2If4uIMRHRr/j+78C9QB/gR50RVJIkSZIkSaqlPuUOzMw/RsSRwE+Kr5aagKMy8/5qhZMkSZIkSZLqRdlFGkBmXhAR9wH/BmwDDKGwJtofgfMz8/HqR5QkSZIkSZJqr6IiDSAzHwOO6IQskiRJkiRJUt2qZI00SZIkSZIkqceqeEZaRASwHjAU6N3amMz8w3LmkiRJkiRJkupKRUVaRBwL/CeFEq0trRZskiRJkiRJUldVdpEWEf8J/AB4C7gC+BuwpJNySZIkSZIkSXWlkhlpXwNmAVtn5pxOyiNJkiRJkiTVpUo2G1gL+J0lmiRJkiRJknqiSoq0Obj2mSRJkiRJknqoSoq0q4BdI6JfZ4WRJEmSJEmS6lUlRdrxwN+B30TEmp2UR5IkSZIkSapLlWw28AjQF9gG+GxEvAG82cq4zMwNqhFOkiRJkiRJqheVFGkDgKSwc2ezhurGkSRJkiRJkupT2UVaZq7RmUEkSZIkSZKkelbJGmmSJEmSJElSj1XJo50fEBGrACtn5uwq5pEkSZIkSerypk+fvnufPn0mZ+ZInMjUFTRFxKtLliw5cdy4cTcva1BFRVpEDAAmAwcDoyismdaneG4CcBxwQmY+0uHYkiRJkiRJXdj06dN379ev33mNjY3vNjQ0zO3Vq1fWOpPa1tTUFAsXLhw8c+bM86ZPn37kssq0shvR4gy0PwDfBv4BPA1EyZC/ADsDEzseW5IkSZIkqWvr06fP5MbGxncHDhy40BKta+jVq1cOHDhwYWNj47t9+vSZvMxxFdzzOGBz4PDM3Bz4n9KTmfkOcBewS0cCS5IkSZIkdQeZObKhoWFRrXOocg0NDYuKj+O2qpIibT/g95n5i+Ln1hrVmYC7e0qSJEmSpJ6slzPRuqbif7dl9mWVFGlrADPaGfM2MLiCe0qSJEmSJEldQiVF2tvAqu2MWQd4veNxPiwiNo6I2yJiQUTMiojvR0TvCq7vFREPRURGxGeqmU2SJEmSJEk9RyW7dj4IfCYiVs7Mt1uejIiRwJ7AjdUKFxFDgVuBJ4C9gXWBMykUgMeVeZvD8XFTSZIkSZJUY43fu2HrWvzcmaft9XA17vPggw/2nzBhwibXXXfdM5/5zGfeKueaH/3oR8NHjBix5JBDDnmzGhlqrZIZaecCw4HrI2K90hPFz78BGorjquXrxXvum5m3ZOYFwInANyNiUHsXF4u4HwD/XcVMkiRJkiRJKsOll1666jXXXDOk1jmqpewiLTNvBE4GtgeeAr4LEBGvFj9vBxyfmfdWMd+ewM2ZOb/k2JUUyrUdyrj+JOA+4LYqZpIkSZIkSVIPVMmMNDLzBGB34P+Ad4qH+wG/B3bPzFOrG48NKZR0pRleAhYUzy1TRGwOfBX4VpUzSZIkSZIkdXunnXbaqiNHjty8oaFhq5133nnsyy+/3Lf0/OTJk0dsuummG62yyipbDhs2bIudd9557OOPP96v+fyECRM2+Mtf/jJg2rRpwyJi64jY+txzzx0GcN555w3beuutNxg8ePCWgwYN2nKbbbZZ/+677x6won/HSlWyRhoAmXkLcEsnZGnNUKC1Z2jnFs+15SfAeZn5XEQ0VjmXJEmSJElSt/WrX/1qyLHHHrvWxIkT/77vvvu+eccdd6xyxBFHNJaOefnll/t+7Wtfe22dddZ5d968eb0uuuiiVbfffvsNn3322ceHDRu29Kc//emLBxxwwLprrbXW4uOPP342wEYbbbQYYObMmX2/8IUvvLHeeustXrx4cVxxxRUf2W233TacPn364xtvvPG7NfiVy1JxkdYVRMTngQ2Az1ZwzSRgEsCoUaN45JFHOild5zpwzNJaR1AP1VX/zUiSJEmSPuz0008ftd12282//PLLXwLYb7/95r/++ut9fvOb3wxvHvPzn//8b81/XrJkCXvvvff8ESNGbHnFFVcMOfLII9/YeuutFw0YMKBp2LBhS3bZZZd3Su//ox/9aHbzn5cuXco+++wzf/311x/4i1/8YljpuXpT70XaXGBwK8eHFs99SESsBPwQOB3oFRFDgOaNCQZGxCqZ+aGdJTLzIuAigPHjx+eWW25Zhfgr3ueufKXWEdRDnTGpa/6bkSRJkiR90HvvvceTTz454JRTTnmp9Pi+++47t7RIu+222wYef/zxo5944omB8+bN6918/JlnnulHO6ZPn97/u9/97urTp09f+R//+Mf7/dSzzz7bv1q/R2cou0iLiPeALGNoZma7f2FleooWa6FFxJrAAFqsnVZiILAG8OPiq9SVwPPA2CrlkyRJkiRJ6lZmz57dZ+nSpYwYMeK90uOjRo1a0vznZ599tu/ee++9/uabb/7OWWed9eIaa6zxbr9+/XKfffZZb9GiRW2uyT937txen/70p9cfPnz4eyeffPLfxowZ825DQ0PTpEmTGhcvXhyd9XtVQyUz0v5E60XaEArFVD/gMWB+K2M66kbg2y1mkR0ELATuWsY1bwM7tTg2ErgC+C/g9irmkyRJkiRJ6lZGjRq1pHfv3syZM2el0uOzZ89+v0e69tprBy1atKjXTTfd9NygQYOaoDCTrXRm2rLccccdK8+ZM2elG2+88ZmtttpqUfPxt956q91ra63sXTsz85OZuV0rr82AEcBUoDcVrEtWhguAxcC0iPhUcR2zKcCPM/P9wi4inouInxdzLsnMO0tfwB+LQx/LzD9VMZ8kSZIkSVK3stJKK7HhhhsuuP7664eUHp82bdr7Gz8uXLiwV0TkSiut9P6kq5///OcfWbp0abS4Vy5evPgD/dOCBQt6ATQ0NDQ1H7vlllsGzpo16wO7gtajsou0thRLrcMozFj7QTXuWbzvXGAXCgXddcCJwFnA5BZD+xTHSJIkSZIkaTl95zvfmX3PPfcMOvjgg9eaNm3aoKOOOmr1O++88/117Hffffe3mpqa4sADD2y89tprVzn55JNXO/HEE1dfZZVVPrAL4tixYxc98MADK1999dWD7r777gGvvvpq7x122OHtAQMGNH31q19tnDZt2qCzzz572Je+9KUxq6222nsfTlJfqrbZQGYujYg7gP2Bf6/ifZ8Adm5nTGM752cCdf2MrSRJkiRJ6t5mnrbXw7XOUK4vfelLb7788ssvnXPOOaOmTZs2bMKECW+df/75M/fbb7/1ACZMmLDw3HPP/etpp502+qCDDhq6wQYbLLj88stfOOSQQ8aU3ufEE0+cdfjhh/c99NBDx7z99tu9zznnnJlHH330G5dddtnzxx577JoTJ04cu9Zaay06++yzXzrzzDNH1ua3LV9klrN/QJk3i7gA+HJmNlTtpjUwfvz4fOihh2odo0Mav3dDrSOoh5p52l61jqCuZkprmzKrIlPm1TqBVD1+Jyw/vxMkqdNFxMOZOb69cTNmzJi5xRZbvL4iMqn6ZsyYMXyLLbZobO1cVR7tBIiICMvQLgAAIABJREFU9YADKOyKKUmSJEmSJHUrZT/aGREXtXGPNYHti3/+bhVySZIkSZIkSXWlkjXSDm/n/HPADzPzZ8uRR5IkSZIkSapLlRRp6y3jeBMwNzPfrEIeSZIkSZIkqS6VXaRlpmufSZIkSZIkqceq2mYDkiRJkiRJUndWyWYDH+/oD8nMP3T0WkmSJEmSJKkeVLJG2r1AdvDn9O7gdZIkSZIkSVJdqKRIOwXYGtgdmAncB7wKjAQ+ATQCNwEPVzWhJEmSJEmSVAcqKdL+F/jP4uvczFzafCIiegP/AZwETM7MB6uaUpIkSZIkSd3avHnzeg0ZMmSrc845Z+bRRx/9Rq3ztKaSIu1k4PbMPKvliWKpdmZE7EKhTNujSvkkSZIkSZK6hymDt67Nz53n04NVUsmunROAP7cz5s/AxzoeR5IkSZIkSfVmyZIlLFq0KGqdo9YqKdJ6AWPaGTOmwntKkiRJkiSpzuy3336Nm2666Ua//OUvh4wdO3aT/v37j7vzzjsHHnDAAY1rrLHGZv379x/X2Ni46dFHHz26tGB7+umn+0bE1j/72c+GTpw4ce1VVlllyxEjRmz+jW98Y/TSpUs/8DMuvfTSIY2NjZv2799/3Pjx4zeYMWNG/5Y5lixZwje/+c3Ro0aN2qxv377jxo4du8kFF1zwkdayXnnllYPXXXfdTRoaGrbacccdx86ZM6f3448/3m+bbbZZv6GhYatNN910oz/96U8Ny/P3UknpdT+wf0S0+thmRHwa2B/4w/IEkiRJkiRJUu298sorfY8//vg1vvnNb86+6qqrngUYOnToklNPPfVvV1999TNHHXXUq1deeeXwr371q2u1vHby5MlrDBw4cOnUqVNf2G+//d44++yzR11yySVDm8/fe++9Aw4//PB1N9poowVTp059bs8993xz4sSJ67a8zze+8Y3Vzz333JGHHHLI61dcccVzH/3oR98+4ogj1rnwwgs/UKbNmjWr70knnTT6hBNOeOXMM898cfr06St/+ctfXvvzn//8mP333/8fl1122fNLliyJiRMnjmlqaurw30kla6QdB9wF3BARtwF3A3OAEcAOwM7AYuC/O5xGkiRJkiRJdeHNN9/sc8MNNzzz8Y9/fGHzsT322OPt5j/vtttubw8cOLDpmGOOaVy0aNFL/fv3z+ZzEyZMeOviiy9+GWCfffaZf/vttw++5pprhh5++OFzAU455ZSRa6+99qIbbrjhhV69enHggQfOf/fdd+OMM85Yvfkec+bM6f2zn/1stWOOOWb2GWecMRtgv/32mz9r1qyVTj311NFf+9rX/tE8dv78+X3uueeepzbZZJPFAI8++uiACy+8cMRPfvKTmUceeeQbAJn5yuc///mxjzzySP9x48Yt6sjfSdkz0oo7ce4OvAB8Cvg+cEHxfZfi8d0z0wXsJEmSJEmSurjVVlvtvdISrampie9///urrbvuupv0799/XN++fbc+4ogj1nn33Xfjueee61t67a677jq/9PN66623cPbs2Ss1f54xY8bA3Xff/c1evf5ZTR100EFvll4zffr0hkWLFvWaOHHi3NLj+++//9wXX3yx36xZs96fIDZ69OjFzSUawNixYxcB7Lnnnu/n2GijjRYBvPTSSyvRQZXMSCMz74mI9YHtgHHAYGAeMB24JzOzreslSZIkSZLUNQwfPvy90s8nnXTSaieddNKaRxxxxKs77bTTW8OGDVty//33Dzz22GPXWrhw4Qc2Ihg6dOgHFkTr27dvLl68+P3W7PXXX19ptdVWW1I6ZvTo0R/4eS+//PJKAKuvvvoHjo8aNeo9gL///e+9R48evQRg0KBBH/p5xd/h/eP9+vVLgIULF3Z4ff+KijSAYll2d/ElSZIkSZKkbijig5t0XnPNNR/ZY4895v7kJz95pfnYo48+2qHF+4cPH/7ea6+99oFeatasWR+YKbbGGmu813x85MiR7xdizTPbVl111Q/uXrACdKiBi4iGiNgsIratdiBJkiRJkiTVn0WLFvXq27fvB1bqv/LKKz+yrPFt2Xzzzd+5+eabh5Qu/P+b3/xmSOmYcePGLezfv3/Tr3/966Glx6+++uqha6+99uLm2WgrUkUz0iJiFHA28Lnitdl8j4j4BPBT4MjMdLaaJEmSJElSN7LDDjvMv+SSS1Y77bTT3llvvfUW/+pXv/rIiy++2L8j9zr22GNf3WmnnTbaa6+9xhx22GGvP/roow2XX375qqVjRowYsfTwww9/7ZxzzhnVp0+fnDBhwoKrrrpqyF133TX4wgsvfKE6v1Vlyi7SImIk8AAwCvg/YDiwTcmQB4DVgQPxsU9JkiRJkqQPmjKvS2/QePrpp896/fXX+5x66qmrA+yxxx5zf/jDH740ceLEsZXea/vtt19w8cUXvzBlypTVDz744LGbbrrpO5dffvnzO+6440al484666xX+vTpk5deeulqZ555Zp+11lpr8fnnn//XSZMmzV3WvTtTlLs/QET8FPhXYI/MvDUiJgMnZGbvkjHXAGMyc/NOSbuCjB8/Ph966KFax+iQxu/dUOsI6qFmnrZXrSOoq5kyuNYJur4p82qdQKoevxOWn98JktTpIuLhzBzf3rgZM2bM3GKLLV5fEZlUfTNmzBi+xRZbNLZ2rpI10vYC/jczb21jzEvA6AruKUmSJEmSJHUJlRRpI4Bn2hmzGBjY8TiSJEmSJElSfaqkSJsLrNHOmPWAVzseR5IkSZIkSapPlRRp9wH/EhGrtXYyItYF9gTurEIuSZIkSZIkqa5UUqT9CBgA3BkRuwL9ASKiX/HzdUACP656SkmSJEmSpK6jqampKWodQpUr/ndrWtb5PuXeKDPvj4gjgPOAm0pOLSi+LwUOy8zHOhJUkiRJkiSpO4iIVxcuXDh44MCBC2udRZVZuHBh/4hY5rJllcxIIzMvBrYAzgemAy8CjwIXAVtm5i+XI6skSZIkSVKXt2TJkhNnzpzZ95133mlwZlrX0NTUFO+8807DzJkz+y5ZsuTEZY0re0Zas8x8CjhqudJJkiRJkiR1U+PGjbt5+vTpRz7//POTM3MkFU5kUk00RcSrS5YsOXHcuHE3L2tQ2UVaRDwD3JSZR1clniRJkiRJUjdVLGOWWcioa6qkER0FvN1ZQSRJkiRJkqR6VkmR9gQwprOCSJIkSZIkSfWskiLtPOCzEbFpZ4WRJEmSJEmS6lUlmw08D9wG/CEizgceBF4FsuXAzPxDdeJJkiRJkiRJ9aGSIu1eCqVZAN+hlQKtRO/lCSVJkiRJkiTVm0qKtFNouzyTJEmSJEmSuq2yi7TMPK4zg0iSJEmSJEn1rJLNBiRJkiRJkqQeq80iLSJOiIjtV1QYSZIkSZIkqV61NyNtCrBj6YGIOCYiXuisQJIkSZIkSVI96sijnUOAtasdRJIkSZIkSapnrpEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUhnKKtCERsVbzi8IaaUTEmqXHW4ypmojYOCJui4gFETErIr4fEb3bueajEXFJRDxXvO7piJgcEf2rmU2SJEmSJEk9R58yxhxTfLU0cxnjs8z7tisihgK3Ak8AewPrAmdSKACPa+PSg4pjTweeBTYHTiq+71eNbJIkSZIkSepZ2iu8XqJQjNXK14EGYN/MnA/cEhGDgCkRcUbxWGtOy8zXSz7fGRGLgAsjYu3MfLGTc0uSJEmSJKmbabNIy8zGFZRjWfYEbm5RmF1JYabZDsB1rV3UokRr9ufi+2jAIk2SJEmSJEkVqffNBjYEnio9kJkvAQuK5yqxLdAEPF+daJIkSZIkSepJ6r1IGwq82crxucVzZYmIkRTWVPtlZr5WpWySJEmSJEnqQaqyKUA9i4i+wP8AbwPfaGPcJGASwKhRo3jkkUdWTMAqO3DM0lpHUA/VVf/NqIbWPLTWCbo+/92pO/E7Yfn5nSBJUqer9yJtLjC4leNDi+faFBEBTAU2AT6Rmcu8JjMvAi4CGD9+fG655ZYdClxrn7vylVpHUA91xqSu+W9GNXTNpbVO0PUddk6tE0jV43fC8vM7QZKkTlfvRdpTtFgLLSLWBAbQYu20ZTgb2BvYNTPLGS9JkiRJkiS1qt7XSLsR2D0iVik5dhCwELirrQsj4ljgSOCLmXlv50WUJEmSJElST1DvRdoFwGJgWkR8qriO2RTgx5k5v3lQRDwXET8v+TwROIXCY52vRMTHSl6rrthfQZIkSZIkSd1BXT/amZlzI2IX4DzgOgo7eJ5FoUwr1QfoXfJ5t+L7ocVXqa8Al1Y3qSRJkiRJkrq7iou04oyu/YCNgIGZeXjJ8XWAxzJzYbUCZuYTwM7tjGls8flQPlygSZIkSZIkSR1WUZEWEYcB5wL9gQASOLx4egRwPzAJ+HmrN5AkSZIkSZK6qLLXSIuIXYGLgGeAfYCflp7PzMeBvwCfq2ZASZIkSZIkqR5UMiPtu8BsYIfMnB8RW7Uy5lFg26okkyRJkiRJkupIJbt2jgeuL90tsxUvAyOXL5IkSZIkSZJUfyop0voC77QzZgiwtONxJEmSJEmSpPpUSZE2E9i6nTHbAE93OI0kSZIkSZJUpyop0q4FtouIA1o7GRFfATYHrq5GMEmSJEmSJKmeVLLZwBnA54ErImJ/YDBARBwJbAfsCzwL/KTaISVJkiRJkqRaK7tIy8y5EbEDMBUonZV2bvH9HmBiZra3jpokSZIkSZLU5VQyI43MfAnYMSI2B7YFhgHzgD9m5sOdkE+SJEmSJEmqCxUVac0y81Hg0SpnkSRJkiRJkupW2ZsNRMQZEbFRZ4aRJEmSJEmS6lUlM9K+BfxnRDwMXAZckZn/6JxYkqRqaPzeDbWOsEwz+9c6QddX1/99T9ur1hEkSZKkqit7RhrwBeBmYCsKGwzMioirIuKzEdG7U9JJkiRJkiRJdaLsIi0zf5OZnwbWAL4LPAvsC1xDoVT7cURs2TkxJUmSJEmSpNqqeLOBzJwD/Aj4UURsBRxKYbbafwDHRMRjmWmhpg+Z2X9irSN0eY2Lfl3rCJIkSVLnmzK41gm6vinzap1A6pYqebTzQzLzz5l5DDAa+DawBNisGsEkSZIkSZKkelLxjLRSETEYOAj4MvAxIABrb0mSJEmSJHU7FRdpEdEL2J1CefYvQD8ggdso7OY5rZoBJUmSJEmSpHpQdpEWEZsBXwIOBkZQmH32DDAVmJqZL3dKQkmSJEmSJKkOVDIjbUbxfR7wM+DSzLy/+pEkSZIkSZKk+lNJkfZ74FLgd5m5uHPiSJIkSZIkSfWp7CItM/fozCCSJEmSJElSPetV6wCSJEmSJElSV7DMGWkR8QsKu3H+V2bOKX4uR2bmYVVJJ0mSJEmSJNWJth7tPJRCkXY6MKf4uRwJWKRJkiRJkiSpW2mrSFun+P5Ki8+SJEmSJElSj7PMIi0zX2zrsyRJkiRJktSTlL3ZQEScEBHbtzNmu4g4YfljSZIkSZIkSfWlkl07pwA7tjNme2ByR8NIkiRJkiRJ9aqSIq0cKwFNVb6nJEmSJEmSVHPVLtLGAa9X+Z6SJEmSJElSzbW1aycRcXuLQ4dGxI6tDO0NrAmsDVxRnWiSJEmSJElS/WizSOODa6Il0Fh8tdQEvAH8BvhGFXJJkiRJkiRJdaXNIi0z33/0MyKagCmZ+f1OTyVJkiRJkiTVmfZmpJX6CvDnzgoiSZIkSZIk1bOyi7TMvKwzg0iSJEmSJEn1rJIZae+LiDWA1YF+rZ3PzLuXJ5QkSZIkSZJUbyoq0iJiN+AsYMN2hvbucCJJkiRJkiSpDvVqf0hBRHwMuB4YApwHBHA3cDHwVPHzdYCbEUiSJEmSJKnbKbtIA44FFgEfzcxjisfuyMyvA5sCJwOfAq6qbkRJkiRJkiSp9iop0rYF/jczZ7W8PgtOAJ4ETqxiPkmSJEmSJKkuVFKkDQZeKvn8LjCwxZj7gO2XN5QkSZIkSZJUbyop0l4Dhrb4vG6LMSsBDcsbSpIkSZIkSao3lRRpz/DB4uyPwK4RsT5ARIwE9gOerV48SZIkSZIkqT5UUqTdBOwQER8pfj6HwuyzP0fEgxR27lwVOLu6ESVJkiRJkqTaq6RIu5DC+mfvAWTmfcABwF8p7No5GzgiM6dWO6QkSZIkSZJUa2UXaZk5PzP/lJlvlRz7XWZumpkNmblRZl5U7YARsXFE3BYRCyJiVkR8PyJ6l3Hd4Ii4JCLmRsS8iLg8IoZVO58kSZIkSZJ6hj61DtCWiBgK3Ao8AexNYY22MykUgMe1c/n/AOsDhwNNwOnANcB2nZVXkiRJkiRJ3VddF2nA1ymsw7ZvZs4HbomIQcCUiDijeOxDImJbYDdgh8y8u3jsFeBPEfGpzLx1BeWXJEmSJElSN7HMIi0iXujgPTMz121/WFn2BG5uUZhdSWF22Q7AdW1cN6e5RCuGeiAi/lo8Z5EmSZJ6nMbv3VDrCMs0s3+tE3R9df3f97S9ah1BkqSqaGuNtF5AdOBVyQYG7dmQwm6g78vMl4AFxXNlX1f0ZDvXSZIkSZIkSa2KzKx1hmWKiPeAb2fm2S2OvwxMzcz/WsZ1twDvZObnWhz/FTAmMz/eyjWTgEnFjxsAT1fhV1DXMhx4vdYhJNUNvxMklfI7QVJLfi/0TGtn5qq1DqHaqfc10laY4o6jVd91VF1HRDyUmeNrnUNSffA7QVIpvxMkteT3gtQzdfgxzIgYGhFrVjNMK+YCg1s5PrR4rtrXSZIkSZIkSa2qqEiLiJUj4syIeJXCFNa/lpzbJiL+LyLGVTHfU7RY06xY3g2g9TXQlnld0bLWTpMkSZIkSZLaVHaRFhGDgfuBbwCzKCzcHyVDHgO2A75QxXw3ArtHxColxw4CFgJ3tXPdyIj4ZPOBiBgPjCmek1rjo72SSvmdIKmU3wmSWvJ7QeqByt5sICLOAL4FHJqZUyNiMnBCZvYuGXM9MDozqzIrLSKGAk8AjwOnUyjCfgycnZnHlYx7DrgrMw8rOXYzsF4xc1Px+tcyc7tqZJMkSZIkSVLPUsmjnfsCN2fm1DbGvAisvnyR/ikz5wK7AL2B64ATgbOAyS2G9imOKXUQhVlrvwCmAg8D+1QrmyRJkiRJknqWSnbtXAO4up0xb9P6Iv8dlplPADu3M6axlWNvAl8pviRJkiRJkqTlUsmMtLeA1doZsw6FTQgkSZIkSZKkbqWSIu1B4DMtFv5/X0SMAj4N3FuNYJIkSZIkSVI9qaRIOwcYBvxfRGxUeqL4+bdAf+Dc6sWTJEmSJEmS6kPZu3YCFHfqnAwk8B6wEjAXGAoE8N3M/GEn5JQkSZIkSZJqqqIiDSAidgKOBj5GYYbaPOCPwFmZeXvVE0qSJEmSJEl1oOIiTZIkSZIkSeqJKlkjrSwRsWq17ylJkiRJkiTVWtWKtIgYHBGnAM9X656SJEmSJElSvehTzqCIWBvYmsIGAw9k5pySc/2BbwDforDpwIJOyClJkiRJkiTVVLsz0iLiXAqzzH4LXAPMjIh/K57bEXgaOBkYAJwDjOmssJIkSZIkSVKttLnZQER8GbgEaAKeKh7esPh+GHAh0Bu4GDg5M2d1XlRJkiRJkiSpdtor0u4AtgV2ysz7i8e2B26hUKC9DHw2Mx9bAVklSZIkSZKkmmnv0c7Ngd81l2gAmXk3hUc8A/iqJZokSZIkSZJ6gvaKtMHAc60cf7b4fn8r5yRJkiRJkqRup70irReFnTpbeg8gMxdWPZEkSZIkSZJUh9rdtRNY9iJqkiRJkiRJUg/R3mYDTVRepGVm9lmuVJIkSd1IREwBJlPYwOnO2qaRJElSR5UzIy0qfJVzT0mSpBUiIgZFxNkRcU9EzIqIRRHxWkQ8EBH/EREDa51xRYqIHSMi23idVuuMkiRJ9arNmWOZaSkmSZK6uo8Ak4AHgBuAv1PYUGln4CzgXyNi28ycX7uINXEXcGcrx+9dwTkkSZK6DB/BlCRJ3d3fgMGZ+aENlCLiV8DBwNeBM1Z0sBq7MzOn1DqEJElSV+KMM0mS1K6IWDki3o2I+1ocbyg+KpkRcUiLc0cUj391xab9oMxc2lqJVvTb4vt61fhZEbF1RNwUEW9FxPyIuDUitq3GvSVJklR7zkiTJEntysy3I+IBYJuIWCUz3yqe+gTQr/jnXYBflly2S/H9thUUsyM+W3x/dHlvFBEfB24F+gLTgOeALSk8Pnn78t6/E4yNiCOBQcCrwD2Z+WyNM0mSJNU1izRJklSu2ykUZ9tTWGsMCmXZUgrrbTUXZ0REL2An4IXMfLG9G0fEEOA/KsxzTWY+Uu7giOgDHFf8+BFgOwpF1x3AxRX+7Jb3DuAXQAPwucy8tuTcMcDZFd5vS+BzFcY4OzPfrGD8wcVX6c+9GvjXzJxb4c+WJEnqESIza51BkiR1ARGxA4XZVWdl5jeLxx4AEpgKnAdskJnPRMQ44GHg4sycVMa9G4G/VhjpK5l5aQX5+wMLWxz+JfBvmfl2hT+75b0/QWGR/rszc4cW53oDTwPrAjtl5p1l3O9Q4JIKY6yTmTPLuPcmwGcolKEzgf7AeOAUYCvgPmD7zGyq8OdLkiR1e66RJkmSynU/hSJqF4CIGAyMo/DoZvOji82z0nYuvpf1SGNmzszMqPB1aSXhM3NRZgaF//2zBnAo8CngoWKRtzzGFd/vauXnLqXCnTAz89IO/H3MLPPef8nM0zPz8cx8OzNfz8ybgB0plJmf4J+PvEqSJKmERZokSSpLZr5LoRDaLCJWpVC89AZuy8wngdn8s0jbhcJMtbpbGywLXsnMy4B9gQ0ozKZbHoOL73OWcf7V5bx/p8vM+cCvix+3r2UWSZKkeuUaaZIkqRK3A7tSKMo+Diyi8Chg87k9I6IfhfXH/pKZr5Vz0xWxRlprMvOPEfEmhVJwecwrvo9YxvmRldxsBa2R1pq/F98HLud9JEmSuiWLNEmSVInmHTh3AbYF/pCZi0rOHQwcQaGIqWS3ziHA5AqzzASWq0iLiFUo7Fr5Vntj2zG9+L5DyxPFNdI+WeH9tqTyv49LgeUt0j5WfH9hOe8jSZLULflopyRJqsR0CrOv9gY24YNlWfNjnMe2+NyuzlwjLSI2K2400PJ4XwqPdPbin7uQlp7PiCh3V6Y/UNhQYPuI2LvFuSMpbDRQts5cIy0ixi/j+BeBg4B3gf+pJK8kSVJP4Yw0SZJUtsxcGhF3UijSoKRIy8wXI+J5CqXRUlpZeL9GDgO+EhH3AS9SmLU1GtiNwiOXTwPfKr0gIpr/n41Ly/kBmZkRcRhwC3B1REwDnqMws2wX4CZgj+X/VariqohYAjwEvExh186PAhOAJcDXyi3lJEmSehqLNEmSVKnbKBRp8ymUMS3PrQs8nJnzWl5YI78FVqbwKOq2wCoUsj8BnAmcn5kLWlyzWfH9ynJ/SGbeFxHbAT8A9iwe/hOF9dd2p36KtJ9S2K30E8BwIIBXKDwaenZmzqhdNEmSpPoWmeU+sbDiRcRY4NsU/kfvJsA9mbljGdcNBs6msEhvL+B64OjMfKPz0kqSpO4iIo6m8L8lNsvMv9Q6jyRJkupDvc9I2wT4NPBHYKUKrvsfYH3gcKAJOB24hsIOYpIkSe3ZAfhfSzRJkiSVqvcZab0ys6n456uA4e3NSIuIbSks+LtDZt5dPDaBwqMVu2bmrZ2bWpIkSZIkSd1RXe/a2VyiVWhPYE5ziVa8zwPAX/nneiWSJEmSJElSReq6SOugDYGnWjn+ZPGcJEmSJEmSVLHuWKQNpbCtfUtzi+ckSZIkSZKkitX7ZgMrTERMAiYBNDQ0bN3Y2FjbQJIkSZIkqa48+eSTr2fmqrXOodrpjkXaXKC1/6MeWjzXqsy8CLgIYPz48fnQQw91TjpJkiRJktQlRcSLtc6g2uqOj3Y+RetroS1r7TRJkiRJkiSpXd2xSLsRGBkRn2w+EBHjgTHFc5IkSZIkSVLF6vrRzogYAHy6+HF1YFBE7F/8/H+ZuSAingPuyszDADLz/oj4PTA1Ir4FNAGnA/dm5q0r+FeQJEmSJElSN1HXRRqwGvDbFseaP68DzKTwO/RuMeYg4CzgFxRm3V0PHN1pKSVJkiRJktTt1XWRlpkzgWhnTGMrx94EvlJ8SZIkSZIkScutO66RJkmSJEmSJFWdRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWwSJMkSZIkSZLKYJEmSZIkSZIklcEiTZIkSZIkSSqDRZokSZIkSZJUBos0SZIkSZIkqQwWaZIkSZIkSVIZLNIkSZIkSZKkMlikSZIkSZIkSWWo+yItIjaOiNsiYkFEzIqI70dE7zKuGx8Rv4+IfxRft0b8//buPty2qq4X+PcnxxdIwONbHBNBySupt+hGJSqXAM18KdRSsvKqyUNapqXSVaPrAR99hAItLZFEjcroxXcNSUBR82qpoDeRFAtJSE08SAgqL+P+MecWOQPoAAAgAElEQVTO5Wbts8c57L3XOnt/Ps+zn3X2GHPM+Vtzn72AL2OMWT++FjUDAAAAsP7MdZBWVZuTnJukJTkqyYlJnpfkhGXG7TuO25TkyePXpiTvrar9VrNmAAAAANanTbMuYBnPSLJ7kse31q7JEITtlWRrVZ08tk3z6CR7Jnlca+3rSVJVH07y1SSPSvKa1S8dAAAAgPVkrmekJXlkknMWBWZnZQjXDtvOuNsmuTHJNybarh3baqWLBAAAAGD9m/cg7cAkl0w2tNYuT3Ld2LeUN4/HnFJVd6+quyd5RZJtSf56lWoFAAAAYB2b96Wdm5NcPaV929g3VWvtyqo6PMm7kjx7bP73JI9orf3Hilc5R/Z/wbtnXQIb1GUvf/SsSwAAAIBVNe9B2k6pqi0ZZp59PMkxY/OvJXl3VT14nNW2eMyxSY5Nki1btuSiiy5aq3JX1BPvc9OsS2CD2lV/ZwAAAKDXvAdp25LsPaV989i3lOMy7JP2c621G5Kkqs5P8rkkz893Zqn9l9ba6UlOT5KDDz64HXTQQbeu8hl57FlXzLoENqiTj901f2cAAACg17zvkXZJFu2FVlX7Jtkji/ZOW+TAJJ9eCNGSpLX27SSfTnLAKtQJAAAAwDo370Ha2UkeUVV7TrQdneT6JBdsZ9wXkjywqm630FBVt0/ywCSXrUKdAAAAAKxz8x6knZbkW0neUlUPG/cx25rk1NbaNQsHVdWlVXXGxLjXJblHkrdW1aOr6jFJ3pZkS8blmwAAAACwI+Y6SGutbUtyZJLdkrwzyQlJXpHkxYsO3TQeszDu40l+KsmeSf40yZkZloM+vLX2ydWvHAAAAID1Zt4fNpDW2sVJjljmmP2ntJ2X5LxVKgsAAACADWauZ6QBAAAAwLwQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHSY+yCtqu5fVedV1XVVdWVVnVhVu3WOfXxV/WNVXV9VV1XVe6rqe1a7ZgAAAADWn7kO0qpqc5Jzk7QkRyU5McnzkpzQMfaYJG9KcnaSRyY5JsnnkmxarXoBAAAAWL/mPVR6RpLdkzy+tXZNkvdW1V5JtlbVyWPbLVTVXZO8Ismvt9b+eKLrrateMQAAAADr0lzPSMswk+ycRYHZWRnCtcO2M+6J4+ufrFZhAAAAAGws8x6kHZjkksmG1trlSa4b+5by40n+OcnTq+qLVXVDVX20qh68eqUCAAAAsJ7N+9LOzUmuntK+bexbyj5J7pfk+CS/leSq8fU9VXXf1tqXFw+oqmOTHJskW7ZsyUUXXXQrS5+NJ97nplmXwAa1q/7OAAAAQK95D9J2ViW5Y5IntNbekyRV9eEkX0jyrCS/s3hAa+30JKcnycEHH9wOOuigtat2BT32rCtmXQIb1MnH7pq/MwAAANBr3pd2bkuy95T2zWPf9sa1JO9faBj3Wft4kvuvYH0AAAAAbBDzHqRdkkV7oVXVvkn2yKK90xb5TIZZabWovZLcvJIFAgAAALAxzHuQdnaSR1TVnhNtRye5PskF2xn3rvH18IWGqto7yY8k+eRKFwkAAADA+jfvQdppSb6V5C1V9bDxgQBbk5w6LtVMklTVpVV1xsL3rbWPJXl7kjOq6ilV9egk70hyQ5I/XMs3AAAAAMD6MNdBWmttW5Ijk+yW5J1JTkjyiiQvXnTopvGYSb+U5G1JTk3yNxlCtCPGcwIAAADADpn7p3a21i5OcsQyx+w/pe3aJM8cvwAAAADgVpnrGWkAAAAAMC8EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQYe6DtKq6f1WdV1XXVdWVVXViVe22A+NvU1Ufq6pWVY9ZzVoBAAAAWL82zbqA7amqzUnOTXJxkqOSHJDklAwB4PGdpzkmyT1XpUAAAAAANox5n5H2jCS7J3l8a+29rbXTkpyQ5LlVtddyg8cg7qVJfnt1ywQAAABgvZv3IO2RSc5prV0z0XZWhnDtsI7xL0ny90nOW4XaAAAAANhA5j1IOzDJJZMNrbXLk1w39i2pqn4wyS8nef6qVQcAAADAhjHXe6Ql2Zzk6int28a+7XlVkle31i6tqv2Xu1BVHZvk2CTZsmVLLrrooh2rdE488T43zboENqhd9XcGAAAAes17kLZTqurnk9wvyU/3jmmtnZ7k9CQ5+OCD20EHHbRK1a2ux551xaxLYIM6+dhd83cGAAAAes370s5tSfae0r557LuFqrptkt9NclKS21TVnZIsPJjge6pqz9UoFAAAAID1bd6DtEuyaC+0qto3yR5ZtHfahO9Jcs8kp2YI27Yl+eTYd1aSC1elUgAAAADWtXlf2nl2kuOqas/W2n+ObUcnuT7JBUuMuTbJ4Yva9knyF0lelOT81SgUAAAAgPVt3oO005I8O8lbquqkJPdJsjXJqa21axYOqqpLk1zQWnt6a+3GJO+fPMnEwwb+X2vto6tfNgAAAADrzVwHaa21bVV1ZJJXJ3lnhid4viJDmDZpU5Ld1rY6AAAAADaSuQ7SkqS1dnGSI5Y5Zv9l+i9LUitXFQAAAAAbzdwHaQCsU1unPZSZHbL167OuAFaOz4Rbz2cCAKy6eX9qJwAAAADMBUEaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAB0EaAAAAAHQQpAEAAABAh7kP0qrq/lV1XlVdV1VXVtWJVbXbMmN+tKreUFWXjuP+uapeXFV3WKu6AQAAAFhfNs26gO2pqs1Jzk1ycZKjkhyQ5JQMAeDx2xl69HjsSUk+l+QHk7xkfP3ZVSwZAAAAgHVqroO0JM9IsnuSx7fWrkny3qraK8nWqjp5bJvm5a21r058//6q+maS11bVfq21L6xy3QAAAACsM/O+tPORSc5ZFJidlSFcO2ypQYtCtAUXjq/3WLnyAAAAANgo5j1IOzDJJZMNrbXLk1w39u2IQ5LcnOTzK1MaAAAAABvJvAdpm5NcPaV929jXpar2ybCn2p+21r6yQrUBAAAAsIHM+x5pt1pV3S7JXyW5Nslvbue4Y5McmyRbtmzJRRddtDYFrrAn3uemWZfABrWr/s4wQ/s+ddYV7Pr83rGe+Ey49XwmAMCqm/cgbVuSvae0bx77tquqKsmZSR6Q5CGttSXHtNZOT3J6khx88MHtoIMO2qmCZ+2xZ10x6xLYoE4+dtf8nWGG3vbGWVew63v678+6Alg5PhNuPZ8JALDq5j1IuySL9kKrqn2T7JFFe6ct4ZVJjkry8NZaz/EAAAAAMNW875F2dpJHVNWeE21HJ7k+yQXbG1hVL0zyrCS/1Fr70OqVCAAAAMBGMO9B2mlJvpXkLVX1sHEfs61JTm2tXbNwUFVdWlVnTHz/C0lelmFZ5xVV9aCJr7ut7VsAAAAAYD2Y66WdrbVtVXVkklcneWeGJ3i+IkOYNmlTkt0mvv/J8fWp49ekpyV548pWCjCf9n/Bu2ddwpIuu8OsK9j1zfXP9+WPnnUJAACw4uY6SEuS1trFSY5Y5pj9F33/1NwyQAMAAACAnTbvSzsBAAAAYC4I0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADoI0gAAAACggyANAAAAADpsmnUBbByX3eEXZl3CLm//b75p1iUAAMDq27r3rCvY9W39+qwrgHXJjDQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6CBIAwAAAIAOgjQAAAAA6LBp1gUsp6run+RVSQ5JcnWS1yU5obV20zLj9k7yyiSPzRAYvivJs1trV61uxQAA82n/F7x71iUs6bI7zLqCXd9c/3xf/uhZlwAAK2Kug7Sq2pzk3CQXJzkqyQFJTskQjB2/zPC/SvLfkhyT5OYkJyV5W5JDV6teAAAAANavuQ7Skjwjye5JHt9auybJe6tqryRbq+rkse0WquqQJD+Z5LDW2gfGtiuSfLSqHtZaO3eN6gcAAABgnZj3PdIemeScRYHZWRnCtcOWGfflhRAtSVpr/5DkX8c+AAAAANgh8x6kHZjkksmG1trlSa4b+7rHjT6zzDgAAAAAmGrel3ZuzvCAgcW2jX07M+4+0wZU1bFJjh2/vbaq/nkH6qRDzbqA5d01yVdnXcT2PWbWBSypTpp1BexqfCasBJ8JrB8+E1aCzwRYY/P9uXDCLvDJumvab9YFMFvzHqStmdba6UlOn3UdzE5Vfay1dvCs6wDmg88EYJLPBGAxnwuwMc370s5tSfae0r557FvpcQAAAAAw1bwHaZdk0Z5mVbVvkj0yfQ+0JceNlto7DQAAAAC2a96DtLOTPKKq9pxoOzrJ9UkuWGbcPlX10IWGqjo4w/5oZ69GoawLlvYCk3wmAJN8JgCL+VyADahaa7OuYUlVtTnJxUn+KclJGYKwU5O8srV2/MRxlya5oLX29Im2c5LcN8nzk9w8jv9Ka+3QtXsHAAAAAKwXcz0jrbW2LcmRSXZL8s4kJyR5RZIXLzp003jMpKMzzFp7fZIzk3w8yeNWs14AAAAA1q+5npEGAAAAAPNirmekwVqoqvtX1XlVdV1VXVlVJ1bV4hmOwAZQVd9fVa+tqk9V1U1V9f5Z1wTMTlU9oareUVVXVNW1VfXxqnrSrOsCZqOqfq6qPlxVV1XVN6vqn6vq+Kq63axrA9bOplkXALM07sN3boa9+I5KckCSUzKEzMdvZyiwPj0gyaOSfCTJbWdcCzB7z03yr0l+M8lXM3w+vKmq7tpae9VMKwNm4S5Jzk/yu0muTvJjSbYm2SfJs2ZXFrCWLO1kQ6uqFyb5rST7tdauGdt+K+M/EBfagI2hqm7TWrt5/PPfJLlra+0nZlsVMCtjYPbVRW1vSnJIa+3eMyoLmCNV9dIkv5Zkc/Mf17AhWNrJRvfIJOcsCszOSrJ7ksNmUxIwKwshGkCSLA7RRhcmucda1wLMrauSWNoJG4ggjY3uwCSXTDa01i5Pct3YBwAw6ZAkn511EcDsVNVuVbVHVT00ybOTvMZsNNg47JHGRrc5w/4Gi20b+wAAkiRVdWSSxyb55VnXAszUN5LcfvzzmUmOm2EtwBozIw0AAJZRVfsneVOSt7fW3jjTYoBZe3CSQ5M8L8MDy14923KAtWRGGhvdtiR7T2nfPPYBABtcVd05ydlJvpDkF2dcDjBjrbVPjH/8UFV9NcmfVNUprbXPz7IuYG2YkcZGd0kW7YVWVfsm2SOL9k4DADaeqtojybsybCb+mNbadTMuCZgvC6GaJ/nCBiFIY6M7O8kjqmrPibajk1yf5ILZlAQAzIOq2pTkr5PcN8lPtda+MuOSgPnzkPH1X2daBbBmLO1kozstw5N23lJVJyW5T5KtSU5trV0zy8KAtTfOPHnU+O33Jdmrqn5u/P5vzUSBDeePMnwmPCfJXarqLhN9F7bWvjWbsoBZqKr3JDk3yaeT3JQhRHtekr+0rBM2jvKUXja6qrp/hg1CD8nwBM/XJdnaWrtppoUBa27cTHyp/6N879baZWtWDDBzVXVZkv2W6PaZABtMVb0kyeOS7J/kxiT/kuQNSU5rrd0ww9KANSRIAwAAAIAO9kgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNAAAAADoIEgDAAAAgA6CNACgW1U9tapaVT111rXMk6r6YlVdugLn+bPx/t5zJepaaVW1d1W9uqouq6obx1ofOOu6AADWiiANADqMgUFb5pjLxuP2X5uqqKq7VtXNVfWlJfoPWfjZVdXhSxzzhbH/Xqtb7epYqRCv0ylJfi3JJ5O8LMkJSb6yvQFV9aGJn8FSX8evQe0AALfaplkXAADsUt6a5CNJ/n3WhSRJa+2rVfWpJD9UVQ9orX160SFHLhya5Igk75vsrKrvT3KvJJ9rrV1+K0o5bLzGeveYJBe31o7aibFvSLLUPf7AzpcEALB2BGkAQLfW2teTfH3WdSxyfpIfyhCULQ7Sjkjy+STXjH/+nSn9SXLerSmgtfb5WzN+V1BVuyX53iT/tJOneH1r7UMrWBIAwJqztBMAVllVPXbc++qzVfWN8evjVfXsqrrFP4ur6o3jcrd7V9WzquriqvrmuHT0RVVV43FPqKp/GM/3lXHvqt2nnK9V1fur6nur6vVV9eVxzIer6tDxmO+pqt8dlzl+q6o+XVVPmHKuqXukjbVdNnGey8fzXFpV/3uh5kVjqqqeM/H+rhjfw94L5+u8xQsh2BGTjVV1hySHZJiF9r4kP1pVd1w0dskgraoeWVVnV9VV43v5fFWdXFV7TTl26vLKqrpTVf3B+N6+WVWfqarfqKr7jvfxdUu8p6qqX62qfxrHfamqTpu8dlU9bFxu/H1JDli0VHKp8y6+yD2q6jUTP/evVNWbq+qHFx33oSQ3jt8eOXGdc3uusyMW3ldVHV9VD6qqv62qr9XE3nEL93v8u/LKsf4bamKJ6HjvT6qqz4338GtV9Z6qOmJnrgkAkJiRBgBr4eVJbk7y0SRXJNk7Q4Dz+0l+NMmTlxj3e0l+Isk7k/xdkp9J8tIkt6uqr43nfVuSDyZ5eIa9q3ZL8swp57pTkr9P8p9J/iLJnZP8fJJzquqQJK8d296V5LZJnpTkL6vq31prH+l8n7dNck6SeyQ5O0Pw8tixzjtk2E9r0h+OtV6Z5PQk3x7f44+N57qh87ofGK/1E1V1m9bazWP7Q8brnj++7+cm+Z9J/jYZkqokh2dYkrl4yeeJGWavXZXh/v9HhllvxyX5qap6cGvt2u0VVVV7jOc9KMknkvxpks1JXpxhKej2nJLhZ/quDPf0yCS/kuSAsT1J/iXDPX3u+P7/YGL8J5Y5f6rqgCQfSrJPknOTvCnDMtcnJHl0VT2utXb2ePjrM9zH30nyr0nOnKhhtTw0yf/J8PM9I8nd891/J+6Q5P1J9kryngw/48uSpKrunOHv+4FJ/iHJm5PcLckTk5xbVce21qaFjctdEwDY4Kq1jbCdBwDcOvWdBw0sDoMm/UaGkOzerbXLJsYesHjpXw0z0d6Q5H8leVBr7aMTfW9M8pQkX0jykNbaFWP7nZJcmmT3JNcl+Z+ttc+MfbdPcmGGoGXf1tpXJs63UPtrk/zqQtBUVU/OEIhsyxA6PKG19s2x79AMYcLbWmuPmzjXU8e6n9Zae+NE+2VJ9ssQoP1sa+36sf3uST47Hna31toNi87/2SQ/3lq7emy/XYZQ59AkX2it7b/07f6u+/nhDLPPfrS19rGx7aVJXpRky3i/vpbkla2154/9/z3Jp5Jc2Fr7HxPneniG4PJDSR4zLmdd6DsmyR8n+b3W2nET7V9M8s3W2vdPtJ2QIZT58yRPbuO/dFXVfhmCrjsnOaO1dszEmD9L8osZAqFDW2tfHNtvm+SC8T3+SGvtExNjbnHtznt2XoZA9wWttZMm2g/NEFB9Lcl+rbXrxvZNGUKl81prD9uB63woQ6i5vT3S/mjh72xVPSzJe8f2Y1prZ0w55xczzMQ7J8njF2qc6D8jyS8neU1r7Vcn2g9M8o8Zgtr7ttb+rfeaAACJpZ0AsKNevJ2vvacNmLZ/1hhm/f747SOWuNZLFkK0cczVSd6RZI8MAcFnJvq+leQvk9wuyQ9MOdd1SY6bmK2VDDOQbswwS+o5CyHaeL4PZghzDlqitqU8eyFEG8/zlSRvz3Bv7jdx3FPG15cuhGjj8d9O8sIdvGYyfXnnEUk+01r7Umvtmgzh1eL+ybH/9R7G12MmQ7Sxvtdl2CPsFztqekqSm5K8cCFEG8/xhXz37LFpTlgI0cYxN2QIopJhxt6tUsOTZY/IMLvslMm+8Wf/V0nummFG4Up5Wpb+3bn7lOM/1hFoPW9KiHb7JL+QYV+8F032tdYuSfLqJLfP9JmgPdcEADYwQRoA7IDWWi31lWEG2S1U1V2q6uVV9amqunZhf6kkHx8P+b4lLvexKW1Xjq8fn9K3ELpN29Pps621/1z0Xm5K8uUkV7fWpi3Ru2KJcy3l6621W+wTluTfxtfNE20Le3BN23z+I/nOfly9zh9fj0iSqtozycH57iWb78vwdM87Tx6bWwZphyT5VpInVdXWxV8ZtsbYUlVTg9Px+pszzNC7fGHW0yLLbbo/7Wc/7T7urIX7/4HW2rR7ff6i41bCodv5/Zn2AIN/WOZ835jylNYkuX+GZZ8XToa0E7b33pa7JgCwwdkjDQBW0bgc8x+T3DvDf6SfmWHJ3I0Z9i17TobZMdNMezrmjR19t+0818KY7fXtyL8rTAstJuvabaJtIYT68uKDW2s3VdVVO3DdJPlwkuuTHDougzwsQ+3nTxzz/iS/leTwqnrbeMy3MywxnXTnJJVhptT23DFL37sl398y7Qum3ctp93FnLdT370v0L7TfaQWutbO+tEz/Uvfw1ry35a4JAGxwgjQAWF3HZAjRTmitbZ3sGDf5f84sipoD14yv35tFG9ZX1W5J7pLvzLBbVmvtW+M+aUcmeVCG2WYtQ3i24IMZwqgjMszu2jvDjKzrvvtsuSbJt1tr05Yb9pp8f9Ms1b5WFgLAfZbo37LouFlYbiPfpfpvzXuzeTAAsF2WdgLA6lrYAP7NU/qWe3Ljenbh+PrQKX0Pys79z77JfdKOSPKp1tp/zWwbn7L5sYn+yTGTPpLkblV1vyl9XVprX8uwsf69qmrfKYdMe98766bs+Cy1hft/6BhcLnb4+Lrs0z/n0GcyLM394araa0r/rvzeAIAZE6QBwOq6bHz9icnGqvrh7Nym+uvFmePrb0/uNTY+tfNlO3nOhWWcT0jyg/nu/dEWvC/JgfnOwwKmBWmnju5dFusAAAMSSURBVK+vq6otizur6o5V9eMd9ZyZIeB6WVXVxPh75TsPNFgJVyW5+7jJfpfxqbLvy/CU11+f7KuqhyQ5ejzv21euzLUxPjTjTRlmHJ442VdV903yrAxLev9s7asDAHZ1lnYCwOo6M8lxSV5ZVYcn+VyS+yZ5TJK3ZAgsNpzW2gVVdXqSY5N8uqrenOSGJD+dYcndlUlu3s4ppvnYOPYB4/fnTznmfRkCzAcmuTZTNpdvrf1dVR2f5CVJPldVZ2d4uuUdk+yfYSbh+zL8DLfn5UmOSvJLSX6gqs7NsC/XE5NckOGJmDv6Hqc5L8PG+e+pqg9mCIkubK29e5lxv5LhoQevqKpHZniAxb0yBJE3Jnlqa+0bK1Dfgl+uqoct0feJ1to7VvBax2WY9fecqvqxDPf7bhnu/R2TPLO1dvkKXg8A2CAEaQCwilprV1bVoRlClYcmeUSSS5L8apJzs0GDtNEzM9yLX0nyjAwzoN6a5EVJvpjk8ztysvEhBRck+ZkMyx0XP0QgSf4+Q9B0uwz7o92wxLleOoZSz07ykAyB2NfHuk5L8ucd9Xyjqg7LEMg9PslvZtgP7sQkH80QpF2z9Bm6nZBkrwzB3qEZZsGdkWS7QVpr7XNV9SNJjk/yqAxLHq8Zx72stTbtyaG3xtO203dGkhUL0lprV42zBl+U5HFJnpvkuiT/N8nvttbOXalrAQAbS7VmT1UAYH6My+8+m+Ss1tqTZl3PaqiqZyb5oyTHtNbOmHU9AAD0sUcaADATVbVPVd1mUdseSV45fvvWta9qZVXVPaa07ZfktzMsZV1u+SUAAHPE0k4AYFZ+I8mTqur9Sf49yT5JjkxyzyRnJ/nr2ZW2Yt4+PmfgE0muTnLvDEswd09yXGvtSzOsDQCAHWRpJwAwE1V1ZJLnJzkoyZ0zbHD/2QxPXHzlUvuX7Uqq6tczPCH0vhn2Mbs2Q6j2qtba22ZZGwAAO06QBgAAAAAd7JEGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQQZAGAAAAAB0EaQAAAADQ4f8D2HriwdRw2HAAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -1399,12 +1258,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1426,14 +1285,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: 0.6246, 4: 0.6292, 5: 0.6138}, 3: {3: 0.7116, 4: 0.7308000000000001, 5: 0.6768}, 4: {3: 0.7167, 4: 0.7055000000000001, 5: 0.7293000000000001}}\n" + "{2: {3: 0.6529999999999999, 4: 0.632, 5: 0.6298}, 3: {3: 0.6756, 4: 0.6828000000000001, 5: 0.7012}, 4: {3: 0.7204999999999999, 4: 0.7421, 5: 0.7487}}\n" ] } ], @@ -1444,7 +1303,7 @@ "\n", "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", "# this is equivalently wrapped up in the following\n", - "# assert pr_succ_arr == get_success_probabilites(noisy_results, ideal_results)\n", + "assert pr_succ_arr == get_success_probabilites(noisy_results, ideal_results)\n", "pr_succ_rand = [1/2**w for w in widths]\n", "\n", "ideal_distrs = {w: np.asarray([[1] + [0 for _ in range(w)]]).T for w in widths}\n", @@ -1494,9 +1353,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'depth_vec' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth_vec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpcheck\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Sucess Probablity'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth_vec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpcheck_rand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'random guess'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.05\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Depth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pr(success)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'depth_vec' is not defined" + ] + } + ], "source": [ "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", From 40ec261deea8c984af8b2929cd7366f5d5fa3260 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 29 Jul 2019 13:30:13 -0400 Subject: [PATCH 23/49] Clean up and add qaoa helpers. --- examples/volumetrics.ipynb | 1467 ++++++++++++++++------------ forest/benchmarking/volumetrics.py | 123 ++- 2 files changed, 914 insertions(+), 676 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 7e38044d..226bd44c 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -24,7 +24,6 @@ "import itertools\n", "import networkx as nx\n", "import numpy as np\n", - "import pandas as pd\n", "import time\n", "# from scipy.spatial.distance import hamming\n", "# import scipy.interpolate\n", @@ -79,7 +78,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -215,31 +214,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "Z 1\n", + "Z 0\n", + "I 1\n", "X 2\n", - "I 3\n", + "X 3\n", "Z 4\n", - "X 5\n", + "I 5\n", "X 6\n", - "X 7\n", - "Z 8\n", + "Z 7\n", + "X 8\n", "I 0\n", "I 3\n", "CZ 0 1\n", - "CZ 1 4\n", "I 1\n", - "I 2\n", - "I 2\n", - "I 5\n", - "I 3\n", - "I 6\n", - "I 3\n", "I 4\n", + "CZ 1 2\n", + "CZ 2 5\n", + "CZ 3 6\n", + "CZ 3 4\n", "I 4\n", "I 7\n", "CZ 4 5\n", - "CZ 5 8\n", + "I 5\n", + "I 8\n", "CZ 6 7\n", "I 7\n", "I 8\n", @@ -262,19 +259,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi) 0\n", - "RZ(-pi/2) 1\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 1\n", "RX(-pi/2) 1\n", - "RZ(-pi) 2\n", + "RZ(-pi/2) 2\n", "RX(-pi) 2\n", "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 5\n", + "RX(-pi) 3\n", + "RX(-pi) 4\n", "RZ(pi/2) 5\n", - "RZ(-pi) 6\n", - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 8\n", "RZ(-pi/2) 8\n", "RX(-pi/2) 8\n", "\n" @@ -302,10 +301,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 1\n", - "X 4\n", - "I 1\n", - "X 4\n", + "I 3\n", + "I 6\n", + "X 3\n", + "I 6\n", "\n" ] } @@ -324,8 +323,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "CNOT 7 8\n", - "CNOT 7 8\n", + "I 0\n", + "I 3\n", + "I 0\n", + "I 3\n", "\n" ] } @@ -366,20 +367,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 1\n", - "CZ 0 1\n", - "RX(-pi/2) 1\n", - "RX(-pi/2) 0\n", - "CZ 0 1\n", - "RZ(pi/2) 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "CZ 0 1\n", - "RX(-pi/2) 0\n", + "CZ 4 7\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "RX(-pi/2) 4\n", + "CZ 4 7\n", + "RX(-pi/2) 4\n", + "CZ 4 7\n", + "RX(pi/2) 4\n", + "CZ 4 7\n", + "RX(pi/2) 7\n", + "RX(pi/2) 4\n", + "CZ 4 7\n", + "RX(-pi/2) 4\n", + "RZ(pi/2) 4\n", + "RX(-pi/2) 4\n", "\n" ] } @@ -399,33 +401,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-2.0251704131299655) 1\n", + "RZ(1.4594683462000786) 0\n", + "RX(pi/2) 0\n", + "RZ(1.0243900534343162) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.9310896318515138) 1\n", "RX(pi/2) 1\n", - "RZ(1.671564714694243) 1\n", + "RZ(2.1890957456453397) 1\n", "RX(-pi/2) 1\n", - "RZ(-1.3830973810959275) 2\n", - "RX(pi/2) 2\n", - "RZ(1.2413369689433633) 2\n", - "RX(-pi/2) 2\n", - "CZ 2 1\n", - "RZ(-2.8836019621636186) 1\n", - "RX(pi/2) 1\n", - "RZ(0.9759904846486989) 2\n", - "RX(-pi/2) 2\n", - "CZ 2 1\n", + "CZ 1 0\n", + "RZ(-0.6622997930375671) 0\n", + "RX(pi/2) 0\n", + "RZ(1.9828557178909971) 1\n", "RX(-pi/2) 1\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(2.593711329555103) 1\n", + "CZ 1 0\n", + "RX(-pi/2) 0\n", "RX(pi/2) 1\n", - "RZ(2.471415229947945) 1\n", + "CZ 1 0\n", + "RZ(-1.058016381779542) 0\n", + "RX(pi/2) 0\n", + "RZ(2.41382240722402) 0\n", + "RX(-pi/2) 0\n", + "RZ(-1.285005356809755) 0\n", + "RZ(-2.4846463293283545) 1\n", + "RX(pi/2) 1\n", + "RZ(2.123977194138851) 1\n", "RX(-pi/2) 1\n", - "RZ(-0.6337679516296877) 1\n", - "RZ(0.10419300890719785) 2\n", - "RX(pi/2) 2\n", - "RZ(1.8274486780321837) 2\n", - "RX(-pi/2) 2\n", - "RZ(0.4809127509292681) 2\n", + "RZ(0.4963073279217695) 1\n", "\n" ] } @@ -445,38 +447,38 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(1.229760623484783) 3\n", - "RX(pi/2) 3\n", - "RZ(1.021574308763614) 3\n", - "RX(-pi/2) 3\n", - "RZ(-1.8486661215139757) 4\n", - "RX(pi/2) 4\n", - "RZ(1.1613694661492238) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RZ(-2.107290395324421) 3\n", - "RX(pi/2) 3\n", - "RZ(2.482724125564382) 3\n", - "RX(-pi/2) 3\n", - "RZ(-0.14029325543178395) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.9416631561063133) 3\n", - "RX(-pi/2) 3\n", - "RZ(1.8868409863344517) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(1.722512065613851) 3\n", - "RX(pi/2) 3\n", - "RZ(1.1538066968468772) 3\n", - "RX(-pi/2) 3\n", - "RZ(-3.0483439973439523) 3\n", - "RZ(-0.3273187044737542) 4\n", - "RX(pi/2) 4\n", - "RZ(1.3899233354854028) 4\n", - "RX(-pi/2) 4\n", - "RZ(-3.0375946996294916) 4\n", + "RZ(1.8557603976473196) 2\n", + "RX(pi/2) 2\n", + "RZ(0.7251833997059203) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.1999478754078161) 5\n", + "RX(pi/2) 5\n", + "RZ(0.8979922527579525) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RZ(2.25700516641314) 2\n", + "RX(pi/2) 2\n", + "RZ(2.399886548789372) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.424775614897118) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RX(pi/2) 2\n", + "RZ(-1.727052862487164) 2\n", + "RX(-pi/2) 2\n", + "RZ(1.4404838838072855) 5\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(2.8183740988822845) 2\n", + "RX(pi/2) 2\n", + "RZ(1.1724242573000176) 2\n", + "RX(-pi/2) 2\n", + "RZ(0.9844594158963185) 2\n", + "RZ(-2.5389634138457637) 5\n", + "RX(pi/2) 5\n", + "RZ(1.9227325522942578) 5\n", + "RX(-pi/2) 5\n", + "RZ(-3.032666685630888) 5\n", "\n" ] } @@ -502,24 +504,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 1\n", + "X 2\n", "I 4\n", - "X 6\n", - "I 7\n", - "I 1\n", + "I 5\n", + "X 8\n", + "CNOT 2 5\n", + "CNOT 4 5\n", + "CNOT 5 8\n", + "X 2\n", "I 4\n", - "CNOT 4 7\n", - "I 6\n", - "I 7\n", - "I 1\n", - "X 4\n", - "I 6\n", - "I 7\n", - "I 1\n", + "I 5\n", + "X 8\n", + "I 2\n", + "I 5\n", "I 4\n", - "CNOT 4 7\n", - "I 6\n", - "I 7\n", + "I 5\n", + "I 5\n", + "I 8\n", "\n" ] } @@ -545,22 +546,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 5\n", - "H 8\n", - "I 5\n", - "Z 8\n", - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "Z 5\n", - "I 8\n", - "I 5\n", - "I 8\n", - "H 5\n", - "H 8\n", + "H 3\n", + "H 6\n", + "I 3\n", + "Z 6\n", + "H 3\n", + "CZ 3 6\n", + "H 3\n", + "Z 3\n", + "Z 6\n", + "H 3\n", + "CZ 3 6\n", + "H 3\n", + "Z 3\n", + "I 6\n", + "I 3\n", + "I 6\n", + "H 3\n", + "H 6\n", "\n" ] } @@ -585,42 +588,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(-pi) 8\n", - "CZ 7 8\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RZ(-pi) 8\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 7\n", - "RZ(-pi) 7\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(-pi) 8\n", - "RX(-pi) 8\n", - "RX(pi/2) 7\n", - "RZ(-pi) 7\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RZ(pi/2) 8\n", - "RZ(-pi) 7\n", - "CZ 7 8\n", - "RZ(-pi/2) 7\n", - "RX(pi) 7\n", - "RZ(pi/2) 8\n", + "RX(pi/2) 3\n", + "RX(-pi) 6\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RX(pi/2) 3\n", + "CZ 3 6\n", + "RZ(pi/2) 3\n", + "RZ(-pi/2) 3\n", + "RX(-pi) 3\n", + "RX(pi/2) 6\n", + "RZ(-pi) 6\n", + "RX(pi/2) 6\n", + "RX(pi/2) 3\n", + "CZ 3 6\n", + "RX(pi/2) 6\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 3 6\n", + "RZ(-pi/2) 3\n", + "RX(-pi) 3\n", + "RX(-pi/2) 6\n", + "RX(-pi/2) 3\n", + "CZ 3 6\n", + "RX(pi/2) 6\n", + "RX(-pi/2) 3\n", + "CZ 3 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(1.598215249600061) 3\n", + "RX(-pi/2) 3\n", + "RX(pi/2) 6\n", + "RZ(0.6500806106097443) 6\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "RZ(0.9207157161851534) 3\n", + "RX(pi/2) 6\n", + "RZ(3.11417373078463) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 6\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -657,392 +672,560 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-0.8641532304474019) 1\n", - "RX(pi/2) 1\n", - "RZ(1.7808942053169874) 1\n", - "RX(-pi/2) 1\n", - "RZ(-2.6643255812379083) 4\n", + "RZ(0.758348409269925) 3\n", + "RX(pi/2) 3\n", + "RZ(1.7630700871127511) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.06888378729568245) 3\n", + "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 6 7\n", + "RZ(2.5953963254157664) 2\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(1.13234180443474) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 8\n", + "RZ(2.7205361452995973) 4\n", "RX(pi/2) 4\n", - "RZ(2.0043626992256676) 4\n", + "RZ(1.2543184374584302) 4\n", "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RZ(-2.799167617521931) 1\n", - "RX(pi/2) 1\n", - "RZ(0.7455745773745162) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RX(-pi/2) 1\n", + "RZ(1.208609474957872) 7\n", + "RX(pi/2) 7\n", + "RZ(0.94147495985692) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RZ(-2.0520996460608902) 4\n", "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(-0.20077346339921417) 6\n", + "RZ(-2.53949116123707) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", + "RX(-pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RX(pi/2) 5\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "CZ 5 8\n", + "RZ(-0.5221305937638101) 3\n", + "RX(pi/2) 3\n", + "RZ(2.558816414461174) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.949929968488697) 6\n", "RX(pi/2) 6\n", - "RZ(2.3449414168436298) 6\n", + "RZ(2.9357743332570747) 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1263,7 +1446,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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+yZoBAAAAmE+zfrOBpyU5MMmJrbWr0wVhByfZVlWn9dNWcnySg5L8RGvtqiSpqg8l+WqSxyb508mXDgAAAMA8mekz0pIcl+RdywKzN6QL147cw3o3S/KdJN9YMu3r/bQad5EAAAAAzL9ZD9IOT3L50gmttc8nuaaft5rt/TIvrao7VtUdk5yRZFeSN0+oVgAAAADm2Kxf2nlokt0rTN/Vz1tRa+3KqnpkknckeUY/+V+THNta+/eV1qmqk5OcnCSHHXZYLr300jUV/IR7Xr+m9fY1az2+wOa0ffv2bN++PUmye/fudfWAeeyzeiKwXuPqs5ulx+qbAExLtdamXcOqqurbSZ7TWjtz2fQvJnlta+03V1nvsCTvS/KJfHc8tP+R5IeSPLQ/q21VCwsLbceOHWuqeesp565pvX3NzlOPn3YJwJQsLCxkrT02mc8+qycC47SePrtZeqy+CUxLVV3SWluYdh1Mz6yfkbYrySErTD+0n7ea56QbJ+2nWmvfTpKqek+STyV5dr57lhoAAAAADDLrY6RdnmVjoVXV3ZLcMsvGTlvm8CQfXwzRkqS19q0kH09yrwnUCQAAAMCcm/Ug7bwkx1bVQUumnZTk2iQX7WG9K5L8YFXdfHFCVd0iyQ8m2TmBOgEAAACYc7MepL0syTeTvLWqjulvCLAtyemttasXF6qqT1fVq5as98okd07y11V1fFU9Lsk5SQ5L8vINqx4AAACAuTHTQVprbVeSo5Psn+TtSV6Y5IwkL1i26JZ+mcX1LknymCQHJXldktemuxz00a21f5x85QAAAADMm1m/2UBaa59I8qi9LLN1hWkXJrlwQmUBAAAAsI+Z6TPSAAAAAGBWCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABpj5IK2q7ltVF1bVNVV1ZVW9qKr2H7juiVX191V1bVV9rareWVW3mnTNAAAAAMyfmQ7SqurQJBckaUlOSPKiJP8zyQsHrPvUJK9Pcl6S45I8NcmnkmyZVL0AAAAAzK9ZD5WeluTAJCe21q5Ocn5VHZxkW1Wd1k+7iaq6fZIzkjy9tfaKJbP+euIVAwAAADCXZvqMtHRnkr1rWWD2hnTh2pF7WO8J/eNrJlUYAAAAAPuWWQ/SDk9y+dIJrbXPJ7mmn7eahyT5ZJKnVNUXq+rbVfXRqnro5EoFAAAAYJ7N+qWdhybZvcL0Xf281dwpyX2SPC/Jryf5Wv/4zqq6d2vty8tXqKqTk5ycJIcddlguvfTSNRX8hHtev6b19jVrPb7A5rR9+/Zs3749SbJ79+519YB57LN6IrBe4+qzm6XH6psATEu11qZdw6qq6ttJntNaO3PZ9C8meW1r7TdXWe/dSR6d5LjW2jv7aQcnuSLJWa2139rTfhcWFtqOHTvWVPPWU85d03r7mp2nHj/tEoApWVhYyFp7bDKffVZPBMZpPX12s/RYfROYlqq6pLW2MO06mJ5Zv7RzV5JDVph+aD9vT+u1JO9dnNCPs3ZJkvuOsT4AAAAA9hGzHqRdnmVjoVXV3ZLcMsvGTlvmsiTV/9xo9SQ3jLNAAAAAAPYNsx6knZfk2Ko6aMm0k5Jcm+SiPaz3jv7xkYsTquqQJA9K8o/jLhIAAACA+TfrQdrLknwzyVur6pj+hgDbkpzeX6qZJKmqT1fVqxaft9Z2JPmbJK+qqp+vquOTvC3Jt5P88Ua+AAAAAADmw0wHaa21XUmOTrJ/krcneWGSM5K8YNmiW/pllvrZJOckOT3JW9KFaI/qtwkAAAAAI9ky7QL2prX2iSSP2ssyW1eY9vUkv9T/AAAAAMC6zPQZaQAAAAAwKwRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAANsmXYBsM/adsi0K7ipbVdNuwIAAACYWc5IAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGCAmQ/Squq+VXVhVV1TVVdW1Yuqav8R1t+vqnZUVauqx02yVgAAAADm15ZpF7AnVXVokguSfCLJCUnuleSl6QLA5w3czFOT3HUiBQIAAACwz5j1M9KeluTAJCe21s5vrb0syQuT/FpVHby3lfsg7neT/K/JlgkAAADAvJv1IO24JO9qrV29ZNob0oVrRw5Y/7eTfDDJhROoDQAAAIB9yKwHaYcnuXzphNba55Nc089bVVX9lyS/kOTZE6sOAAAAgH3GrAdphybZvcL0Xf28PfmjJGe11j499qoAAAAA2OfM9M0G1qqqfjrJfZL8+AjrnJzk5CQ57LDDcumll65p30+45/VrWm9fs9bjO1fu9qRpV3BTfi9MyPbt27N9+/Ykye7du9fVA+axz+qJwHqNq89ulh6rbwIwLdVam3YNq6qqryT549baC5dN/0aSba21319hnZsl+WyS05P8eT/5e5P8Y5KfTvK3rbX/3NN+FxYW2o4dO9ZU89ZTzl3TevuanaceP+0Spm/bIdOu4Ka2XTXtCtgHLCwsZK09NpnPPqsnAuO0nj67WXqsvglMS1Vd0lpbmHYdTM+sX9p5eZaNhVZVd0tyyywbO22JWyW5a7ogbVf/84/9vDck+YeJVAoAAADAXJv1SzvPS/KcqjpoyVlkJyW5NslFq6zz9SSPXDbtTkn+KslvJnnPJAoFAAAAYL7NepD2siTPSPLWqnpJknsm2Zbk9Nba1YsLVdWnk1zUWntKa+07Sd67dCNVtbX/4/9urX108mUDAAAAMG9mOkhrre2qqqOTnJXk7enu4HlGujBtqS1J9t/Y6gAAAADYl8x0kJYkrbVPJHnUXpbZupf5O5PU+KoCAAAAYF8z80EaADDDNvIOxPvanYUdW2Azm0QP06uAGTDrd+0EAAAAgJkgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAbYMu0CWNnOA5447RJWtPW610+7BNgcth0y7QpuattV064AAObXJP7v93/3/PD3A+aGM9IAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAA8x8kFZV962qC6vqmqq6sqpeVFX772WdH66qP6+qT/frfbKqXlBVB2xU3QAAAADMly3TLmBPqurQJBck+USSE5LcK8lL0wWAz9vDqif1y74kyaeS/Jckv90//uQESwYAAABgTs10kJbkaUkOTHJia+3qJOdX1cFJtlXVaf20lZzaWvvqkufvrarrkvxZVd29tXbFhOsGAAAAYM7M+qWdxyV517LA7A3pwrUjV1tpWYi26B/6xzuPrzwAAAAA9hWzHqQdnuTypRNaa59Pck0/bxRHJLkhyWfGUxoAAAAA+5JZD9IOTbJ7hem7+nmDVNWd0o2p9rrW2lfGVBsAAAAA+5BZHyNt3arq5knelOTrSZ61h+VOTnJykhx22GG59NJL17S/J9zz+jWtt9yl+z9pLNsZtydcP6bXt8bjO1fu9qRpV3BTfi/j4/d7I9u3b8/27duTJLt3715XDxhXn50lm7onbuTf9c18nNbCsWUE4+qzm6XHzmTfnMS/2Vl8nUM5HjfmeMDcqNbatGtYVVV9Jckft9ZeuGz6N5Jsa639/l7WryR/leTRSX60tXb5npZftLCw0Hbs2LGmmreecu6a1ltu5wFPHMt2xm3rda8fy3Z2nnr8WLazqW07ZNoV3NS2q6Zdwfzw+13VwsJC1tpjk/H12VmyqXviRv5dn5G/wxvGsWWN1tNnN0uPncm+OYl/s5v536bjcWOOx9yoqktaawvTroPpmfUz0i7PsrHQqupuSW6ZZWOnreLMJCckefTQEA0AAAAAVjLrY6Sdl+TYqjpoybSTklyb5KI9rVhVz03yK0l+trX2gcmVCAAAAMC+YNaDtJcl+WaSt1bVMf04ZtuSnN5au3pxoar6dFW9asnzJyZ5cZLXJvlSVf3Ikp87bOxLAAAAAGAezPSlna21XVV1dJKzkrw93R08z0gXpi21Jcn+S57/WP/4pP5nqScnOXu8lQIAAAAw72Y6SEuS1tonkjxqL8tsXfb8SblpgAYAAAAAazbrl3YCAAAAwEwQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGCAmb9rJwAAwLRsPeXcQcvtPGCK+z71+PHvHIAVOSMNAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAG2DLtAgBg0nYe8MQN29fW616/YfsC1mjbIRu4r6s2bl8AwMQJ0gCAJMnWU84deZ2dB0ygkFWsqb5Tj59AJQAA7KsEaTCitXyQW8lGfvgcamyvzQdXAAAA5pAx0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAA2yZdgEAAMDms/OAJ459m1uve/3YtwnMoG2HTGCbV41/m7ACZ6QBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYICZD9Kq6r5VdWFVXVNVV1bVi6pq/wHrHVJVf15Vu6rqqqr6y6q63UbUDAAAAMD82TLtAvakqg5NckGSTyQ5Icm9krw0XQD4vL2s/qYk35/kqUluSPKSJOckefik6gUAAJhnW085d9ByOw+Y4r5PPX78OwfozXSQluRpSQ5McmJr7eok51fVwUm2VdVp/bSbqKojkvxYkiNba+/rp30pyUer6pjW2gUbVD8AAAAAc2LWg7TjkrxrWWD2hnRnlx2Z5O17WO/LiyFakrTWLq6qz/XzBGkAwIYZehbFUpM4m2M1a6rPGR8AwD5o1oO0w5O8Z+mE1trnq+qafh1eTJIAACAASURBVN5qQdrhSS5fYfpl/TyAFa3lw+RKNvID8FBje20+PAMAJHGpK+yLqrU27RpWVVXfTvKc1tqZy6Z/MclrW2u/ucp65yf5Rmvt8cum/0WSe7bWHrrCOicnObl/ep8knxzDS5gVt0/y1WkXwcT4/c6/efgd3z7JHfo/H5jkY1OsZah5OO6zyrGdHMd2cmb92M5yn531Y7fRHI+bckxuzPG4sVk7Hndvrd1h74sxr2b9jLQN01p7eZKXT7uOSaiqHa21hWnXwWT4/c4/v+PpcNwnx7GdHMd2chzbtXPsbszxuCnH5MYcjxtzPJg1+027gL3YleSQFaYf2s8b93oAAAAAsKJZD9Iuz7IxzarqbklumZXHQFt1vd5qY6cBAAAAwB7NepB2XpJjq+qgJdNOSnJtkov2st6dquphixOqaiHJPft5+5q5vGSV/8vvd/75HU+H4z45ju3kOLaT49iunWN3Y47HTTkmN+Z43JjjwUyZ9ZsNHJrkE0n+OclL0gVhpyc5s7X2vCXLfTrJRa21pyyZ9q4k907y7CQ39Ot/pbX28I17BQAAAADMi5k+I621tivJ0Un2T/L2JC9MckaSFyxbdEu/zFInpTtr7dVJXpvkkiQ/Mcl6AQAAAJhfM31GGgAAAADMipk+Iw0AAAAAZoUgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaUCqaltVtao6atq1AMwjfRZgcqrq7L7Hbp12LcD8E6TBlFXVXarq6VV1XlXtrKpvVtXXqur8qjpx2vVNW3XO798ctaraMu2agM2lqg6uqjOr6v1VdWVVXVdVX6mqi6vqV6vqVtOucZr0WWDcqup5S3rKMdOuZ5qq6hZV9c/9sfjitOsB1k+QBtP39CR/mOQ+Sf4uyelJ3pXk4Um2V9XpU6xtFvxKkkcmuW7ahQCb1m2TnJzk+iTnpuuzb05yUJIzklxcVQdPr7yp02eBsamqByZ5fpKvT7uWGfHiJHefdhHA+PjGEabv4iRHtdYuWjqxqn4gyUeSPKuq/rK1dslUqpuiqrpPkpck+YMkPx1vQoC1+UKSQ1pr314+o6r+IsnPJHlaktM2urBp02eBcaqqA5K8LsnfJ/lMkp+bbkXT1V/O/6wkv5zkT6dbDTAuI5+RVlW3q6qnVtVLq+ply6Y/sG+esKGq6tZV9a2q+uCy6Qf2l/C0qvq5ZfN+qZ/+Cxtb7Y211t66PETrp1+W5I3906PGsa+qelBVvbOq/rOqrq6qC6rqiHFse9z6S4tel+SzSV4w5XJgn7fJ++z1K4VovTf3j/cex770WWAtNnOPXeb3ktwjyZOS3DDujVfVMf1l+t+oqv+oqnOq6vBx72cc+jOdz05yYWvtZXtZHNhERgrSqurnk+xM8mfpkvX/b8nsu6T75uGJ4yoOhmqtfT3dmV0PrqqDlsz60SS36P989LLVFp9fOOHy1mPxg9931ruhqnpokvcnOSbJeUnOSvKtJO9N8pD1bn8Cnpfkh5I8qbX2zWkXA/u6Oe6zP94//tN6N6TPAms1Dz22qh6V5JlJntta+9QEtv9T6YY/WUj3JcifJbldkg+nC+9mzR8mOTTJU6ZdCDBegy/trKqjk7w6yceTbEvy6HTjjSRJWmv/VFWXJXl8vxxstPeke7PxiHRj4CTdG4zrk1yUJW8+qmq/dOPBfLa1dsXeNlxVt0nyqyPWc05r7dIR11m6z4OT/GSSluTda91Ov61K9+/ywCSPb639zZJ5z0xy5ojbe0C6f+ujOLO1tnvg9n84yf9KcmprbceI+wEmZ1P32f4MrOf1T2+bbizKB6Qbn/IVI+57+bb1WWC9Nm2PrapD0p199f50AdJYVdWt0wVnNyR5+NK+VVVnZMTX1l9yedQo67TWto2w/Z9I8vNJntpa+/wo+wFm3yhjpP1Gkn9L17iuqqr7r7DMpUl+ZCyVweguTPJb6d5kLH3zcUmStyY5q6q+v7X2L+k+ON02yfaB275NRr/sZWe6fxMj6z+QvTLJ9yT5k/4yz/V4aLqbGbxv6Ye73lnpbnhwrxG294CMfjzOTrLXD3hVdWC6S40+nuRFI+4DmKzN3me3rLCP1yX55dbaegfa12eB9drMPfaP+nqOaq21EfczxAn99l+7Qvi/LcmTkxwywvaOyujHY9uQharqe5K8PMl5rbVXjbgPYBMY5dLOH07yjtbaVXtY5otJ7rS+kmDNPpzk2vTf1vXfjD0w3ZuS9/TLLH6T96j+8T0ZoLW2s7VWI/6cvY7X8tIk/y3dt3q/to7tLHpg/7jSWGzXJ/nAKBtrrZ29huOxc+DmT0tyzyQ/v4cxjYDp2NR9trV2XWut0r3/uWu6MXyOSbKjqraOsq0V6LPAem3KHltVP5nupgK/3lr77KBXOro99dirMuKX1621baMejxE2/4p0X9w8dZSagM1jlCDtgCT/uZdlbpMJDCoJQ7TWvpXug8r9q+oO6b5p2j/dAJ+XJfnXfPfNx9HpLpkc9OZjI1XVaenGIHxfkseOadyaxW/ovrzK/H8bwz7WraqOTPI/kvxOa+0fp10PcGPz0mdb50uttdckOTHdmWRnrXOz+iywLpuxx1bVbZO8LF3YN8m7Um6WHvvf0429+czW2pXTrgeYjFEu7dyZ5EF7WebBSf5lzdXA+r0n3fh9R6e7zOa6JB9cMu+4qrpFunFxPt5a+8qQjW7UGGlLxnj4uySPa61dM+I+V7N4Jun3rDJ/pDNJJzh2zw8lqSQvrKoXrrLMt7srX/NDox5fYCw2dZ9drrX2karanfXfHVmfBcZhs/XY701y+77eG/resdz5/fRntdZGGi9yiXH32KMymTHSFs+ce01VvWaF+XepqsVLXw8d0LOBGTRKkPa2JM+uqhNba29dPrNP3/9ruuv6YVoW71p0dJIjknyofXfcmwuT/EySX0pyq4x2h6OJjivRj4l2VpJfTnJ+khNaa9eOuL89+Vj/eOQK+94/ycNG3N6kxu755ySrjSVxUpJbpxvMuyX52oj7B8ZjU/bZ1fR3xzs4ez/rfm/0WWAcNluP/VpW7ymPSHLvdHcxvjJd/1mrpT32Rje26y+BfcCI2zsqkxkj7cPp+uhKnpLkmiR/1T93t2TYpKoNHAuyP233Y+nGFHljulv5Hpvu8oCHJ3lCks8meVDrbt8MG67/sPK1JN9Kcock/6u19uJ+3t3TvSH4SpI7pgur3jalUv+vPkR7ebpxFM5LcmIbMOj14rdZQ8Zs6PdxWbrLl/Z0N7lHttbeO+pr2AhVtTPJ3ZPcrLX2nSmXA/usTdpn75/kU8t7a1XdPN1YNv89yetbaz+zbL4+C2yozdhjV1NVZ6e7c+WjW2sXLJu3NcnnklzRWts6YFu3TvL5JAclOaKtftfOe4wwXuSG6v9P+VJr7a7TrgVYn8FnpLXW/qM/BfYvkvy/S2b9Sf/44SQ/LURjmlpr11fVe9Pd2SdZ8k1da+2KqvpMurumLd5GfBY8P12Idm26b/1OWeG0+Etba+csPulveZ50r2OvWmutqp6S7my37VX11iSfTvft3dFJ3pnkMet5EcC+YZP22ackeXJVfTDJFenO2rpzkh9LdznQJ5M8e+kK+iwwDZu0x67FYo8dFNq31r5eVSenO6Hj/VX1xnRjxj0syQ+mG1v4EZMoFGC5US7tTJ/uP6yqHpjuVOPbpbte/SOttY+OvzxYkwvTvfm4Osny22NfmO7NxyVtz3eg3Uj36B8PTPLcVZZ5TZJzljy/f//4hqE7aa19sKoenuR3kxzXT/5oulPbj40PeMBwm63PvjndpTZH9D8Hpav9E+nukvwnK4xJqc8C07LZeuxarKXHvqWqHpPukswnpLs08n3p+vopEaQBG2TwpZ3TUFXfl+Q56Zrj/ZK8v7V21ID1Dkl3CcXj033b8Y4kz2itGeuDuVBVz0j3d/z+rbWPT7segHmjzwJMTlWdnuQXk9y9tfbVadcDMIr99r5Ip6puUVV3rqqbrTL/5v38W4yvvNwvyWPTXXIxyt1A35Tu29+nJnlSkh/Ojc/mgc3uyCRv8+EOYGL0WYDJOTLJK4RowGY0ys0GfjfJryW560pndlXV7ZJ8MclprbVR74Cy2j73a63d0P/5LUluv7cz0qrqiCQfSnJka+19/bQHp7u04iYDXQIAAADAEIPPSEt3ZtiFq10e2U8/P8njxlFYv80b1rDacUm+vBii9du5ON1dYY5bdS0AAAAA2INRgrR7pLvEck/+JcnWNVczHocnuXyF6Zf18wAAAABgZKMEaTfL3m8Bf0O6Ow9O06Hpbmu/3K5+HgAAAACMbMsIy34u3aCQe3Jkks+vvZzpqaqTk5ycJAceeOCDtm7dOt2CAObIrl27snt39x1HVUWPBRgvfRZgY1x22WVfba3dYdp1MD2jBGlvS/IbVfVrrbXTl8+sqmcnWUjyB+Mqbo12JVnpL/Wh/bwVtdZenuTlSbKwsNB27NgxmeoA9nELCwvRYwEmR58FmJyqumLaNTBdowRpf5DkZ5P8flU9Icm7k3wpyV2SHJsuRPtiktPGXeSILk/y8BWmH57knA2uBQAAAIA5MThIa639R1UdleSvkjy4/2lJql/k4iRPXO2unhvovCS/VVUPa619IEmqaiHJPft5AAAAADCyUc5IS2vts0keUlUPTvIjSW6TbmD/j7TWLh53cVV1yySP7Z/eJcnBVfVT/fO/ba1dU1WfTnJRa+0pfY0frqp3J3ltf7npDUlekuQDrbULxl0jAAAAAPuGkYK0RX1oNvbgbAV3TPLmZdMWn98jyc50r2H/ZcuclOSMJK9Od2fSdyR5xsSqBAAAAGDurSlI2yittZ357qWjqy2zdYVpu5M8uf8BAAAAgHUbKUirqi1JHpdufLRDc9MzwZKktdZ+cQy1AQAAAMDMGBykVdWdkpyf5L7Z81liLYkgDQAAAIC5MsoZaS9Ncr90Y5S9IskXknxnEkUBAAAAwKwZJUg7Nt2dL0+aVDEAAAAAMKv2G2HZA5N8eFKFAAAAAMAsGyVI+3iS751UIQAAAAAwy0YJ0l6a5P+pqsMnVQwAAAAAzKpRxkj7QpJ3JPlwVZ2e5JIku1dasLX2oTHUBgAAAAAzY5Qg7QNJWpJKsm0vy+6/1oIAAAAAYBaNEqS9OF2QBgAAAAD7nMFBWmvteZMsBAAAAABm2Sg3GwAAAACAfdYol3YmSapqS5KjkvxAklu31n6vn37zJLdOsqu15hJQAAAAAObKSGekVdUxST6b5F1J/v8kv7Nk9oOS/HuSk8ZWHQAAAADMiMFBWlU9MMk70p3F9pwkb1g6v7X24SQ7k/zEGOsDAAAAgJkwyhlpz09ybZKF1trpST65wjJ/n+QB4ygMAAAAAGbJKEHaw5L8dWvtyj0s8/kkh62vJAAAAACYPaMEabdONwbanhw44jYBAAAAYFMYJfT6UpL77WWZByT53NrLAQAAAIDZNEqQ9q4kj6mqI1aaWVU/luRH092QAAAAAADmyihB2ouTXJXkgqr63SSHJ0lVHds/357ky0lOH3uVAAAAADBlW4Yu2Fr7YlUdm+RNSZ6bpCWpJH/bP+5McmJrbW/jqAEAAADApjM4SEuS1tqOqvr+JCck+ZEkt0t3ltpH0t3R81vjLxEAAAAApm9wkFZVd07y7f6Ms+39DwAAAADsE0YZI+0LSU6bVCEAAAAAMMtGCdJ2J/nKpAoBAAAAgFk2SpD20SQ/NKlCAAAAAGCWjXKzgRcmeV9VPam1dvaE6tn0tp5y7rRL2BR2nnr8tEsAAAAAGMkoQdrRSd6T5FVV9bQkf5/k35K0Zcu11trvjak+AAAAAJgJowRpv7Pkzw/uf1bSkgjSAAAAAJgrowRpj55YFQAAAAAw4wYHaa21CydZCAAAAPwf9u47XK6q3v/4+5uEFCIpUlJoAUI1ciFEuKJ0ERAVKYJGUQQuyo8mXhtcShAuTSE0EQQUUQTvJREucAHpXSmRJlI1tIQgmEJJITnf3x8zhzs5OWXmZJ/M5Jz363nmmcxaa+/5nH1gG7+svZYkNbKqd+2MiD9ExIQuzCJJkiRJkiQ1rKoLacAngb5dFUSSJEmSJElqZLUU0l4A1uyqIJIkSZIkSVIjq6WQdhnwmYhYo6vCSJIkSZIkSY2qll07JwE7AfdHxGnAw8DrQLYcmJnTioknSZIkSZIkNYZaCmkvUyqaBfDTdsZljeeVJEmSJEmSGl4tBa/f0srsM0mSJEmSJKknqLqQlplf7cogkiRJkiRJUiOrZbMBSZIkSZIkqceykCZJkiRJkiRVoepHOyPi51UOzcz8ZifzSJIkSZIkSQ2pls0GDu6gv3lHzwQspEmSJEmSJKlbqaWQtn4b7UOAjwHHAfeW3yVJkiRJkqRupZZdO19sp/vRiLgJeAK4BWhvrCRJkiRJkrTcqWVGWrsy86WIuA74NnB5UeeNiE2A84GPA7OAS4GTMnNRB8eNA04FxpWbpgD/kZl/KiqbJGnZG/XDG+sdoXBTT9+93hEkSZIkVaHoXTtnABsUdbKIGArcRmndtT2AHwH/DpzUwXFrlo/rA+xffvUBbo2ItYvKJ0mSJEmSpJ6jsBlpEdEL2AGYU9Q5gW8BA4C9MnMOpULYIGBCRJxZbmvN7sBKwJ6ZObuc7wHgTeAzwM8KzChJkiRJkqQeoOpCWkRs3c451gQOBDYHLisgV7PdgFtaFMyuBs4AtgOub+O4FYCFwLsVbe+U26LAfJIkSZIkSeohapmRdh+lRyzbEsADwPeXKtHiNgLuqGzIzJcj4r1yX1uFtEmUHgM9KyL+s9x2AjAT+O8C80mSJEmSJKmHqKWQdiqtF9KaKBWoHsrMBwpJ9X+GUtpgoKWZ5b5WZea0iNgBuAE4stw8HdglM/9RcEZJkiRJkiT1AFUX0jLzuK4MUqSIGEFp5tmjwMHl5sOAGyNi68x8uZVjDgEOARgxYgSPPfZYp75733Xb3UxUZZ29vpKWT5MmTWLSpEkAzJo1a6nuAd3xPus9UdLSKvI+K0mS2haZ7T2tWV8R8Qbw08w8qUX7u8CEzPxxG8edDewFrJ+Z75fb+gLPA9dl5pGtHdds3Lhx+cgjj3Qq86gf3tip43qaqafvXu8Ikupk3LhxdPYeC93zPus9UVKRlvY+K0lqW0Q8mpnj6p1D9VPLZgObU1r8/7LMnNFK/zDgIOCGzHyioHzPUFoLrfJ71gRWLPe1ZSPgL81FNIDMXBARfwHWKyibJEmS1K0sL/+xwv8AIUmql141jP0ucCjwRhv9/wC+BXxnaUNVuAnYJSJWqmjbD5gL3N3OcS8BY8qz0ACIiH7AGGBqgfkkSZIkSZLUQ9RSSNsauDPbeBY0M5so7bD5ySKClV0EzAcmR8SnyuuYTQDOzsw5zYMi4oWIuKziuEuBkcDvI2L3iPgscC0wAvh5gfkkSZIkSZLUQ9RSSBsOvNLBmNcoFasKkZkzgZ2A3sD1wEnARODEFkP7lMc0H/cosCuwEvBr4ApKj4PunJmPF5VPkiRJkiRJPUfVa6QB7wGrdjBmVWBB5+MsKTOfBnbsYMyoVtpuB24vMoskSZIkSZJ6rlpmpD0OfD4iBrbWWV7H7PPlcZIkSZIkSVK3Uksh7RJgNeCWiPhIZUdEjAFupjQj7dLi4kmSJEmSJEmNoepHOzPzqojYHRgPPB4R0yitibY6pYX9ewFXZuZvuiSpJEmSJEmSVEe1rJFGZn41Ih4AjgA2BNYodz0DnJeZFxWcT5IkSZIkSWoINRXSADLzQuDCiBgEDAFmZeacwpNJkiRJkiRJDaTmQlqzcvHMApokSZIkSZJ6hKo3G4iIzSLi2IgY1kb/sHL/psXFkyRJkiRJkhpDLbt2fg84FHijjf5/AN8CvrO0oSRJkiRJkqRGU0shbWvgzszM1jozswm4A/hkEcEkSZIkSZKkRlJLIW048EoHY14DRnQ+jiRJkiRJktSYaimkvQes2sGYVYEFnY8jSZIkSZIkNaZaCmmPA5+PiIGtdUbESsDny+MkSZIkSZKkbqWWQtolwGrALRHxkcqOiBgD3ExpRtqlxcWTJEmSJEmSGkOfagdm5lURsTswHng8IqZRWhNtdWAkpaLclZn5my5JKkmSJEmSJNVR1YU0gMz8akQ8ABwBbAisUe56BjgvMy8qOJ8kSZIkSZLUEGoqpAFk5oXAhRExCBgCzMrMOYUnkyRJkiRJkhpIzYW0ZuXimQU0SZIkSZIk9Qg1FdIi4hPAJyitiQYwDbg/M+8vOpgkSZIkSZLUSKoqpEXEJ4GfAZs0N5Xfs9z/F+BQC2qSJEmSJEnqrjospEXEnsDVwArADOBu4JVy95rAdsAY4I6I2Dczr+uirJIkSZIkSVLdtFtIi4gRwBVAE6WdOi/OzIUtxvQB/g04C/h1RGyYmdO7KK8kSZIkSZJUF7066P82MBDYPzN/2rKIBpCZCzPzZ8D+wIeAo4qPKUmSJEmSJNVXR4W0XYGHM/Oajk6UmZOAh4DdiggmSZIkSZIkNZKOCmmjgPtqON/95WMkSZIkSZKkbqWjQtoKwIIazregfIwkSZIkSZLUrXRUSJtOaUfOan0EeL3zcSRJkiRJkqTG1FEh7V5g54jYoKMTRcSGwC7APUUEkyRJkiRJkhpJR4W0nwJ9gRvKhbJWlQtt1wN9gAuLiydJkiRJkiQ1hj7tdWbmwxFxNvAd4LGI+G/gduCV8pA1gU8B+wD9gHMy86EuzCtJkiRJkiTVRbuFtLLvAe8BxwBfBb7Soj+AJuA04LhC00mSJEmSJEkNosNCWmYmcEJEXA4cBHwCGFHufh24D/hlZr7QVSElSZIkSZKkeqtmRhoAmfk34D+6MIskSZIkSZLUsDrabECSJEmSJEkSFtIkSZIkSZKkqlhIkyRJkiRJkqpgIU2SJEmSJEmqgoU0SZIkSZIkqQoW0iRJkiRJkqQqtFlIi4g3IuK7FZ+PjYhPLptYkiRJkiRJUmNpb0baKsCKFZ9PAXbs2jiSJEmSJElSY2qvkDYDWH1ZBZEkSZIkSZIaWZ92+h4C9o+IBcD0ctu2EXFsB+fMzDytkHSSJEmSJElSg2ivkPY94DrgsIq2Hen48c4ELKRJkiRJkiSpW2mzkJaZz0XEGGA0pUc8bwOuAH69jLJJkiRJkiRJDaO9GWlk5iLgWeDZiAD4W2beviyCSZIkSZIkSY2k3UJaCysATV0VRJIkSZIkSWpkVRfSyrPTAIiIEcBmwBBgNvDnzJze1rGSJEmSJEnS8q5XLYMjYo2IuAF4FbgB+A1wPfBqRNwQEWsVHTAiNomI2yPivYiYFhE/iojeVR67V0Q8HBFzI+KtiLg5IgYWnVGSJEmSJEndX9Uz0iJiGHA/sCbwCnAvMB0YAXwC+AxwX0R8LDNnFBEuIoZS2uTgaWAPYD3gLEoFwOM6OPZg4ALgTEo7kA6ltONoLY+zSpIkSZIkSUBtRaXjKBXR/gP4cWYubO6IiD7Ad4FTy+OOKCjft4ABwF6ZOQe4NSIGARMi4sxy2xIiYhVgInBEZl5S0fX7gnJJkiRJkiSph6nl0c7PArdl5mmVRTSAzFyYmacDt5bHFWU34JYWBbOrKRXXtmvnuH3L778qMIskSZIkSZJ6sFoKaSOAhzsY80h5XFE2Ap6pbMjMl4H3yn1t2Qp4FjgoIl6NiPcj4k8RsXWB2SRJkiRJktSD1PJo5xygo80E1iyPK8pQYFYr7TPLfW0ZDmxI6THT7wNvld9vjoj1W1vDLSIOAQ4BGDFiBI899linAu+77qKOB6nT11fS8mnSpElMmjQJgFmzZi3VPaA73me9J0paWkXdZ5eXe6z3TUlSvURmVjcw4vfArsD2mfmnVvrHUdqA4KbM3KuQcBHvA9/LzHNatL8KXJGZx7Zx3B+AnYHdMvPmctsg4CXggsw8vr3vHTduXD7yyCOdyjzqhzd26rieZurpu9c7gqQ6GTduHJ29x0L3vM96T5RUpKW5zy4v91jvm5LqJSIezcxx9c6h+qllRtp/UtqZ896IuBK4k9KuncOB7YGvlsedVmC+mcDgVtqHlvvaOy6Bu5obMnNORDwKbFJgPkmSJEmSJPUQVRfSMvORiNgP+CXwdeBrFd1B6RHMgzKzo3XUavEMLdZCi4g1gRVpsXZaC38tZ4oW7QE0FZhPkiRJkiRJPUQtmw2QmddSWiftAOB84Iry+zeAtTPz9wXnuwnYJSJWqmjbD5gL3N3OcTeU33doboiIwcAWwOMFZ5QkSZIkSVIPUMujnQBk5tuUCmhXFB9nCRcBRwKTI+IMYF1gAnB2Zn6wqUFEvADcnZkHlTM+EhHXAZdFxA+BNyltNvA+8NNlkFuSJEmSJEndTE0z0pa1zJwJ7AT0Bq4HTgImAie2GNqnPKbSV4FrgbOBaygV0XYsn1OSJEmSJEmqSc0z0pa1zHwa2LGDMaNaaXsHOLT8kiRJkiRJkpZKQ89IkyRJkiRJkhqFhTRJkiRJkiSpChbSJEmSJEmSpCpYSJMkSZIkSZKqUHUhLSJW6cogkiRJkiRJUiOrZUbaKxFxZURs22VpJEmSJEmSpAZVSyHt78CXgTsj4umIOCoihnZRLkmSJEmSJKmhVF1Iy8xNgO2Bq4B1gInAaxHxq4jYumviSZIkSZIkSY2hps0GMvOezPwqMBL4d2AqsD9wb0Q8GRGHRcSg4mNKkiRJkiRJ9dWpXTszc2ZmTqyYpfZbYDRwHjAtIi6NiM2LiylJkiRJkiTVV6cKaS28BkwH3gECGAAcCDwSEddExJACvkOSJEmSJEmqq04V0iKid0TsExG3As8C3wVmA98HDHOlTgAAIABJREFUVgM+DdwG7AVcWFBWSZIkSZIkqW761DI4ItYB/g34BqWCWQI3Ahdm5i0VQ28DbouIycCuBWWVJEmSJEmS6qbqQlpE3ALsRGkW2wzgNODizHylncMeBvZYqoSSJEmSJElSA6hlRtrOwL2UHtWcnJnvV3HMDcAbnQkmSZIkSZIkNZJaCmkfzcy/1HLyzHwSeLK2SJIkSZIkSVLjqXqzgVqLaJIkSZIkSVJ3UnUhLSL2jog/RMTqbfSPLPe7JpokSZIkSZK6naoLaZR261w1M19rrTMzpwErA4cUEUySJEmSJElqJLUU0j5KaRfO9jwM/Evn40iSJEmSJEmNqZZC2ip0vAPnW+VxkiRJkiRJUrdSSyHtTWB0B2PWA2Z1Po4kSZIkSZLUmPrUMPZ+4PMRsUFmPteyMyI2BPYA/reocFK3NmFwvRMsacLseieQJEmSJKlh1TIj7WygL3BfRPy/iFg3IvqV3w8D7qNUmPtJVwSVJEmSJEmS6qnqGWmZ+ceIOBw4v/xqqQk4IjMfLCqcJEmSJEmS1ChqebSTzLwoIu4H/h+wFTCE0ppofwQuzMynio8oSZIkSZIk1V9NhTSAzHwSOLQLskiSJEmSJEkNq5Y10iRJkiRJkqQeq+YZaRERwPrAUKB3a2My84GlzCVJkiRJkiQ1lJoKaRFxDPDvlIpo7Wm1wCZJkiRJkiQtr6oupEXEvwP/CbwNXAW8AizsolySJEmSJElSQ6llRto3gWnAFpk5o4vySJIkSZIkSQ2pls0G1gJ+bxFNkiRJkiRJPVEthbQZuPaZJEmSJEmSeqhaCmnXADtHRL+uCiNJkiRJkiQ1qloKaccD/wB+FxFrdlEeSZIkSZIkqSHVstnAY0BfYCvgcxHxFjCrlXGZmRsWEU6SJEmSJElqFLUU0lYEktLOnc0GFBtHkiRJkiRJakxVF9Iyc42uDCJJkiRJkiQ1slrWSJMkSZIkSZJ6rFoe7VxMRKwEfCgzpxeYR5IkSZIkabk3ZcqUXfr06XNiZg7HiUzLg6aIeH3hwoUnjR079pa2BtVUSIuIFYETga8AIyitmdan3LclcBxwQmY+1unYkiRJkiRJy7EpU6bs0q9fvwtGjRq1YMCAATN79eqV9c6k9jU1NcXcuXMHT5069YIpU6Yc3lYxreqKaHkG2gPA94B/As8CUTHkL8COwPjOx5YkSZIkSVq+9enT58RRo0YtGDhw4FyLaMuHXr165cCBA+eOGjVqQZ8+fU5sc1wN5zwO2BQ4ODM3Bf6rsjMz3wXuBnbqTGBJkiRJkqTuIDOHDxgwYF69c6h2AwYMmFd+HLdVtRTS9gb+kJm/KH9uraI6FXB3T0mSJEmS1JP1ciba8qn8e2uzXlZLIW0N4PEOxrwDDK7hnJIkSZIkSdJyoZZC2jvAqh2MWQd4s/NxlhQRm0TE7RHxXkRMi4gfRUTvGo7vFRGPRERGxGeLzCZJkiRJkqSeo5ZdOx8GPhsRH8rMd1p2RsRwYDfgpqLCRcRQ4DbgaWAPYD3gLEoFwOOqPM3B+LipJEmSJEmqs1E/vHGLenzv1NN3f7SI8zz88MP9t9xyy49cf/31z332s599u5pjfvKTn6wybNiwhfvvv/+sIjLUWy0z0s4DVgFuiIj1KzvKn38HDCiPK8q3yufcKzNvzcyLgJOA70TEoI4OLhfi/hP4jwIzSZIkSZIkqQqXX375qtdee+2QeucoStWFtMy8CTgF2BZ4BvgBQES8Xv68DXB8Zt5XYL7dgFsyc05F29WUimvbVXH8ycD9wO0FZpIkSZIkSVIPVMuMNDLzBGAX4H+Bd8vN/YA/ALtk5mnFxmMjSkW6ygwvA++V+9oUEZsCBwLfLTiTJEmSJElSt3f66aevOnz48E0HDBiw+Y477jj61Vdf7VvZf+KJJw4bM2bMxiuttNJmK6+88r/suOOOo5966ql+zf1bbrnlhn/5y19WnDx58soRsUVEbHHeeeetDHDBBResvMUWW2w4ePDgzQYNGrTZVltttcE999yz4rL+GWtVyxppAGTmrcCtXZClNUOB1p6hnVnua8/5wAWZ+UJEjCo4lyRJkiRJUrf1m9/8Zsgxxxyz1vjx4/+x1157zbrzzjtXOvTQQ0dVjnn11Vf7fvOb33xjnXXWWTB79uxeP//5z1fddtttN3r++eefWnnllRf97Gc/e+mLX/ziemuttdb8448/fjrAxhtvPB9g6tSpfb/85S+/tf7668+fP39+XHXVVR/+9Kc/vdGUKVOe2mSTTRbU4UeuSs2FtOVBRHwJ2BD4XA3HHAIcAjBixAgee+yxTn33vusu6tRxPU1nr2+3suYB9U6wJH8v6iKTJk1i0qRJAMyaNWup7gHd8T7rPVHS0irqPru83GO9b0pS1zvjjDNGbLPNNnOuvPLKlwH23nvvOW+++Waf3/3ud6s0j7nsssteaf7zwoUL2WOPPeYMGzZss6uuumrI4Ycf/tYWW2wxb8UVV2xaeeWVF+60007vVp7/Jz/5yfTmPy9atIg999xzzgYbbDDwF7/4xcqVfY2m0QtpM4HBrbQPLfctISJWAH4MnAH0ioghQPPGBAMjYqXMXGJnicz8OfBzgHHjxuVmm23WqcBfuPq1Th3X05x5SOeub7dy7eX1TrCkg86tdwJ1U5ttthknn3wyAOPGjaOz91jonvdZ74mSllZR99nl5R7rfVOSutb777/PX//61xVPPfXUlyvb99prr5mVhbTbb7994PHHHz/y6aefHjh79uzeze3PPfdcPzowZcqU/j/4wQ9WnzJlyof++c9/flCfev755/sX9XN0haoLaRHxPpBVDM3M7PCCVekZWqyFFhFrAivSYu20CgOBNYCzy69KVwMvAqMLyidJkiRJktStTJ8+vc+iRYsYNmzY+5XtI0aMWNj85+eff77vHnvsscGmm2767sSJE19aY401FvTr1y/33HPP9efNm9fumvwzZ87s9ZnPfGaDVVZZ5f1TTjnllXXXXXfBgAEDmg455JBR8+fPj676uYpQy4y0P9F6IW0IpcJUP+BJYE4rYzrrJuB7LWaR7QfMBe5u45h3gB1atA0HrgKOBe4oMJ8kSZIkSVK3MmLEiIW9e/dmxowZK1S2T58+/YM60nXXXTdo3rx5vW6++eYXBg0a1ASlmWyVM9Pacuedd35oxowZK9x0003Pbb755vOa299+++0Oj623qnftzMxPZuY2rbw+CgwDrgB6U8O6ZFW4CJgPTI6IT5XXMZsAnJ2ZHxTsIuKFiLisnHNhZt5V+QL+WB76ZGb+qcB8kiRJkiRJ3coKK6zARhtt9N4NN9wwpLJ98uTJH2z8OHfu3F4RkSussMIHk64uu+yyDy9atChanCvnz5+/WP3pvffe6wUwYMCApua2W2+9deC0adMW2xW0EVVdSGtPuah1EKUZa/9ZxDnL550J7ESpQHc9cBIwETixxdA+5TGSJEmSJElaSt///ven33vvvYO+8pWvrDV58uRBRxxxxOp33XXXB+vY77LLLm83NTXFvvvuO+q6665b6ZRTTlntpJNOWn2llVZabOea0aNHz3vooYc+NGnSpEH33HPPiq+//nrv7bbb7p0VV1yx6cADDxw1efLkQeecc87KX/va19ZdbbXV3l8ySWMpbLOBzFwUEXcC+wCHFXjep4EdOxgzqoP+qUBDP2MrSZIkSZK6t6mn7/5ovTNU62tf+9qsV1999eVzzz13xOTJk1fecsst377wwgun7r333usDbLnllnPPO++8v59++ukj99tvv6Ebbrjhe1deeeXf9t9//3Urz3PSSSdNO/jgg/secMAB677zzju9zz333KlHHnnkW7/61a9ePOaYY9YcP3786LXWWmveOeec8/JZZ501vD4/bfWK3rWzL6UdNSVJUk8wobXNtbvqu2Yvu+9qBF5bScuzrriHea+Slrljjz32H8cee+w/Ktsy84Ni4GGHHfbPww477J+V/a+99tqTlZ832WSTBQ888MBzLc+9zz77zNlnn33+Utm23377Nfy/6IU82gkQEesDX6S0K6YkSZIkSZLUrVQ9Iy0ift7OOdYEti3/+QcF5JIkSZIkSZIaSi2Pdh7cQf8LwI8z89KlyCNJkiRJkiQ1pFoKaeu30d4EzMzMWQXkkSRJkiRJkhpS1YW0zHTtM0mSJEmSJPVYhW02IEmSJEmSJHVntWw2sHVnvyQzH+jssZIkSZIkSVIjqGWNtPuA7OT39O7kcZIkSZIkSVJDqKWQdiqwBbALMBW4H3gdGA58AhgF3Aw8WmhCSZIkSZIkqQHUUkj7H+Dfy6/zMnNRc0dE9Aa+DZwMnJiZDxeaUpIkSZIkSd3a7Nmzew0ZMmTzc889d+qRRx75Vr3ztKaWQtopwB2ZObFlR7modlZE7ESpmLZrQfkkSZIkSZK6hwmDt6jP98726cGC1LJr55bAnzsY82fgXzsfR5IkSZIkSY1m4cKFzJs3L+qdo95qKaT1AtbtYMy6NZ5TkiRJkiRJDWbvvfceNWbMmI1//etfDxk9evRH+vfvP/auu+4a+MUvfnHUGmus8dH+/fuPHTVq1JgjjzxyZGWB7dlnn+0bEVtceumlQ8ePH7/2SiuttNmwYcM2Pfroo0cuWrRose+4/PLLh4waNWpM//79x44bN27Dxx9/vH/LHAsXLuQ73/nOyBEjRny0b9++Y0ePHv2Riy666MOtZb366qsHr7feeh8ZMGDA5ttvv/3oGTNm9H7qqaf6bbXVVhsMGDBg8zFjxmz8pz/9acDSXJdail4PAvtERKuPbUbEZ4B9gAeWJpAkSZIkSZLq77XXXut7/PHHr/Gd73xn+jXXXPM8wNChQxeedtppr0yaNOm5I4444vWrr756lQMPPHCtlseeeOKJawwcOHDRFVdc8be99977rXPOOWfEL3/5y6HN/ffdd9+KBx988Hobb7zxe1dcccULu+2226zx48ev1/I8Rx999OrnnXfe8P333//Nq6666oWPfexj7xx66KHrXHzxxYsV06ZNm9b35JNPHnnCCSe8dtZZZ700ZcqUD339619f+0tf+tK6++yzzz9/9atfvbhw4cIYP378uk1NTZ2+JrWskXYccDdwY0TcDtwDzACGAdsBOwLzgf/odBpJkiRJkiQ1hFmzZvW58cYbn9t6663nNrftuuuu7zT/+dOf/vQ7AwcObDrqqKNGzZs37+X+/ftnc9+WW2759iWXXPIqwJ577jnnjjvuGHzttdcOPfjgg2cCnHrqqcPXXnvteTfeeOPfevXqxb777jtnwYIFceaZZ67efI4ZM2b0vvTSS1c76qijpp955pnTAfbee+8506ZNW+G0004b+c1vfvOfzWPnzJnT5957733mIx/5yHyAJ554YsWLL7542Pnnnz/18MMPfwsgM1/70pe+NPqxxx7rP3bs2HmduSZVz0gr78S5C/A34FPAj4CLyu87ldt3yUwXsJMkSZIkSVrOrbbaau9XFtGampr40Y9+tNp66633kf79+4/t27fvFoceeug6CxYsiBdeeKFv5bE777zznMrP66+//tzp06ev0Pz58ccfH7jLLrvM6tXr/0pT++2336zKY6ZMmTJg3rx5vcaPHz+zsn2fffaZ+dJLL/WbNm3aBxPERo4cOb+5iAYwevToeQC77bbbBzk23njjeQAvv/zyCnRSLTPSyMx7I2IDYBtgLDAYmA1MAe7NzGzveEmSJEmSJC0fVllllfcrP5988smrnXzyyWseeuihr++www5vr7zyygsffPDBgcccc8xac+fOXWwjgqFDhy62IFrfvn1z/vz5H1TN3nzzzRVWW221hZVjRo4cudj3vfrqqysArL766ou1jxgx4n2Af/zjH71Hjhy5EGDQoEFLfF/5Z/igvV+/fgkwd+7cTq/vX1MhDaBcLLun/JIkSZIkSVI3FLH4Jp3XXnvth3fdddeZ559//mvNbU888USnFu9fZZVV3n/jjTcWq0tNmzZtsZlia6yxxvvN7cOHD/+gINY8s23VVVddfPeCZaBTFbiIGBARH42IjxcdSJIkSZIkSY1n3rx5vfr27bvYSv1XX331h9sa355NN9303VtuuWVI5cL/v/vd74ZUjhk7duzc/v37N/32t78dWtk+adKkoWuvvfb85tloy1JNM9IiYgRwDvCF8rHZfI6I+ATwM+DwzHS2miRJkiRJUjey3XbbzfnlL3+52umnn/7u+uuvP/83v/nNh1966aX+nTnXMccc8/oOO+yw8e67777uQQcd9OYTTzwx4Morr1y1csywYcMWHXzwwW+ce+65I/r06ZNbbrnle9dcc82Qu+++e/DFF1/8t2J+qtpUXUiLiOHAQ8AI4H+BVYCtKoY8BKwO7IuPfUqSJEmSJC1uwuzleoPGM844Y9qbb77Z57TTTlsdYNddd5354x//+OXx48ePrvVc22677XuXXHLJ3yZMmLD6V77yldFjxox598orr3xx++2337hy3MSJE1/r06dPXn755audddZZfdZaa635F1544d8POeSQmW2duyvVMiPtREpFtF0z87aIOJGKQlpmvh8R9wLbFpxRkiRJkiRJy9CkSZOmtmwbPHhw0zXXXLNE+5e//OUPCoQbbrjhgsxcomDY2vkOPPDAmQceeOBiBbGWx/bp04eJEydOmzhx4rRash555JFvHXnkkW9VtrWVrRa1rJG2O/A/mXlbO2NeBkYuTSBJkiRJkiSpEdVSSBsGPNfBmPnAwM7HkSRJkiRJkhpTLYW0mcAaHYxZH3i983EkSZIkSZKkxlRLIe1+4PMRsVprnRGxHrAbcFcBuSRJkiRJkqSGUksh7SfAisBdEbEz0B8gIvqVP18PJHB24SklSZIkSZKWH01NTU1R7xCqXfn31tRWf9W7dmbmgxFxKHABcHNF13vl90XAQZn5ZGeCSpIkSZIkdQcR8frcuXMHDxw4cG69s6g2c+fO7R8RbS5bVsuMNDLzEuBfgAuBKcBLwBPAz4HNMvPXS5FVkiRJkiRpubdw4cKTpk6d2vfdd98d4My05UNTU1O8++67A6ZOndp34cKFJ7U1ruoZac0y8xngiKVKJ0mSJEmS1E2NHTv2lilTphz+4osvnpiZw6lxIpPqoikiXl+4cOFJY8eOvaWtQVUX0iLiOeDmzDyykHiSJEmSJEndVLkY02ZBRsunWiqiI4B3uiqIJEmSJEmS1MhqKaQ9DazbVUEkSZIkSZKkRlZLIe0C4HMRMaarwkiSJEmSJEmNqpbNBl4EbgceiIgLgYeB14FsOTAzHygmniRJkiRJktQYaimk3UepaBbA92mlgFah99KEkiRJkiRJkhpNLYW0U2m/eKYCTe0/vt4RWjVq3m/rHUFaPkwYXO8ES5owu94JJEnqvrrif/v93+7uw38+pG6j6kJaZh7XlUEkSZIkSZKkRlbLZgOSJEmSJElSj9VuIS0iToiIbZdVGEmSJEmSJKlRdTQjbQKwfWVDRBwVEX/rqkCSJEmSJElSI+rMo51DgLWLDiJJkiRJkiQ1MtdIkyRJkiRJkqpgIU2SJEmSJEmqgoU0SZIkSZIkqQrVFNKGRMRazS9Ka6QREWtWtrcYU5iI2CQibo+I9yJiWkT8KCJ6d3DMxyLilxHxQvm4ZyPixIjoX2Q2SZIkSZIk9Rx9qhhzVPnV0tQ2xmeV5+1QRAwFbgOeBvYA1gPOolQAPK6dQ/crjz0DeB7YFDi5/L53EdkkSZIkSZLUs3RU8HqZUmGsXr4FDAD2ysw5wK0RMQiYEBFnlttac3pmvlnx+a6ImAdcHBFrZ+ZLXZxbkiRJkiRJ3Uy7hbTMHLWMcrRlN+CWFgWzqynNNNsOuL61g1oU0Zr9ufw+ErCQJkmSJEmSpJo0+mYDGwHPVDZk5svAe+W+WnwcaAJeLCaaJEmSJEmSepJGL6QNBWa10j6z3FeViBhOaU21X2fmGwVlkyRJkiRJUg9SyKYAjSwi+gL/BbwDHN3OuEOAQwBGjBjBY4891qnv23fdRZ06rqXHeh9QyHmKtu+ign6+Tl7fbmXNA+qdYEn+Xorj73cxkyZNYtKkSQDMmjVrqe4BRd1nG8lyfU9clv+sL8/XqTO8tqpBUffZ5eUe25D3za74d7YRf85qeT0W5/WQuo3IrOdeAu2LiDeAn2bmSS3a3wUmZOaPOzg+gKuAnYFPZOYz7Y1vNm7cuHzkkUc6lXnUD2/s1HEtTe0/vpDzFG3UvN8Wcp6pp+9eyHmWaxMG1zvBkibMrneC7sPfb5vGjRtHZ++xUNx9tpEs1/fEZfnPeoP8M7zMeG3VSUtzn11e7rENed/sin9nl+d/N70ei/N6dBsR8Whmjqt3DtVPo89Ie4YWa6FFxJrAirRYO60N5wB7ADtXW0STJEmSJEmSWtPoa6TdBOwSEStVtO0HzAXubu/AiDgGOBz4ambe13URJUmSJEmS1BM0eiHtImA+MDkiPlVex2wCcHZmzmkeFBEvRMRlFZ/HA6cCVwCvRcS/VrxWXbY/giRJkiRJkrqDhn60MzNnRsROwAXA9ZR28JxIqZhWqQ/Qu+Lzp8vvB5Rflb4BXF5sUkmSJEmSJHV3NRfSyjO69gY2BgZm5sEV7esAT2bm3KICZubTwI4djBnV4vMBLFlAkyRJkqSaVLsBw9T+dfzuRtx8QZK6qZoKaRFxEHAe0B8IIIGDy93DgAeBQ4DLWj2BJEmSJEmStJyqeo20iNgZ+DnwHLAn8LPK/sx8CvgL8IUiA0qSJEmSJEmNoJYZaT8ApgPbZeaciNi8lTFPAB8vJJkkSZIkSZLUQGoppI0Drq7cLbMVrwLDly6SJEmqh2rX4qnUFWsCtaVT+Vw3SJIkSQWq+tFOoC/wbgdjhgCLOh9HkiRJkiRJaky1FNKmAlt0MGYr4NlOp5EkSZIkSZIaVC2FtOuAbSLii611RsQ3gE2BSUUEkyRJkiRJkhpJLWuknQl8CbgqIvYBBgNExOHANsBewPPA+UWHlCRJkiRJkuqt6kJaZs6MiO2AK4DKWWnnld/vBcZnZkfrqEmSJEmSJEnLnVpmpJGZLwPbR8SmwMeBlYHZwB8z89EuyCdJkiRJkiQ1hJoKac0y8wngiYKzSJIkSZIkSQ2r6s0GIuLMiNi4K8NIkiRJkiRJjaqWXTu/CzwVEQ9FxGER8eGuCiVJkiRJkiQ1mloKaV8GbgE2p7TBwLSIuCYiPhcRvbsknSRJkiRJktQgqi6kZebvMvMzwBrAD4Dngb2AaykV1c6OiM26JqYkSZIkSZJUXzVvNpCZM4CfAD+JiM2BAyjNVvs2cFREPJmZFtQkSQ1jav/xy+y7Rs377TL7LkmdNGHwMvyu2cvuuyRJUper5dHOJWTmnzPzKGAk8D1gIfDRIoJJkiRJkiRJjaTmGWmVImIwsB/wdeBfgQD8z26SJEmSJEnqdmoupEVEL2AXSsWzzwP9gARuB34FTC4yoCRJkiRJktQIqi6kRcRHga8BXwGGUZp99hxwBXBFZr7aJQmlBjPqhzcWcp6p/Qs5TaEK+9lO372Q80iSJEmS1EhqmZH2ePl9NnApcHlmPlh8JEmSJEmSJKnx1FJI+wNwOfD7zJzfNXEkSZIkSZKkxlR1IS0zd+3KIJIkSZIkSVIj61XvAJIkSZIkSdLyoM0ZaRHxC0q7cR6bmTPKn6uRmXlQIekkSZIkSZKkBtHeo50HUCqknQHMKH+uRgIW0iRJkiRJktSttFdIW6f8/lqLz5IkSZIkSVKP02YhLTNfau+zJEmSJEmS1JNUvdlARJwQEdt2MGabiDhh6WNJkiRJkiRJjaWWXTsnANt3MGZb4MTOhpEkSZIkSZIaVS2FtGqsADQVfE5JkiRJkiSp7ooupI0F3iz4nJIkSZIkSVLdtbdrJxFxR4umAyJi+1aG9gbWBNYGriommiRJkiRJktQ42i2ksfiaaAmMKr9aagLeAn4HHF1ALkmSJEmSJKmhtFtIy8wPHv2MiCZgQmb+qMtTSZIkSWpoU/uPL/yco+b9tvBzSmpAEwZ3wTlnF39OqRUdzUir9A3gz10VRJIkSZIkSWpkVRfSMvNXXRlEkiRJkiRJamS1zEj7QESsAawO9GutPzPvWZpQkiRJkiRJUqOpqZAWEZ8GJgIbdTC0d6cTSZIkSZIkSQ2oV8dDSiLiX4EbgCHABUAA9wCXAM+UP18PuBmBJEmSJEmSup2qC2nAMcA84GOZeVS57c7M/BYwBjgF+BRwTbERJUmSJEmSpPqrpZD2ceB/MnNay+Oz5ATgr8BJBeaTJEmSJEmSGkIthbTBwMsVnxcAA1uMuR/YdmlDSZIkSZIkSY2mlkLaG8DQFp/XazFmBWDA0oaSJEmSJEmSGk0thbTnWLxw9kdg54jYACAihgN7A88XF0+SJEmSJElqDH1qGHszcEpEfDgz/wmcC+wF/DkingbWB1YCvl98TEmSJElSvY364Y1VjZvav47fffruxX+5JJXVMiPtYkrrn70PkJn3A18E/k5p187pwKGZeUXRISVJkiRJkqR6q7qQlplzMvNPmfl2RdvvM3NMZg7IzI0z8+dFB4yITSLi9oh4LyKmRcSPIqJ3FccNjohfRsTMiJgdEVdGxMpF55MkSZIkSVLPUMujnctcRAwFbgOeBvagtEbbWZQKgMd1cPh/ARsABwNNwBnAtcA2XZVXkiSpNdU+jlSpKx6Lakun8vnolCRJ6oEaupAGfIvSLqB7ZeYc4NaIGARMiIgzy21LiIiPA58GtsvMe8ptrwF/iohPZeZtyyi/JEmSJEmSuok2C2kR8bdOnjMzc72Oh1VlN+CWFgWzqynNLtsOuL6d42Y0F9HKoR6KiL+X+yykSZIkSZIkqSbtzUjrBWQnzhmdzNKajYA7Khsy8+WIeK/c11YhbSPgmVba/1ruk6RWdebxptYsy0eyqlXYz+bjXJJ5sal8AAAgAElEQVQkSYC7mLa0PFwP8O+zWjqR2Zla2bIREe8D38vMc1q0vwpckZnHtnHcrcC7mfmFFu2/AdbNzK1bOeYQ4JDyxw2BZwv4ERrFKsCb9Q6hLuPvt/vrDr/jVYBVy38eAEypY5ZqdYfr3qi8tl3Ha9t1Gv3aNvJ9ttGv3bLm9ViS12RxXo/FNdr1WDszV+14mLqrRl8jbZkp7zha+K6jjSAiHsnMcfXOoa7h77f783dcH173ruO17Tpe267jte08r93ivB5L8poszuuxOK+HGk2vzh4YEUMjYs0iw7RiJjC4lfah5b6ij5MkSZIkSZJaVVMhLSI+FBFnRcTrlKZW/r2ib6uI+N+IGFtgvmdosaZZuXi3Iq2vgdbmcWVtrZ0mSZIkSZIktavqQlpEDAYeBI4GplFauL9yY4EngW2ALxeY7yZgl4hYqaJtP2AucHcHxw2PiE82N0TEOGDdcl9P0y0fWdUH/P12f/6O68Pr3nW8tl3Ha9t1vLad57VbnNdjSV6TxXk9Fuf1UEOperOBiDgT+C5wQGZeEREnAidkZu+KMTcAIzOzkFlpETEUeBp4CjiDUiHsbOCczDyuYtwLwN2ZeVBF2y3A+uXMTeXj38jMbYrIJkmSJEmSpJ6llkc79wJuycwr2hnzErD60kX6P5k5E9gJ6A1cD5wETARObDG0T3lMpf0ozVr7BXAF8CiwZ1HZJEmSJEmS1LPUsmvnGsCkDsa8Q+uL/HdaZj4N7NjBmFGttM0CvlF+SZIkSZIkSUullhlpbwOrdTBmHUqbEEiSJEmSJEndSi2FtIeBz7ZY+P8DETEC+AxwXxHBJEmSJEmSpEZSSyHtXGBl4H8jYuPKjvLn/wb6A+cVF0+SJEmSJElqDFXv2glQ3qnzRCCB94EVgJnAUCCAH2Tmj7sgpyRJkiRJklRXNRXSACJiB+BI4F8pzVCbDfwRmJiZdxSeUJIkSZIkSWoANRfSJEmSJEmSpJ6oljXSqhIRqxZ9TkmSJEmSJKneCiukRcTgiDgVeLGoc0qSJEmSJEmNok81gyJibWALShsMPJSZMyr6+gNHA9+ltOnAe12QU5IkSZIkSaqrDmekRcR5lGaZ/TdwLTA1Iv5fuW974FngFGBF4Fxg3a4KK0mSJEmSJNVLu5sNRMTXgV8CTcAz5eaNyu8HARcDvYFLgFMyc1rXRZUkSZIkSZLqp6NC2p3Ax4EdMvPBctu2wK2UCmivAp/LzCeXQVZJkiRJkiSpbjp6tHNT4PfNRTSAzLyH0iOeARxoEU2SJEmSJEk9QUeFtMHAC620P19+f7CVPkmSJEmSJKnb6aiQ1ovSTp0tvQ+QmXMLTyRJkiRJkiQ1oA537QTaXkRNkiRJkiRJ6iGqKaRNiIhFlS/gBICW7eXXwq6NLKloETEhIjIitq93FknqjrzPSlLXiYjLy/fYUfXOIqn7q6aQFjW+qjmnpLKIWD0ijoiImyJiakTMj4i3IuLWiNir3vmWtYjYvvwXobZep9c7o6TlS0QMiohzIuLeiJgWEfMi4o2IeCgivh0RA+udcVnyPiupq0XEcRX3lE/VO8+yFBEHdHCP/Va9M0paOn3a68xMi2JS1zsC+AHwd+BO4HVgbWAv4FMRMTEzv1PHfPVyN3BXK+33LeMckpZ/HwYOAR4CbgT+QWlDpR2BicC/RcTHM3NO/SLWhfdZSYWLiLGUnmB6B/hQnePU03XAY620P7Ksg0gqVruFNEnLxEPA9pl5d2VjRGwM/BE4OiKuzMxH65Kufu7KzAn1DiGpW3gFGJyZS2ygFBG/Ab4CfAs4c1kHqzPvs5IKFRH9gV8DDwMvAvvXN1FdXZuZl9c7hKTiOeNM3UJEfCgiFkTE/S3aB5Qf4cmI2L9F36Hl9gOXbdrFZebklkW0cvtfgd+VP25fxHdFxBYRcXNEvB0RcyLitoj4eBHnltS9Lef32UWtFdHK/rv8vn4R3+V9VlJnLM/32BZOA9YBDgCaij55RHyq/Jj+uxHxz4i4NiI2Kvp7JKk9zkhTt5CZ70TEQ8BWEbFSZr5d7voE0K/8550o/RcyKj4D3L6MYnZG8//xW+pNPCJia+A2oC8wGXgB2IzSYz13LO35u8DoiDgcGETpcdd7M/P5OmeSeqxufJ/9XPn9iaU9kfdZSZ3VHe6xEbEjcBRwdGY+HxFFn38fSv+ReUH5fTrwSeBBCriHd4HNIuLbQH/gNeDOzHy1zpkkFcBCmrqTOyj9ZWNbSmvgQOkvGIsorQPT/JcNIqIXsAPwt8x8qaMTR8QQ4Ns15rk2M1tbF6EqETEI2BtI4A+dPU/5XAH8AhgAfCEzr6voOwo4p8bzbQZ8ocYY52TmrBrGf6X8qvzeScC/ZebMGr9bUjGW6/tsRPQBjit//DCwDaVC153AJTV+d8tze5+VtLSW23tsRAwGLgfuBc6r8XuqOf+HgIspzXLbJjMfqeibSI0/W5R2UN6+lmM68Sj8US0+L4r/3969h+la1/Xif38EFVBAPAWWspRMtnZuVaIiCpohtk1L3VrtNLnwUGkHKTLbLvDSCzXRvcM0EzMqsmyZJ8QDqHhKa6HYT4UUc2FKSuBaEALK4fv7475HnzXrmTX3zJpnnmdmXq/rmuth7uPnuWf4rJn3fO/vXfX6JL/VWrtxiccCZoggjfXkgiR/lO6HjNEfPi5KNzLgzKr6gdba59P94nTnJFsHHvtOSV64xHq2Z/wEo4vqfyF7fZLvSfKn/W2ee+NBSe6X5EOjv9z1zkz3wIMjlnC8H83Sr8cbkwz5Be+/kpyS7mu4Pd1f8TYneUm6YPHQqnpoa23FbxcAFrXW++y+Y87xV0mevQK/1OizwN5ayz32T/p6HtZaa0s8zxCP7Y9/9miI1tuS5GnpHiIz1MOy9OuxZeB2X0rX89+b5Cvp6npIutten5FuFPBTlnhuYIaYI4315J+S3JD+r3X9X8Z+PN0PJXO31Mz9Je/Y/nXQrTatte2ttVrixxv34r28IskT0v1VbyWe2Pnj/eu4udhuyRKf0NZae+Myrsf2gcf+bGvtpa21z7TWrmutXdVae3e6H3i+lO4vtT+3x4MAk7Km+2xr7cbWWqX7+ef70s3h84gk26pq01KONYY+C+ytNdljq+oX0j1U4Pdaa/8+6J0u3Z567DVZ4h+vW2tblno9lnDsC1trZ7bWPt9au7619p+ttTenG0G4I8mTq+pHllIvMFsEaawbrbVvp/tF5Yeq6m7pfiHYJ8kF/Yiu/8x3f/g4Lt0tkzM3Z01VvSzJbyf5UJJHt9a+tQKHnfsL3dcXWP+1FTjHRLXWrk1yTv/pQ6dZC2xU66XPts5XW2t/meTx6UaSnbmXh9Vngb2yFntsVd05yWvThX2vmeCp1kOP/Y8k7+o/1WNhDXNrJ+vN+5M8Mt0PFw9KcmOSj46sO76qbp9uXpzPttauHHLQ1ZojbWSOhw8keUxr7folnnMh1/Sv37PA+kOXcrBVmrtnnP/qX++wl8cBlm9N99n5Wmsfr6qd2funI+uzwEpYaz32Xknu2td76wIPGHhfv/y3W2tLmi9yxEr32Idl8nOkjaPHwjogSGO9mXtq0XFJjkrysfbdeW8uSDep8rPS/eO1lCccTXReiX5OtDOTPDvJ+5I8trV2wxLPtyef7F+PGXPufdLN27AUk5y7Z08e2L9O6rYBYHFrss8upKoOTDdfzX8vtu0i9FlgJay1Hnt1krMWWPfQJPdNcl6SK5J8ZonnHzXaY98wuqK/BfZHl3i8h2Vyc6TtyU/3r3osrGHVJjIXJExH/8vK1ekei323JH/YWntJv+7wdD8QXJnk7unCqrdPqdTv6EO01yU5Md0PGo9vAya9rqqWJEPmbOjPcUm625f29DS5h7fWPrjU97CSqmpz230S2VTVLyc5O8lNSe43dC4gYGWt0T77Q0m+ML+3VtXt0j2t838nOae1Nv8JlvossKrWYo9dSFW9McmvJnlka+38ees2pZuT8fLW2qYBx7pjki8nOTDJUW3hp3bee9q9a1yP7Z+y+vvpHupyVZIj+tvpgTXIiDTWldbaLVX1wXRP9klG/lLXWru8qr6Y7qlpc48RnwX/J12IdkO6v/qdMmZY/MWttbfOfdL/Y5x072NRrbVWVU9PN9pta1W9Jcll6f56d1ySdyf52b15EyvoH6rq5iTb0j3paL8kP5nkp5LcnOQZ0/4BCTayNdpnn57kaVX10SSXpxu1dY8kP5PudqB/S/K80R30WX0WpmGN9tjlmOuxNw/ZuLV2XVWdlOTvkny4qv4u3ZxxD0nyg+nmFp6Vecf+pao+k+TTSb6abn63B6er8/okvyREg7VNkMZ6dEG6Hz6uTfdLwvx1RyS5qH/Czyy4d/+6f5I/WGCbv0zy1pHPf6h/fdPQk7TWPlpVRyd5cZLj+8WfSDe0/VGZnV/wXpPuKXoPTjfnRqX7IeSN6eb/+fT0SgN6a63PvjnJHdPdJnVUuhEN1yb5XLqnJP/pmDkp9VlgWtZaj12O5fTYf6iqn013S+YTk3wrXYB2VJJTMjtB2h+n+8PEsUnunOTWdKPpXp3kjAk+2RRYJTN9a2dVfX+Sk9M1xwck+XBr7WED9js43S0UP5/urx3vTPKc1trVk6sWVk9VPSfd9/gPtdY+O+16ANYbfRZgcqrqjCTPSHJ4a+2qadcDsBSzPiLtAUkeneTjSW67hP3+PskPpLtd7tYkL003mufolS4QpuSYJG/3yx3AxOizAJNzTJI/F6IBa9Gsj0i7TWvt1v6//yHJXRcbkVZVRyX5WJJjWmsf6pf9VLpbK3ab6BIAAAAAhrjN4ptMz1yItkTHJ/n6XIjWH+ef0z0V5vgF9wIAAACAPZjpIG2Zjkxy6Zjll/TrAAAAAGDJ1mOQdki6x9rPt6NfBwAAAABLNusPG1g1VXVSkpOSZP/99/+JTZs2TbcggHVkx44d2bmz+xtHVUWPBVhZ+izA6rjkkkuuaq3dbdp1MD3rMUjbkWTcN/Uh/bqxWmuvS/K6JNm8eXPbtm3bZKoD2OA2b94cPRZgcvRZgMmpqsunXQPTtR5v7bw04+dCW2juNAAAAABY1HoM0s5LcmhVPWRuQVVtTnKffh0AAAAALNlM39pZVQckeXT/6fcmOaiqfrH//F2tteur6rIkF7bWnp4krbV/qqr3Jjm7qp6X5NYkL03ykdba+av8FgAAAABYJ2Y6SEty9yRvnrds7vN7J9me7j3sM2+bJyV5ZZI3pBt1984kz5lYlQAAAACsezMdpLXWtiepRbbZNGbZziRP6z8AAAAAYK+txznSAAAAAGDFCdIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAPtOu4D1ZtMp5067hDVh++knTLsEAAAAgCUxIg0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGCAfaddAAAsxaZTzp12CStu++knTLsEAABggJkfkVZV96+qC6rq+qq6oqpOq6p9Buy3uareW1Xf6D/Or6qfXo2aAQAAAFh/ZjpIq6pDkpyfpCV5bJLTkvxuklMX2e+e/X77JvmV/mPfJO+rqsMnWTMAAAAA69Os39r5zCT7J3l8a+3adEHYQUm2VNXL+mXjnJDkwCSPa61dkyRV9bEkVyV5dJLXTL50AAAAANaTmR6RluT4JO+ZF5i9KV24dswe9rttkpuTfHNk2XX9slrpIgEAAABY/2Y9SDsyyaWjC1prX05yfb9uIVv7bV5RVXevqrsneWWSHUnePKFaAQAAAFjHZj1IOyTJzjHLd/TrxmqtXZHk4Ul+IcnX+4/HJ3lUa+2/JlAnAAAAAOvcrM+RtixVdVi6kWcXJTmxX/zrSc6tqgf1o9rm73NSkpOS5LDDDsvFF1+8rHM/8T63LGu/jWa51xdYm7Zu3ZqtW7cmSXbu3LlXPWA99lk9EdhbK9lnAYCFVWtt2jUsqKquTPLq1tqp85Z/M8mW1trLF9jvjHQj0O7bWrupX3a7JF9I8rbW2nP2dN7Nmze3bdu2LavmTaecu6z9Nprtp58w7RKAKdm8eXOW22OT9dln9URgJe1tnwVgYVV1UWtt87TrYHpm/dbOSzNvLrSqumeSAzJv7rR5jkzy2bkQLUlaa99O8tkkR0ygTgAAAADWuVkP0s5L8qiqOnBk2ZOS3JDkwj3sd3mSH+xHoSVJqur2SX4wyfYJ1AkAAADAOjfrQdprk3wryVuq6hH9PGZbkpzRWrt2bqOquqyqzhrZ7/VJ7pHkH6vqhKp6TJK3JjksyetWrXoAAAAA1o2ZDtJaazuSHJdknyTvSHJqklcmeeG8Tfftt5nb76IkP5vkwCR/leTsdLeDPrK19unJVw4AAADAejPzT+1srX0uybGLbLNpzLILklwwobIAAAAA2GBmekQaAAAAAMwKQRoAAAAADCBIAwAAAIABZn6ONAAAYHVsOuXcaZcwyPbTT5h2CQBsUEakAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAPMfJBWVfevqguq6vqquqKqTquqfQbu+/iq+pequqGqrq6qd1fVHSZdMwAAAADrz0wHaVV1SJLzk7Qkj01yWpLfTXLqgH1PTHJOkvOSHJ/kxCRfSLLvpOoFAAAAYP2a9VDpmUn2T/L41tq1Sd5XVQcl2VJVL+uX7aaq7prklUl+s7X25yOr/nHiFQMAAACwLs30iLR0I8neMy8we1O6cO2YPez3xP71LydVGAAAAAAby6wHaUcmuXR0QWvty0mu79ct5KeT/FuSp1fVV6rqpqr6RFU9aHKlAgAAALCezfqtnYck2Tlm+Y5+3UIOTXK/JC9I8ntJru5f311V922tfX3+DlV1UpKTkuSwww7LxRdfvKyCn3ifW5a130az3OsLrE1bt27N1q1bkyQ7d+7cqx6wHvusngjsrZXqs2ulx+qbAExLtdamXcOCquqmJCe31l41b/lXkpzdWnv+Avu9N8kjkxzfWnt3v+ygJJcnObO19kd7Ou/mzZvbtm3bllXzplPOXdZ+G83200+YdgnAlGzevDnL7bHJ+uyzeiKwkvamz66VHqtvAtNSVRe11jZPuw6mZ9Zv7dyR5OAxyw/p1+1pv5bkg3ML+nnWLkpy/xWsDwAAAIANYtaDtEszby60qrpnkgMyb+60eS5JUv3HLrsnuXUlCwQAAABgY5j1IO28JI+qqgNHlj0pyQ1JLtzDfu/sXx8+t6CqDk7yE0k+vdJFAgAAALD+zXqQ9tok30rylqp6RP9AgC1Jzuhv1UySVNVlVXXW3OettW1J3pbkrKr61ao6Icnbk9yU5NWr+QYAAAAAWB9mOkhrre1IclySfZK8I8mpSV6Z5IXzNt2332bULyd5a5IzkvxDuhDt2P6YAAAAALAk+067gMW01j6X5NhFttk0Ztl1SZ7VfwAAAADAXpnpEWkAAAAAMCsEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAAD7DvtAmDD2nLwtCvY3ZZrpl0BAAAAzCwj0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYYOaDtKq6f1VdUFXXV9UVVXVaVe2zhP1vU1XbqqpV1WMmWSsAAAAA69e+0y5gT6rqkCTnJ/lckscmOSLJK9IFgC8YeJgTk3zfRAoEAAAAYMOY9RFpz0yyf5LHt9be11p7bZJTk/xOVR202M59EPfiJH842TIBAAAAWO9mPUg7Psl7WmvXjix7U7pw7ZgB+78oyUeTXDCB2gAAAADYQGY9SDsyyaWjC1prX05yfb9uQVX1w0l+LcnzJlYdAAAAABvGTM+RluSQJDvHLN/Rr9uTP0lyZmvtsqratNiJquqkJCclyWGHHZaLL754aZX2nnifW5a130az3Ou7rtzzqdOuYHe+LkzI1q1bs3Xr1iTJzp0796oHrMc+qycCe2ul+uxa6bH6JgDTUq21adewoKq6KcnJrbVXzVv+lSRnt9aev8B+/yvJq5L8QGvt2j5I+1KSn2utvXOx827evLlt27ZtWTVvOuXcZe230Ww//YRplzB9Ww6edgW723LNtCtgA9i8eXOW22OT9dln9URgJe1Nn10rPVbfBKalqi5qrW2edh1Mz6zf2rkjybi04ZB+3W6q6rZJXp7kpUluU1V3SjL3YII7VNWBkygUAAAAgPVt1oO0SzNvLrSqumeSAzJv7rQRd0jyfUnOSBe27Ujy6X7dm5J8aiKVAgAAALCuzfocaeclObmqDmyt/Xe/7ElJbkhy4QL7XJfk4fOWHZrkb5M8P8n7J1EoAAAAAOvbrAdpr03ynCRvqaqXJrlPki1JzmitXTu3UVVdluTC1trTW2s3J/ng6EFGHjbw/7XWPjH5sgEAAABYb2Y6SGut7aiq45KcmeQd6Z7g+cp0YdqofZPss7rVAQAAALCRzHSQliSttc8lOXaRbTYtsn57klq5qgAAAADYaGY+SAMAZtiWcQ/XntS5rlm9c80C1xZYyybRw/QqYAbM+lM7AQAAAGAmCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMIAgDQAAAAAGEKQBAAAAwACCNAAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIAB9p12AYy3fb+nTLuEsTbdeM60S4C1YcvB065gd1uumXYFALB+TeLffv92rx++P2DdMCINAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMMDMB2lVdf+quqCqrq+qK6rqtKraZ5F9frKq/qKqLuv3+7eqemFV7bdadQMAAACwvuw77QL2pKoOSXJ+ks8leWySI5K8Il0A+II97PqkftuXJvlCkh9O8qL+9RcmWDIAAAAA69RMB2lJnplk/ySPb61dm+R9VXVQki1V9bJ+2Tint9auGvn8g1V1Y5I/q6rDW2uXT7huAAAAANaZWQ/Sjk/ynnmB2ZvSjTQ7Jsk7xu00L0Sb86n+9R5JBGkAAMCiNp1y7qDttk9gEpnB5z79hJU/OQBjzfocaUcmuXR0QWvty0mu79ctxVFJbk3yxZUpDQAAAICNZNaDtEOS7ByzfEe/bpCqOjTdnGp/1Vq7coVqAwAAAGADmfVbO/daVd0uyd8nuS7Jb+9hu5OSnJQkhx12WC6++OJlne+J97llWfvNd/E+T12R46y0J96yQu9vmdd3XbnnU6ddwe58XVaOr+8utm7dmq1btyZJdu7cuVc9YKX67CxZ0z1xNb/X1/J1Wg7XliVYqT67VnrsavbNoddkEj+/D/3Zeyb/HZlED5vF9zmU6wHrRrXWpl3DgqrqyiSvbq2dOm/5N5Nsaa29fJH9K8nfJnlkkge31i7d0/ZzNm/e3LZt27asmofOY7CY7fs9ZUWOs9I23XjOihzHPA5Jthw87Qp2t+WaaVewfvj6Lmjz5s1Zbo9NVq7PzpI13RNX83t9Rr6HV41ryzLtTZ9dKz12Nfvm8DnSVv7n96E/e8/kvyOT6GFruVe5HutGVV3UWts87TqYnlkfkXZp5s2FVlX3THJA5s2dtoBXJXlskonIYtAAABhaSURBVEcODdEAAAAAYJxZnyPtvCSPqqoDR5Y9KckNSS7c045V9QdJfiPJL7fWPjK5EgEAAADYCGY9SHttkm8leUtVPaKfx2xLkjNaa9fObVRVl1XVWSOfPyXJS5KcneSrVfXAkY+7re5bAAAAAGA9mOlbO1trO6rquCRnJnlHuid4vjJdmDZq3yT7jHz+M/3rU/uPUU9L8saVrRQAAACA9W6mg7Qkaa19Lsmxi2yzad7nT83uARoAAAAALNus39oJAAAAADNBkAYAAAAAAwjSAAAAAGAAQRoAAAAADDDzDxsAAFbHplPOXfI+2/ebQCELWFZ9p58wgUoAANiojEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIABBGkAAAAAMMC+0y4AACZt+35PWbVzbbrxnFU7F7BMWw5exXNds3rnAgAmTpAGS7TplHNX5Djb91uRw6yoFXtvp5+wIscBAACAWeLWTgAAAAAYQJAGAAAAAAMI0gAAAABgAEEaAAAAAAwgSAMAAACAAQRpAAAAADCAIA0AAAAABhCkAQAAAMAAgjQAAAAAGECQBgAAAAADCNIAAAAAYABBGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhg32kXAAAArD3b93vKih9z043nrPgxgRm05eAJHPOalT8mjGFEGgAAAAAMIEgDAAAAgAEEaQAAAAAwgCANAAAAAAYQpAEAAADAAII0AAAAABhAkAYAAAAAAwjSAAAAAGAAQRoAAAAADCBIAwAAAIAB9p12AQAAAKwNm045d9B22/eb4rlPP2HlTw7QMyINAAAAAAaY+SCtqu5fVRdU1fVVdUVVnVZV+wzY7+Cq+ouq2lFV11TV31TVXVajZgAAAADWn5m+tbOqDklyfpLPJXlskiOSvCJdAPiCRXb/+yQ/kOTEJLcmeWmStyY5elL1AgCMM/R2pFGTuC1qIcuqz61TAMAGNNNBWpJnJtk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" ] @@ -1285,14 +1468,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: 0.6529999999999999, 4: 0.632, 5: 0.6298}, 3: {3: 0.6756, 4: 0.6828000000000001, 5: 0.7012}, 4: {3: 0.7204999999999999, 4: 0.7421, 5: 0.7487}}\n" + "{2: {3: 0.8758000000000001, 4: 0.8847999999999999, 5: 0.8708}, 3: {3: 0.869, 4: 0.8192, 5: 0.8215999999999999}, 4: {3: 0.8262, 4: 0.7928, 5: 0.765}}\n", + "{2: {3: 0.9950000000000001, 4: 0.9947999999999999, 5: 0.9942}, 3: {3: 0.991, 4: 0.9838, 5: 0.9845999999999999}, 4: {3: 0.9976, 4: 0.9941999999999999, 5: 0.9962000000000001}}\n", + "{2: {3: 0.6258000000000001, 4: 0.6347999999999999, 5: 0.6208}, 3: {3: 0.744, 4: 0.6942, 5: 0.6965999999999999}, 4: {3: 0.7637, 4: 0.7303, 5: 0.7024999999999999}}\n" ] } ], @@ -1303,45 +1488,39 @@ "\n", "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", "# this is equivalently wrapped up in the following\n", - "assert pr_succ_arr == get_success_probabilites(noisy_results, ideal_results)\n", - "pr_succ_rand = [1/2**w for w in widths]\n", + "assert pr_succ_arr == get_success_probabilities(noisy_results, ideal_results)\n", + "\n", + "# count as success even if there are log many bits incorrect.\n", + "pr_succ_allow_log_errors = get_success_probabilities(noisy_results, ideal_results, \n", + " allowed_errors = basement_log_function)\n", "\n", - "ideal_distrs = {w: np.asarray([[1] + [0 for _ in range(w)]]).T for w in widths}\n", - "rand_distrs = {w: np.asarray([get_random_hamming_wt_distr(w)]).T for w in widths}\n", + "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", + "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", + "\n", + "pr_succ_rand = {w: 1/2**w for w in widths}\n", + "pr_succ_rand_allow_log_errors = {w: sum(rand_distrs[w][0:basement_log_function(w)+1]) for w in widths}\n", "\n", "# total variation distance\n", - "tvd_noisy_ideal = {w: {d: tvd(np.asarray([distr]).T, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", + "tvd_noisy_ideal = {w: {d: get_total_variation_dist(distr, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", " for w, d_distrs in avg_err_hamm_distrs.items()}\n", "\n", + "# tvd_noisy_ideal is equivalent to 1 - success probability.\n", "np.testing.assert_allclose([pr for d_vals in pr_succ_arr.values() for pr in d_vals.values()], \n", " [1 - val for d_vals in tvd_noisy_ideal.values() for val in d_vals.values()])\n", "\n", - "tvd_noisy_rand = {w: {d: tvd(np.asarray([distr]).T, rand_distrs[w]) for d, distr in d_distrs.items()}\n", + "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", " for w, d_distrs in avg_err_hamm_distrs.items()}\n", "\n", - "print(tvd_noisy_rand)\n", - "\n", - "# pcheck_log_errors = []\n", - "# pcheck_log_errors_rand = []\n", - "# tvd_rand = []\n", - "# tvd_ideal = []\n", - "\n", - "# for dep in range(1, df_fn_depth.Depth.max()+1):\n", - "# idx = df_fn_depth['Depth']== dep\n", - "# depth_vec.append(dep)\n", - "# pcheck.append(df_fn_depth[idx]['Pr. success data'].mean()) \n", - "# pcheck_rand.append(df_fn_depth[idx]['Pr. success rand'].mean())\n", - "# pcheck_log_errors.append(df_fn_depth[idx]['Pr. success loge data'].mean())\n", - "# pcheck_log_errors_rand.append(df_fn_depth[idx]['Pr. success loge rand'].mean())\n", - "# tvd_ideal.append(df_fn_depth[idx]['TVD(data, ideal)'].mean())\n", - "# tvd_rand.append(df_fn_depth[idx]['TVD(data, rand)'].mean())" + "print(pr_succ_arr)\n", + "print(pr_succ_allow_log_errors)\n", + "print(tvd_noisy_rand)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Success probablity and success probablity including a small number of errors" + "## Success probablity and success probablity including a small number of errors" ] }, { @@ -1353,28 +1532,29 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 81, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'depth_vec' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m--------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth_vec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpcheck\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Sucess Probablity'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdepth_vec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpcheck_rand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'random guess'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.05\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Depth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pr(success)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'depth_vec' is not defined" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", - "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", + "w=3\n", + "plt.scatter(depths, [pr_succ_arr[w][d] for d in depths], label='Sucess Probability')\n", + "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", "plt.ylim([-0.05,1.05])\n", "plt.xlabel('Depth')\n", + "plt.xticks(depths)\n", "plt.ylabel('Pr(success)')\n", - "plt.title('Pr(success) vs Depth for Width = {}'.format(wid))\n", + "plt.title('Pr(success) vs Depth for Width = {}'.format(w))\n", "plt.legend()\n", "plt.show()" ] @@ -1385,40 +1565,52 @@ "source": [ "**Sucess if we allow for a small number of errors**\n", "\n", - "Some near term algorithms have robustness to noise. In light of that we might want to consider answers that are only a little wrong successes.\n", + "Some near term algorithms have robustness to noise. In light of that we might want to consider as successes answers that are only a little wrong.\n", "\n", - "To make this notion formal we allow a logarithmic number of bits to flip from the correct answer and call all such instances \"success\".\n", + "To make this notion formal we allow a logarithmic number of bits to be flipped from the correct answer and call all such instances \"success\".\n", "\n", "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", "\n", - "$\\log_2 ({\\rm number\\ of\\ bits}) -1$\n", + "$\\log_2 ({\\rm number\\ of\\ bits})$\n", "\n", "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", "\n", "\n", - "Supose we have a circuit of width 4, this means correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4)-1 = 1$.\n", + "Supose we have a circuit of width 4 so that the correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4) = 2$.\n", "\n", - "So any string with hamming weight zero or one counts as a success.\n", + "So any string with hamming weight zero, one, or two counts as a success.\n", "\n", "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.figure()\n", - "plt.scatter(depth_vec,pcheck,label='Sucess Probablity')\n", - "plt.plot(depth_vec,pcheck_rand,label='random guess')\n", - "plt.scatter(depth_vec,pcheck_log_errors,label='Sucess Probablity + log errors')\n", - "plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", - "plt.ylim([-0.05,1.05])\n", + "w=4\n", + "plt.scatter(depths, [pr_succ_arr[w][d] for d in depths], label='Sucess Prob')\n", + "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", + "plt.scatter(depths, [pr_succ_allow_log_errors[w][d] for d in depths], label='Sucess Prob w/ log errors')\n", + "plt.plot(depths, [pr_succ_rand_allow_log_errors[w] for _ in depths], label='random guess w/ log errors')\n", + "plt.ylim([-0.05, 1.05])\n", "plt.xlabel('Depth')\n", - "plt.ylabel('Pr(success+log errors)')\n", - "plt.title('Pr(success+log errors) vs Depth for Width = {}'.format(wid))\n", - "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.xticks(depths)\n", + "plt.ylabel('Pr(success)')\n", + "plt.title('Pr(success) (& w/ log errors) vs Depth for Width = {}'.format(w))\n", + "plt.legend()\n", "plt.show()" ] }, @@ -1431,19 +1623,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure()\n", - "plt.scatter(depth_vec,tvd_ideal,label='TVD(data, ideal)')\n", - "plt.scatter(depth_vec,tvd_rand,label='TVD(data, rand)')\n", - "plt.scatter(depth_vec,1-np.asarray(pcheck),label='1-Sucess Probablity',alpha=0.33,marker='^',s=80)\n", - "#plt.plot(depth_vec,pcheck_log_errors_rand,label='random guess + log errors')\n", + "plt.scatter(depths, [tvd_noisy_ideal[w][d] for d in depths], label='TVD(data, ideal)')\n", + "plt.scatter(depths, [tvd_noisy_rand[w][d] for d in depths], label='TVD(data, rand)')\n", + "plt.scatter(depths, 1-np.asarray([pr_succ_arr[w][d] for d in depths]),\n", + " label='1 - Pr[Success]', alpha=0.33, marker='^', s=80)\n", "plt.ylim([-0.05,1.05])\n", "plt.xlabel('Depth')\n", + "plt.xticks(depths)\n", "plt.ylabel('Total variation distance')\n", - "plt.title('Width = {}'.format(wid))\n", + "plt.title('Width = {}'.format(w))\n", "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", "plt.show()" ] @@ -1452,76 +1656,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Plot depth = width" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "max_idx = min([max(res_df['Depth']),max(res_df['Width'])])\n", - "\n", - "for idx in range(1,max_idx+1):\n", - " distz = get_hamming_dist(res_df, idx, idx)\n", - " # combine data from different subgraphs\n", - " avg_dist = distz['Hamming dist. data'].mean()\n", - " # rand data\n", - " rand_dist = distz['Hamming dist. rand'][0]\n", - " dep = idx\n", - " wid = idx\n", - " x_labels = np.arange(0, len(avg_dist))\n", - " plt.subplot(1,max_idx,idx)\n", - " plt.bar(x_labels, avg_dist, width=0.61, align='center')\n", - " plt.bar(x_labels, rand_dist, width=0.31, align='center')\n", - " plt.xticks(x_labels)\n", - " plt.xlabel('Hamming Weight of Error')\n", - " plt.ylabel('Relative Frequency of Occurence')\n", - " plt.ylim([0,1])\n", - " plt.grid(axis='y', alpha=0.75)\n", - " plt.legend(['data','random'])\n", - " plt.title('Depth = {}, Width = {}'.format(dep,wid))\n", - "plt.subplots_adjust(bottom=0.1, right=3.2, top=0.9)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the distribution of sublattice widths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G = perfect_qc.qubit_topology()\n", - "len(perfect_qc.qubit_topology())\n", - "# distribution of graph lengths\n", - "distr = []\n", - "for num_nodes in range(1, len(G.nodes) + 1):\n", - " listg = generate_connected_subgraphs(G, num_nodes)\n", - " distr.append(len(listg))\n", - "\n", - "cir_wid = list(range(1, len(G.nodes) + 1))\n", - "plt.bar(cir_wid, distr, width=0.61, align='center')\n", - "plt.xticks(cir_wid)\n", - "plt.xlabel('sublattice / circuit width')\n", - "plt.ylabel('Frequency of Occurence')\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.title('Distribution of sublattice widths')\n", - "disty" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot success probablity landscape" + "## Plot success probablity landscape" ] }, { @@ -1537,103 +1672,119 @@ "metadata": {}, "outputs": [], "source": [ - "values = np.asarray([munged['Pr. success data'][idx] for idx in munged.index])\n", - "values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "values_rand = np.asarray([munged['Pr. success rand'][idx] for idx in munged.index])\n", - "values_rand" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.arange(min(res_df['Depth']), max(res_df['Depth'])+1)\n", + "widths = list(avg_err_hamm_distrs.keys())\n", + "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", + "\n", + "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "# count as success even if there are log many bits incorrect.\n", + "pr_succ_allow_log_errors = get_success_probabilities(noisy_results, ideal_results, \n", + " allowed_errors = basement_log_function)\n", "\n", - "y = np.arange(min(res_df['Width']), max(res_df['Width'])+1)\n", + "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", + "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", "\n", - "X, Y = np.meshgrid(x, y)" + "pr_succ_rand = {w: 1/2**w for w in widths}\n", + "pr_succ_rand_allow_log_errors = {w: sum(rand_distrs[w][0:basement_log_function(w)+1]) for w in widths}\n", + "\n", + "# total variation distance\n", + "tvd_noisy_ideal = {w: {d: get_total_variation_dist(distr, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ - "(x1,x2) = X.shape\n", - "Zdata = np.reshape(values,(x2,x1)).T\n", - "Zrand = np.reshape(values_rand,(x2,x1)).T" + "X, Y = np.meshgrid(widths, depths)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ - "Zdata" + "Zdata = np.reshape([pr_succ_arr[w][d] for d in depths for w in widths], X.shape)\n", + "Zrand = np.reshape([pr_succ_rand[w] for d in depths for w in widths], X.shape)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", + "extent = min(depths)-0.5, max(depths)+0.5, min(widths)-0.5, max(widths)+0.5\n", "ax = plt.gca()\n", "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "xticks = depths\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(map(str, xticks))\n", "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "yticks = widths\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels(map(str, yticks))\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", "plt.title('Success Probability')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", + "xticks = depths\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(map(str, xticks))\n", "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", + "yticks = widths\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels(map(str, yticks))\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", "plt.title('Success Probability of Random Guess')\n", "plt.show()" ] @@ -1780,6 +1931,48 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the distribution of sublattice widths" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = perfect_qc.qubit_topology()\n", + "len(perfect_qc.qubit_topology())\n", + "# distribution of graph lengths\n", + "distr = []\n", + "for num_nodes in range(1, len(G.nodes) + 1):\n", + " listg = generate_connected_subgraphs(G, num_nodes)\n", + " distr.append(len(listg))\n", + "\n", + "cir_wid = list(range(1, len(G.nodes) + 1))\n", + "plt.bar(cir_wid, distr, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 28033bb2..16a2d9c3 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -4,18 +4,17 @@ import numpy as np import random import itertools -import pandas as pd from scipy.spatial.distance import hamming from scipy.special import comb from dataclasses import dataclass, field -from functools import partial import matplotlib.pyplot as plt from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate from pyquil.quilatom import QubitPlaceholder from pyquil.quil import Program, address_qubits, merge_programs from pyquil.api import QuantumComputer, BenchmarkConnection -from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET +from pyquil.gates import * +from pyquil.paulis import exponential_map, sX, sZ from rpcq.messages import TargetDevice from rpcq._utils import RPCErrorError @@ -33,7 +32,8 @@ def make_default_pattern(num_generators): """ return [(list(range(num_generators)), 'n')] -# TODO: perhaps best for pattern to be sample-time specified given ambiguity in append +# TODO: perhaps best for pattern to be sample-time specified, given ambiguity in append; however, +# it convenient to keep a persistent state. @dataclass class CircuitTemplate: @@ -43,12 +43,6 @@ class CircuitTemplate: def __post_init__(self): self.pattern = make_default_pattern(len(self.generators)) - # def create_unit(self): - # # returns a function that can be used as a generator in another template - # return lambda qc, graph, width, depth, sequence: sum(gen(qc, graph, width, depth, - # sequence) for gen in - # self.generators) - def append(self, other): """ Mutates the CircuitTemplate object by appending new generators @@ -218,12 +212,8 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog -def dagger_all_prior(sequence: List[Program]): - return merge_programs(sequence).dagger() - - -def dagger_previous(sequence: List[Program]): - return sequence[-1].dagger() +def dagger_previous(sequence: List[Program], n: int = 1): + return merge_programs(sequence[-n:]).dagger() def _qubit_perm_to_bitstring_perm(qubit_permutation: List[int]): @@ -252,6 +242,7 @@ def random_qubit_permutation(graph: nx.Graph): def random_su4_pairs(graph: nx.Graph): qubits = list(graph.nodes) prog = Program() + # ignore the edges in the graph for q1, q2 in zip(qubits[::2], qubits[1::2]): matrix = haar_rand_unitary(4) gate_definition = DefGate(f"RSU4_{q1}_{q2}", matrix) @@ -261,6 +252,14 @@ def random_su4_pairs(graph: nx.Graph): return prog +def maxcut_cost_unitary(graph: nx.Graph, layer_number): + prog = Program() + theta = prog.declare('theta_' + str(layer_number), memory_type='REAL') + for edge in graph.edges: + exponential_map(sZ(edge[0] * sZ(edge[1])))(theta) + return prog + + def graph_restricted_compilation(qc, graph, program): qubits = list(graph.nodes) @@ -301,44 +300,40 @@ def graph_restricted_compilation(qc, graph, program): def get_rand_1q_template(gates: Sequence[Gate]): def func(graph, **kwargs): - partial_func = partial(random_single_qubit_gates, gates=gates) - return partial_func(graph) + return random_single_qubit_gates(graph, gates=gates) return CircuitTemplate([func]) def get_rand_2q_template(gates: Sequence[Gate]): def func(graph, **kwargs): - partial_func = partial(random_two_qubit_gates, gates=gates) - return partial_func(graph) + return random_two_qubit_gates(graph, gates=gates) return CircuitTemplate([func]) def get_rand_1q_cliff_template(bm: BenchmarkConnection): def func(graph, **kwargs): - partial_func = partial(random_single_qubit_cliffords, bm=bm) - return partial_func(graph=graph) + return random_single_qubit_cliffords(bm, graph) return CircuitTemplate([func]) def get_rand_2q_cliff_template(bm: BenchmarkConnection): def func(graph, **kwargs): - partial_func = partial(random_two_qubit_cliffords, bm=bm) - return partial_func(graph=graph) + return random_two_qubit_cliffords(bm, graph) return CircuitTemplate([func]) def get_dagger_all_template(): def func(qc, sequence, **kwargs): - prog = dagger_all_prior(sequence) + prog = dagger_previous(sequence, len(sequence)) native_quil = qc.compiler.quil_to_native_quil(prog) # remove gate definition and HALT return Program([instr for instr in native_quil.instructions][:-1]) return CircuitTemplate([func]) -def get_dagger_previous(): +def get_dagger_previous(n: int = 1): def func(qc, sequence, **kwargs): - prog = dagger_previous(sequence) + prog = dagger_previous(sequence, n) native_quil = qc.compiler.quil_to_native_quil(prog) # remove gate definition and HALT return Program([instr for instr in native_quil.instructions][:-1]) @@ -372,6 +367,43 @@ def func(graph, **kwargs): return CircuitTemplate([func]) +def get_all_H_template(): + return get_switch_basis_x_z_template() + + +def get_param_local_RX_template(): + # remember that RX(theta) = e^(i theta X/2) + def func(graph, sequence, **kwargs): + prog = Program() + theta = prog.declare('theta_' + str(len(sequence)), memory_type='REAL') + for node in graph.nodes: + prog += H(node) + prog += RZ(theta, node) + prog += H(node) + return prog + return CircuitTemplate([func]) + + +def get_param_maxcut_graph_cost_template(maxcut_graph=None): + if maxcut_graph is None: + def default_graph_func(graph, qc, sequence, **kwargs): + prog = maxcut_cost_unitary(graph, len(sequence)) + native_quil = qc.compiler.quil_to_native_quil(prog) + # remove gate definition and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([default_graph_func]) + else: + def func(graph, qc, sequence, **kwargs): + if len(maxcut_graph.nodes) > len(graph.nodes): + raise ValueError("The maxcut graph must have fewer nodes than the number of " + "qubits.") + prog = maxcut_cost_unitary(maxcut_graph, len(sequence)) + native_quil = graph_restricted_compilation(qc, graph, prog) + # remove gate definitions and HALT + return Program([instr for instr in native_quil.instructions][:-1]) + return CircuitTemplate([func]) + + def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, graph: nx.Graph = None, pattern = None): @@ -460,8 +492,14 @@ def get_error_hamming_weight_distributions(noisy_results, perfect_results): for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, perfect_ckt_sample_results): + if len(ideal_result) > 1: + raise ValueError("You have provided ideal results with more than one shot; " + "this method is intended to analyze results where the ideal " + "result is deterministic, which makes multiple shots " + "unnecessary.") - hamm_dist_per_shot = [hamming_distance(ideal_result, shot) for shot in noisy_shots] + hamm_dist_per_shot = [hamming_distance(ideal_result, shot) for shot in + noisy_shots] # Hamming weight distribution hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) @@ -475,13 +513,22 @@ def get_average_of_distributions(distrs): for w, d_arr in distrs.items()} -def get_success_probabilites(noisy_results, perfect_results): +def get_success_probabilities(noisy_results, perfect_results, + allowed_errors: Union[int, Callable[[int], int]] = 0): + if isinstance(allowed_errors, int): + error_func = lambda num_bits: allowed_errors + else: + error_func = allowed_errors + avg_distrs = get_average_of_distributions(get_error_hamming_weight_distributions( noisy_results, perfect_results)) - return {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_distrs.items()} + return {w: {d: sum(distr[0:error_func(w)+1]) for d, distr in d_distrs.items()} + for w, d_distrs in avg_distrs.items()} -# def get_total_variation_dist(distrs1, distrs2): + +def get_total_variation_dist(distr1, distr2): + return tvd(np.asarray([distr1]).T, np.asarray([distr2]).T) # TODO: separate these out @@ -615,20 +662,18 @@ def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], w return fig, axs -def basement_function(number: float): - """ - Once you are in the basement you can't go lower. Defined as +def basement_log_function(number: float): + return basement_function(np.log2(number)) - basement_function(number) = |floor(number)*heaviside(number,0)|, - where heaviside(number,0) implies the value of the step function is - zero if number is zero. +def basement_function(number: float): + """ + Return the floor of the number, or 0 if the number is negative. :param number: the basement function is applied to this number. :returns: basement of the number """ - basement_of_number = np.abs(np.floor(number) * np.heaviside(number, 0)) - return basement_of_number + return max(int(np.floor(number)), 0) # ================================================================================================== From 5c08d8c374b4509dfef558fac4c1efdf18a23870 Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 2 Aug 2019 16:19:23 -0400 Subject: [PATCH 24/49] Provide some draft comments. --- forest/benchmarking/volumetrics.py | 69 +++++++++++++++++++++++++++--- 1 file changed, 62 insertions(+), 7 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 16a2d9c3..b9618215 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -37,6 +37,20 @@ def make_default_pattern(num_generators): @dataclass class CircuitTemplate: + """ + We want to be able to specify various families of circuits and, once specified, randomly + sample from the family circuits of various width and depth. 'Width' is simply the number of + qubits. 'Depth' is not simply circuit depth but rather the number of some repeated group of + gates that constitute some distinctive unit. A depth d circuit could consist of d consecutive + rounds of random single qubit, then two qubit gates. It could also mean d consecutive + random Cliffords followed by the d conjugated Cliffords that invert the first d gates. + + Because these families of circuits are quite diverse, specifying the family and drawing + samples can potentially require a wide variety of parameters. The compiler may be required to + map an abstract circuit into native quil; a sample acting on a specific qubit topology + may be desired; the sequence of 'layers' generated so far may be necessary to compute an + inverse. + """ generators: List[Callable] = field(default_factory=lambda : []) pattern: List[Union[int, Tuple[List, int], Tuple[List, str]]] = field(init=False, repr=False) @@ -80,6 +94,45 @@ def __iadd__(self, other): return self def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None, pattern=None): + """ + The introduction of `pattern` is an attempt to enable some flexibility in specifying what + exactly constitutes a single unit of 'depth'. The default behavior is to sample from each + generator in series and consider these combined samples as a single unit. Thus, + the default pattern is + + [(list(range(num_generators)), 'n')] + + indicating that we combine samples from the generators at sequential indices and repeat + this depth many, or 'n' times. + + Another common family this will enable is 'do depth many layers of gates, then invert + them at the end'. If the last generator is the inversion generator this is specified by the + pattern + + [(list(range(num_generators - 1)), 'n'), -1] + + In general, a `pattern` is a list whose elements are either + + 1) an index of a generator + 2) a tuple of a `pattern` and a number of repetitions + 3) a tuple of a `pattern` and 'n', indicating depth many repetitions + + TODO: + A family that does not easily fit into the current paradigm is the following: + + C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t + + where C_j is a clifford, P_j is a random local Pauli. It could be accommodated if we + provided the depth to the inverse layers. + + :param graph: + :param repetitions: + :param qc: + :param width: + :param sequence: + :param pattern: + :return: + """ if width is not None: graph = random.choice(generate_connected_subgraphs(graph, width)) @@ -152,8 +205,7 @@ def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): :return: A program that has two qubit gates randomly placed on the graph edges. """ program = Program() - # do the two coloring with pragmas? - # no point until fencing is over + # TODO: two coloring with pragmas for a, b in graph.edges: gate = random.choice(gates) program += gate(a, b) @@ -384,16 +436,17 @@ def func(graph, sequence, **kwargs): return CircuitTemplate([func]) -def get_param_maxcut_graph_cost_template(maxcut_graph=None): - if maxcut_graph is None: - def default_graph_func(graph, qc, sequence, **kwargs): +def get_param_maxcut_graph_cost_template(graph_family: Callable[[int], nx.Graph] = None): + if graph_family is None: + def default_func(graph, qc, sequence, **kwargs): prog = maxcut_cost_unitary(graph, len(sequence)) native_quil = qc.compiler.quil_to_native_quil(prog) # remove gate definition and HALT return Program([instr for instr in native_quil.instructions][:-1]) - return CircuitTemplate([default_graph_func]) + return CircuitTemplate([default_func]) else: def func(graph, qc, sequence, **kwargs): + maxcut_graph = graph_family(len(graph.nodes)) if len(maxcut_graph.nodes) > len(graph.nodes): raise ValueError("The maxcut graph must have fewer nodes than the number of " "qubits.") @@ -403,7 +456,9 @@ def func(graph, qc, sequence, **kwargs): return Program([instr for instr in native_quil.instructions][:-1]) return CircuitTemplate([func]) - +# ================================================================================================== +# Data acquisition +# ================================================================================================== def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, graph: nx.Graph = None, pattern = None): From 28229af6621324af49f425b4ef8ddb7580ccd689 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 5 Aug 2019 16:12:26 -0400 Subject: [PATCH 25/49] Analyze single circuit first, then average. --- examples/volumetrics.ipynb | 1219 +++++++++++----------------- forest/benchmarking/volumetrics.py | 63 +- 2 files changed, 517 insertions(+), 765 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 226bd44c..81f29c83 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -78,7 +78,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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"RZ(pi/2) 5\n", - "RZ(-pi/2) 6\n", + "RZ(pi/2) 6\n", "RX(-pi/2) 6\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", + "RZ(-pi) 7\n", + "RZ(-pi) 7\n", + "RX(-pi) 8\n", "\n" ] } @@ -301,10 +305,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 3\n", - "I 6\n", - "X 3\n", - "I 6\n", + "I 0\n", + "I 1\n", + "X 0\n", + "X 1\n", "\n" ] } @@ -323,10 +327,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "I 3\n", - "I 0\n", - "I 3\n", + "I 4\n", + "I 5\n", + "CNOT 4 5\n", "\n" ] } @@ -345,10 +348,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 1\n", - "H 2\n", + "H 3\n", "H 4\n", - "H 7\n", + "H 5\n", + "H 8\n", "\n" ] } @@ -367,21 +370,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "CZ 4 7\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", "RX(-pi/2) 4\n", - "CZ 4 7\n", + "CZ 3 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", "RX(-pi/2) 4\n", - "CZ 4 7\n", + "CZ 3 4\n", "RX(pi/2) 4\n", - "CZ 4 7\n", - "RX(pi/2) 7\n", + "CZ 3 4\n", "RX(pi/2) 4\n", - "CZ 4 7\n", - "RX(-pi/2) 4\n", - "RZ(pi/2) 4\n", + "RX(-pi/2) 3\n", + "CZ 3 4\n", "RX(-pi/2) 4\n", + "RZ(-pi/2) 4\n", "\n" ] } @@ -401,33 +403,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(1.4594683462000786) 0\n", - "RX(pi/2) 0\n", - "RZ(1.0243900534343162) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.9310896318515138) 1\n", - "RX(pi/2) 1\n", - "RZ(2.1890957456453397) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RZ(-0.6622997930375671) 0\n", - "RX(pi/2) 0\n", - "RZ(1.9828557178909971) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(-pi/2) 0\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(-1.058016381779542) 0\n", - "RX(pi/2) 0\n", - "RZ(2.41382240722402) 0\n", - "RX(-pi/2) 0\n", - "RZ(-1.285005356809755) 0\n", - "RZ(-2.4846463293283545) 1\n", - "RX(pi/2) 1\n", - "RZ(2.123977194138851) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.4963073279217695) 1\n", "\n" ] } @@ -447,38 +422,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(1.8557603976473196) 2\n", + "RZ(0.8406899999638024) 1\n", + "RX(pi/2) 1\n", + "RZ(2.847991243602221) 1\n", + "RX(-pi/2) 1\n", + "RZ(-0.9649014576461203) 1\n", + "RZ(-3.0197488646781547) 2\n", "RX(pi/2) 2\n", - "RZ(0.7251833997059203) 2\n", + "RZ(1.175907213650433) 2\n", "RX(-pi/2) 2\n", - "RZ(-0.1999478754078161) 5\n", - "RX(pi/2) 5\n", - "RZ(0.8979922527579525) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RZ(2.25700516641314) 2\n", - "RX(pi/2) 2\n", - "RZ(2.399886548789372) 2\n", + "RZ(-1.296233838595962) 2\n", + "CZ 2 1\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(2.281028907841513) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 2\n", "RX(-pi/2) 2\n", - "RZ(-1.424775614897118) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", + "CZ 2 1\n", + "RX(pi/2) 1\n", + "RZ(-2.002236765648214) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.6738697650167795) 2\n", "RX(pi/2) 2\n", - "RZ(-1.727052862487164) 2\n", + "CZ 2 1\n", + "RZ(0.6154267530935176) 1\n", + "RX(pi/2) 1\n", + "RZ(1.6626538921651697) 1\n", + "RX(-pi/2) 1\n", + "RZ(-2.239540857961898) 1\n", + "RZ(1.7272246029442417) 2\n", "RX(-pi/2) 2\n", - "RZ(1.4404838838072855) 5\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(2.8183740988822845) 2\n", - "RX(pi/2) 2\n", - "RZ(1.1724242573000176) 2\n", + "RZ(1.2470234421229809) 2\n", "RX(-pi/2) 2\n", - "RZ(0.9844594158963185) 2\n", - "RZ(-2.5389634138457637) 5\n", - "RX(pi/2) 5\n", - "RZ(1.9227325522942578) 5\n", - "RX(-pi/2) 5\n", - "RZ(-3.032666685630888) 5\n", + "RZ(-0.3451740588607606) 2\n", "\n" ] } @@ -504,23 +481,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 2\n", + "X 1\n", + "I 2\n", + "I 3\n", + "X 4\n", + "I 1\n", "I 4\n", - "I 5\n", - "X 8\n", - "CNOT 2 5\n", - "CNOT 4 5\n", - "CNOT 5 8\n", - "X 2\n", + "I 1\n", + "I 2\n", + "I 3\n", "I 4\n", - "I 5\n", - "X 8\n", + "X 1\n", "I 2\n", - "I 5\n", + "X 3\n", + "X 4\n", + "CNOT 1 4\n", + "CNOT 1 2\n", + "I 3\n", "I 4\n", - "I 5\n", - "I 5\n", - "I 8\n", "\n" ] } @@ -546,24 +524,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 3\n", "H 6\n", - "I 3\n", - "Z 6\n", - "H 3\n", - "CZ 3 6\n", - "H 3\n", - "Z 3\n", + "H 7\n", "Z 6\n", - "H 3\n", - "CZ 3 6\n", - "H 3\n", - "Z 3\n", + "Z 7\n", + "H 6\n", + "CZ 6 7\n", + "H 6\n", "I 6\n", - "I 3\n", + "Z 7\n", "I 6\n", - "H 3\n", + "I 7\n", + "Z 6\n", + "I 7\n", + "I 6\n", + "I 7\n", "H 6\n", + "H 7\n", "\n" ] } @@ -588,54 +565,44 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 3\n", - "RX(-pi) 6\n", - "RZ(-pi/2) 6\n", - "RX(-pi/2) 6\n", - "CZ 3 6\n", - "RX(pi/2) 3\n", - "CZ 3 6\n", - "RZ(pi/2) 3\n", - "RZ(-pi/2) 3\n", - "RX(-pi) 3\n", - "RX(pi/2) 6\n", - "RZ(-pi) 6\n", - "RX(pi/2) 6\n", - "RX(pi/2) 3\n", - "CZ 3 6\n", - "RX(pi/2) 6\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 3 6\n", - "RZ(-pi/2) 3\n", - "RX(-pi) 3\n", - "RX(-pi/2) 6\n", - "RX(-pi/2) 3\n", - "CZ 3 6\n", - "RX(pi/2) 6\n", - "RX(-pi/2) 3\n", - "CZ 3 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(1.598215249600061) 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 6\n", - "RZ(0.6500806106097443) 6\n", - "RX(-pi/2) 6\n", - "CZ 3 6\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RX(pi/2) 6\n", - "CZ 3 6\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RZ(0.9207157161851534) 3\n", - "RX(pi/2) 6\n", - "RZ(3.11417373078463) 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 6\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "RX(-pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 7\n", + "RZ(-pi) 8\n", + "RX(pi/2) 7\n", + "CZ 7 8\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RX(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 8\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RX(pi/2) 8\n", + "RX(-pi/2) 7\n", + "CZ 7 8\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "RZ(-pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(-pi/2) 7\n", + "RZ(pi/2) 8\n", + "RX(pi) 8\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -672,560 +639,349 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(0.758348409269925) 3\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "CZ 1 0\n", + "RZ(pi/2) 3\n", "RX(pi/2) 3\n", - "RZ(1.7630700871127511) 3\n", + "CZ 3 4\n", + "RZ(-pi/2) 3\n", "RX(-pi/2) 3\n", - "RZ(0.06888378729568245) 3\n", - "RZ(-pi/2) 6\n", - "RX(pi/2) 6\n", - "CZ 6 7\n", - "RZ(2.5953963254157664) 2\n", - "RX(pi/2) 2\n", - "RZ(-pi/2) 5\n", - "RX(pi/2) 5\n", - "RZ(1.13234180443474) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RX(-pi/2) 8\n", - "RZ(2.7205361452995973) 4\n", "RX(pi/2) 4\n", - "RZ(1.2543184374584302) 4\n", + "CZ 4 3\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", "RX(-pi/2) 4\n", - "RZ(1.208609474957872) 7\n", - "RX(pi/2) 7\n", - "RZ(0.94147495985692) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RZ(-2.0520996460608902) 4\n", + "CZ 3 4\n", + "RZ(pi) 0\n", + "RX(pi) 0\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(-2.53949116123707) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RX(-pi/2) 4\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RX(pi/2) 5\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "CZ 5 8\n", - "RZ(-0.5221305937638101) 3\n", - "RX(pi/2) 3\n", - "RZ(2.558816414461174) 3\n", - "RX(-pi/2) 3\n", - "RZ(-1.949929968488697) 6\n", - "RX(pi/2) 6\n", - "RZ(2.9357743332570747) 6\n", - "RX(-pi/2) 6\n", - "CZ 3 6\n", - "RZ(1.5884307184269622) 3\n", - "RX(-pi/2) 3\n", - "RZ(-0.011319042316977779) 6\n", - "RX(pi/2) 6\n", - "CZ 3 6\n", + "CZ 1 4\n", + "RX(pi/2) 1\n", + "CZ 0 1\n", + "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", - "RX(-pi/2) 6\n", - "CZ 3 6\n", - "RZ(0.6751470631856724) 4\n", - "RX(pi/2) 4\n", - "RZ(2.6655130904014928) 4\n", + "RZ(pi/2) 4\n", "RX(-pi/2) 4\n", - "CZ 5 4\n", - "RZ(-2.9349743099037617) 3\n", - "RX(pi/2) 3\n", - "RZ(1.9087088772488927) 3\n", + "CZ 3 4\n", "RX(-pi/2) 3\n", - "RZ(1.5277383498463577) 4\n", + "RZ(pi) 4\n", "RX(pi/2) 4\n", - "RZ(0.45916430288874793) 4\n", + "CZ 4 3\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", "RX(-pi/2) 4\n", "CZ 3 4\n", - "RZ(0.0024518619620117477) 3\n", + "RX(pi/2) 0\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", + "RX(-pi/2) 0\n", + "CZ 0 1\n", + "RZ(-pi/2) 0\n", + "RX(pi) 0\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "CZ 3 0\n", + "RZ(pi) 0\n", + "RX(-pi/2) 0\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(pi/2) 4\n", + "RZ(pi/2) 3\n", "RX(pi/2) 3\n", + "CZ 4 3\n", + "RZ(pi) 1\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", + "RZ(pi/2) 4\n", + "CZ 0 1\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", "CZ 3 4\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(-0.8648293577208861) 4\n", + "CZ 4 3\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 0\n", + "RX(pi) 0\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "CZ 3 0\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(1.0245999986208698) 5\n", - "RX(pi/2) 5\n", - "CZ 4 5\n", + "CZ 3 4\n", + "RZ(pi) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 3\n", + "RX(pi) 3\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", + "RZ(pi/2) 4\n", + "RZ(1.1243912032248942) 0\n", + "RX(pi/2) 0\n", + "RZ(1.2180109156170746) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.6134967010491525) 0\n", + "RZ(-2.6007950357484777) 1\n", + "RX(pi/2) 1\n", + "RZ(3.07677097365376) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.2330554369238811) 1\n", + "CZ 1 0\n", + "RZ(-pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(2.2688338135521353) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 0\n", + "RZ(-1.7600137491541061) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.9455860910933556) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(-1.765614659103938) 3\n", "RX(pi/2) 3\n", + "RZ(2.333860458736699) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.3189846424964307) 3\n", + "RZ(-1.9457738910099138) 4\n", + "RX(pi/2) 4\n", + "RZ(1.6615752928737941) 4\n", "RX(-pi/2) 4\n", + "RZ(3.1146598749504513) 4\n", "CZ 4 3\n", - "RZ(-2.658904419553418) 7\n", - "RX(pi/2) 7\n", - "RZ(0.5745905015087214) 7\n", - "RX(-pi/2) 7\n", - "RZ(0.03842194804433141) 8\n", - "RX(pi/2) 8\n", - "RZ(1.8397221653434963) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(0.6426269118296801) 7\n", - "RX(-pi/2) 7\n", - "RZ(-2.299604875835275) 8\n", - "RX(pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 7\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(-2.682428350701045) 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(2.208149269791826) 3\n", "RX(-pi/2) 3\n", - "CZ 4 5\n", "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 3\n", + "RZ(-1.772289877464722) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.7972582129469177) 4\n", "RX(pi/2) 4\n", - "RZ(2.972685011137046) 4\n", + "CZ 4 3\n", + "RZ(-1.8550831521821793) 0\n", + "RX(pi/2) 0\n", + "RZ(1.023046143496381) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.19389593670807215) 0\n", + "RZ(0.8350132758730009) 1\n", + "RX(pi/2) 1\n", + "RZ(1.4034616514737628) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.5951974037155017) 1\n", + "RZ(-3.0630317874417194) 3\n", + "RX(pi/2) 3\n", + "RZ(2.6647202108971375) 3\n", + "RX(-pi/2) 3\n", + "RZ(-2.3997638535696124) 3\n", + "RZ(-1.479001253061274) 4\n", "RX(-pi/2) 4\n", - "RZ(3.1167222954807863) 7\n", - "RX(pi/2) 7\n", - "RZ(2.294313832241617) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", + "RZ(1.932316588815607) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.5458526604808727) 4\n", + "RX(-pi/2) 0\n", + "RZ(3*pi/4) 1\n", + "RX(pi) 1\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(-1.8562367540991378) 7\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", + "CZ 1 4\n", + "RZ(-pi/2) 0\n", + "RX(pi/2) 0\n", + "CZ 1 0\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(pi) 4\n", + "RX(pi) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(pi) 4\n", "RX(pi/2) 4\n", - "RZ(pi/2) 5\n", - 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- "RZ(-1.2864053521598962) 5\n", - "RZ(-0.10395248961445913) 6\n", - "RX(pi/2) 6\n", - "RZ(1.1255661636898064) 6\n", - "RX(-pi/2) 6\n", - "RZ(2.5670064382514095) 6\n", - "RZ(-1.6335402841420965) 7\n", - "RX(pi/2) 7\n", - "RZ(1.2372046695134922) 7\n", - "RX(-pi/2) 7\n", - "RZ(0.7997699118812944) 7\n", + "RZ(2.7885886179362576) 4\n", "\n" ] } @@ -1251,7 +1007,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 3: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 4: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}}\n" + "{2: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 3: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 4: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}}\n" ] } ], @@ -1281,7 +1037,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: 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]), array([0.796, 0.184, 0.02 , 0. , 0. ]), array([0.69 , 0.268, 0.038, 0.004, 0. ]), array([0.628, 0.304, 0.064, 0.004, 0. ]), array([0.866, 0.124, 0.01 , 0. , 0. ]), array([0.68 , 0.256, 0.046, 0.018, 0. ]), array([0.842, 0.146, 0.012, 0. , 0. ]), array([0.808, 0.168, 0.02 , 0.004, 0. ]), array([0.848, 0.14 , 0.012, 0. , 0. ])]}}\n" ] } ], @@ -1317,12 +1073,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: array([0.8758, 0.1192, 0.005 ]), 4: array([0.8848, 0.11 , 0.0052]), 5: array([0.8708, 0.1234, 0.0058])}, 3: {3: array([8.69e-01, 1.22e-01, 8.40e-03, 6.00e-04]), 4: array([0.8192, 0.1646, 0.0142, 0.002 ]), 5: array([0.8216, 0.163 , 0.0142, 0.0012])}, 4: {3: array([0.8262, 0.1582, 0.0132, 0.0024, 0. ]), 4: array([0.7928, 0.1778, 0.0236, 0.0032, 0.0026]), 5: array([7.65e-01, 2.08e-01, 2.32e-02, 3.60e-03, 2.00e-04])}}\n" + "{2: {3: array([0.8894, 0.1064, 0.0042]), 4: array([0.897 , 0.0998, 0.0032]), 5: array([0.898 , 0.0968, 0.0052])}, 3: {3: array([8.168e-01, 1.664e-01, 1.620e-02, 6.000e-04]), 4: array([0.8216, 0.1628, 0.0146, 0.001 ]), 5: array([0.7988, 0.186 , 0.0142, 0.001 ])}, 4: {3: array([7.732e-01, 1.994e-01, 2.600e-02, 1.200e-03, 2.000e-04]), 4: array([0.8008, 0.1844, 0.0136, 0.0012, 0. ]), 5: array([0.7832, 0.1864, 0.0268, 0.0036, 0. ])}}\n" ] } ], "source": [ - "avg_err_hamm_distrs = get_average_of_distributions(err_hamm_distrs)\n", + "avg_err_hamm_distrs = average_distributions(err_hamm_distrs)\n", "print(avg_err_hamm_distrs)" ] }, @@ -1355,7 +1111,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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" ] @@ -1446,7 +1202,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1468,16 +1224,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: 0.8758000000000001, 4: 0.8847999999999999, 5: 0.8708}, 3: {3: 0.869, 4: 0.8192, 5: 0.8215999999999999}, 4: {3: 0.8262, 4: 0.7928, 5: 0.765}}\n", - "{2: {3: 0.9950000000000001, 4: 0.9947999999999999, 5: 0.9942}, 3: {3: 0.991, 4: 0.9838, 5: 0.9845999999999999}, 4: {3: 0.9976, 4: 0.9941999999999999, 5: 0.9962000000000001}}\n", - "{2: {3: 0.6258000000000001, 4: 0.6347999999999999, 5: 0.6208}, 3: {3: 0.744, 4: 0.6942, 5: 0.6965999999999999}, 4: {3: 0.7637, 4: 0.7303, 5: 0.7024999999999999}}\n" + "{2: {3: 0.8894, 4: 0.897, 5: 0.8979999999999999}, 3: {3: 0.8168000000000001, 4: 0.8216000000000001, 5: 0.7988000000000001}, 4: {3: 0.7732, 4: 0.8008000000000001, 5: 0.7831999999999999}}\n", + "{2: {3: 0.9958000000000002, 4: 0.9968, 5: 0.9948}, 3: {3: 0.9832000000000001, 4: 0.9843999999999999, 5: 0.9848000000000001}, 4: {3: 0.9986, 4: 0.9987999999999999, 5: 0.9964000000000001}}\n", + "{2: {3: 0.6394, 4: 0.647, 5: 0.6479999999999999}, 3: {3: 0.6918000000000001, 4: 0.6966000000000001, 5: 0.6738}, 4: {3: 0.7107, 4: 0.7383, 5: 0.7206999999999999}}\n" ] } ], @@ -1486,13 +1242,15 @@ "widths = list(avg_err_hamm_distrs.keys())\n", "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", "\n", - "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "avg_pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", "# this is equivalently wrapped up in the following\n", - "assert pr_succ_arr == get_success_probabilities(noisy_results, ideal_results)\n", + "assert avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results))\n", "\n", "# count as success even if there are log many bits incorrect.\n", - "pr_succ_allow_log_errors = get_success_probabilities(noisy_results, ideal_results, \n", - " allowed_errors = basement_log_function)\n", + "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results, \n", + " allowed_errors = basement_log_function))\n", "\n", "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", @@ -1505,14 +1263,14 @@ " for w, d_distrs in avg_err_hamm_distrs.items()}\n", "\n", "# tvd_noisy_ideal is equivalent to 1 - success probability.\n", - "np.testing.assert_allclose([pr for d_vals in pr_succ_arr.values() for pr in d_vals.values()], \n", + "np.testing.assert_allclose([pr for d_vals in avg_pr_succ_arr.values() for pr in d_vals.values()], \n", " [1 - val for d_vals in tvd_noisy_ideal.values() for val in d_vals.values()])\n", "\n", "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", " for w, d_distrs in avg_err_hamm_distrs.items()}\n", "\n", - "print(pr_succ_arr)\n", - "print(pr_succ_allow_log_errors)\n", + "print(avg_pr_succ_arr)\n", + "print(avg_pr_succ_allow_log_errors)\n", "print(tvd_noisy_rand)" ] }, @@ -1532,12 +1290,12 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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VijNKGmJCRKpFt27d6N69O507d6Zt27bk5OSUu87IkSMZPHgw6enpnH766bRr1w6AHj16kJubS69evQAYOnQo3bt3L7Hppzw33XQTP/zhD0lLS6Nz586kp6fTtGlTAB588EEuvPBCUlNTyc7OJj8/H4BHHnmEm2++ma5du7Jv3z769u3Lo48+ytixY5k3bx716tUjPT2dgQMH8vTTT/PQQw/RsGFDGjduzBNPPJF0jFGrdfcszs7Odt2YRqR8q1atokuXLjUdxhFv//79FBQUkJKSwocffsjZZ5/NmjVrOOqoo2o6tEor6b03syXunl1SeZ0RiEis7d69m/79+1NQUIC7M378+FqdBCpDiUBEYq1Jkyaxv/2tOotFRGJOiUBEJOaUCEREYk6JQEQk5pQIRCQy999/P+np6XTt2pWsrCwWLVpU0yGxbt06GjVqRFZWFmlpadxwww1JjYy6bt06MjIyktpmv379SuyQnjFjBg8++CAAo0aN4uGHHwbg3nvvZe7cuQCMHTs28vGJdNWQiETizTffZObMmSxdupSjjz6aLVu28NVXX9V0WACccMIJLFu2jH379nHmmWcyffr0orGOAPbt20eDBtF/PQ4aNIhBgwYdMn/06NFFz8eOHctVV13FN77xjcji0BmBiAAw/e2N5Dz4Ch1HzCLnwVeY/vbhjQq/adMmWrZsWTTWT8uWLWnVqhVw8A1m8vLy6NevHwD5+flcc801ZGZm0rVrV/72t78B8NJLL3HaaafRo0cPLrvssqJf+I4YMYK0tDS6du3KnXfeCcBzzz1HRkYG3bp1o2/fvmXG2KBBA04//XTWrl3Lq6++yhlnnMGgQYNIS0sDSh7qGoJEMWTIELp06cL3v//9oiP20aNH07NnTzIyMhg2bNhBw2Y/+eSTRUNRFw6HPWnSJIYPH35IXLm5uUydOpVHHnmEzz77jP79+9O/f38mTpzIbbfdVlTuscce4/bbb6/I21EmJQIRYfrbG/n58++ycfseHNi4fQ8/f/7dw0oG5557LuvXr+ekk07ipptu4rXXXit3nV/96lc0bdqUd999l+XLl3PmmWeyZcsWfv3rXzN37lyWLl1KdnY2Y8aMYevWrUybNo0VK1awfPly7rnnHiD4Mp4zZw7vvPMOM2bMKHN7u3fv5uWXXy4arXTp0qX87ne/4/333z9oqOuFCxfy2GOP8fbbbwOwZs0abrrpJlatWsWxxx7L+PHjARg+fDiLFy/mvffeY8+ePcycOfOgbS1btozx48dz7bXXVug1vOWWW2jVqhXz5s1j3rx5XH755bzwwgsUFBQA8Oc//7nCdZVFiUBEeGjOGvYU7D9o3p6C/Tw0Z02l62zcuDFLlixhwoQJpKamcsUVVzBp0qQy15k7dy433/z1oMTNmzdn4cKFrFy5kpycHLKyspg8eTKffPIJTZs2JSUlheuuu47nn3++qOkkJyeH3NxcHnvsMfbv31/idj788EOysrLIycnhggsuYODAgQD06tWLjh07AgcPdd24ceOioa6Bg8ZKuuqqq1iwYAEA8+bNo3fv3mRmZvLKK6+wYsWKom0OHhwMyNy3b1927tzJ9u3bk31Jady4MWeeeSYzZ85k9erVFBQUFCWxw6E+AhHhs+17kppfUfXr16dfv37069ePzMxMJk+eTG5uLg0aNCjqoE0c3rkk7s4555zDU089dciyt956i5dffpmpU6fyhz/8gVdeeYVHH32URYsWMWvWLE455RSWLFlCixYtDlqvsI+guMRhs8uSODx14fTevXu56aabyMvLo23btowaNeqgfStpncoYOnQoDzzwAJ07d+aaa6rmNi46IxARWjVrlNT8ilizZg0ffPBB0fSyZcto3749EPQRLFmyBKCoHwDgnHPOYdy4cUXT27Zt49RTT+WNN95g7dq1AHz55Ze8//775Ofns2PHDs4//3x++9vf8s477wDB0X7v3r0ZPXo0qamprF+/vlLxlzXU9aeffsqbb74JwJQpU+jTp0/Rl37Lli3Jz89n6tSpB9X3zDPPAMGZRtOmTYtGOC1PkyZN2LVrV9F07969Wb9+PVOmTCk6yzhcSgQiwl0DTqZRw/oHzWvUsD53DTi50nXm5+cXDe/ctWtXVq5cyahRo4BgeOlbb72V7Oxs6tf/erv33HMP27ZtK+rsnTdvHqmpqUyaNInBgwfTtWtXTjvtNFavXs2uXbu48MIL6dq1K3369GHMmDHBvtx1F5mZmWRkZHD66afTrVu3SsWfONR17969i4a6Bjj55JMZN24cXbp0Ydu2bdx44400a9aM66+/noyMDAYMGEDPnj0Pqi8lJYXu3btzww03FN1ysyKGDRvGeeedR//+/YvmXX755eTk5BTdse1waRhqkToq2WGop7+9kYfmrOGz7Xto1awRdw04mYu7t44wQqmsCy+8kNtvv52zzjqrxOUahlpEKuXi7q31xX+E2759O7169aJbt26lJoHKUCIQEaklmjVrxvvvv1/l9aqPQKQOq21Nv3L4KvOeKxGI1FEpKSls3bpVySBG3J2tW7eSkpKS1HpqGhKpo9q0acOGDRvYvHlzTYci1SglJYU2bdoktY4SgUgd1bBhw6JfyYqURU1DIiIxp0QgIhJzSgQiIjEXqz4C/XJSoqDPldR2sUkEheOtFw61WzjeOqB/Wqk0fa6kLohN01AU462L6HMldUFsEkFU461LvOlzJXVBpInAzM4zszVmttbMRpSwvJ2ZzTOzt81suZmdH1UsUYy3LqLPlUSpqu8jXZrIEoGZ1QfGAQOBNGCwmaUVK3YP8Ky7dweuBMZHFU8U462L6HMlUYniPtKlifKMoBew1t0/cvevgKeBi4qVceDY8HlT4LOogrm4e2v+49JMWjdrhAGtmzXiPy7NVIeeHBZ9riQq1dn/FOVVQ62BxHvEbQB6FyszCnjJzH4MHAOcXVJFZjYMGAbQrl27Sgek8dYlCvpcSRSqs/+ppjuLBwOT3L0NcD7wpJkdEpO7T3D3bHfPTk1NrfYgRUSqW3X2P0WZCDYCbROm24TzEl0HPAvg7m8CKUDLCGMSEakVqrP/KcpEsBjoZGYdzewogs7gGcXKfAqcBWBmXQgSgcbMFZHYq87+p8j6CNx9n5kNB+YA9YGJ7r7CzEYDee4+A/gJ8JiZ3U7QcZzruouGiAhQff1PkQ4x4e6zgdnF5t2b8HwlkBNlDCIiUraa7iwWEZEapkQgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjHXoKIFzawe0A1oBewB3nP3L6IKTEREqke5icDMTgB+BpwNfABsBlKAk8xsN/BHYLK7H4gyUBERiUZFzgh+Dfw38CN398QFZvZN4AfAvwOTqz48ERGJWrl9BO4+2N3nF08C4bIv3H2su5eYBMzsPDNbY2ZrzWxEKWUuN7OVZrbCzKYkvwsiInI4KtxZbGaXmVmT8Pkvzex5M+tRRvn6wDhgIJAGDDaztGJlOgE/B3LcPR24rRL7ICIihyGZq4Z+6e67zKwPcBbwOEGTUWl6AWvd/SN3/wp4GrioWJnrgXHuvg2CM4wk4hERkSqQTCLYH/69AJjg7rOAo8oo3xpYnzC9IZyX6CSCTuc3zGyhmZ1XUkVmNszM8swsb/PmzUmELCIi5UkmEWw0sz8CVwCzzezoJNcvSQOgE9APGAw8ZmbNihdy9wnunu3u2ampqYe5SRERSZTMF/nlwBxggLtvB44D7iqj/EagbcJ0m3Beog3ADHcvcPePgfcJEoOIiFSTZBLBt4BZ7v6BmfUDLgPeKqP8YqCTmXU0s6OAK4EZxcpMJzgbwMxaEjQVfZRETCIicpiSSQR/A/ab2YnABIKj/VIv93T3fcBwgrOIVcCz7r7CzEab2aCw2Bxgq5mtBOYBd7n71krsh4iIVJKV8POAkguaLXX3Hmb2U2CPu//ezN529+7Rhniw7Oxsz8vLq85NiojUema2xN2zS1qWzBlBgZkNBq4GZobzGh5ucCIiUrOSSQTXAKcB97v7x2bWEXgymrBERKS6VHj0UXdfaWY/A9qF0x8Dv4kqMBERqR7JDDHxXWAZ8L/hdJaZFb8KSEREaplkmoZGEQwbsR3A3ZcB344gJhERqUZJdRa7+45i83QPAhGRWq7CfQTACjP7AVA/HDX0FuAf0YQlIiLVJZkzgh8D6cC/CH5ItgMNGy0iUuslc9XQbuDu8CEiInVEMlcN/T1xZFAza25mc6IJS0REqksyTUMtw1FHAQhvJvPNqg9JRESqUzKJ4ICZtSucMLP2QMUGKhIRkSNWMlcN3Q0sMLPXAAPOAIZFEpWIiFSbZDqL/ze8Wf2p4azb3H1LNGGJiEh1Saaz+BKCH5XNdPeZwD4zuzi60EREpDok00cwMvGXxWHH8ciqD0lERKpTMomgpLLJ9DGIiMgRKJlEkGdmY8zshPAxBlgSVWAiIlI9kh1i4ivgmfDxL+DmKIISEZHqk8xVQ18CIyKMRUREakCFE4GZzaOEH5C5+5lVGpGIiFSrZDp770x4ngJ8D9hXteGIiEh1S6ZpqHjH8Btm9lYVxyMiItUsmaah4xIm6wGnAE2rPCIREalWyTQNLSHoIzCCJqGPgeuiCEpERKpPMk1DHaMMREREakYyYw1dZmZNwuf3mNnz4SB0IiJSiyXzg7JfuvsuM+sDnA08Dvx3NGGJiEh1SSYR7A//XgBMcPdZwFFVH5KIiFSnZBLBRjP7I3AFMNvMjk5yfREROQIl80V+OTAHGBAOQX0ccFckUYmISLUpNxGYWWMAd9/t7s+7+wfh9CZ3fymxTAnrnmdma8xsrZmVOk6RmX3PzNzMsiu3GyIiUlkVOSP4HzP7LzPra2bHFM40s2+b2XVmNgc4r/hKZlYfGAcMBNKAwWaWVkK5JsCtwKLK7oSIiFReuYnA3c8CXgZ+BKwwsx1mthX4C/BvwA/dfWoJq/YC1rr7R+7+FfA0cFEJ5X4F/AbYW8l9EBGRw1DRH5S9CLzr7uuTqLs1kFh+A9A7sUD4O4S27j7LzErtbzCzYcAwgHbt2iURgoiIlKdCncXu7sDsqtywmdUDxgA/qcD2J7h7trtnp6amVmUYIiKxl8xVQ0vNrGcS5TcCbROm24TzCjUBMoBXzWwdcCowQx3GIiLVK5lB53oDV4Vf2l8SDD7n7t61lPKLgU5m1pEgAVwJ/KBwobvvAFoWTpvZq8Cd7p6XzA6IiMjhSSYRDEimYnffZ2bDCX57UB+Y6O4rzGw0kOfuM5KpT0REolFuIjCzFOAG4ETgXeBxd6/QncncfTbF+hbc/d5SyvarSJ0iIlK1KtJHMBnIJkgCA4H/ijQiERGpVhVpGkpz90wAM3sc0O0pRUTqkIqcERQUPqlok5CIiNQeFTkj6GZmO8PnBjQKpwuvGjo2suhERCRy5SYCd69fHYGIiEjN0P0ERERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiLtJEYGbnmdkaM1trZiNKWH6Hma00s+Vm9rKZtY8yHhEROVRkicDM6gPjgIFAGjDYzNKKFXsbyHb3rsBU4D+jikdEREoW5RlBL2Ctu3/k7l8BTwMXJRZw93nuvjucXAi0iTAeEREpQZSJoDWwPmF6QzivNNcBL5a0wMyGmVmemeVt3ry5CkMUEZEjorPYzK4CsoGHSlru7hPcPdvds1NTU6s3OBGROq5BhHVvBNomTLcJ5x3EzM4G7ga+4+7/ijAeEREpQZRnBIuBTmbW0cyOAq4EZiQWMLPuwB+BQe7+RYSxiIhIKSJLBO6+DxgOzAFWAc+6+wozG21mg8JiDwGNgVJwnA8AAAWvSURBVOfMbJmZzSilOhERiUiUTUO4+2xgdrF59yY8PzvK7YuISPmOiM5iERGpOUoEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxF+kPyo5E972wgpWf7azpMEREKiyt1bGM/G56ZPXrjEBEJOZid0YQZVYVEamNdEYgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIiIxp0QgIhJzSgQiIjEXaSIws/PMbI2ZrTWzESUsP9rMngmXLzKzDlHGIyIih4osEZhZfWAcMBBIAwabWVqxYtcB29z9ROC3wG+iikdEREoW5RlBL2Ctu3/k7l8BTwMXFStzETA5fD4VOMvMLMKYRESkmCgTQWtgfcL0hnBeiWXcfR+wA2hRvCIzG2ZmeWaWt3nz5ojCFRGJp1rRWezuE9w9292zU1NTazocEZE6JcpEsBFomzDdJpxXYhkzawA0BbZGGJOIiBQTZSJYDHQys45mdhRwJTCjWJkZwA/D598HXnF3jzAmEREppkFUFbv7PjMbDswB6gMT3X2FmY0G8tx9BvA48KSZrQX+jyBZiIhINYosEQC4+2xgdrF59yY83wtcFmUMIiJStlrRWSwiItFRIhARiTklAhGRmFMiEBGJOattV2ua2Wbgk8OspiWwpQrCEUmkz5VEpSo+W+3dvcRf5Na6RFAVzCzP3bNrOg6pW/S5kqhE/dlS05CISMwpEYiIxFxcE8GEmg5A6iR9riQqkX62YtlHICIiX4vrGYGIiISUCEREYi7SQeeOJGaWAswHjibY76nuPrJmo5K6JLxPdx6w0d0vrOl4pG4ws3XALmA/sC+Ky0hjkwiAfwFnunu+mTUEFpjZi+6+sKYDkzrjVmAVcGxNByJ1Tn93j+zHirFpGvJAfjjZMHyop1yqhJm1AS4A/lTTsYgkKzaJAIJTdzNbBnwB/N3dF9V0TFJnjAV+Chyo6UCkznHgJTNbYmbDothArBKBu+939yyC+yf3MrOMmo5Jaj8zuxD4wt2X1HQsUif1cfcewEDgZjPrW9UbiFUiKOTu24F5wHk1HYvUCTnAoLBT72ngTDP7S82GJHWFu28M/34BTAN6VfU2YpMIzCzVzJqFzxsB5wCrazYqqQvc/efu3sbdOxDcd/sVd7+qhsOSOsDMjjGzJoXPgXOB96p6O3G6auhbwOTwEr96wLPuPrOGYxIRKcvxwDQzg+D7eoq7/29Vb0RDTIiIxFxsmoZERKRkSgQiIjGnRCAiEnNKBCIiMadEICISc0oEIsWY2X4zW2ZmK8zsHTP7iZlV+n/FzH6R8LyDmVX5deAih0OJQORQe9w9y93TCX54OBA4nCHLf1F+EZGao0QgUobwZ/3DgOEWqG9mD5nZYjNbbmY/AjCzfmY238xmmdkaM3vUzOqZ2YNAo/AM469htfXN7LHwjOOl8JfuIjVGiUCkHO7+EVAf+CZwHbDD3XsCPYHrzaxjWLQX8GMgDTgBuNTdR/D1GcaQsFwnYFx4xrEd+F717Y3IoZQIRJJzLnB1OJz5IqAFwRc7wFvu/pG77weeAvqUUsfH7r4sfL4E6BBhvCLlitNYQyKVYmbfJrhN4BeAAT929znFyvTj0BsdlTZ+y78Snu8H1DQkNUpnBCJlMLNU4FHgDx4MzDUHuDG83SlmdlI4KiQE97joGF5hdAWwIJxfUFhe5EikMwKRQzUKm34aAvuAJ4Ex4bI/ETTlLLVgSMjNwMXhssXAH4ATCe53MS2cPwFYbmZLgburYwdEkqHRR0WqQNg0dKe7X1jTsYgkS01DIiIxpzMCEZGY0xmBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzP0/YkhdKJf0g/EAAAAASUVORK5CYII=\n", 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" ] @@ -1548,7 +1306,7 @@ ], "source": [ "w=3\n", - "plt.scatter(depths, [pr_succ_arr[w][d] for d in depths], label='Sucess Probability')\n", + "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Probability')\n", "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", "plt.ylim([-0.05,1.05])\n", "plt.xlabel('Depth')\n", @@ -1585,12 +1343,12 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1601,9 +1359,9 @@ ], "source": [ "w=4\n", - "plt.scatter(depths, [pr_succ_arr[w][d] for d in depths], label='Sucess Prob')\n", + "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Prob')\n", "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", - "plt.scatter(depths, [pr_succ_allow_log_errors[w][d] for d in depths], label='Sucess Prob w/ log errors')\n", + "plt.scatter(depths, [avg_pr_succ_allow_log_errors[w][d] for d in depths], label='Sucess Prob w/ log errors')\n", "plt.plot(depths, [pr_succ_rand_allow_log_errors[w] for _ in depths], label='random guess w/ log errors')\n", "plt.ylim([-0.05, 1.05])\n", "plt.xlabel('Depth')\n", @@ -1623,12 +1381,12 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1641,7 +1399,7 @@ "plt.figure()\n", "plt.scatter(depths, [tvd_noisy_ideal[w][d] for d in depths], label='TVD(data, ideal)')\n", "plt.scatter(depths, [tvd_noisy_rand[w][d] for d in depths], label='TVD(data, rand)')\n", - "plt.scatter(depths, 1-np.asarray([pr_succ_arr[w][d] for d in depths]),\n", + "plt.scatter(depths, 1-np.asarray([avg_pr_succ_arr[w][d] for d in depths]),\n", " label='1 - Pr[Success]', alpha=0.33, marker='^', s=80)\n", "plt.ylim([-0.05,1.05])\n", "plt.xlabel('Depth')\n", @@ -1668,18 +1426,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "widths = list(avg_err_hamm_distrs.keys())\n", "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", "\n", - "pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results))\n", "\n", "# count as success even if there are log many bits incorrect.\n", - "pr_succ_allow_log_errors = get_success_probabilities(noisy_results, ideal_results, \n", - " allowed_errors = basement_log_function)\n", + "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results, \n", + " allowed_errors = basement_log_function))\n", "\n", "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", @@ -1697,7 +1457,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1706,22 +1466,22 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "Zdata = np.reshape([pr_succ_arr[w][d] for d in depths for w in widths], X.shape)\n", + "Zdata = np.reshape([avg_pr_succ_arr[w][d] for d in depths for w in widths], X.shape)\n", "Zrand = np.reshape([pr_succ_rand[w] for d in depths for w in widths], X.shape)" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { - "image/png": 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k4T+UsIvy56xuAPL8aEQTjM0Pr/REt7efH+1c5JkkHQjssP2opH0pflr+76qeJyLqp863n12Fmu2bJB0GzLP9L2VITbG9tc1hBwNXlP1qk4CrbH9/96scEX030UNN0rsoOvNnAC+k6CP7InDyaMfYXgW8fAzqGBF1U+NQ67ZP7b3A8cDjALZ/CRzUq0pFRH11O/G27pNvt9neLhWjGZKmUOusjoieasBLIm+SdDGwr6Q3AN8C/k/vqhURdVbnllq3oXYh8DDFEwXvpvhF5Y/0qlIRUXMTfUqH7UFJ1wDX2H64x3WKiDrrYyusG51ePSRJH5e0CbgXuFfSw5Iqz1uLiAapcUut0+3nByhGPV9le4btGcBxwPGSPtDz2kVELWmwu6UfOoXaecDZtn89tMH2OuBc4G29rFhExLPRqU9tqu1Nwzfafrh8R1pE7Ilq3KfWKdS2P8t9EdFUNR8o6BRq/0XS4yNsF7BPD+oTERPBRA0123nBWUTsaqKGWkTEcKJ/I5vd6PaJgoiIwhg+0C5pgaR7Ja2VdGGbcmdKsqT5nc6ZUIuI6sZg8m35rsVFwCnAUcDZko4aodw04AJgl984GUlCLSKqG5snCo4F1tpeZ3s7sAQ4Y4Ryn6R4a/ZT3VQtoRYRlVW4/ZwpaUXLsrDlNLOAB1rW15fbnr6O9Apgju0fdFu3DBRERHXdj35ust2xH2wkkiYB/wC8vcpxCbWIqMZjNvq5geLnNofMLrcNmQYcA9xYvqD2BcBSSafbXjHaSRNqEVHd2MxTux2YJ+lwijA7Czjnd5ewHwNmDq1LuhH47+0CDdKnFhHPwlhM6bC9EzgfuA5YQ/GLc6slXSLp9Gdbt7TUIqK6MXqiwPYyijdpt24b8X2Ntk/s5pwJtYiopo8vgOxGQi0iKhET+y0dERG7SKhFRLMk1CKiURJqEdEYE/zNtxERu0qoRUST1PklkbUKNSN2uFZVqpXD997Y7yrU3lQ9r99VqLWpGhiT8+T2MyKaI5NvI6JxEmoR0RR5oiAiGkeD9U21hFpEVJM+tYhomtx+RkSzJNQioknSUouIZkmoRURjjN2vSfVEQi0iKsk8tYhoHtc31RJqEVFZWmoR0RyZfBsRTZOBgoholIRaRDSHyUBBRDRLBgoiolkSahHRFJl8GxHNYuclkRHRMPXNtIRaRFSX28+IaA4Duf2MiEapb6Yxqd8ViIiJR+5u6XgeaYGkeyWtlXThCPs/KOluSask/aukwzqdM6EWEZVp0F0tbc8hTQYWAacARwFnSzpqWLGfA/NtvxS4Gvj7TnVLqEVENa6wtHcssNb2OtvbgSXAGc+4lH2D7d+Uq8uB2Z1Omj61iKikmHzbdafaTEkrWtYX215cfp4FPNCybz1wXJtzvRO4ttMFE2oRUV33b+nYZHv+7l5O0rnAfOC1ncom1CKisgottXY2AHNa1meX2555Len1wP8AXmt7W6eTpk8tIqoZuz6124F5kg6XtBdwFrC0tYCklwNfAk63vbGb6vUs1CTNkXRDORy7WtIFvbpWRIyn7kY+O41+2t4JnA9cB6wBrrK9WtIlkk4vi30KeA7wLUl3SFo6yul+p5e3nzuBD9n+maRpwEpJP7R9dw+vGRHjYYxeEml7GbBs2LaPtnx+fdVz9izUbD8EPFR+3ippDcVoR0ItYiLLjxmDpLnAy4HbxuN6EdFje/LrvCU9B/g28H7bj4+wfyGwEGDmIVN7XZ2IGAv1zbTejn5KmkoRaF+3/Z2RythebHu+7fkHzMgMk4iJQIODXS390LMUkSTgUmCN7X/o1XUiYpyZKpNvx10vW2rHA+cBJ5VDsXdIOrWH14uIcSCM3N3SD70c/byZ4jGxiGiaPXmgICIaKKEWEY1R8z61hFpEVNavkc1uJNQioiLn9jMiGsQk1CKiYep795lQi4jq+jUHrRsJtYioLqEWEY1hw0B97z8TahFRXVpqEdEoCbWIaAwDHX5/oJ8SahFRkcHpU4uIpjAZKIiIhkmfWkQ0SkItIpojD7RHRJMYyKuHIqJR0lKLiObIY1IR0SQGZ55aRDRKniiIiEZJn1pENIad0c+IaJi01CKiOYwHBvpdiVEl1CKimrx6KCIap8ZTOib1uwIRMbEY8KC7WjqRtEDSvZLWSrpwhP17S/pmuf82SXM7nTOhFhHVuHxJZDdLG5ImA4uAU4CjgLMlHTWs2DuBLbaPAD4D/F2n6iXUIqIyDwx0tXRwLLDW9jrb24ElwBnDypwBXFF+vho4WZLanbRWfWq/uuu3m8484o77+12PFjOBTf2uRI3l++msbt/RYbt7gq1sue5ffPXMLovvI2lFy/pi24vLz7OAB1r2rQeOG3b878rY3inpMeB5tPlOaxVqtg/sdx1aSVphe36/61FX+X46a+J3ZHtBv+vQTm4/I6JfNgBzWtZnl9tGLCNpCnAA8Ei7kybUIqJfbgfmSTpc0l7AWcDSYWWWAn9Sfn4z8CO7/eMMtbr9rKHFnYvs0fL9dJbvaBRlH9n5wHXAZOAy26slXQKssL0UuBS4UtJaYDNF8LWlDqEXETGh5PYzIholoRYRjZI+tWEk7QP8GNib4vu52vbH+lureipnhK8ANtg+rd/1qRNJ9wFbgQFgZ9OmddRZQm1X24CTbD8haSpws6RrbS/vd8Vq6AJgDbB/vytSU6+zXaeJt3uE3H4O48IT5erUcsloyjCSZgNvAr7S77pEtEqojUDSZEl3ABuBH9q+rd91qqHPAh8G6vsOmv4ycL2klZIW9rsye5KE2ghsD9h+GcUM52MlHdPvOtWJpNOAjbZX9rsuNfYa26+geAPFeyWd0O8K7SkSam3YfhS4Aaj1s259cDxwetkZvgQ4SdLX+lulerG9ofznRuC7FG+kiHGQUBtG0oGSppef9wXeANzT31rVi+2LbM+2PZdihvePbJ/b52rVhqT9JE0b+gy8Ebirv7Xac2T0c1cHA1eU0xUmAVfZ/n6f6xQTy/OB75av/ZoCfMP2P/e3SnuOPCYVEY2S28+IaJSEWkQ0SkItIholoRYRjZJQi4hGSagFkj4j6f0t69dJ+krL+qclXSzp6lGOv1HS/PLzxS3b50rK/KwYVwm1ALgFeDWApEkUP+t2dMv+V1NMsH1zF+e6uHORiN5JqAXArcDvl5+Pppj9vlXScyXtDRwJbB5qdUnaV9ISSWskfRfYt9z+t8C+ku6Q9PXyfJMlfVnSaknXl09pRPRMQi2w/SCwU9KhFK2ynwC3UQTdfOBOYHvLIX8K/Mb2kcDHgFeW57kQ+K3tl9l+a1l2HrDI9tHAo8CZ4/AnxR4soRZDbqUItKFQ+0nL+i3Dyp4AfA3A9ipgVZvz/tr2HeXnlcDcsatyxK4SajFkqF/tJRS3n8spWmqvpgi8Z2tby+cB8rxx9FhCLYbcCpwGbC7fJ7cZmE4RbMND7cfAOQDlu+Ze2rJvR/ka9Ii+SKjFkDspRj2XD9v22Ajv2f8C8BxJa4BLKG4rhywGVrUMFESMq7ylIyIaJS21iGiUhFpENEpCLSIaJaEWEY2SUIuIRkmoRUSjJNQiolH+P2LFGZevEVkqAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -1754,7 +1514,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1791,9 +1551,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'munged' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtvd_rand_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, rand)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmunged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtvd_ideal_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, ideal)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmunged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mZtvd_rand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvd_rand_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mZtvd_ideal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvd_ideal_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'munged' is not defined" + ] + } + ], "source": [ "tvd_rand_values = np.asarray([munged['TVD(data, rand)'][idx] for idx in munged.index])\n", "tvd_ideal_values = np.asarray([munged['TVD(data, ideal)'][idx] for idx in munged.index])\n", @@ -1803,9 +1575,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'tvd_ideal_values' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtvd_ideal_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtvd_rand_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'tvd_ideal_values' is not defined" + ] + } + ], "source": [ "tvd_ideal_values\n", "tvd_rand_values" @@ -1940,20 +1724,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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tpwJTAdra2ujq6soR1QarfTZeVki7/fl/00pZoJg8rZQF+p/H+idP4R9Z1XMnIhZLGp2n8XQhl450OuglwGZ5g0XEDGAGQGdnZ3R0dOTd1Aahvc6dX0i7J0zt+/+bVsoCxeRppSzQ/zzWP3mGel6SNLGyIGlLYElfdpJOB70O+BCwuqTKG856QDH/k8zMrK48Pf7DgAskLSCbtmEdYHJvG0l6N/B6RLwgaRSwM9mB3euAvYFzgSnApf3MbmZm/ZBnyoZbJW0GjE+rHoyI13O03QbMSuP8KwDnR8Tlku4DzpV0PHAHcGo/s5uZWT/0WPglrQUcDPxjWnUv2UHahb01HBF3AVvUWf8IsHWfk5qZ2YDodoxf0jbArWnx9PQDMCfdZ2Zmg1BPPf4fAntFxB1V6y6TdAlwCvCBQpOZmVkhejqrZ9Waog9ARHQBY4qLZGZmReqp8EvSGnVWrtnLdmZm1sJ6KuAnAVdL2l7SmPSzA/CbdJ+ZmQ1C3Y7xR8SMdO7+d8jO6gngPuD4iPh1g/KZmdkA6/F0zoi4HLi8QVnMzKwBPFZvZlYyLvxmZiXT0xe4Dk3/+staZmZDSE89/s+lf3/SiCBmZtYYPR3cvV/SQ8C6ku6qWi+yC2y9v9hoZmZWhJ5O5/yUpHWA3wJ7NC6SmZkVqbfTOZ8CNpe0IvAPaXXeaZnNzKwF9Tofv6TtyWbmnEc2zLO+pCkRcWPB2czMrAB5rsB1IvDRiHgQQNI/AOcAWxYZzMzMipHnPP4RlaIPEBF/BkYUF8nMzIqUp8d/m6SZwJlpeT/gtuIimZlZkfIU/i+SXX7xK2n5JuBnhSUyM7NC5bnY+qtk4/wnFh/HzMyK5rl6zMxKxoXfzKxkei38kv6pEUHMzKwx8vT4fyZpjqQvSVqt8ERmZlaoXgt/RGxLdgrn+sBcSWdL2rnwZGZmVohcY/wR8RBwDHAksD3wY0kPSPpEkeHMzGzg5Rnjf7+kk4D7gR2B3SPiven2SQXnMzOzAZbnC1w/AWYCR0fEksrKiFgg6ZjCkpmZWSHyFP5dgSURsQxA0grAyIh4OSLOKDSdFap92hUD3ua86bsOeJtmNrDyjPFfC4yqWh6d1pmZ2SCUp/CPjIjFlYV0e3RxkczMrEh5Cv9LkiZWFiRtCSzp4fFmZtbC8ozxHwZcIGkB2RW41gEmF5rKzMwKk2d2zlslbQaMT6tyXXNX0vpkl2xcGwhgRkScLGlN4DygnexyjvtExPP9i29mZn2Vd5K2rYD3AxOBT0n69xzbLAUOj4gJwAeBgyVNAKYBsyNiU2B2WjYzswbJc7H1M4D3AF3AsrQ6yHrz3YqIJ4En0+0XJd0PjAP2BHZID5sFXE/2jWAzM2uAPGP8ncCEiIj+7kRSO7AFcAuwdnpTAHiKbCio3jZTgakAbW1tdHV19Xf31o19Nl7W+4P6qL+/pyKyQP/ytFIW8O/JBl6ewn8P2QHdJ3t7YD2SVgEuAg6LiEWS3rwvIkJS3TeUiJgBzADo7OyMjo6O/uzeerDXufMHvM0Tpvbv91REFuhfnlbKAv492cDLU/jHAvdJmgO8WlkZEXv0tqGkEWRF/6yIuDitflpSW0Q8KakNWNiP3GZm1k95Cv+x/WlYWdf+VOD+iKi+Xu9lwBRgevr30v60b2Zm/ZPndM4bJG0IbBoR10oaDQzL0fY2wGeBuyVVBvCOJiv450s6AHgM2Kd/0c3MrD/ynNVzINlB1jXJzu4ZB/wcmNTTdhFxM9kXvurpcVszMytOnvP4DybrvS+CNy/KslaRoczMrDh5Cv+rEfFaZUHScLLz+M3MbBDKU/hvkHQ0MCpda/cC4NfFxjIzs6LkKfzTgGeAu4EvAFeSXX/XzMwGoTxn9bwB/CL9mJnZIJfnrJ5HqTOmHxEbF5LIzMwKlXeunoqRwL+RndppZmaDUK9j/BHxXNXP/Ij4EdkF2M3MbBDKM9QzsWpxBbJPAHk+KZiZWQvKU8B/WHV7KemqWYWkMTOzwuU5q+cjjQhiZmaNkWeo52s93V8z86aZmbW4vGf1bEU2nTLA7sAc4KGiQpmZWXHyFP71gIkR8SKApGOBKyLiM0UGMzOzYuSZsmFt4LWq5dfo5jq5ZmbW+vL0+E8H5ki6JC3vBcwqLpKZmRUpz1k935X0G2DbtOpzEXFHsbHMzKwoeYZ6AEYDiyLiZOAJSRsVmMnMzArUa+GX9C3gSOCotGoEcGaRoczMrDh5evz/CuwBvAQQEQuAMUWGMjOz4uQp/K9FRJCmZpa0crGRzMysSHkK//mSTgFWl3QgcC2+KIuZ2aCV56yeH6Rr7S4CxgP/FRHXFJ7MzMwK0WPhlzQMuDZN1OZib2Y2BPQ41BMRy4A3JK3WoDxmZlawPN/cXQzcLeka0pk9ABHxlcJSmZlZYfIU/ovTj5mZDQHdFn5JG0TE4xHheXnMzIaQnnr8vwImAki6KCI+2ZhIQ1f7tCsKaXfe9F0LadfMhqaeDu6q6vbGRQcxM7PG6KnwRze3zcxsEOtpqGdzSYvIev6j0m3SckTEqoWnMzOzAddt4Y+IYY0MYmZmjZF3Pv4+k/RLSQsl3VO1bk1J10h6KP27RlH7NzOz+gor/MBpwC4166YBsyNiU2B2WjYzswYqrPBHxI3A32pW78lb1+udRXb9XjMza6Aie/z1rB0RT6bbTwFrN3j/Zmall2fKhkJEREjq9jRRSVOBqQBtbW10dXU1LFtR9tl4WSHt9ve1KSJPK2WB/uVppSzg35MNvEYX/qcltUXEk5LagIXdPTAiZgAzADo7O6Ojo6NRGQuz17nzC2n3hKn9e22KyNNKWaB/eVopC/j3ZAOv0UM9lwFT0u0pwKUN3r+ZWekVeTrnOcAfgfGSnpB0ADAd2FnSQ8BOadnMzBqosKGeiPhUN3dNKmqf9XhiNLOhyX/b/dfooR4zM2syF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+M7OSceE3MysZF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+M7OSceE3MysZF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+M7OSceE3MysZF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+M7OSceE3MysZF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+M7OSceE3MysZF34zs5Jx4TczK5nhzQ5gZjYUtE+7YsDbnDd91wFvE5rU45e0i6QHJT0saVozMpiZlVXDC7+kYcD/AB8DJgCfkjSh0TnMzMqqGT3+rYGHI+KRiHgNOBfYswk5zMxKSRHR2B1KewO7RMTn0/JngQ9ExCE1j5sKTE2L44EHGxBvLPBsA/aTRytlgdbK4yzda6U8ztK9RuXZMCLeXbuyZQ/uRsQMYEYj9ynptojobOQ+u9NKWaC18jhL91opj7N0r9l5mjHUMx9Yv2p5vbTOzMwaoBmF/1ZgU0kbSVoR2Be4rAk5zMxKqeFDPRGxVNIhwG+BYcAvI+LeRufoRkOHlnrRSlmgtfI4S/daKY+zdK+peRp+cNfMzJrLUzaYmZWMC7+ZWcm48AOSfilpoaR7WiDL+pKuk3SfpHslHdrELCMlzZF0Z8pyXLOyVGUaJukOSZe3QJZ5ku6W1CXptiZnWV3ShZIekHS/pA81Mcv49JpUfhZJOqyJeb6a/v/eI+kcSSObmOXQlOPepr4mHuMHSdsBi4HTI+J9Tc7SBrRFxO2SxgBzgb0i4r4mZBGwckQsljQCuBk4NCL+1OgsVZm+BnQCq0bEbs3KkbLMAzojoulfDJI0C7gpImams+VGR8QLLZBrGNnp2h+IiMeasP9xZP9vJ0TEEknnA1dGxGlNyPI+spkKtgZeA64CDoqIhxudxT1+ICJuBP7W7BwAEfFkRNyebr8I3A+Ma1KWiIjFaXFE+mlaT0HSesCuwMxmZWhFklYDtgNOBYiI11qh6CeTgL80o+hXGQ6MkjQcGA0saFKO9wK3RMTLEbEUuAH4RDOCuPC3MEntwBbALU3MMExSF7AQuCYimpYF+BHwdeCNJmaoFsDVkuamKUaaZSPgGeD/0jDYTEkrNzFPtX2Bc5q184iYD/wAeBx4Evh7RFzdpDj3ANtKepek0cDHefuXWRvGhb9FSVoFuAg4LCIWNStHRCyLiA6yb1hvnT6uNpyk3YCFETG3GfvvxocjYiLZTLMHpyHDZhgOTAT+NyK2AF4Cmj7deRpy2gO4oIkZ1iCbBHIjYF1gZUmfaUaWiLgf+B5wNdkwTxewrBlZXPhbUBpPvwg4KyIubnYegDR0cB2wS5MibAPskcbVzwV2lHRmk7IAb/YmiYiFwCVkY7fN8ATwRNWnsQvJ3gia7WPA7RHxdBMz7AQ8GhHPRMTrwMXAPzcrTEScGhFbRsR2wPPAn5uRw4W/xaQDqqcC90fEiU3O8m5Jq6fbo4CdgQeakSUijoqI9SKinWz44HcR0ZSeG4CkldPBd9KwykfJPso3XEQ8BfxV0vi0ahLQ8JMB6vgUTRzmSR4HPihpdPrbmkR23KwpJK2V/t2AbHz/7GbkaNnZORtJ0jnADsBYSU8A34qIU5sUZxvgs8DdaWwd4OiIuLIJWdqAWenMjBWA8yOi6adRtoi1gUuyWsJw4OyIuKqJeb4MnJWGVx4BPtfELJU3w52BLzQzR0TcIulC4HZgKXAHzZ0u4SJJ7wJeBw5u1kF4n85pZlYyHuoxMysZF34zs5Jx4TczKxkXfjOzknHhNzMrGRd+aypJx0o6os769t5mS02P+XTVcoekj1ct7yFpQL7BKmlfSd/I+diZkiYM0H6vTDNvri7pS++0nTrr33z9Je0vad2q++ZJGtvffVrrcuG3wawd+HTVcgfZ/CcARMRlETF9gPb1MbKv2fcqIj5fbzbV9H2IPomIj6dzvVcH+l34q9rpyf5k0xrYEOfCbwMqfaP1ijSH/z2SJqf1b/YeJXVKur5qs80l/VHSQ5IOrNNmu6SbJN2efipfuZ9ONulVl6QjgW8Dk9Py5NSD/WlqY21Jl6Rcd1bakPQZZdcc6JJ0Sr3inL7x2UH2JaDq9cMk/SA9z7skfTmtv15SZ7q9WNIPJd0JfEjSVpL+kDLMkTSmOmfa5nJJO9S8btOB96Sc36/J8Z+SvpJunyTpd+n2jpLOqvP6f0PSnyXdDIxP6/Ymm+76rLSPUan5L6fX/G5Jm9X7ndvg42/u2kDbBVgQEbvCm1MG9+b9wAeBlYE7JF1Rc/9CYOeIeEXSpmTTAHSSTUR2RGVefklPk82Pf0ha3r+qjR8DN0TEv6bivoqk9wKTgW0i4nVJPwP2A06v2f8WwJ2x/Lcdp5J96uiIiKWS1qzz3FYmm4r38PSt2geAyRFxq6RVgSU5Xh/Sc31fmjCv1k3A4ek5dgIrKZvvaVvgxuoHStqSbMqLDrK//9uBuRFxoaRDyF7P29JjAZ6NiIlpmOkI4PM581oLc4/fBtrdwM6Svidp24j4e45tLo2IJemCJtex/GRnI4BfSLqbbKbH/oyf7wj8L7w54+jfyeZt2RK4Vdn0GJOAjetsuwvwmzrrdwJOSXOrExH1rumwjGzCPch6109GxK3p8Ysq275Dc4Et0xvJq8Afyd4AtiV7U6i2LXBJmhN+EXBZL21XJgmcS/YmZ0OAe/w2oCLiz5Imko21Hy9pdkR8m2yelEpHo/bSd7U96drlrwJPA5unNl4ZoLgCZkXEUb087qPAJ/u5j1ciorepd6tfG1j+9elR+rTyKNkY/R+Au4CPAJvwzickezX9uwzXiyHDPX4bUOmskJcj4kzg+7w1PfA8st41LF9E91R2fd93kU2Wd2vN/auR9ZTfIJvArjIO/yIwpupxtcvVZgNfTBmHpSGo2cDeemvGxDUlbVjzfFYDhkfEc3XavAb4grIrO9HNUE+1B4E2SVulx49J284DOiStIGl96k/v3NNzg6xnfwTZ0M5NwEHAHXWGp24E9pI0Stnsorv3YR82RLjw20D7J2BOGjr5FnB8Wn8ccLKyi5LX9oDvIhvi+RPwnYiovTTez4Ap6QDpZmQXGqlstywdKP1qamNC5eBuTRuHAh9Jw0Vzya7Beh9wDNlVtO4iK+RtNdvtDFzbzXOdSTbt710p26e7eRyQXe7XVrsAAACCSURBVBKR7JjCT9LjryHr3f8eeJRsKuUfU3MQOW37HPD7dCD5+7X3kxX7NuCPaf77V1h+mId0Wc/zgDvJhq+q32RPA35ec3DXhiDPzmnWA0kzgZnNvMC82UBz4TczKxkP9ZiZlYwLv5lZybjwm5mVjAu/mVnJuPCbmZWMC7+ZWcn8f9uZRQy4c7vWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "G = perfect_qc.qubit_topology()\n", "len(perfect_qc.qubit_topology())\n", diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index b9618215..f5368d37 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -529,11 +529,11 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = # ================================================================================================== # Analysis # ================================================================================================== -def get_error_hamming_weight_distributions(noisy_results, perfect_results): +def get_error_hamming_weight_distributions(noisy_results, ideal_results): - # allow for perfect result to depend only on width (pass in a list) - if not isinstance(perfect_results, dict): - perfect_results = {width: {depth: perfect_results[width] for depth in depth_array.keys()} + # allow for ideal result to depend only on width (pass in a list) + if not isinstance(ideal_results, dict): + ideal_results = {width: {depth: ideal_results[width] for depth in depth_array.keys()} for width, depth_array in noisy_results.items()} distrs = {width: {depth: [] for depth in depth_array.keys()} @@ -543,10 +543,10 @@ def get_error_hamming_weight_distributions(noisy_results, perfect_results): for depth, samples in depth_array.items(): noisy_ckt_sample_results = noisy_results[width][depth] - perfect_ckt_sample_results = perfect_results[width][depth] + ideal_ckt_sample_results = ideal_results[width][depth] for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, - perfect_ckt_sample_results): + ideal_ckt_sample_results): if len(ideal_result) > 1: raise ValueError("You have provided ideal results with more than one shot; " "this method is intended to analyze results where the ideal " @@ -562,54 +562,33 @@ def get_error_hamming_weight_distributions(noisy_results, perfect_results): return distrs -def get_average_of_distributions(distrs): - # take in output of `get_error_hamming_weight_distributions` - return {w: {d: sum(distr_list) / len(distr_list) for d, distr_list in d_arr.items()} - for w, d_arr in distrs.items()} - - -def get_success_probabilities(noisy_results, perfect_results, +def get_single_target_success_probabilities(noisy_results, ideal_results, allowed_errors: Union[int, Callable[[int], int]] = 0): if isinstance(allowed_errors, int): error_func = lambda num_bits: allowed_errors else: error_func = allowed_errors - avg_distrs = get_average_of_distributions(get_error_hamming_weight_distributions( - noisy_results, perfect_results)) + hamming_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results) + + return {w: {d: [sum(distr[0:error_func(w)+1]) for distr in distrs] + for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} + - return {w: {d: sum(distr[0:error_func(w)+1]) for d, distr in d_distrs.items()} - for w, d_distrs in avg_distrs.items()} +def average_distributions(distrs): + """ + E.g. take in output of :func:`get_error_hamming_weight_distributions` or + :func:`get_single_target_success_probabilities` + :param distrs: + :return: + """ + return {w: {d: sum([np.asarray(distr) for distr in distr_list]) / len(distr_list) + for d, distr_list in d_arr.items()} for w, d_arr in distrs.items()} def get_total_variation_dist(distr1, distr2): return tvd(np.asarray([distr1]).T, np.asarray([distr2]).T) - # TODO: separate these out - - # Probability of success with basement[ log_2(width) - 1 ] errors - # I.e. error when you allow for a logarithmic number of bit flips from the answer - # num_bit_flips_allowed_from_answer = int(basement_function(np.log2(width) - 1)) - # pr_suc_log_err_data = sum( - # [wt_dist_data[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) - # pr_suc_log_err_rand = sum( - # [wt_dist_rand[idx] for idx in range(0, num_bit_flips_allowed_from_answer + 1)]) - # - # - # sample_stats = { - # 'Hamming dist. data': wt_dist_data, - # 'TVD(data, ideal)': tvd_data_ideal, - # 'TVD(data, rand)': tvd_data_rand, - # 'Pr. success data': pr_suc_data, - # # 'Pr. success rand': pr_suc_rand, - # 'loge = basement[log_2(Width)-1]': num_bit_flips_allowed_from_answer, - # 'Pr. success loge data': pr_suc_log_err_data} - # # 'Pr. success loge rand': pr_suc_log_err_rand} - # - # samples.append(sample_stats) - - # return stats - def hamming_distance(arr1, arr2): """ From 208df9b5e3183bbbef757f2bdaac1d8d2b8bd067 Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 9 Aug 2019 17:04:50 -0400 Subject: [PATCH 26/49] Add basic plot of successes. --- examples/volumetrics.ipynb | 942 ++++++++++++++--------------- forest/benchmarking/volumetrics.py | 53 +- 2 files changed, 504 insertions(+), 491 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 81f29c83..b9960865 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -78,7 +78,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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5\n", - "RZ(pi/2) 6\n", + "RX(-pi/2) 5\n", "RX(-pi/2) 6\n", + "RZ(pi/2) 6\n", "RZ(-pi) 7\n", - "RZ(-pi) 7\n", + "RZ(pi/2) 8\n", "RX(-pi) 8\n", "\n" ] @@ -305,10 +301,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "I 1\n", - "X 0\n", - "X 1\n", + "I 4\n", + "X 7\n", + "I 4\n", + "I 7\n", "\n" ] } @@ -327,9 +323,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "I 5\n", - "CNOT 4 5\n", + "CNOT 1 2\n", + "CNOT 1 2\n", "\n" ] } @@ -348,10 +343,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 3\n", + "H 0\n", + "H 1\n", "H 4\n", - "H 5\n", - "H 8\n", + "H 7\n", "\n" ] } @@ -370,20 +365,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 3\n", - "CZ 3 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 6 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 6\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "CZ 6 7\n", + "RX(-pi/2) 7\n", + "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 6 7\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", "\n" ] } @@ -403,6 +397,35 @@ "name": "stdout", "output_type": "stream", "text": [ + "RZ(-1.2331567623564417) 1\n", + "RX(pi/2) 1\n", + "RZ(1.7404467610762533) 1\n", + "RX(-pi/2) 1\n", + "RZ(-2.407885832151905) 1\n", + "RZ(-0.4968338474688072) 2\n", + "RX(pi/2) 2\n", + "RZ(1.8242564813704634) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.872651637214672) 2\n", + "CZ 2 1\n", + "RZ(-2.5391621160551203) 1\n", + "RX(pi/2) 1\n", + "RZ(1.7005564940842257) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(-2.8844964068236463) 1\n", + "RX(pi/2) 1\n", + "RZ(1.4042518086522815) 1\n", + "RX(-pi/2) 1\n", + "RZ(-1.117519772304104) 1\n", + "RZ(2.506641595928895) 2\n", + "RX(pi/2) 2\n", + "RZ(0.2886431488211289) 2\n", + "RX(-pi/2) 2\n", + "RZ(-2.8643254313406334) 2\n", "\n" ] } @@ -422,40 +445,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(0.8406899999638024) 1\n", - "RX(pi/2) 1\n", - "RZ(2.847991243602221) 1\n", - "RX(-pi/2) 1\n", - "RZ(-0.9649014576461203) 1\n", - "RZ(-3.0197488646781547) 2\n", - "RX(pi/2) 2\n", - "RZ(1.175907213650433) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.296233838595962) 2\n", - "CZ 2 1\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(2.281028907841513) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi/2) 2\n", - "CZ 2 1\n", - "RX(pi/2) 1\n", - "RZ(-2.002236765648214) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.6738697650167795) 2\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(0.6154267530935176) 1\n", - "RX(pi/2) 1\n", - "RZ(1.6626538921651697) 1\n", - "RX(-pi/2) 1\n", - "RZ(-2.239540857961898) 1\n", - "RZ(1.7272246029442417) 2\n", - "RX(-pi/2) 2\n", - "RZ(1.2470234421229809) 2\n", - "RX(-pi/2) 2\n", - "RZ(-0.3451740588607606) 2\n", + "RZ(-2.1004961533263593) 3\n", + "RX(pi/2) 3\n", + "RZ(1.658041343629602) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.6905613828918837) 3\n", + "RZ(2.4772894438271122) 4\n", + "RX(pi/2) 4\n", + "RZ(1.3967333178418608) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.9397427934868001) 4\n", + "CZ 4 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(2.818246600476062) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 3\n", + "RX(pi/2) 3\n", + "RZ(-1.7999267560042078) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.5761532093595472) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(-1.0008998454603715) 3\n", + "RX(pi/2) 3\n", + "RZ(2.4241875951702756) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.1651060870208385) 3\n", + "RZ(1.6218604008940751) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.236139528756533) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.9972127678928056) 4\n", "\n" ] } @@ -482,23 +505,23 @@ "output_type": "stream", "text": [ "X 1\n", - "I 2\n", "I 3\n", "X 4\n", + "X 7\n", "I 1\n", "I 4\n", - "I 1\n", - "I 2\n", "I 3\n", "I 4\n", - "X 1\n", - "I 2\n", - "X 3\n", + "I 4\n", + "I 7\n", + "I 1\n", + "I 3\n", "X 4\n", + "X 7\n", "CNOT 1 4\n", - "CNOT 1 2\n", "I 3\n", "I 4\n", + "CNOT 4 7\n", "\n" ] } @@ -524,23 +547,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 6\n", - "H 7\n", - "Z 6\n", - "Z 7\n", - "H 6\n", - "CZ 6 7\n", - "H 6\n", - "I 6\n", - "Z 7\n", - "I 6\n", - "I 7\n", - "Z 6\n", - "I 7\n", - "I 6\n", - "I 7\n", - "H 6\n", - "H 7\n", + "H 1\n", + "H 4\n", + "I 1\n", + "Z 4\n", + "I 1\n", + "I 4\n", + "I 1\n", + "I 4\n", + "I 1\n", + "I 4\n", + "I 1\n", + "I 4\n", + "H 1\n", + "CZ 1 4\n", + "H 1\n", + "H 1\n", + "H 4\n", "\n" ] } @@ -565,44 +588,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "RX(-pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 7\n", - "RZ(-pi) 8\n", - "RX(pi/2) 7\n", - "CZ 7 8\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RX(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RX(pi/2) 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "RZ(-pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "RZ(-pi/2) 7\n", - "RZ(pi/2) 8\n", - "RX(pi) 8\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 2\n", + "RZ(pi/2) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RX(pi/2) 2\n", + "RZ(-pi) 2\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 1\n", + "RZ(-pi) 1\n", + "RX(-pi) 1\n", + "RX(pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RZ(-pi) 2\n", + "RX(-pi/2) 1\n", + "RZ(-0.14370725766325165) 1\n", + "RX(pi) 1\n", + "RX(pi/2) 2\n", + "CZ 1 2\n", + "RZ(-1.7145035844581487) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -639,192 +661,171 @@ "name": "stdout", "output_type": "stream", "text": [ + "RZ(pi/2) 1\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", "RX(-pi/2) 0\n", "RZ(-pi/2) 4\n", + "RX(pi) 4\n", + "RZ(pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "CZ 1 0\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 3 4\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", + "CZ 1 4\n", + "RZ(pi/2) 5\n", "RZ(pi) 0\n", - "RX(pi) 0\n", - "RZ(-pi/2) 1\n", + "RX(pi/2) 0\n", + "RZ(-2.552556931782524) 0\n", + "RX(pi/2) 0\n", "RX(pi/2) 1\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 1 4\n", + "RZ(-0.9712714822298043) 1\n", "RX(pi/2) 1\n", "CZ 0 1\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(-pi/2) 3\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", "RX(pi/2) 0\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RX(-pi/2) 0\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(pi) 0\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "CZ 3 0\n", - "RZ(pi) 0\n", - "RX(-pi/2) 0\n", - "RX(-pi/2) 1\n", + "RX(pi/2) 1\n", "CZ 0 1\n", - "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(pi/2) 4\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 4 3\n", + "CZ 5 4\n", + "RX(pi/2) 0\n", + "RZ(0.9712714822298032) 0\n", + "RX(pi/2) 2\n", + "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "CZ 2 5\n", + "RZ(-pi/2) 2\n", + "RX(-pi/2) 2\n", + "RZ(pi) 5\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RX(pi/2) 1\n", + "RZ(2.7631528388701625) 1\n", + "RX(pi/2) 1\n", + "RZ(2.6820883434244958) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", "RZ(pi) 1\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", + "RX(pi/2) 1\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(pi/2) 4\n", - "CZ 0 1\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "CZ 3 4\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", + "RZ(pi) 1\n", + "RX(pi/2) 1\n", + "RZ(-2.030300636960194) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 2\n", + "RZ(0.21059590708763665) 2\n", + "RZ(pi) 0\n", + "RX(-pi/2) 1\n", + "CZ 1 4\n", + "CZ 1 2\n", "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RZ(-pi/2) 0\n", - "RX(pi) 0\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "CZ 3 0\n", - "RX(-pi/2) 3\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RZ(pi) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 3\n", - "RX(pi) 3\n", - "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", + "RX(pi/2) 0\n", + "CZ 1 0\n", "RZ(pi/2) 1\n", - "RZ(pi/2) 3\n", + "RX(pi/2) 1\n", + "CZ 1 4\n", + "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", "RZ(pi/2) 3\n", - "RZ(pi/2) 4\n", - "RZ(1.1243912032248942) 0\n", - "RX(pi/2) 0\n", - "RZ(1.2180109156170746) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.6134967010491525) 0\n", - "RZ(-2.6007950357484777) 1\n", - "RX(pi/2) 1\n", - "RZ(3.07677097365376) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.2330554369238811) 1\n", - "CZ 1 0\n", "RZ(-pi/2) 0\n", "RX(pi/2) 0\n", - "RZ(2.2688338135521353) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", "RX(-pi/2) 1\n", "CZ 1 0\n", + "RZ(pi) 4\n", "RX(pi/2) 0\n", - "RZ(-1.7600137491541061) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", - "RZ(1.9455860910933556) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(-1.765614659103938) 3\n", + "RZ(pi/2) 3\n", "RX(pi/2) 3\n", - "RZ(2.333860458736699) 3\n", + "CZ 3 0\n", + "RZ(pi) 0\n", + "RX(pi/2) 0\n", "RX(-pi/2) 3\n", - "RZ(1.3189846424964307) 3\n", - "RZ(-1.9457738910099138) 4\n", + "CZ 0 3\n", + "CZ 1 4\n", + "RZ(-pi/2) 1\n", + "RX(pi) 1\n", + "RX(pi/2) 4\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "RZ(pi) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(pi) 4\n", "RX(pi/2) 4\n", - "RZ(1.6615752928737941) 4\n", - "RX(-pi/2) 4\n", - "RZ(3.1146598749504513) 4\n", - "CZ 4 3\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", "RZ(pi/2) 3\n", "RX(pi/2) 3\n", - "RZ(2.208149269791826) 3\n", - "RX(-pi/2) 3\n", "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", + "RX(pi) 4\n", "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.772289877464722) 3\n", - "RX(-pi/2) 3\n", - "RZ(0.7972582129469177) 4\n", "RX(pi/2) 4\n", + "CZ 4 1\n", + "RX(-pi/2) 4\n", "CZ 4 3\n", - "RZ(-1.8550831521821793) 0\n", - "RX(pi/2) 0\n", - "RZ(1.023046143496381) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.19389593670807215) 0\n", - "RZ(0.8350132758730009) 1\n", "RX(pi/2) 1\n", - "RZ(1.4034616514737628) 1\n", + "CZ 4 1\n", + "RZ(-pi/2) 0\n", + "RX(pi) 0\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(pi) 2\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RZ(pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(pi/2) 5\n", + "RX(pi) 7\n", + "RZ(1.731435598539156) 1\n", + "RX(pi/2) 1\n", + "RZ(2.0048517099354908) 1\n", "RX(-pi/2) 1\n", - "RZ(0.5951974037155017) 1\n", - "RZ(-3.0630317874417194) 3\n", - "RX(pi/2) 3\n", - "RZ(2.6647202108971375) 3\n", - "RX(-pi/2) 3\n", - "RZ(-2.3997638535696124) 3\n", - "RZ(-1.479001253061274) 4\n", + "RZ(-1.579266854495088) 1\n", + "RZ(1.748887767548628) 4\n", + "RX(pi/2) 4\n", + "RZ(2.2686650825133166) 4\n", "RX(-pi/2) 4\n", - "RZ(1.932316588815607) 4\n", + "RZ(-0.6082077459138557) 4\n", + "RZ(-1.5322946152827703) 5\n", + "RX(pi/2) 5\n", + "RZ(1.6117592090946185) 5\n", + "RX(-pi/2) 5\n", + "RZ(-1.7756393468666194) 5\n", + "CZ 5 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.2697056415606447) 4\n", "RX(-pi/2) 4\n", - "RZ(0.5458526604808727) 4\n", - "RX(-pi/2) 0\n", - "RZ(3*pi/4) 1\n", - "RX(pi) 1\n", - "RZ(pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", "RX(pi/2) 4\n", - "CZ 1 4\n", - "RZ(-pi/2) 0\n", - "RX(pi/2) 0\n", - "CZ 1 0\n", - "RZ(pi/2) 3\n", + "RZ(-1.7089281306267985) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.418946472860818) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(2.767710837344638) 3\n", "RX(pi/2) 3\n", - "RZ(pi) 4\n", - "RX(pi) 4\n", + "RZ(1.0527489270784798) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", "CZ 3 4\n", - "RZ(-pi/2) 3\n", + "RZ(2.796660940434048) 3\n", "RX(-pi/2) 3\n", "RZ(pi) 4\n", "RX(pi/2) 4\n", @@ -833,155 +834,120 @@ "RX(pi/2) 3\n", "RX(-pi/2) 4\n", "CZ 3 4\n", - "RZ(pi) 0\n", - "RZ(pi/4) 1\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 4\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "CZ 1 4\n", + "CZ 4 1\n", + "RX(pi/2) 1\n", + "RZ(2.5653973171329856) 1\n", "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(pi/2) 4\n", "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(-pi/2) 3\n", - "RZ(pi) 4\n", + "CZ 4 1\n", + "RX(pi/2) 1\n", + "RZ(-1.8139258482310732) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.113814843437046) 4\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 3\n", + "CZ 4 1\n", + "RZ(-0.00740429708791579) 1\n", + "RX(pi/2) 1\n", + "RZ(1.9610827675672056) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.381319135689587) 1\n", + "RZ(-0.24619098689844998) 3\n", "RX(pi/2) 3\n", + "RZ(0.9937032604069961) 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array([0.778, 0.204, 0.016, 0.002, 0. ]), array([0.91 , 0.088, 0.002, 0. , 0. ]), array([0.786, 0.188, 0.024, 0. , 0.002]), array([0.758, 0.224, 0.016, 0.002, 0. ]), array([0.792, 0.18 , 0.024, 0.002, 0.002]), array([0.758, 0.202, 0.036, 0.002, 0.002]), array([0.842, 0.154, 0.004, 0. , 0. ]), array([0.776, 0.192, 0.026, 0.006, 0. ]), array([0.846, 0.138, 0.016, 0. , 0. ])]}}\n" ] } ], @@ -1073,7 +1039,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: array([0.8894, 0.1064, 0.0042]), 4: array([0.897 , 0.0998, 0.0032]), 5: array([0.898 , 0.0968, 0.0052])}, 3: {3: array([8.168e-01, 1.664e-01, 1.620e-02, 6.000e-04]), 4: array([0.8216, 0.1628, 0.0146, 0.001 ]), 5: array([0.7988, 0.186 , 0.0142, 0.001 ])}, 4: {3: array([7.732e-01, 1.994e-01, 2.600e-02, 1.200e-03, 2.000e-04]), 4: array([0.8008, 0.1844, 0.0136, 0.0012, 0. ]), 5: array([0.7832, 0.1864, 0.0268, 0.0036, 0. ])}}\n" + "{2: {3: array([0.8916, 0.1036, 0.0048]), 4: array([0.9064, 0.0904, 0.0032]), 5: array([0.8796, 0.1134, 0.007 ])}, 3: {3: array([8.316e-01, 1.592e-01, 8.600e-03, 6.000e-04]), 4: array([8.344e-01, 1.518e-01, 1.320e-02, 6.000e-04]), 5: array([0.849 , 0.138 , 0.0112, 0.0018])}, 4: {3: array([8.032e-01, 1.776e-01, 1.860e-02, 6.000e-04, 0.000e+00]), 4: array([7.612e-01, 2.126e-01, 2.460e-02, 1.200e-03, 4.000e-04]), 5: array([8.018e-01, 1.772e-01, 1.900e-02, 1.400e-03, 6.000e-04])}}\n" ] } ], @@ -1111,7 +1077,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1175,7 +1141,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -1202,7 +1168,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1224,16 +1190,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: 0.8894, 4: 0.897, 5: 0.8979999999999999}, 3: {3: 0.8168000000000001, 4: 0.8216000000000001, 5: 0.7988000000000001}, 4: {3: 0.7732, 4: 0.8008000000000001, 5: 0.7831999999999999}}\n", - "{2: {3: 0.9958000000000002, 4: 0.9968, 5: 0.9948}, 3: {3: 0.9832000000000001, 4: 0.9843999999999999, 5: 0.9848000000000001}, 4: {3: 0.9986, 4: 0.9987999999999999, 5: 0.9964000000000001}}\n", - "{2: {3: 0.6394, 4: 0.647, 5: 0.6479999999999999}, 3: {3: 0.6918000000000001, 4: 0.6966000000000001, 5: 0.6738}, 4: {3: 0.7107, 4: 0.7383, 5: 0.7206999999999999}}\n" + "{2: {3: 0.8916000000000001, 4: 0.9064, 5: 0.8795999999999999}, 3: {3: 0.8315999999999999, 4: 0.8344000000000001, 5: 0.849}, 4: {3: 0.8032, 4: 0.7612, 5: 0.8017999999999998}}\n", + "{2: {3: 0.9952, 4: 0.9968, 5: 0.993}, 3: {3: 0.9907999999999999, 4: 0.9862000000000002, 5: 0.9870000000000001}, 4: {3: 0.9994, 4: 0.9984, 5: 0.998}}\n", + "{2: {3: 0.6416000000000001, 4: 0.6563999999999999, 5: 0.6295999999999999}, 3: {3: 0.7066, 4: 0.7094, 5: 0.724}, 4: {3: 0.7407, 4: 0.6987, 5: 0.7393}}\n" ] } ], @@ -1290,12 +1256,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1343,12 +1309,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1381,12 +1347,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1426,38 +1392,7 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "widths = list(avg_err_hamm_distrs.keys())\n", - "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", - "\n", - "avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", - " ideal_results))\n", - "\n", - "# count as success even if there are log many bits incorrect.\n", - "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", - " ideal_results, \n", - " allowed_errors = basement_log_function))\n", - "\n", - "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", - "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", - "\n", - "pr_succ_rand = {w: 1/2**w for w in widths}\n", - "pr_succ_rand_allow_log_errors = {w: sum(rand_distrs[w][0:basement_log_function(w)+1]) for w in widths}\n", - "\n", - "# total variation distance\n", - "tvd_noisy_ideal = {w: {d: get_total_variation_dist(distr, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", - " for w, d_distrs in avg_err_hamm_distrs.items()}\n", - "\n", - "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", - " for w, d_distrs in avg_err_hamm_distrs.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1466,7 +1401,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1476,12 +1411,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1514,7 +1449,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1551,7 +1486,60 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "successes = determine_successes_from_ckt_success_probs(avg_pr_succ_arr, .8)\n", + "plot_success(successes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Need to update all that follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1561,7 +1549,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtvd_rand_values\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtvd_rand_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, rand)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmunged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtvd_ideal_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, 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P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t - where C_j is a clifford, P_j is a random local Pauli. It could be accommodated if we - provided the depth to the inverse layers. + where C_j is a clifford, P_j is a random local Pauli. We could accomplish this with a + 'alternate conjugate and random local pauli layer' that is applied as the last step + after P_N is added to the sequence and steps through the entire sequence in reverse. + An alternative accommodation is to allow for some post-processing of the sequence, + e.g. do a sequential build phase that appends sequence elements and then a transform + phase that takes in a sequence and outputs a new sequence. This makes conjugation in + general more natural, and easily compatible with pauli frame randomization. (we could + also achieve this by requiring each layer to take in a sequence and output a sequence) :param graph: :param repetitions: @@ -172,7 +178,7 @@ def _do_pattern(patt): return sequence - def sample_program(self, graph, repetitions, qc=None, width=None, sequence = None, + def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None, pattern = None): return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence, pattern)) @@ -524,8 +530,6 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = # # return [] - - # ================================================================================================== # Analysis # ================================================================================================== @@ -575,6 +579,12 @@ def get_single_target_success_probabilities(noisy_results, ideal_results, for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} +def determine_successes_from_ckt_success_probs(ckt_success_probs, + threshold_probability: float = 2/3): + return {w: {d: prob > threshold_probability for d, prob in d_ckt_succ_probs.items()} + for w, d_ckt_succ_probs in ckt_success_probs.items()} + + def average_distributions(distrs): """ E.g. take in output of :func:`get_error_hamming_weight_distributions` or @@ -644,10 +654,10 @@ def get_random_hamming_wt_distr(num_bits: int): def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], widths=None, depths=None, plot_rand_distr=False): if widths is None: - widths = distr_arr.keys() + widths = list(distr_arr.keys()) if depths is None: - depths = list(distr_arr.values())[0].keys() + depths = list(list(distr_arr.values())[0].keys()) legend = ['data'] if plot_rand_distr: @@ -696,6 +706,33 @@ def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], w return fig, axs +def plot_success(successes, widths=None, + depths=None): + if widths is None: + widths = list(successes.keys()) + + if depths is None: + depths = list(set(d for w in widths for d in successes[w].keys())) + + fig, ax = plt.subplots(figsize=(len(widths), len(depths))) + + margin = .5 + ax.set_xlim(widths[0] - margin, widths[-1] + margin) + ax.set_ylim(depths[0] - margin, depths[-1] + margin) + ax.set_xticks(widths) + ax.set_yticks(depths) + + for w_idx, w in enumerate(widths): + depth_succ = successes[w] + for d_idx, (d, succ) in enumerate(depth_succ.items()): + color = 'white' + if succ: + color = 'lightblue' + ax.scatter(w, d, marker='s', s=1000, color=color, edgecolors='black') + + return fig, ax + + def basement_log_function(number: float): return basement_function(np.log2(number)) From 37c470131094def93bd3ede77e56f14da2c1a2cc Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 12 Aug 2019 12:27:54 -0400 Subject: [PATCH 27/49] Fix success plotting formatting. --- forest/benchmarking/volumetrics.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 0b60b6a5..ffff5736 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -706,29 +706,38 @@ def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], w return fig, axs -def plot_success(successes, widths=None, - depths=None): +def plot_success(successes, widths=None, depths=None, boxsize=None): if widths is None: widths = list(successes.keys()) if depths is None: depths = list(set(d for w in widths for d in successes[w].keys())) - fig, ax = plt.subplots(figsize=(len(widths), len(depths))) + if boxsize is None: + boxsize = 1500 + + fig_width = min(len(widths), 15) + fig_depth = min(len(depths), 15) + + fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) margin = .5 - ax.set_xlim(widths[0] - margin, widths[-1] + margin) - ax.set_ylim(depths[0] - margin, depths[-1] + margin) - ax.set_xticks(widths) - ax.set_yticks(depths) + ax.set_xlim(-margin, len(widths) + margin - 1) + ax.set_ylim(-margin, len(depths) + margin - 1) + plt.xticks(ticks=np.array(range(len(widths))), labels=widths) + plt.yticks(ticks=np.array(range(len(depths))), labels=depths) for w_idx, w in enumerate(widths): + if w not in successes.keys(): + continue depth_succ = successes[w] - for d_idx, (d, succ) in enumerate(depth_succ.items()): + for d_idx, d in enumerate(depths): + if d not in depth_succ.keys(): + continue color = 'white' - if succ: + if depth_succ[d]: color = 'lightblue' - ax.scatter(w, d, marker='s', s=1000, color=color, edgecolors='black') + ax.scatter(w_idx, d_idx, marker='s', s=boxsize, color=color, edgecolors='black') return fig, ax From 0f0497e622971d6fd07fa6b6bc9f5bc49d1c0e29 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 12 Aug 2019 17:33:24 -0400 Subject: [PATCH 28/49] Adjustments to plotting, add pareto plot, update notebook. --- examples/volumetrics.ipynb | 1501 +++++++++++++++------------- forest/benchmarking/volumetrics.py | 91 +- 2 files changed, 899 insertions(+), 693 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index b9960865..942b10c7 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -25,8 +25,6 @@ "import networkx as nx\n", "import numpy as np\n", "import time\n", - "# from scipy.spatial.distance import hamming\n", - "# import scipy.interpolate\n", "\n", "from matplotlib import pyplot as plt\n", "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", @@ -49,8 +47,8 @@ "metadata": {}, "outputs": [], "source": [ - "# if you want to run on a \"real lattice\"\n", "from pyquil import *\n", + "# if you want to run on a \"real lattice\"\n", "#list_quantum_computers()\n", "#perfect_qc = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", "#noisy_qc = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", @@ -78,7 +76,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -215,22 +213,22 @@ "output_type": "stream", "text": [ "X 0\n", - "I 1\n", - "X 2\n", - "Z 3\n", + "X 1\n", + "Z 2\n", + "I 3\n", "X 4\n", - "X 5\n", + "Z 5\n", "X 6\n", - "X 7\n", + "Z 7\n", "Z 8\n", - "I 0\n", - "I 3\n", + "CZ 0 3\n", "CZ 0 1\n", - "CZ 1 4\n", + "I 1\n", + "I 4\n", "CZ 1 2\n", - "CZ 2 5\n", - "I 3\n", - "I 6\n", + "I 2\n", + "I 5\n", + "CZ 3 6\n", "CZ 3 4\n", "CZ 4 7\n", "I 4\n", @@ -258,24 +256,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi) 0\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", + "RX(pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi) 1\n", + "RX(-pi) 1\n", "RX(-pi/2) 2\n", - "RZ(-pi) 2\n", - "RX(pi/2) 3\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 5\n", - "RZ(pi/2) 5\n", + "RZ(pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi) 4\n", "RX(-pi/2) 5\n", + "RZ(-pi) 5\n", + "RX(pi/2) 6\n", + "RZ(-pi/2) 6\n", "RX(-pi/2) 6\n", - "RZ(pi/2) 6\n", - "RZ(-pi) 7\n", + "RX(-pi) 7\n", + "RX(-pi/2) 8\n", "RZ(pi/2) 8\n", - "RX(-pi) 8\n", "\n" ] } @@ -301,10 +298,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "X 7\n", - "I 4\n", - "I 7\n", + "X 3\n", + "X 6\n", + "I 3\n", + "X 6\n", "\n" ] } @@ -323,8 +320,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "CNOT 1 2\n", - "CNOT 1 2\n", + "I 6\n", + "I 7\n", + "I 6\n", + "I 7\n", "\n" ] } @@ -343,10 +342,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 0\n", "H 1\n", + "H 3\n", "H 4\n", - "H 7\n", + "H 6\n", "\n" ] } @@ -365,19 +364,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 7\n", - "CZ 6 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 6\n", "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 6 7\n", + "RX(-pi/2) 7\n", + "CZ 6 7\n", + "RZ(-pi) 7\n", "RX(-pi/2) 6\n", + "RX(-pi/2) 7\n", "CZ 6 7\n", "RX(-pi/2) 7\n", - "RZ(-pi/2) 6\n", "RX(pi/2) 6\n", "CZ 6 7\n", - "RZ(-pi/2) 7\n", "RX(-pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 6\n", "\n" ] } @@ -397,35 +398,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-1.2331567623564417) 1\n", + "RZ(-3.086927607437598) 1\n", "RX(pi/2) 1\n", - "RZ(1.7404467610762533) 1\n", + "RZ(2.40107122228458) 1\n", "RX(-pi/2) 1\n", - "RZ(-2.407885832151905) 1\n", - "RZ(-0.4968338474688072) 2\n", - "RX(pi/2) 2\n", - "RZ(1.8242564813704634) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.872651637214672) 2\n", - "CZ 2 1\n", - "RZ(-2.5391621160551203) 1\n", + "RZ(-1.8346006571742377) 1\n", + "RZ(-0.05466504615219536) 4\n", + "RX(pi/2) 4\n", + "RZ(0.7405214313052141) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.8346006571742375) 4\n", + "CZ 4 1\n", + "RZ(2.5422306773748558) 1\n", "RX(pi/2) 1\n", - "RZ(1.7005564940842257) 2\n", - "RX(-pi/2) 2\n", - "CZ 2 1\n", + "RZ(-0.5993619762149378) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 1\n", "RX(-pi/2) 1\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(-2.8844964068236463) 1\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RZ(0.963711916263757) 1\n", "RX(pi/2) 1\n", - "RZ(1.4042518086522815) 1\n", + "RZ(1.116799765442358) 1\n", "RX(-pi/2) 1\n", - "RZ(-1.117519772304104) 1\n", - "RZ(2.506641595928895) 2\n", - "RX(pi/2) 2\n", - "RZ(0.2886431488211289) 2\n", - "RX(-pi/2) 2\n", - "RZ(-2.8643254313406334) 2\n", + "RZ(1.0622751320808765) 1\n", + "RZ(2.1778807373260367) 4\n", + "RX(pi/2) 4\n", + "RZ(1.1167997654423574) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.0622751320808765) 4\n", "\n" ] } @@ -445,40 +446,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-2.1004961533263593) 3\n", - "RX(pi/2) 3\n", - "RZ(1.658041343629602) 3\n", - "RX(-pi/2) 3\n", - "RZ(0.6905613828918837) 3\n", - "RZ(2.4772894438271122) 4\n", - "RX(pi/2) 4\n", - "RZ(1.3967333178418608) 4\n", - "RX(-pi/2) 4\n", - "RZ(-0.9397427934868001) 4\n", - "CZ 4 3\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(2.818246600476062) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.7999267560042078) 3\n", - "RX(-pi/2) 3\n", - "RZ(1.5761532093595472) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(-1.0008998454603715) 3\n", - "RX(pi/2) 3\n", - "RZ(2.4241875951702756) 3\n", - "RX(-pi/2) 3\n", - "RZ(-1.1651060870208385) 3\n", - "RZ(1.6218604008940751) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.236139528756533) 4\n", - "RX(-pi/2) 4\n", - "RZ(-0.9972127678928056) 4\n", + "RZ(-1.7424710365298193) 6\n", + "RX(pi/2) 6\n", + "RZ(1.6814681504417381) 6\n", + "RX(-pi/2) 6\n", + "RZ(-1.3152662010862128) 6\n", + "RZ(0.1473029953361833) 7\n", + "RX(pi/2) 7\n", + "RZ(2.1164344969391715) 7\n", + "RX(-pi/2) 7\n", + "RZ(-0.3421885644722391) 7\n", + "CZ 7 6\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "RZ(2.274539616534053) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 6\n", + "RX(pi/2) 6\n", + "RZ(-1.734060431751839) 6\n", + "RX(-pi/2) 6\n", + "RZ(1.711847788157013) 7\n", + "RX(pi/2) 7\n", + "CZ 7 6\n", + "RZ(1.6727182122252418) 6\n", + "RX(pi/2) 6\n", + "RZ(1.0239436735316791) 6\n", + "RX(-pi/2) 6\n", + "RZ(-2.040801702902205) 6\n", + "RZ(2.769966602149135) 7\n", + "RX(-pi/2) 7\n", + "RZ(1.433767837709585) 7\n", + "RX(-pi/2) 7\n", + "RZ(0.38430040772537577) 7\n", "\n" ] } @@ -504,24 +505,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 1\n", - "I 3\n", - "X 4\n", - "X 7\n", "I 1\n", "I 4\n", - "I 3\n", - "I 4\n", + "I 6\n", + "X 7\n", + "CNOT 1 4\n", "I 4\n", "I 7\n", + "CNOT 6 7\n", "I 1\n", - "I 3\n", "X 4\n", - "X 7\n", + "X 6\n", + "I 7\n", "CNOT 1 4\n", - "I 3\n", "I 4\n", - "CNOT 4 7\n", + "I 7\n", + "I 6\n", + "I 7\n", "\n" ] } @@ -547,23 +547,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 1\n", - "H 4\n", - "I 1\n", - "Z 4\n", - "I 1\n", - "I 4\n", - "I 1\n", - "I 4\n", - "I 1\n", - "I 4\n", - "I 1\n", - "I 4\n", - "H 1\n", - "CZ 1 4\n", - "H 1\n", - "H 1\n", - "H 4\n", + "H 7\n", + "H 8\n", + "Z 7\n", + "I 8\n", + "H 7\n", + "CZ 7 8\n", + "H 7\n", + "I 7\n", + "Z 8\n", + "H 7\n", + "CZ 7 8\n", + "H 7\n", + "Z 7\n", + "Z 8\n", + "I 7\n", + "I 8\n", + "H 7\n", + "H 8\n", "\n" ] } @@ -588,43 +589,47 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 1\n", - "RX(pi/2) 2\n", - "RZ(pi/2) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RX(pi/2) 2\n", - "RZ(-pi) 2\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", - "CZ 1 2\n", - "RX(-pi/2) 1\n", - "RZ(-pi) 1\n", - "RX(-pi) 1\n", - "RX(pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RZ(-pi) 2\n", - "RX(-pi/2) 1\n", - "RZ(-0.14370725766325165) 1\n", - "RX(pi) 1\n", - "RX(pi/2) 2\n", - "CZ 1 2\n", - "RZ(-1.7145035844581487) 1\n", - "RX(pi/2) 1\n", - "RZ(pi/2) 1\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", + "RZ(pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi) 6\n", + "RX(-pi) 6\n", + "CZ 3 6\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RX(-pi/2) 3\n", + "CZ 3 6\n", + "RZ(-pi) 3\n", + "RX(-pi) 3\n", + "RX(pi/2) 6\n", + "RX(pi/2) 3\n", + "CZ 3 6\n", + "RZ(pi/2) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 3\n", + "RZ(pi/2) 6\n", + "RX(-pi) 6\n", + "CZ 3 6\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RX(pi/2) 6\n", + "RX(-pi/2) 3\n", + "CZ 3 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 3\n", + "RX(-pi) 3\n", + "RZ(-2.7514871497345705) 3\n", + "RX(pi) 3\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RZ(-1.180690822939675) 3\n", + "RX(pi) 3\n", + "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "RZ(pi/2) 6\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -661,293 +666,359 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(pi/2) 1\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 4 7\n", - "RX(-pi/2) 0\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", "RZ(-pi/2) 4\n", - "RX(pi) 4\n", - "RZ(pi/2) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 4\n", "RX(pi/2) 4\n", - "CZ 1 4\n", - "RZ(pi/2) 5\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "RZ(-2.552556931782524) 0\n", + "RZ(pi/2) 4\n", + "RZ(pi/2) 0\n", "RX(pi/2) 0\n", - "RX(pi/2) 1\n", - 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4\n", - "RX(pi/2) 4\n", - "RZ(-1.7643050612030988) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.8515397051369078) 5\n", "RX(pi/2) 5\n", "CZ 5 4\n", - "RZ(1.0713144339540546) 3\n", - "RX(pi/2) 3\n", - "RZ(1.7843875032906464) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.806401663076292) 3\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", "RZ(pi) 4\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 4 1\n", - "RX(pi/2) 1\n", - "RZ(2.9032606770560987) 1\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "RZ(-pi/2) 8\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "CZ 1 2\n", + "RZ(-pi/2) 1\n", "RX(-pi/2) 1\n", - "RX(-pi/2) 4\n", - "CZ 4 1\n", + "RZ(pi) 2\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(pi) 1\n", "RX(pi/2) 1\n", - "RZ(-1.8753240539510152) 1\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "CZ 5 8\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "CZ 5 2\n", + "RZ(-pi/2) 8\n", "RX(-pi/2) 1\n", - "RZ(1.5808549020659255) 4\n", - "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(1.3415289360694957) 1\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RX(pi) 5\n", + "CZ 8 5\n", + "RZ(-pi/2) 0\n", + "RX(pi) 0\n", + "RZ(pi) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RZ(pi/2) 2\n", + "RZ(-pi/2) 4\n", + "RX(pi) 4\n", + "RX(pi/2) 5\n", + "RZ(pi/2) 5\n", + "RZ(-pi/2) 8\n", + "RZ(2.4199401839953483) 0\n", + "RX(pi/2) 0\n", + "RZ(2.514809287526923) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.7430590019224299) 0\n", + "RZ(-1.2296368684699086) 1\n", "RX(pi/2) 1\n", - "RZ(1.5763567661690538) 1\n", + "RZ(2.3487071393705357) 1\n", "RX(-pi/2) 1\n", - "RZ(-0.32283314658952666) 1\n", - "RZ(-2.4446757072195027) 3\n", - "RX(pi/2) 3\n", - "RZ(0.7054924828239257) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.407085750277984) 3\n", - "RZ(2.566137534569804) 4\n", - "RX(pi/2) 4\n", - "RZ(0.6989653107290661) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.2506377587718038) 4\n", - "RZ(-0.1185336069480023) 5\n", + "RZ(0.35269628057958613) 1\n", + "CZ 1 0\n", + "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(2.2936383231902973) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 0\n", + "RZ(-1.6237793109153458) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.26535808895675) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(-0.33025559980241503) 2\n", + "RX(pi/2) 2\n", + "RZ(0.9721239057466267) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.170507541045568) 2\n", + "RZ(2.624627633759631) 5\n", "RX(pi/2) 5\n", - "RZ(0.492602541091333) 5\n", + "RZ(0.5330448874228438) 5\n", + "RX(-pi/2) 5\n", + "RZ(2.1366798531052416) 5\n", + "CZ 5 2\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "RZ(2.547124086494814) 2\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 5\n", "RX(-pi/2) 5\n", - "RZ(-1.3269530029895265) 5\n", + "CZ 5 2\n", + "RX(pi/2) 2\n", + "RZ(-1.6655157631631923) 2\n", + "RX(-pi/2) 2\n", + "RZ(1.3694752670292285) 5\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(-2.124177304890633) 0\n", + "RX(pi/2) 0\n", + "RZ(0.7698476520272532) 0\n", + "RX(-pi/2) 0\n", + "RZ(-1.1906845624131532) 0\n", + "RZ(-1.7877124981731334) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.38884819540667714) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.07180093401376109) 1\n", + "RZ(-2.2680484910923973) 2\n", + "RX(pi/2) 2\n", + "RZ(0.8982684580372229) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.6403599470421588) 2\n", + "RZ(1.2896722030099204) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.8100832922742016) 5\n", + "RX(-pi/2) 5\n", + "RZ(-1.3333663386475023) 5\n", "\n" ] } @@ -973,15 +1044,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 3: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}, 4: {3: [, , , , , , , , , ], 4: [, , , , , , , , , ], 5: [, , , , , , , , , ]}}\n" + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" ] } ], "source": [ - "widths = [2, 3, 4]\n", - "depths = [3, 4, 5]\n", + "widths = [2, 3, 4, 5]\n", + "depths = [2, 3, 4, 5, 10]\n", "ckt = classical_1q_2q\n", - "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=10)\n", + "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=20)\n", "print(prog_array)" ] }, @@ -1003,7 +1074,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {3: [array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 1]]), array([[0, 0]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 1]]), array([[0, 0]])], 4: [array([[1, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 0]]), array([[0, 1]]), array([[0, 1]]), array([[0, 0]]), array([[0, 0]])], 5: [array([[1, 1]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[0, 1]]), array([[1, 1]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[0, 0]])]}, 3: {3: [array([[0, 0, 1]]), array([[0, 1, 0]]), array([[1, 1, 1]]), array([[0, 1, 0]]), array([[1, 1, 0]]), array([[0, 1, 1]]), array([[1, 0, 1]]), array([[0, 0, 0]]), array([[0, 0, 1]]), array([[1, 0, 0]])], 4: 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\n", 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\n", 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": {}, @@ -1168,9 +1239,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1352,7 +1423,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1411,12 +1482,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1426,18 +1497,16 @@ } ], "source": [ - "extent = min(depths)-0.5, max(depths)+0.5, min(widths)-0.5, max(widths)+0.5\n", + "extent = -0.5, len(widths) - 0.5, -0.5, len(depths) - 0.5\n", "ax = plt.gca()\n", "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + " cmap='viridis', origin='lowerleft', vmin=0.0, vmax=1.0)\n", "\n", - "xticks = depths\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", "\n", - "yticks = widths\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", @@ -1449,12 +1518,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1468,13 +1537,11 @@ "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", "\n", - "xticks = depths\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", "\n", - "yticks = widths\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", @@ -1486,25 +1553,25 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(
,\n", - " )" + "(
,\n", + " )" ] }, - "execution_count": 40, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1512,213 +1579,137 @@ } ], "source": [ - "successes = determine_successes_from_ckt_success_probs(avg_pr_succ_arr, .8)\n", - "plot_success(successes)" + "success_threshold = .8\n", + "successes = determine_successes_from_ckt_success_probs(avg_pr_succ_arr, success_threshold)\n", + "plot_success(successes, f\"Volumetric Benchmark\\n Random Classical Circuits\\n Pr[Success] > {success_threshold}\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Need to update all that follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, + "execution_count": 134, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'munged' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtvd_rand_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, rand)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmunged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtvd_ideal_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmunged\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TVD(data, ideal)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmunged\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mZtvd_rand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvd_rand_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mZtvd_ideal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvd_ideal_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'munged' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "[7, 3, 1, 0, None]\n" ] - } - ], - "source": [ - "tvd_rand_values = np.asarray([munged['TVD(data, rand)'][idx] for idx in munged.index])\n", - "tvd_ideal_values = np.asarray([munged['TVD(data, ideal)'][idx] for idx in munged.index])\n", - "Ztvd_rand = np.reshape(tvd_rand_values,(x2,x1)).T\n", - "Ztvd_ideal = np.reshape(tvd_ideal_values,(x2,x1)).T" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tvd_ideal_values\n", - "tvd_rand_values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", - "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" + }, + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fake_successes = successes\n", + "fake_successes[4][2] = True\n", + "fake_successes[3][5] = False\n", + "plot_pareto_frontier(successes, 'Pareto Frontier', widths=[2,3,4,5,6], depths = [2,3,4,5,7,10,15])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot total variation distance landscape" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "loge_rand_values = np.asarray([munged['Pr. success loge rand'][idx] for idx in munged.index])\n", - "loge_data_values = np.asarray([munged['Pr. success loge data'][idx] for idx in munged.index])\n", - "Zlge_rand = np.reshape(loge_rand_values,(x2,x1)).T\n", - "Zlge_data = np.reshape(loge_data_values,(x2,x1)).T" + "Ztvd_ideal = np.reshape([tvd_noisy_ideal[w][d] for d in depths for w in widths], X.shape)\n", + "Ztvd_rand = np.reshape([tvd_noisy_rand[w][d] for d in depths for w in widths], X.shape)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", - "img = ax.imshow(Zlge_data, interpolation='none', extent=extent,\n", + "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Total Variation Distance of Noisy to Ideal')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "extent = min(res_df['Depth'])-0.5, max(res_df['Depth'])+0.5, min(res_df['Width'])-0.5, max(res_df['Width'])+0.5\n", "ax = plt.gca()\n", - "img = ax.imshow(Zlge_rand, interpolation='none', extent=extent,\n", + "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", "\n", - "xticks = np.arange(1,max(res_df['Depth'])+1)\n", - "ax.set_xticks(xticks)\n", - "ax.set_xticklabels(map(str, xticks))\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", "\n", - "yticks = np.arange(1,max(res_df['Width'])+1)\n", - "ax.set_yticks(yticks)\n", - "ax.set_yticklabels(map(str, yticks))\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", "\n", "ax.set_aspect('equal')\n", "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Depth')\n", - "plt.ylabel('Width')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the distribution of sublattice widths" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "G = perfect_qc.qubit_topology()\n", - "len(perfect_qc.qubit_topology())\n", - "# distribution of graph lengths\n", - "distr = []\n", - "for num_nodes in range(1, len(G.nodes) + 1):\n", - " listg = generate_connected_subgraphs(G, num_nodes)\n", - " distr.append(len(listg))\n", - "\n", - "cir_wid = list(range(1, len(G.nodes) + 1))\n", - "plt.bar(cir_wid, distr, width=0.61, align='center')\n", - "plt.xticks(cir_wid)\n", - "plt.xlabel('sublattice / circuit width')\n", - "plt.ylabel('Frequency of Occurence')\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.title('Distribution of sublattice widths')\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Total Variation Distance of Noisy to Random')\n", "plt.show()" ] }, @@ -1726,12 +1717,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data exploration" + "## Data exploration" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1740,40 +1731,55 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "size = Y.shape\n", - "width_1d = Y.reshape((1,np.prod(size)))\n", - "depth_1d = X.reshape((1,np.prod(size)))" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 20)\n", + "(1, 20)\n" + ] + } + ], "source": [ - "data_1d = Zdata.reshape((1,np.prod(size)))\n", - "data_1d.shape\n", - "width_1d.shape\n" + "shape = Zdata.shape\n", + "size = Zdata.size\n", + "width_1d = X.reshape((1,size))\n", + "depth_1d = Y.reshape((1,size))\n", + "data_1d = Zdata.reshape((1,size))\n", + "print(data_1d.shape)\n", + "print(width_1d.shape)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0],\n", + " [ 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5,\n", + " 2, 3, 4, 5],\n", + " [ 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5,\n", + " 10, 10, 10, 10]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dims = np.zeros_like(width_1d)\n", - "dims[0,0] = size[0]\n", - "dims[0,1] = size[1]\n", - "\n", - "xdata = np.vstack((dims,width_1d, depth_1d))\n", - "\n", - "\n", + "dims[0,0] = shape[0]\n", + "dims[0,1] = shape[1]\n", "\n", + "xdata = np.vstack((dims, width_1d, depth_1d))\n", "xdata" ] }, @@ -1781,7 +1787,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Fitting models" + "### Fitting models" ] }, { @@ -1798,18 +1804,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ - "def two_param(x,pw,pd):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = (1-pw)**(width) * (1-pd)**depth\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", + "def two_param(x, pw, pd):\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = (1-pw)**(widths) * (1-pd)**depths\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", " return rpcheck.ravel()" ] }, @@ -1824,18 +1828,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def one_param(x,p):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = (1-p)**(width*depth)\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = (1-p)**(widths * depths)\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", " return rpcheck.ravel()" ] }, @@ -1843,25 +1845,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "From my prior work a better model to fit to is\n", + "Josh: \"From my prior work a better model to fit to is \"\n", "\n", "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def two_param_exp(x,p,a,b):\n", - " temp = x[0]\n", - " wid = temp[0]\n", - " dep = temp[1]\n", - " width = x[1].reshape(wid,dep)\n", - " depth = x[2].reshape(wid,dep)\n", - " pcheck = np.exp(-(a*p + b) * width * depth)\n", - " rpcheck = pcheck.reshape((1,wid*dep))\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = np.exp(-(a*p + b) * widths * depths)\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", " return rpcheck.ravel()" ] }, @@ -1874,7 +1874,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1884,9 +1884,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The estimated error is p = 0.0111\n", + "The estimated product of the one and two qubit fidelity is F = 0.9889\n" + ] + } + ], "source": [ "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", @@ -1895,37 +1904,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ - "zfit = one_param(xdata,popt)\n", - "Z_fit = zfit.reshape(size)" + "zfit = one_param(xdata, popt)\n", + "Z_fit = zfit.reshape(shape)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.pcolormesh(X,Y, Z_fit)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", + "ax = plt.gca()\n", + "img = ax.imshow(Z_fit, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('One parameter fit to success prob')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.pcolormesh(X,Y,Zdata)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Success prob')\n", "plt.show()" ] }, @@ -1938,61 +1993,141 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "pguess2d = [0.0276, 0.01, 0.4]" + "# pguess2d_exp = [0.0276, 0.01, 0.4]\n", + "# popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d, bounds=(0., 1))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ - "popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d , bounds=(0., 1))" + "popt2d, pcov2d = curve_fit(two_param, xdata, data_1d.ravel(), bounds=(0., 1))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05850703, 0.00244478])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "popt2d" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.88208017 0.83047228 0.78188381 0.73613811]\n", + " [0.87992368 0.82844195 0.77997228 0.73433842]\n", + " [0.87777246 0.8264166 0.77806542 0.73254312]\n", + " [0.8756265 0.82439619 0.77616322 0.73075222]\n", + " [0.86497514 0.81436802 0.76672176 0.72186315]]\n" + ] + } + ], "source": [ - "zfit2d = two_param(xdata,popt2d[0],popt2d[1])\n", - "Z_fit2d = zfit2d.reshape(size)" + "zfit2d = two_param(xdata, popt2d[0], popt2d[1])\n", + "Z_fit2d = zfit2d.reshape(shape)\n", + "print(Z_fit2d)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.pcolormesh(X,Y, Z_fit2d)\n", - "plt.xticks(list(range(1,circuit_depth+1)))\n", - "plt.yticks(list(range(1,circuit_width+1)))\n", - "plt.colorbar()\n", + "ax = plt.gca()\n", + "img = ax.imshow(Z_fit2d, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0, vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Two parameter fit success prob')\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the distribution of sublattice widths" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "1-1.02319786e-01" + "G = perfect_qc.qubit_topology()\n", + "len(perfect_qc.qubit_topology())\n", + "# distribution of graph lengths\n", + "distr = []\n", + "for num_nodes in range(1, len(G.nodes) + 1):\n", + " listg = generate_connected_subgraphs(G, num_nodes)\n", + " distr.append(len(listg))\n", + "\n", + "cir_wid = list(range(1, len(G.nodes) + 1))\n", + "plt.bar(cir_wid, distr, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "plt.show()" ] }, { diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index ffff5736..d1ec58b5 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -706,15 +706,12 @@ def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], w return fig, axs -def plot_success(successes, widths=None, depths=None, boxsize=None): +def plot_success(successes, title, widths=None, depths=None, boxsize=1500): if widths is None: widths = list(successes.keys()) if depths is None: - depths = list(set(d for w in widths for d in successes[w].keys())) - - if boxsize is None: - boxsize = 1500 + depths = list(set(d for w in successes.keys() for d in successes[w].keys())) fig_width = min(len(widths), 15) fig_depth = min(len(depths), 15) @@ -724,8 +721,14 @@ def plot_success(successes, widths=None, depths=None, boxsize=None): margin = .5 ax.set_xlim(-margin, len(widths) + margin - 1) ax.set_ylim(-margin, len(depths) + margin - 1) - plt.xticks(ticks=np.array(range(len(widths))), labels=widths) - plt.yticks(ticks=np.array(range(len(depths))), labels=depths) + ax.set_xticks(range(len(widths))) + ax.set_xticklabels(widths) + ax.set_yticks(range(len(depths))) + ax.set_yticklabels(depths) + ax.set_xlabel('Width') + ax.set_ylabel('Depth') + + colors = ['white', 'lightblue'] for w_idx, w in enumerate(widths): if w not in successes.keys(): @@ -734,11 +737,79 @@ def plot_success(successes, widths=None, depths=None, boxsize=None): for d_idx, d in enumerate(depths): if d not in depth_succ.keys(): continue - color = 'white' + color = colors[0] if depth_succ[d]: - color = 'lightblue' - ax.scatter(w_idx, d_idx, marker='s', s=boxsize, color=color, edgecolors='black') + color = colors[1] + ax.scatter(w_idx, d_idx, marker='s', s=boxsize, color=color, + edgecolors='black') + + # legend + labels = ['Fail', 'Pass'] + for color, label in zip(colors, labels): + plt.scatter([], [], marker='s', c=color, label=label, edgecolors='black') + ax.legend() + + ax.set_title(title) + + return fig, ax + + +def plot_pareto_frontier(successes, title, widths=None, depths=None): + if widths is None: + widths = list(successes.keys()) + + if depths is None: + depths = list(set(d for w in successes.keys() for d in successes[w].keys())) + + fig_width = min(len(widths), 15) + fig_depth = min(len(depths), 15) + + fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) + + margin = .5 + ax.set_xlim(-margin, len(widths) + margin - 1) + ax.set_ylim(-margin, len(depths) + margin - 1) + ax.set_xticks(range(len(widths))) + ax.set_xticklabels(widths) + ax.set_yticks(range(len(depths))) + ax.set_yticklabels(depths) + ax.set_xlabel('Width') + ax.set_ylabel('Depth') + + min_depth_failure_at_width = [] + for w_idx, w in enumerate(widths): + if w not in successes.keys(): + min_depth_failure_at_width.append(None) + continue + depth_succ = successes[w] + min_depth_failure = len(depths) + for d_idx, d in enumerate(depths): + if d not in depth_succ.keys(): + continue + if not depth_succ[d]: + min_depth_failure = d_idx + break + min_depth_failure_at_width.append(min_depth_failure) + + for idx, depth in enumerate(min_depth_failure_at_width): + if depth is None: + continue # the depth was not determined, so leave this boundary open + + # horizontal line for this width + if depth < len(depths): + ax.plot((idx - margin, idx + margin), (depth - margin, depth - margin), color='black') + + # vertical lines + if idx < len(min_depth_failure_at_width) - 1: + for d_idx in range(len(depths)): + if depths[d_idx] not in [d for d in successes[widths[idx]].keys()]: + continue # do not plot line if this depth was not measured + if depth > d_idx >= min_depth_failure_at_width[idx + 1]: + ax.plot((idx + margin, idx + margin), (d_idx - margin, d_idx + margin), + color='black') + + ax.set_title(title) return fig, ax From a87ce7ea0cfb697d25376a45ebce3d18474b0123 Mon Sep 17 00:00:00 2001 From: Kyle Date: Tue, 13 Aug 2019 15:53:56 -0400 Subject: [PATCH 29/49] Add sequence_transformations and some documentation. --- forest/benchmarking/volumetrics.py | 107 ++++++++++++++++++++++++----- 1 file changed, 90 insertions(+), 17 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index d1ec58b5..b728b1fd 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -33,15 +33,17 @@ def make_default_pattern(num_generators): return [(list(range(num_generators)), 'n')] # TODO: perhaps best for pattern to be sample-time specified, given ambiguity in append; however, -# it convenient to keep a persistent state. +# it is convenient to keep a persistent state. Appending sequence_transforms is also not well +# motivated, so instead maybe it is better to remove support for appending CircuitTemplates +# altogether? @dataclass class CircuitTemplate: """ We want to be able to specify various families of circuits and, once specified, randomly sample from the family circuits of various width and depth. 'Width' is simply the number of - qubits. 'Depth' is not simply circuit depth but rather the number of some repeated group of - gates that constitute some distinctive unit. A depth d circuit could consist of d consecutive + qubits. 'Depth' is not simply circuit depth, but rather the number of some repeated group of + gates that constitute some distinct unit. A depth d circuit could consist of d consecutive rounds of random single qubit, then two qubit gates. It could also mean d consecutive random Cliffords followed by the d conjugated Cliffords that invert the first d gates. @@ -50,8 +52,23 @@ class CircuitTemplate: map an abstract circuit into native quil; a sample acting on a specific qubit topology may be desired; the sequence of 'layers' generated so far may be necessary to compute an inverse. + + The primary purpose of this class is to sample circuits, which we represent by a list of + pyquil Programs, or a 'sequence'; this core functionality is found in :func:`sample_sequence`. + In this function `generators` are applied in series according to the order specified by + `pattern`. Each call to a generator will contribute an element to the output sequence, + and some combination of the generators will constitute a unit of depth. After a sequence is + generated from the output of the various `generators`, each `sequence_transform` is then + applied in series on the sequence to create a final output sequence. See + :func:`sample_sequence` for more information. + + .. [Vol] A volumetric framework for quantum computer benchmarks. + Blume-Kohout and Young. + arXiv:1904.05546v2 (2019) + https://arxiv.org/pdf/1904.05546.pdf """ generators: List[Callable] = field(default_factory=lambda : []) + sequence_transforms: List[Callable] = field(default_factory=lambda : []) pattern: List[Union[int, Tuple[List, int], Tuple[List, str]]] = field(init=False, repr=False) def __post_init__(self): @@ -59,7 +76,8 @@ def __post_init__(self): def append(self, other): """ - Mutates the CircuitTemplate object by appending new generators + Mutates the CircuitTemplate object by appending new generators. It is ambiguous how to + append patterns, so we reset the pattern to the default. :param other: :return: @@ -68,6 +86,7 @@ def append(self, other): self.generators += other elif isinstance(other, CircuitTemplate): self.generators += other.generators + self.sequence_transforms += other.sequence_transforms # make default pattern since it is unclear how to compose general patterns. self.pattern = make_default_pattern(len(self.generators)) else: @@ -117,19 +136,18 @@ def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None 2) a tuple of a `pattern` and a number of repetitions 3) a tuple of a `pattern` and 'n', indicating depth many repetitions - TODO: - A family that does not neatly fit into the current paradigm is the following: + The sequence_transforms are distinct from generators in that they take in a sequence and + output a new sequence. These are applied in series after the entire sequence has been + generated. A family of interest that is not easily generated by generators + patterns + alone is given by C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t where C_j is a clifford, P_j is a random local Pauli. We could accomplish this with a - 'alternate conjugate and random local pauli layer' that is applied as the last step + bespoke 'alternate conjugate and random local pauli layer' that is applied as the last step after P_N is added to the sequence and steps through the entire sequence in reverse. - An alternative accommodation is to allow for some post-processing of the sequence, - e.g. do a sequential build phase that appends sequence elements and then a transform - phase that takes in a sequence and outputs a new sequence. This makes conjugation in - general more natural, and easily compatible with pauli frame randomization. (we could - also achieve this by requiring each layer to take in a sequence and output a sequence) + Instead, we introduce sequence_transforms that Conjugation of a sequence and Pauli frame + randomization are sequence level operations that can be conceptually distinguished. :param graph: :param repetitions: @@ -176,6 +194,9 @@ def _do_pattern(patt): _do_pattern(pattern) + for sequence_transform in self.sequence_transforms: + sequence = sequence_transform(sequence) + return sequence def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None, @@ -184,7 +205,7 @@ def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None, # ================================================================================================== -# Gate Sets +# Generators # ================================================================================================== def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): """ @@ -351,11 +372,23 @@ def graph_restricted_compilation(qc, graph, program): return native_quil +### +# Sequence Transforms +### +def dagger_sequence(sequence: List[Program]): + return sequence + [prog.dagger() for prog in reversed(sequence)] + +def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph): + paulis = [I, X, Y, Z] + random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] + new_sequence = [None for _ in range(2*len(sequence) + 1)] + new_sequence[::2] = random_paulis + new_sequence[1::2] = sequence + return new_sequence ### # Templates ### - def get_rand_1q_template(gates: Sequence[Gate]): def func(graph, **kwargs): return random_single_qubit_gates(graph, gates=gates) @@ -516,7 +549,7 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = return results - +# TODO: # def do_volumetric_measurements(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], # depths: List[int], # num_circuit_samples: int, graph: nx.Graph = None, pattern = None, @@ -653,6 +686,15 @@ def get_random_hamming_wt_distr(num_bits: int): def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], widths=None, depths=None, plot_rand_distr=False): + """ + For each width and depth plot the distribution of errors provided in distr_arr. + + :param distr_arr: + :param widths: + :param depths: + :param plot_rand_distr: + :return: + """ if widths is None: widths = list(distr_arr.keys()) @@ -707,6 +749,19 @@ def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], w def plot_success(successes, title, widths=None, depths=None, boxsize=1500): + """ + Plot the given successes at each width and depth. + + If a given (width, depth) is not recorded in successes then nothing is plotted for that + point. Successes are displayed as filled boxes while failures are simply box outlines. + + :param successes: + :param title: + :param widths: + :param depths: + :param boxsize: + :return: + """ if widths is None: widths = list(successes.keys()) @@ -755,6 +810,26 @@ def plot_success(successes, title, widths=None, depths=None, boxsize=1500): def plot_pareto_frontier(successes, title, widths=None, depths=None): + """ + Given the successes at measured widths and depths, draw the frontier that separates success + from failure. + + Specifically, the frontier is drawn as follows:: + + For a given width, draw a line separating all low-depth successes from the minimum + depth failure. For each depth smaller than the minimum failure depth, draw a line + separating the neighboring (width +/- 1, depth) cell if depth is less than the + minimum depth failure for that neighboring width. + + If a requested (width, depth) cell is not specified in successes then no lines will be drawn + around that cell. + + :param successes: + :param title: + :param widths: + :param depths: + :return: + """ if widths is None: widths = list(successes.keys()) @@ -830,8 +905,6 @@ def basement_function(number: float): # ================================================================================================== # Graph tools # ================================================================================================== - - def generate_connected_subgraphs(G: nx.Graph, n_vert: int): """ Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all From ed48add73630b5a2ab6c6b08986484102bdf8c6f Mon Sep 17 00:00:00 2001 From: Kyle Date: Tue, 13 Aug 2019 15:56:26 -0400 Subject: [PATCH 30/49] Pass standard arguments to sequence transforms. --- forest/benchmarking/volumetrics.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index b728b1fd..e43432cd 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -195,7 +195,7 @@ def _do_pattern(patt): _do_pattern(pattern) for sequence_transform in self.sequence_transforms: - sequence = sequence_transform(sequence) + sequence = sequence_transform(graph=graph, qc=qc, width=width, sequence=sequence) return sequence @@ -375,11 +375,11 @@ def graph_restricted_compilation(qc, graph, program): ### # Sequence Transforms ### -def dagger_sequence(sequence: List[Program]): +def dagger_sequence(sequence: List[Program], **kwargs): return sequence + [prog.dagger() for prog in reversed(sequence)] -def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph): +def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **kwargs): paulis = [I, X, Y, Z] random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] new_sequence = [None for _ in range(2*len(sequence) + 1)] From 1df0e09f9836b19bba3b6f9482a4365c6eb36d26 Mon Sep 17 00:00:00 2001 From: Kyle Date: Thu, 15 Aug 2019 15:43:57 -0400 Subject: [PATCH 31/49] Base success off lowerbound and correct pareto frontier. --- examples/volumetrics.ipynb | 1004 +++++++++++++++------------- forest/benchmarking/volumetrics.py | 85 ++- 2 files changed, 607 insertions(+), 482 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 942b10c7..71686196 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -76,7 +76,7 @@ }, { "data": { - "image/png": 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ZfRr1Dd2oDz51doopmu4U0xR8wzPqg2/OnDkcOnSI9vZ206WIZXQ9n5imM/qGZ9QH37hx45g8eTLl5eWmSxHLLFq0iLfffptLly6ZLkUspTP6hmfUBx9onU/MiImJYc6cOfzhD38wXYpYTNOdQ+eI4NM6n5ii6U4x7dZbb2XPnj00NjaaLmXUcETw6ZIGMUUXsotpOqNv6BwRfLNmzaKyslLveCTo5s+fz5EjRzh79qzpUsRimu4cGkcEX3h4OIWFhbz11lumSxHLREZGsmjRIkpLS02XIhbTGX1D44jgA013ijma7hTTdEbf0Dgq+NTgIiaowUVCwbp16zTdOUgut0OONqioqGDRokWcPn3adClimc7OTpKSknj33XdJS0szXY5Y6siRIyxZsoTTp08TFuaYMU1AOOZvJycnh/b2dgWfBN2YMWMoLi7WqE+Mys/PJyEhQTNfg+CY4HO5XFrnE2M03SmhYP369ezYscN0GSHPMcEHWucTc7RhtYQCXdYwOI4KPu3gIqZMnz6dlpYWjh8/broUsdicOXO4ePGizugbgKOCb968eezfv5/Ozk7TpYhlXC6XLmsQ48LCwli3bp2mOwfgqOBLSEggJSVF73bECE13SijQdOfAHBV8oOlOMcfb4OKQK4RklFq6dCkffPAB1dXVpksJWY4LPnV2iim5ubnExMTw3nvvmS5FLBYZGcmqVat0Rl8/HBl8GvGJKZrulFCg6c7+OS74rr/+ej744AOam5tNlyIW0vV8EgpWrlzJ7t27uXDhgulSQpLjgi8qKoprr72WgwcPmi5FLLRs2TJKS0vVWSxGjR8/nsWLF+uMPj8cF3ygdT4xJzU1lfT0dL3xEuM03emfI4NPnZ1ikqY7JRTojD7/HBl8anARk3Qhu4SC1NRUrr32Wl5//XXTpYQcRwbftGnTaGhooK6uznQpYqHi4mL27NlDW1ub6VLEcpru9M2RwRcWFsbcuXMpKyszXYpYaOLEiUyfPl3rzGKc97SGrq4u06WEFEcGH2idT8zSdKeEgvz8fOLj4zUI6MOxwafOTjFJF7JLqNB055VcboduLFhdXc2sWbOor6/H5XKZLkcsc/nyZVJSUqipqSE2NtZ0OWKxsrIyPv/5z/PHP/7RdCkhw7EjvrS0NGJjYzl27JjpUsRCsbGxFBUVsXv3btOliOV0Rt+VHBt8oMsaxKwVK1ZonU+M0xl9V3J88GmdT0zRhewSKrTO15tj1/gASktL2bRpE3v27DFdiliora2NxMRETpw4QXx8vOlyxGJtbW3MSk5m/1/8BRMqKqCxEeLiYPZs2LABkpJMlxhUjg6+S5cukZKSwvnz54mIiDBdjlho5cqV/Pmf/znr1683XYrYqqwMtmyhbccOwsaMIby9/ZP7oqPB7YZVq2DTJpg3z1ydQeToqc5x48YxefJkysvLTZciltJ0pxi1dSsUF8P27UR2dfUOPYDmZmhpge3bPY/butVElUHn6OADNbiIWQo+MWbrVti4EZqaPKO6/rjdnsdt3GhF+Dk++LSDi5hUWFhIVVUVZ86cMV2K2KSs7JPQ6+H7wFxgLHC/r+d5w2///oCXaJLjg0+dnWLSmDFjWLp0Ka+99prpUsQmW7Z4pjH7SAe+BjzQ33Obmz3PdzDHB9+sWbOorKyksbHRdCliKU13SlDV1sLOnT6nN+8E1gOT+nu+2w0lJeDg020cH3zh4eEUFhby1ltvmS5FLKUL2SWotm27+tdwuUbmdUKU44MP1OAiZs2YMYOmpiaOHz9uuhSxQXm5p1PzajQ3w+HDI1NPCAo3XUAwzJ8/n5///OemyxBLuVwuli9fzmuvvUZeXp7pcsQB2traOH36NCdOnKCysrLX50179rBsJP6Qc+dG4lVCkhXBt2DBAv72b//WdBliMe905wMP9NtWIAJAY2Njd5j1DbbKykpqa2tJS0sjJyeH7OxscnJymDt3LnfddReFY8fC889ffREO3m3IiuDLycmhvb2d06dPk5GRYbocsdDy5cv52te+htvt1jFZluvs7OTMmTM+R2vez52dneTk5PQKttmzZ3d/n56eTni4n3++y8vhlVd8Tnd2fPzR+fFHC54QuOKVoqOhoGAEf+rQ4ugty3pau3YtDzzwAHfeeafpUsRSeXl5lJSUMGPGDNOlSAA1NTVx8uRJv8F2+vRpEhISeoVa388TJ04c/huk2lrIyfEZfF8HvtHnts0f395LVBRUVjp2D08rRnzwSYOLgk9MWb58Obt27VLwjWJut5v6+nq/oVZZWcmFCxfIysrqFWTFxcXd32dlZTF27NjAFZmc7Nl7c/v2Ky5p+Do+Qq4vlwtWr3Zs6IFFI76XXnqJxx57TNdTiTE/+9nP+PWvf81vfvMb06WIH/01jXiDLTo62u9ILTs7m+TkZMLCDDfMl5V59t7ss3PLoMTEQGkpzJ074mWFCmuC7+zZs+Tm5nLu3DnGjBljuhyxUHV1NTNnzqSurk6/g4Y0Njb6XVfz1zTS83N2djbjxo0z/WMMTs+9OgcrJgaeeAIefDBwdYUAa6Y6ExISSElJ4f3332fmzJmmyxELpaWlkZqayqFDhygqKjJdjuP4axrp+XVHR8fwm0ZGG294bdzouS6vvzGOy+VpaLEg9MCi4INPNqxW8Ikp3ssaFHxDN9SmkezsbKZPn84tt9zSfVt8fLxdXbUPPug5Y2/LFs82ZC5Xrz08OyIjCQ8L86zpbdrk6OnNnqyZ6gR48sknee+99/jBD35guhSx1LPPPstTTz3Fzp07TZcSUrxNI/1NQ/pqGun5OeBNI6NdXZ1nG7LDh+HcOQ5VVnIqIYE1v/yloxtZfLEq+Pbu3ctDDz3EgQMHTJciljp79ixzs7P58P/+X8LffRcaGyEuDmbPhg0bHPsPkGOaRhzk+eef58knn+Tll182XUrQWRV8LS0tTJo0ifr6eqKjo02XI7YpK4MtW2jdvp3wiAjGtLV9cl90tGcNZtUqz5TTvHnm6hyGoTaN9A22rKwsxo8fb/rHsEpVVRXXXXcdtbW1dk3/YlnwAcybN4/vfe97LFy40HQpYhNvh90obDIYbtNIz8+OahpxCLfbTWpqKvv37ycrK8t0OUFl3W+i92BaBZ8EjZ+28iNAAfBp4L+9N7rdn5yCDUEJv8E2jfQMMuubRhzA5XJRVFTEgQMHFHxON3/+fF588UXTZYgtysr8Xkv1ZcDvhKY3/ObNu6pOu+E0jWRnZ1NcXNwddJmZmURFRQ27BgldRUVFHDx4kHXr1pkuJaisC74FCxbwD//wD6bLEFts2dKrfdzr58BEYCFw1N9zm5s9z3/mGb8vP9ymkYULF6ppRCgsLOQnP/mJ6TKCzro1vq6uLhISEjhy5AhJDu2gkxDhZ7PgC8Bc4FXgh3iC77+vfDYA7rFj+eNLL3Hs4sV+m0b8dUOqaUT6c+zYMZYuXcrJkydNlxJU1o34wsLCmDt3LmVlZaxevdp0OeJk27b5vPnvgS8AmYN4iebWVl74zGd4bc4c5+80IkGXl5fHxYsXqa2tJTk52XQ5QWPl/zHeHVwUfBJI7vJyXH1Ge28DrwAHB/kaMcAjt97KIxZOR0nguVwuCgsLOXjwILfeeqvpcoLGyuCbP3++dm+Rq+Z2u2loaKCiooLjx49z/Pjx7q8rKir49ocf0vet1etABZD98feX8BwI+h7gd1uFc+cCUL2Ih7fBRcHncPPnz+eBBx7QadgyoMbGxl6B1jfcwsPDycvLIzc3l7y8PGbMmMHq1avJy8tj2j/+I/ziF71e70vA53p8/wSeINzaXxHx8SP9Y4l0KyoqYseOHabLCCorgy8tLY3Y2FiOHTvGlClTTJcjBl2+fLlXkPUNufb2dvLy8nqF27Jly7q/nzhxov8XLyqCHTt6NbfEfPzhNQ6IAvy2WUVHQ0HBVf+cIv4UFhayefNm02UElXVdnV6f/vSnueuuu7j77rtNlyIB1NLSQmVlpc/R2vHjx7l48SI5OTm9gq3n15MmTRr+rICfrs4hiYqCykrH7uEp5nV2dhIXF8fp06eJi4szXU5QWDnig092cFHwjW7t7e2cPHnS52itoqKC+vp6srKyuoMsNzeXdevWdX+dkpISuGvYkpM9e29u397/NmX+uFye42IUehJAY8aMoaCggLfffpulS5eaLicorA6+Rx991HQZMoDOzk6qqqp8jtYqKiqorq4mNTW112jtU5/6VHewZWRkmD3tfNMmeOmloZ2C7RUd7Xm+SIB5ty6zJfisneq8dOkSKSkpnD9/noiICNPlWMvtdlNTU+O3geTkyZNMmjTJ71RkZmYmkZGRpn+M/vnZq7NfMTEhtVG1ONuPfvQjSktLrdnFxdoR37hx45g8eTLl5eXMmTPHdDmO1bflv+/nEydOEBsb2yvQ5syZw1133UVeXh45OTmjf59Ib3iN0tMZxPkKCwv5zne+Y7qMoLF2xAfwhS98gblz5/Kg/oG5Ko2NjX7b/Y8fP054eLjP0Vpubi65ubmMGzfO9I8QHPv3e/beLCnxBFyPPTw7IiMJDwvzrOlt2nRVG1OLDFVrayvx8fHU19cTExMz8BNGOauD76mnnuKNN97gP/7jP0yXEtK8Lf/+Gkja2tquCLSeIddvy7+N6uo825kdPgznznGospKT8fGs/dWv1MgixhQVFbF161YWLFhgupSAs3aqEzwNLt/97ndNl2Fca2srJ06c8NtAcuHChSta/ufPn9/9fWJiojYCGIqkJHjkke5vz5eW8g+PPMJahZ4YVFhYyIEDBxR8TjcrOZk7jh6l7bOfJbKpCeLiYPZs2LDBUe+829vbOXXqlN8Gkvr6ejIzM3sF2+233979dUBb/oUFCxbw3nvvceHCBSZMmGC6HLGUd+syG9g51VlW5llr2bmTltZWonr+FURHe5oPVq3yrLXM83tUaMjwtvz7ayDp2/Lf97Pxln9h2bJlfOUrX2HVqlWmSxFLvfHGGzz88MPs37/fdCkBZ1/weVvLR1F3nbfl318DycmTJ0lISPDbQJKVlRX6Lf+W+/rXv05zczOPPfaY6VLEUpcvXyYpKYnGxkbHX+Jl11Rnn+up+vYSNgMPAf8KnlBsavI8HgIafm63m7Nnz/rdL7Jny7830IqKirjzzjvJy8sjOzub6OjogNUngVdcXMxXv/pV02WIxWJjY8nNzeW9997juuuuM11OQNkz4isrg+JivxcRXwJSgRJgSd87Y2KgtPSqWswvXLjgt92/oqKCsLAwn6M172drWv4t1dLSQmJiItXV1ToxXYy59957WbFiBRs2bDBdSkDZM+LbsqXXdVN9PQMkA4t93dnc7Hn+M8/4ff7ly5ev6Izs+XVbW9sVU5HFxcXdt6nl325RUVHMnTuX3bt3s3LlStPliKW8nZ0KPieorYWdO/td0/tP4POAz6Z8txv3Cy9wbO9ePvp45NY33Lwt/z1HaWr5l6FYunQpr7/+uoJPjCkqKuLZZ581XUbA2RF827b1e/cJoBT4UT+PaW5t5Zdr1rCrsLA73G677bbuYEtNTVXLv1yV4uJiNmlTajGosLCQQ4cO0dnZ6ehObzuCr7y83zPR/gu4Ccjr5yVigE1r1rDJkk1cJfhuuOEG3nnnHS5duqQ1XTFi4sSJJCUlceTIEaZPn266nICxY4jS2Njv3T8B/nQwr3Pu3EhUI+JTdHQ0c+bMYffu3aZLEYvZcCG7HcHXz6nCe4DTwJ8M5nXi40eoIBHfvOt8IqZ4G1yczI7gmz0b/Bxt85/AncCADeTR0VBQMMKFifRWXFys4BOjbBjx2XEdX20t5OT0u843oKgoqKx01B6eEnqamppITk7mzJkzWucTI2pqapgxYwYNDQ2O7US3Y8SXnOzZe3O4/xFdLs85aQo9CbCYmBiKiorYs2eP6VLEUikpKURFRXHixAnTpQSMHcEHng2nh7utV3S05/kiQaB1PjHN6dOd9gTfvHmeDaeHerpwTIzneToRW4JE63xiWlFRkaMbXOwJPvBsNO0Nv4GmPV2uT0LP8OkMYpcbb7yR8vJyLl++bLoUsZTTOzvtCj7whFhpKdxxh6dhpc/0Z+uYMXSEh3vuLy1V6EnQxcTEUFhYqDgxzfgAABIOSURBVHU+McbpU512dHX6U1fn2c7s8GHPxenx8ZS73Xzr9Gl+8eqrpqsTi33ta1/D7XbzrW99y3QpYiG3201iYiLvvPMOaWlppssZcXZsWeZPUhI88kivm3IvXKAkI0PbRolRxcXFbN682XQZYimXy0VhYSEHDx50ZPDZN9U5gAkTJnDDDTfwyiuvmC5FLHbjjTdy6NAhrfOJMU6e7lTw+bB27Vqef/5502WIxWJjY7n++ut54403TJcilnJyZ6eCz4e1a9fywgsv0NXVZboUsZiu5xOTnNzZqeDzYcqUKUycONGx/9FldND1fGJSfn4+9fX1nHPgqTQKPj803SmmLVy4kLfffpumpibTpYiFwsLCuP766x25zqfg80PBJ6bFxsZy3XXXaZ1PjPF2djqNgs+PhQsXcuzYMaqqqkyXIhbTdKeY5NQGFwWfHxEREdx6662UlJSYLkUspgYXMUnBZyFNd4ppCxcu5ODBg1rnEyNmzJjBiRMnHHc9qYKvHytXruS1116j5WoOsBW5CuPGjWP27Nns3bvXdClioYiICGbOnMmhQ4dMlzKiFHz9mDRpErNnz9ZUkxildT4xyYnX8yn4BrBmzRpNd4pRWucTk5y4dZmCbwDedT6bD7EQsxYtWsSBAwdobm42XYpYyIkNLgq+AcycOROAd99913AlYqtx48ZRUFCgdT4xoqCggPfff5/W1lbTpYwYBd8AXC6XujvFOK3ziSnR0dFMnTrVUW/+FXyDoOAT07TOJyY5bbpTwTcIxcXFlJeXU19fb7oUsdSiRYt46623dGmNGOG0rcsUfIMQFRXF8uXLefHFF02XIpYaP348s2bN0jqfGKERn6U03SmmaZ1PTLn++us5fPgwnZ2dpksZEQq+QVq9ejUvvfQS7e3tpksRS2mdT0yZMGECaWlpfPDBB6ZLGREKvkFKT09nypQp7N6923QpYqlFixaxf/9+rfOJEU6a7lTwDYGmO8WkCRMmMHPmTN58803TpYiFFHyWUvCJaVrnE1Oc1Nmp4BuCoqIiGhsbOXLkiOlSxFJa5xNTvMHX1dVlupSrpuAbgrCwMNasWcMLL7xguhSx1E033URZWZnW+STokpKSmDBhAsePHzddylVT8A2RpjvFpAkTJnDttdeyb98+06WIhZwy3angG6Kbb76ZN998kwsXLpguRSyldT4xxSkNLgq+IRo3bhyLFi3i5ZdfNl2KWErrfGKKUw6lVfANg6Y7xSTvOp+TjomR0cE74hvt55Mq+IZhzZo17Ny50xHdTTL6xMXFMX36dK3zSdBlZGQAUFVVZbiSq6PgG4a8vDySkpIoKyszXYpYSut8YoLL5XJEg4uCb5g03SkmaZ1PTHFCg4uCb5gUfGLSTTfdxL59+7TOJ0Gn4LPYDTfcQGVlJadOnTJdilho4sSJXHPNNZpul6DTVKfFwsPDWbVqlXZxEWO0zicmTJ48mfPnz1NfX2+6lGFT8F0FTXeKSQo+MSEsLGzUj/oUfFfh1ltvpbS0lKamJtOliIVuuukm3nzzTdra2kyXIpZR8FksPj6eoqIiXnvtNdOliIUmTpzItGnTtM4nQTfaG1wUfFdJ051ikqY7xQQFn+W8wTfat/CR0UnBJyZcc801nD59etRu1q/gu0rXXHMNY8eOpby83HQpYqHFixezd+9erfNJUIWHh1NQUMChQ4dMlzIsCr6r5HK5NN0pxkycOJH8/Hz2799vuhSxzGie7lTwjQAFn5ik6U4x4cYpU0j9yU/g3nvhtts8nx9/HOrqTJc2IJdbi1NXra2tjeTkZD788EOSk5NNlyOW+e1vf8v3v/99nREpwVFWBlu20PXCC7S1txPVM0Kio8HthlWrYNMmmDfPXJ390IhvBERGRnLzzTezc+dO06WIhbzrfO3t7aZLEafbuhWKi2H7dsLa2nqHHkBzM7S0wPbtnsdt3WqiygEp+EaIpjvFlPj4eKZMmaJ1PgmsrVth40ZoavKM6vrjdnset3FjSIafgm+ErFq1it/97nfqrhMjtM4nAVVW9kno9XAWuAOIBXKAn/V9njf8QuxNmYJvhKSkpDB9+nT+93//13QpYiEFnwTUli2eacw+vgxEAjXAT4EHgXf7Pqi52fP8EKLmlhH0zW9+k4aGBr7zne+YLkUsc/bsWXJzc2loaCAiIsJ0OeIktbWQk+NZu+vhMhAPvANM+/i2+4AM4J/7vkZUFFRWQlJSgIsdHI34RtDatWt57rnntIuLBF1CQgKTJ0/mrbfeMl2KOM22bT5v/hAI55PQA7gOHyM+AJfL7+uYoOAbQddddx0tLS18+OGHpksRC2m6UwKivPyK0R7AJWBCn9vigIu+XqO5GQ4fHvnahknBN4K0i4uYpOCTkXDx4kUOHjzIr371K/7pn/6Jt0tLfT5uHNB3p84LwHh/L3zu3MgVeZXCTRfgNGvXruXb3/42f/d3f2e6FLHM4sWL+fznP097e7vW+aRfjY2NHD16lKNHj3LkyJFeX1+6dImpU6d2f8RmZMCpU1e8xjSgAzgC5H982yFgpr8/ND4+ED/KsCj4Rtjy5cu55557OH/+PBMnTjRdjlhk0qRJ5OXlceDAARYsWGC6HDHs3Llz3YHWN+Cam5t7hdvixYt54IEHmDp1Kmlpabhcrk9e6PHH4dChK6Y7Y4E7gf8H/BB4G9gB7PFVTHQ0FBQE6CcdOnV1BsDatWu57777+OxnP2u6FLHMX/3VX5Gens5Xv/pV06VIEJw9e/aKEZv367a2NqZOnUp+fn53wHm/TklJ6R1u/fHT1Qme6/geAH4HTMLTzXmPr9cIsa5OBV8A/OAHP2D37t3813/9l+lSxDLPPvssTz31lLbPcwi3201DQ4PPKcmjR4/S2dlJfn5+r3DzBlxSUtLgw20gd97p2YZsOHHhcsEdd8Azz4xMLSNAwRcAJ0+epLCwkJqaGsaMGWO6HLFIfX09U6ZMoaGhgfBwrWSMBm63m7q6uivCzfu9y+XqDra+o7dJkyaNXLj1p6zMs/dmn51bBiUmBkpLYe7cES9ruPR/RgBkZWWRmZnJ3r17WbRokelyxCKJiYnk5ORw4MAB5s+fb7oc+Zjb7aampsbnlOTRo0cJDw/vFWpr167t/nrSpEmmy/ecsvDEEz63LetXTIzneSEUeqDgCxjvZQ0KPgk272UNCr7gcrvdVFdX+w23qKioXuG2fv16pk6dypQpU0hISDBd/sAefNDzeeNGz3V5/U0WulyehpYnnvjkeSFEU50BsnfvXr74xS9yOIQu2hQ7/OY3v+GHP/whJSUlpktxnK6uLqqrq32ut3300UfExsb6nJKcMmWKc7q89+/37L1ZUuIJuJ57eHrP41u92nMeX4iN9LwUfAHS2dlJWloa+/btIzc313Q5YhGt812drq4uTp8+7Tfc4uLi/IbbhAl99zJxsLo6zzZkhw97Lk6Pj/dcsnD//SHTvemPgi+A7r//fubNm8eXv/xl06WIZQoKCvjxj3/MvBA9Adu0zs5OTp065bNb8tixYyQkJFxxCYD3Y9y4cabLl6ukt4MBtHbtWn70ox8p+CTovOt8NgdfZ2cnlZWVPtfcjh8/TmJiYq9wW7hwYfeaW2xsrOnyJYA04gugCxcukJGRQXV1td4lSlA9/+MfU/v44zwwdy40NkJcHMyeDRs2hPw01FB0dHRw4sQJn+FWUVFBcnKyzwu4J0+eTExMjOnyxRAFX4DdfPPNPPzww6xbt850KWKDsjLYsgV3SQktra1E97zP23iwapWn8WCUjAbb29upqKjwuf1WZWUlqampPi/gzsvLIzo6euA/QKyj4Auw7373u7z77rs8/fTTpksRp9u6ddS2mre1tVFRUeGzoeTUqVOkp6f7bCjJy8tj7NixpsuXUUbBF2BHjx5lyZIlnDp1irAwnQIlAeINveFcXByk8GttbeX48eM+G0qqqqrIzMz02UySl5dHZGRkUGoUOyj4gmD69On89Kc/Zc6cOaZLESfys53UvcAu4DKQCnwF+LO+zx3h7aRaWlo4duyYzwu4q6uryc7O9tktmZubq6OUJGgUfEGwceNGxo8fz+bNm02XIk7kZwPhd4GpwFjgfaAYeAHo9fZrGBsINzc389FHH/lsKKmpqSE3N/eK9bapU6eSk5Oj6wolJCj4guD111/nK1/5Cvv27TNdijhNP0fG9PQBnuD7HvCZvnf6ODKmqanJ71ludXV15OXl+eyWzMrKUrhJyFPwBUF7ezspKSm89957pKammi5HnOTxx2HzZr/B9xCwDWgGCoHfA30vrOmIjOT3K1bws/T07oA7e/YskydP9tlQkpmZqVNHZFTTW7MgiIiI4JZbbqGkpIQHHnjAdDniJOXl/Y72/g34V+AN4HU80559hbe1EXfiBPPXr+eee+4hPz+fjIwMNWOJY+k3O0i8pzWIXC232019fT3l5eXUHjky4OPHADcBp4Ctfh4zZ/JkvvSlL7F8+XKysrIUeuJoGvEFycqVK/nyl79Ma2urrjsSn9xuN+fOnaOqqqr7o7q6utf3VVVVnDlzhtjYWNLT0/luQwM3D/L1O4CP/N0ZHz8yP4TIKKDgC5LExEQKCgooLS3llltuMV2OBJHb7aaxsfGKAOsbatXV1URHR5OWlkZ6enr3R35+PkuWLOn+PjU19ZMdSfys8dUCrwJrgWjgFeB/Pv64QnS0Z1d9EUuouSWI/vmf/5mqqiqefPJJ06XICHC73Vy4cMFngPUNucjISNLT068ItZ7fp6WlDX3/SD9dnXXAp4FDQBeQA/wl8EVfr+Gjq1PEyRR8QfTOO+9w++2389FHH+FyuUyXI/24ePGizwDre5vL5SIjI6PfUEtLSwvsJuV+ruMblGFcxycy2in4gsjtdpOXl0dJSQnXXnut6XKsdPnyZb+jsp63dXV1+R2Z9fx+/Pjxpn8kvzu3DMoI79wiMhpojS+IXC4Xa9as4fnnn1fwjbCmpqbu4Opv6rGtrc1ngF1//fW9bpswYcLoGZXPm+fZc3O4e3Uq9MQyCr4gW7t2LVu/8Q2+Ap5rsBx8VtpIaGlp8RtoPW9rbm72OSqbNWtWr9vi4uJGT6ANhXej6VF6OoNIMGmqM5jKyuj85jdp/+1vGRsVhatnQ8IoPSttuFpbWzlz5ky/HY5VVVVcunSJtLS0fptC0tPTiY+Pd2agDdX+/bBlC5SUeAKuufmT+7y/Y6tXe37HNNITSyn4gmUUn5U2FO3t7f0Gmve2xsZGUlNTrwiwvqGWkJCgi6mHo64Otm2Dw4fh3DnPdXoFBXD//ZpVEOsp+IJhFJyVNpCOjg5qamoGbNs/f/48ycnJPkdlPW9LTExUoImIEQq+QOun4+7nwDeASjznpW0DFvd8QBA67jo7O6mtrR2wbf/s2bMkJib6nWr03paUlKQNjEUkpCn4As3PNVa/w3Mo6C+A+UD1x7dn9HzQVVxj1dXVRV1d3YCBVl9fT0JCQr/rZ+np6SQnJyvQRMQRFHyB1M9ZaQuBL3z80a8+u2p0dXVRX18/4HVotbW1xMfHD9gUkpKSovPTRMQq+hcvkLZt83lzJ7AfuB3PCdktwHrgX/Dsq9hTW3s7/7NsGVvHjeveoDguLu6KECsoKOCWW27pFWiRkZGB+9lEREYpBV8g+TkrrQZoB34N/C8QAawDvgl8q89jIzs7KU5MZNqWLd0bFOt0BxGR4VNbXSA1Nvq82TuqexhIAxKBvwVK/LxMzvjx3HjjjeTk5Cj0RESukoIvkOLifN4cD2QCPS+37vfSa52VJiIyYhR8gTR7tqc5xYcNwL/iOTftHPAdPGenXUFnpYmIjCh1dQZSP12d7cBfAT8DooDPAI9//HUvOitNRGREacQXSMnJnr03fewhGQH8G3AeOAM8iY/Qc7k8+yoq9ERERoxGfIGms9JEREKKRnyB5j0rLSZmaM/TWWkiIgGh6/iCQWeliYiEDE11BpPOShMRMU7BZ4LOShMRMUbBJyIiVlFzi4iIWEXBJyIiVlHwiYiIVRR8IiJiFQWfiIhYRcEnIiJWUfCJiIhVFHwiImIVBZ+IiFhFwSciIlZR8ImIiFUUfCIiYhUFn4iIWEXBJyIiVlHwiYiIVRR8IiJiFQWfiIhYRcEnIiJWUfCJiIhVFHwiImIVBZ+IiFjl/wOdtyd7vQEUEAAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] @@ -216,27 +216,29 @@ "X 1\n", "Z 2\n", "I 3\n", - "X 4\n", + "Z 4\n", "Z 5\n", - "X 6\n", - "Z 7\n", + "Z 6\n", + "I 7\n", "Z 8\n", "CZ 0 3\n", - "CZ 0 1\n", + "I 0\n", + "I 1\n", + "CZ 1 4\n", "I 1\n", - "I 4\n", - "CZ 1 2\n", "I 2\n", - "I 5\n", + "CZ 2 5\n", "CZ 3 6\n", - "CZ 3 4\n", - "CZ 4 7\n", + "I 3\n", + "I 4\n", + "I 4\n", + "I 7\n", "I 4\n", "I 5\n", "CZ 5 8\n", - "I 6\n", + "CZ 6 7\n", "I 7\n", - "CZ 7 8\n", + "I 8\n", "\n" ] } @@ -259,19 +261,18 @@ "RX(pi/2) 0\n", "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", - "RZ(-pi) 1\n", - "RX(-pi) 1\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", "RX(-pi/2) 2\n", - "RZ(pi/2) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi) 4\n", - "RX(-pi/2) 5\n", - "RZ(-pi) 5\n", - "RX(pi/2) 6\n", + "RZ(-pi/2) 2\n", + "RZ(-pi/2) 3\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi) 5\n", "RZ(-pi/2) 6\n", "RX(-pi/2) 6\n", - "RX(-pi) 7\n", - "RX(-pi/2) 8\n", + "RX(-pi/2) 7\n", "RZ(pi/2) 8\n", "\n" ] @@ -298,10 +299,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 3\n", - "X 6\n", - "I 3\n", - "X 6\n", + "X 1\n", + "I 4\n", + "I 1\n", + "I 4\n", "\n" ] } @@ -320,10 +321,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 6\n", - "I 7\n", - "I 6\n", - "I 7\n", + "CNOT 0 3\n", + "I 0\n", + "I 3\n", "\n" ] } @@ -342,10 +342,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 1\n", - "H 3\n", "H 4\n", "H 6\n", + "H 7\n", + "H 8\n", "\n" ] } @@ -364,21 +364,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 6\n", - "RX(pi/2) 6\n", "CZ 6 7\n", "RX(-pi/2) 7\n", + "RX(pi/2) 6\n", "CZ 6 7\n", - "RZ(-pi) 7\n", + "RZ(pi/2) 7\n", "RX(-pi/2) 6\n", - "RX(-pi/2) 7\n", + "RZ(-pi) 6\n", "CZ 6 7\n", - "RX(-pi/2) 7\n", "RX(pi/2) 6\n", "CZ 6 7\n", - "RX(-pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 6\n", + "RX(pi/2) 7\n", "\n" ] } @@ -398,35 +394,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-3.086927607437598) 1\n", - "RX(pi/2) 1\n", - "RZ(2.40107122228458) 1\n", - "RX(-pi/2) 1\n", - "RZ(-1.8346006571742377) 1\n", - "RZ(-0.05466504615219536) 4\n", - "RX(pi/2) 4\n", - "RZ(0.7405214313052141) 4\n", - "RX(-pi/2) 4\n", - "RZ(-1.8346006571742375) 4\n", - "CZ 4 1\n", - "RZ(2.5422306773748558) 1\n", - "RX(pi/2) 1\n", - "RZ(-0.5993619762149378) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RX(-pi/2) 1\n", - "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(0.963711916263757) 1\n", - "RX(pi/2) 1\n", - "RZ(1.116799765442358) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.0622751320808765) 1\n", - "RZ(2.1778807373260367) 4\n", - "RX(pi/2) 4\n", - "RZ(1.1167997654423574) 4\n", - "RX(-pi/2) 4\n", - "RZ(-1.0622751320808765) 4\n", + "RZ(-0.9972031830559858) 2\n", + "RX(pi/2) 2\n", + "RZ(2.499986143155603) 2\n", + "RX(-pi/2) 2\n", + "RZ(2.774737393757615) 2\n", + "RZ(2.4545623393205607) 5\n", + "RX(pi/2) 5\n", + "RZ(0.6293682455256661) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.793139262913046) 5\n", + "CZ 5 2\n", + "RZ(-0.4929538141728491) 2\n", + "RX(pi/2) 2\n", + "RZ(2.648638839416944) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 5\n", + "CZ 5 2\n", + "RZ(2.195187298908836) 2\n", + "RX(pi/2) 2\n", + "RZ(1.4859323114149499) 2\n", + "RX(-pi/2) 2\n", + "RZ(2.3749224706830834) 2\n", + "RZ(1.117192567208857) 5\n", + "RX(pi/2) 5\n", + "RZ(1.100128590553038) 5\n", + "RX(-pi/2) 5\n", + "RZ(-1.3228472411340935) 5\n", "\n" ] } @@ -446,40 +442,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-1.7424710365298193) 6\n", - "RX(pi/2) 6\n", - "RZ(1.6814681504417381) 6\n", - "RX(-pi/2) 6\n", - "RZ(-1.3152662010862128) 6\n", - "RZ(0.1473029953361833) 7\n", + "RZ(-2.340746467015041) 4\n", + "RX(pi/2) 4\n", + "RZ(1.7938386866903413) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.6990997351796275) 4\n", + "RZ(2.0274187165352697) 7\n", "RX(pi/2) 7\n", - "RZ(2.1164344969391715) 7\n", + "RZ(1.5585146643016898) 7\n", "RX(-pi/2) 7\n", - "RZ(-0.3421885644722391) 7\n", - "CZ 7 6\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "RZ(2.274539616534053) 6\n", - "RX(-pi/2) 6\n", + "RZ(0.7513186312249509) 7\n", + "CZ 7 4\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.569499219983433) 4\n", + "RX(-pi/2) 4\n", "RZ(-pi/2) 7\n", "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RX(pi/2) 6\n", - "RZ(-1.734060431751839) 6\n", - "RX(-pi/2) 6\n", - "RZ(1.711847788157013) 7\n", + "CZ 7 4\n", + "RX(pi/2) 4\n", + "RZ(-1.6555312195555723) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.163901379855961) 7\n", "RX(pi/2) 7\n", - "CZ 7 6\n", - "RZ(1.6727182122252418) 6\n", - "RX(pi/2) 6\n", - "RZ(1.0239436735316791) 6\n", - "RX(-pi/2) 6\n", - "RZ(-2.040801702902205) 6\n", - "RZ(2.769966602149135) 7\n", + "CZ 7 4\n", + "RZ(-0.2642123127790188) 4\n", + "RX(pi/2) 4\n", + "RZ(1.5763311584646886) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.995763740841851) 4\n", + "RZ(-1.9563592134079801) 7\n", "RX(-pi/2) 7\n", - "RZ(1.433767837709585) 7\n", + "RZ(0.9008098742333037) 7\n", "RX(-pi/2) 7\n", - "RZ(0.38430040772537577) 7\n", + "RZ(-1.6152828259033303) 7\n", "\n" ] } @@ -506,22 +502,23 @@ "output_type": "stream", "text": [ "I 1\n", - "I 4\n", - "I 6\n", - "X 7\n", - "CNOT 1 4\n", - "I 4\n", - "I 7\n", - "CNOT 6 7\n", + "X 4\n", + "X 5\n", + "I 8\n", "I 1\n", + "I 4\n", + "CNOT 4 5\n", + "CNOT 5 8\n", + "X 1\n", "X 4\n", - "X 6\n", - "I 7\n", - "CNOT 1 4\n", + "I 5\n", + "X 8\n", + "I 1\n", "I 4\n", - "I 7\n", - "I 6\n", - "I 7\n", + "I 4\n", + "I 5\n", + "I 5\n", + "I 8\n", "\n" ] } @@ -549,18 +546,17 @@ "text": [ "H 7\n", "H 8\n", - "Z 7\n", + "I 7\n", "I 8\n", "H 7\n", "CZ 7 8\n", "H 7\n", "I 7\n", - "Z 8\n", - "H 7\n", - "CZ 7 8\n", - "H 7\n", + "I 8\n", + "I 7\n", + "I 8\n", "Z 7\n", - "Z 8\n", + "I 8\n", "I 7\n", "I 8\n", "H 7\n", @@ -589,47 +585,44 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(pi/2) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi) 6\n", - "RX(-pi) 6\n", - "CZ 3 6\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "CZ 3 6\n", - "RX(-pi/2) 3\n", - "CZ 3 6\n", - "RZ(-pi) 3\n", - "RX(-pi) 3\n", - "RX(pi/2) 6\n", - "RX(pi/2) 3\n", - "CZ 3 6\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", - "RZ(pi/2) 3\n", - "RZ(pi/2) 6\n", - "RX(-pi) 6\n", - "CZ 3 6\n", - "RX(-pi/2) 6\n", - "CZ 3 6\n", - "RX(pi/2) 6\n", - "RX(-pi/2) 3\n", - "CZ 3 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 3\n", - "RX(-pi) 3\n", - "RZ(-2.7514871497345705) 3\n", - "RX(pi) 3\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "CZ 3 6\n", - "RZ(-1.180690822939675) 3\n", - "RX(pi) 3\n", - "RZ(-pi/2) 6\n", - "RX(pi/2) 6\n", - "RZ(pi/2) 6\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RX(pi/2) 1\n", + "CZ 1 2\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "RX(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RZ(-pi) 2\n", + "RZ(-pi) 2\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RX(pi/2) 2\n", + "RZ(pi/2) 2\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 1\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 1\n", + "CZ 1 2\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 1\n", + "RZ(1.1027544334978108) 2\n", + "RX(pi) 2\n", + "CZ 1 2\n", + "RX(pi) 1\n", + "RZ(-2.038838220091982) 2\n", + "RX(pi) 2\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -666,260 +659,268 @@ "name": "stdout", "output_type": "stream", "text": [ + "RX(pi/2) 2\n", "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(pi/2) 4\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.848077986470681) 0\n", - "RX(-pi/2) 0\n", - "RZ(-2.314764035370268) 1\n", - "RX(pi/2) 1\n", - "CZ 0 1\n", - "RX(pi/2) 0\n", - "RX(pi/2) 1\n", - "CZ 0 1\n", - "RZ(pi/2) 3\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "CZ 0 3\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RZ(1.8643109939140077) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 4\n", - "RZ(pi) 1\n", - "RX(-pi/2) 1\n", + "RX(pi) 3\n", "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", "RX(-pi/2) 4\n", - "CZ 1 4\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(pi) 4\n", "CZ 3 4\n", - "RZ(-2.314764035370267) 0\n", - "RX(pi) 0\n", - "RX(-pi/2) 3\n", - "CZ 3 0\n", - "RZ(-0.944668461720567) 3\n", "RX(pi) 3\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 0\n", - "RZ(-pi/2) 1\n", - "RX(pi) 1\n", - "RZ(2.196924191869227) 3\n", - "RX(pi/2) 3\n", - "RZ(pi/2) 3\n", - "RZ(pi/2) 4\n", + "RZ(pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 4 5\n", + "RZ(pi) 4\n", "RX(-pi/2) 4\n", - "RZ(1.8003162900275091) 0\n", - "RX(pi/2) 0\n", - "RZ(0.8087903164453999) 0\n", - "RX(-pi/2) 0\n", - "RZ(-1.4880927392391687) 0\n", - "RZ(0.14870205699094785) 1\n", - "RX(pi/2) 1\n", - "RZ(0.915229100411908) 1\n", - "RX(-pi/2) 1\n", - "RZ(-1.2104045299775477) 1\n", - "CZ 1 0\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.09318363201377) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(pi/2) 0\n", - "RZ(-1.7999230442359675) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.9550221491598734) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(1.8578486339322868) 2\n", - "RX(pi/2) 2\n", - 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1\n", "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RZ(0.44487534203122825) 2\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(pi) 5\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RZ(-0.9420657351918189) 0\n", + "RX(pi/2) 0\n", + "RZ(1.64332552049446) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.428808686378022) 0\n", + "RZ(2.7792681094301965) 3\n", + "RX(pi/2) 3\n", + "RZ(1.1631859231446993) 3\n", + "RX(-pi/2) 3\n", + "RZ(-2.363464482159524) 3\n", + "CZ 3 0\n", + "RZ(-pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(2.438314082991957) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "CZ 3 0\n", + "RX(pi/2) 0\n", + "RZ(-2.172735112541817) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.401167176798845) 3\n", + "RX(pi/2) 3\n", + "CZ 3 0\n", + "RZ(-2.233562388363687) 4\n", "RX(pi/2) 4\n", - "RZ(pi) 5\n", + "RZ(0.8900537274511487) 4\n", + "RX(-pi/2) 4\n", + "RZ(-2.265901978436181) 4\n", + "RZ(-0.41550158831996953) 5\n", "RX(pi/2) 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"RZ(-pi/2) 3\n", + "RX(pi) 3\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", "RZ(-pi/2) 4\n", - "RX(pi) 4\n", - "RX(pi/2) 5\n", "RZ(pi/2) 5\n", - "RZ(-pi/2) 8\n", - "RZ(2.4199401839953483) 0\n", + "RX(pi/2) 5\n", + "RX(pi/2) 6\n", + "RZ(-pi/2) 6\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RZ(1.2304146581194013) 0\n", "RX(pi/2) 0\n", - "RZ(2.514809287526923) 0\n", + "RZ(1.1382568824321002) 0\n", "RX(-pi/2) 0\n", - "RZ(0.7430590019224299) 0\n", - "RZ(-1.2296368684699086) 1\n", - "RX(pi/2) 1\n", - "RZ(2.3487071393705357) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.35269628057958613) 1\n", - "CZ 1 0\n", - "RZ(pi/2) 0\n", + "RZ(0.24616777028857695) 0\n", + "RZ(-1.0333574559029208) 3\n", + "RX(pi/2) 3\n", + "RZ(2.0185591596724395) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.655898737415182) 3\n", + "CZ 3 0\n", + "RZ(-pi/2) 0\n", "RX(pi/2) 0\n", - "RZ(2.2936383231902973) 0\n", + "RZ(2.0270775750639185) 0\n", "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "CZ 3 0\n", "RX(pi/2) 0\n", - "RZ(-1.6237793109153458) 0\n", + "RZ(-1.6058528141427315) 0\n", "RX(-pi/2) 0\n", - "RZ(1.26535808895675) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(-0.33025559980241503) 2\n", - "RX(pi/2) 2\n", - "RZ(0.9721239057466267) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.170507541045568) 2\n", - "RZ(2.624627633759631) 5\n", + "RZ(1.9713546836128826) 3\n", + "RX(pi/2) 3\n", + "CZ 3 0\n", + "RZ(0.8112763219059625) 4\n", + "RX(pi/2) 4\n", + "RZ(0.7307760929392239) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.7042084291828412) 4\n", + "RZ(1.5078974643001466) 5\n", "RX(pi/2) 5\n", - "RZ(0.5330448874228438) 5\n", + "RZ(1.4458393724909477) 5\n", "RX(-pi/2) 5\n", - "RZ(2.1366798531052416) 5\n", - "CZ 5 2\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "RZ(2.547124086494814) 2\n", - "RX(-pi/2) 2\n", + "RZ(-1.6091458319749625) 5\n", + "CZ 5 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.317951876858162) 4\n", + "RX(-pi/2) 4\n", "RZ(-pi/2) 5\n", "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RX(pi/2) 2\n", - "RZ(-1.6655157631631923) 2\n", - "RX(-pi/2) 2\n", - "RZ(1.3694752670292285) 5\n", + "CZ 5 4\n", + "RX(pi/2) 4\n", + "RZ(-1.7446341113456105) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.0988163645486764) 5\n", "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(-2.124177304890633) 0\n", + "CZ 5 4\n", + "RZ(1.2493760157498446) 0\n", "RX(pi/2) 0\n", - "RZ(0.7698476520272532) 0\n", + "RZ(1.049945776092154) 0\n", "RX(-pi/2) 0\n", - "RZ(-1.1906845624131532) 0\n", - "RZ(-1.7877124981731334) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.38884819540667714) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.07180093401376109) 1\n", - "RZ(-2.2680484910923973) 2\n", - "RX(pi/2) 2\n", - "RZ(0.8982684580372229) 2\n", - "RX(-pi/2) 2\n", - "RZ(-0.6403599470421588) 2\n", - "RZ(1.2896722030099204) 5\n", - "RX(-pi/2) 5\n", - "RZ(1.8100832922742016) 5\n", + "RZ(-2.464682462861407) 0\n", + "RZ(2.685424325928155) 3\n", + "RX(pi/2) 3\n", + "RZ(1.8704206078353496) 3\n", + "RX(-pi/2) 3\n", + 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\n", 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\n", 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GAAAAek17SNtfleTJSf5Ba+2GJKmqzya5L8lbkvz2+A6tte1JtifJli1b2ubNm1dv2mV0zlX3T3oEDlGXbD04f2YAAACg17Rf2rk7yfoF1jcM2/a1X0tyy9zCcJ+1O5OcuIzzAQAAAHCImPaQdk/G7oVWVccmOTJj904bc3dGZ6XV2HoleXw5BwQAAADg0DDtIe36JGdU1bp5a+cleTTJrfvY72PD48vmFqpqfZLnJ/nicg8JAAAAwOyb9pD2niTfTXJNVb18+ECAbUkuHS7VTJJU1b1V9b655621O5J8OMn7quofV9VZST6S5PtJ/u1qfgMAAAAAzIapDmmttd1JTk9yeJKPJrkoyWVJ3jH20jXDa+Z7XZJrk1ya5E8ziminDccEAAAAgCWZ+k/tbK3dleS0RV6zaYG17yR58/AFAAAAAAdkqs9IAwAAAIBpIaQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADlMf0qrqxKraUVWPVNUDVXVxVR2+hP0Pq6o7qqpV1atWclYAAAAAZteaSQ+wL1W1IclNSe5KcnaS45O8O6MA+PbOw5yf5CdWZEAAAAAADhnTfkbaBUnWJjm3tXZja+09SS5K8r9X1dGL7TyEuN9J8i9XdkwAAAAAZt20h7Qzk3y8tfbwvLWrMoprp3bs/6+SfCbJjhWYDQAAAIBDyLSHtBOS3DN/obX21SSPDNv2qqr+XpJfTfLWFZsOAAAAgEPGVN8jLcmGJA8usL572LYvv5fkitbavVW1abE3qqqtSbYmycaNG7Nz586lTTolXnPcnkmPwCHqYP2ZAQAAgF7THtL2S1X9wyTPSfJLvfu01rYn2Z4kW7ZsaZs3b16h6VbWOVfdP+kROERdsvXg/JkBAACAXtN+aefuJOsXWN8wbPsRVfWEJP93knclOayqnpJk7oMJjqqqdSsxKAAAAACzbdpD2j0ZuxdaVR2b5MiM3TttnqOS/ESSSzOKbbuTfHHYdlWSv1yRSQEAAACYadN+aef1Sd5WVetaa98e1s5L8miSW/eyz3eSvGxs7Zgkf5Lk/0xy80oMCgAAAMBsm/aQ9p4kFya5pqreleS4JNuSXNpae3juRVV1b5JbW2tvaq39IMkt8w8y78MG/ktr7baVHxsAAACAWTPVIa21truqTk9yRZKPZvQJnpdlFNPmW5Pk8NWdDgAAAIBDyVSHtCRprd2V5LRFXrNpke27ktTyTQUAAADAoWbqQxoAM2rbQh/KzJJse2jSE8Dy8TvhwPmdAAArbto/tRMAAAAApoKQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAh6kPaVV1YlXtqKpHquqBqrq4qg5fZJ+/X1X/oaruHfb7clW9o6qetFpzAwAAADBb1kx6gH2pqg1JbkpyV5Kzkxyf5N0ZBcC372PX84bXvivJV5L8vST/anj8lRUcGQAAAIAZNdUhLckFSdYmObe19nCSG6vq6CTbquqSYW0h72ytfXPe81uq6rEk/29V/WRr7b4VnhsAAACAGTPtl3aemeTjY8Hsqozi2ql722ksos35y+HxWcs3HgAAAACHimkPaSckuWf+Qmvtq0keGbYtxclJHk/yV8szGgAAAACHkmm/tHNDkgcXWN89bOtSVcdkdE+1D7bWvrGX12xNsjVJNm7cmJ07dy592inwmuP2THoEDlEH688ME3TsGyY9wcHPzx2zxO+EA+d3AgCsuGkPaQesqo5I8qEk30nyz/f2utba9iTbk2TLli1t8+bNqzPgMjvnqvsnPQKHqEu2Hpw/M0zQtVdOeoKD35v+n0lPAMvH74QD53cCAKy4aQ9pu5OsX2B9w7Btn6qqknwgyXOTvKi1tug+AAAAALCQaQ9p92TsXmhVdWySIzN277S9uDzJ2Ule0VrreT0AAAAALGjaP2zg+iRnVNW6eWvnJXk0ya372rGqfivJW5K8rrX26ZUbEQAAAIBDwbSHtPck+W6Sa6rq5cMHAmxLcmlr7eG5F1XVvVX1vnnP/1GS383oss77q+qF876esbrfAgAAAACzYKov7Wyt7a6q05NckeSjGX2C52UZxbT51iQ5fN7zXxge3zB8zffGJFcu76QAAAAAzLqpDmlJ0lq7K8lpi7xm09jzN+RHAxoAAAAA7Ldpv7QTAAAAAKaCkAYAAAAAHab+0k4A9t+m37xu0iPs1a4nTXqCg99U//2+86xJjwAAAMvOGWkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0WDPpATh07HrSP5r0CAe9TY/98aRHAACAlbdt/aQnOPhte2jSE8BMckYaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6TH1Iq6oTq2pHVT1SVQ9U1cVVdXjHfuur6j9U1e6qeqiq/qiqnrYaMwMAAAAwe9ZMeoB9qaoNSW5KcleSs5Mcn+TdGQXAty+y+4eSPDvJ+UkeT/KuJNcmeclKzQsAAADA7JrqkJbkgiRrk5zbWns4yY1VdXSSbVV1ybD2I6rq5CS/kOTU1tonh7X7k9xWVS9vrd20SvMDAEyNTb953aRH2KtdT5r0BAe/qf77fedZkx4BAJbFtF/aeWaSj48Fs6syimunLrLf1+ciWpK01m5P8tfDNgAAAABYkmkPaSckuWf+Qmvtq0keGbZ17ze4e5H9AAAAAGBB035p54YkDy6wvnvYtj/7HbcMcwEAABzUpvpyYJd7H7Cp/vt1uTcHsWkPaaumqrYm2To8/U5VfXmS88yimvQAi3t6km9Oeoh9e9WkB9iretekJ+Bg43fCcvA7gdnhd8Jy8DuB2XEQ/E5Ipv73gt8JK+QnJz0AkzXtIW13kvULrG8Ytu1rv2csZb/W2vYk25c6ILOjqu5orW2Z9BzAdPA7AZjP7wRgnN8LcGia9nuk3ZOxe5pV1bFJjszC90Db636Dvd07DQAAAAD2adpD2vVJzqiqdfPWzkvyaJJbF9nvmKp68dxCVW3J6P5o16/EoAAAAADMtmkPae9J8t0k11TVy4f7mG1Lcmlr7eG5F1XVvVX1vrnnrbXPJflEkg9U1blVdU6SP0ry6dbaTav6HXAwcWkvMJ/fCcB8ficA4/xegENQtdYmPcM+VdWJSa5IcnJGn8T53iTbWmt75r1mV5JbWmtvmLf2lCSXJfnljILhx5Jc2Fqb4ptBAgAAADCtpj6kAQAAAMA0mPZLOwEAAABgKghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAGCFVdW2qmpV9dJJzwIAwP4T0gCAmVZVR1fV5VX1qap6oKoeq6pvVNXtVfXrVXXUpGdcTVV1UlW9t6r+sqr+tqq+W1V/U1U3VdW5VVWTnhEAYFoJaQDArHtqkq1J9iS5LsmlSa5Osi7JZUlur6qjJzfeqnt+knOS3J/kQ0neneTGJP9bkj9L8v7JjQYAMN3WTHoAAIAV9jdJ1rfWvj++oar+MMlrk1yQ5JLVHmxC/qS1duX44hATP5/k9VV1RWvt9lWfDABgyjkjDQBYVFU9uaq+V1WfGVtfO1wq2arq9WPb3jys/+rqTvt3tdb2LBTRBlcPjz+zHO9VVc+vqhuq6ttV9fBwueTJy3Hs5dJa++5e1h9O8vHh6bL8eQAAzBohDQBYVGvtO0luT/JzVbVu3qYXJXni8N+nj+0293zHCo93IH5pePzPB3qgqjolyaeSvDzJ9UmuSPK9JLckecGBHn+lVdWRSU4bnv6XSc4CADCtXNoJAPS6OaNw9vMZ3WssGcWyPUluzbyQVlWHJXlZkv/eWrtvsQNX1VOS/PoS57m2tbaz98VVtSbJ24enT03ykiSbk/xFkt9f4nuPH7uS/EGStUnOaa19eN62f5bk8iUeb3NG9zFbistbaw8u4T1+Osnrkhye5MeSnJXkWUn+dWvtgMMiAMAsEtIAgF47kvx2RsFsfki7M8k1Sa6oqme31v5bRoHqqRndvL7HU5K8Y4nz7ErSHdIy+nfP+Ht8MMk/aa09tsT3HndKkuck+eT8iDa4IsmvJTl+CcfbnKX/eVyZpDukJfnpsff4XpK3ZfThAwAALMClnQBAr88leTTDmWdVtT7J8zIKbDcPr5k7K23uEsGb06G1tqu1Vkv8unIpw7fWHmutVUb//vmJJG/I6DLMO6pq01KOtYDnDY+3LvC+e5J8eikHa61duR9/HruW+B43DH8eR2QU1X4nye8m+UhVHbGUYwEAHCqENACgS2vtexkFoZOq6hlJXprRZYE7Wmt3J/lafhjSTk/S0hnSVlMbub+19v4k52Z0JtkVB3jY9cPj1/ey/X8e4PFXTGvt+621v2qtXZzk/0ryqiQXTngsAICp5NJOAGApbk7yioxC2SlJHkvymXnbzqyqJ2Z0/7Evtda+0XPQ1bhH2kJaa5+vqgczioIH4qHh8cf2sv2YpRxsNe6RthfXJ/nXGf15/JsDPBYAwMwR0gCApZj7BM7Tk5yc5LPz7i+2I8lrk7w5yVFZ2qd1rsY90n7E8AmkRyf59oEcJ8kXhsdTF3iPw5O8eInHW417pC3kx4fHHxzgcQAAZpJLOwGApfhCRmdfnZ3kufm7sWzuMs7fGnu+qJW8R1pVnVRVT1pg/YiMLuk8LD/88IT521tVtc5v4bNJvpzk56vq7LFtb8nSPmhgRe+RVlVb9rL+jCTvHJ7+yJ8HAABJtdb770MAgKSqrs0opCXJC1trt83bdm9G0WhPkqe11h5a4BCrqqouT/LGjC5BvS+js7aeleQXMrrk8stJXtZa+9q8fQ7L6HvY01rrOoO/ql6U5MaMbt5/TZJ7Mzqz7PSMouIvDu9zy7J8Y/upqnYmeVqS25N8NaPvc1OSVyZZm+TaJK8ePiQBAIB5XNoJACzVjoxC2sNJ7lhg2/FJ7pyGiDQ70/YAACAASURBVDa4OsmTM7oU9eQk6zKa/a4k707y71prj4ztc9LweFXvm7TWPlNVL8no0y/PHJZvy+h+Y2dkFNKmwb/J6P5rz8toriOSfDOj2PfBJB9q/p9WAIAFTfUZaVX100neltE/ep+b5FOttZd27Lc+yeUZ/SPxsCQfS3Jha+1bKzctADArqurCjP4tcVJr7UuTngcAgOkw7WekPTejyww+n+QJS9jvQ0meneT8JI8neVdGlym8ZLkHBABm0qlJPiKiAQAw37SfkXZYa+3x4b//NMnTFzsjrapOzuiGv6e21j45rP1cRpdWvKK1dtPKTg0AAADALJrqT+2ci2hLdGaSr89FtOE4tyf56/zwfiUAAAAAsCRTHdL20wlJ7llg/e5hGwAAAAAs2bTfI21/bMjoY+3H7U5y3N52qqqtSbYmydq1a5+/adOmFRkOAAAAODjdfffd32ytPWPSczA5sxjS9ktrbXuS7UmyZcuWdscdd0x4IgAAAGCaVNV9k56ByZrFSzt3J1m/wPqGYRsAAAAALNkshrR7svC90PZ27zQAAAAAWNQshrTrkxxTVS+eW6iqLRndH+36iU0FAAAAwEFtqu+RVlVHJnnl8PTHkxxdVa8env/H1tojVXVvkltba29Kktba56rqE0k+UFVvTfJ4kncl+XRr7aZV/hYAAAAAmBFTHdKSPDPJ1WNrc89/KsmujL6Hw8dec16Sy5L8QUZn3X0syYUrNiUAAAAAM2+qQ1prbVeSWuQ1mxZYezDJG4cvAAAAADhgs3iPNAAAAABYdkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAhzWTHoDltek3r5v0CByidr3zrEmPAAAAACvKGWkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOgw9SGtqk6sqh1V9UhVPVBVF1fV4R37bamqT1TV/zd83VRVL1iNmQEAAACYPVMd0qpqQ5KbkrQkZye5OMlvJLlokf2OHfZbk+T1w9eaJDdW1U+u5MwAAAAAzKY1kx5gERckWZvk3NbawxmFsKOTbKuqS4a1hZyVZF2SX26tPZQkVfXZJN9M8sok/37lRwcAAABglkz1GWlJzkzy8bFgdlVGce3Ufez3hCQ/SPK/5q19Z1ir5R4SAAAAgNk37SHthCT3zF9orX01ySPDtr35s+E1766qZ1bVM5NclmR3kqtXaFYAAAAAZti0h7QNSR5cYH33sG1BrbUHkrwsya8k+frwdW6SM1prf7sCcwIAAAAw46b9Hmn7pao2ZnTm2Z1Jzh+W/2mS66rqlOGstvF9tibZmiQbN27Mzp07V2vcZfWa4/ZMegQOUQfrzwwAAAD0mvaQtjvJ+gXWNwzb9uZtGd0n7dWtte8nSVXdnOQrSd6a5MLxHVpr25NsT5ItW7a0zZs3H9jkE3LOVfdPegQOUZdsPTh/ZgAAAKDXtF/aeU/G7oVWVccmOTJj904bc0KSL81FtCRprX0vyZeSHL8CcwIAAAAw46Y9pF2f5IyqWjdv7bwkjya5dR/73ZfkZ6vqiLmFqnpikp9NsmsF5gQAAABgxk17SHtPku8muaaqXj7cx2xbkktbaw/Pvaiq7q2q983b771JnpXkz6vqrKp6VZJrk2zMcPkmAAAAACzFVIe01truJKcnOTzJR5NclOSyJO8Ye+ma4TVz+92Z5BeTrEvywSQfyOhy0Fe01r648pMDAAAAMGum/cMG0lq7K8lpi7xm0wJrO5LsWKGxAAAAADjETPUZaQAAAAAwLYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOUx/SqurEqtpRVY9U1QNVdXFVHd6577lV9Z+q6tGq+lZV3VBVR630zAAAAADMnqkOaVW1IclNSVqSs5NcnOQ3klzUse/5Sf44yfVJzkxyfpKvJFmzUvMCAAAAMLumPSpdkGRtknNbaw8nubGqjk6yraouGdZ+RFU9PcllSX6ttfb78zb9+YpPDAAAAMBMmuoz0jI6k+zjY8Hsqozi2qn72O81w+P7V2owAAAAAA4t0x7STkhyz/yF1tpXkzwybNubFyT5cpI3VdX/qKrvV9VtVXXKyo0KAAAAwCyb9ks7NyR5cIH13cO2vTkmyXOSvD3Jv0jyreHxhqr6mdba18d3qKqtSbYmycaNG7Nz584DHH0yXnPcnkmPwCHqYP2ZAQAAgF7THtL2VyV5cpJ/0Fq7IUmq6rNJ7kvyliS/Pb5Da217ku1JsmXLlrZ58+bVm3YZnXPV/ZMegUPUJVsPzp8ZAAAA6DXtl3buTrJ+gfUNw7Z97deS3DK3MNxn7c4kJy7jfAAAAAAcIqY9pN2TsXuhVdWxSY7M2L3Txtyd0VlpNbZeSR5fzgEBAAAAODRMe0i7PskZVbVu3tp5SR5Ncus+9vvY8PiyuYWqWp/k+Um+uNxDAgAAADD7pj2kvSfJd5NcU1UvHz4QYFuSS4dLNZMkVXVvVb1v7nlr7Y4kH07yvqr6x1V1VpKPJPl+kn+7mt8AAAAAALNhqkNaa213ktOTHJ7ko0kuSnJZkneMvXTN8Jr5Xpfk2iSXJvnTjCLaacMxAQCA/5+9e4+3tK7rBf75wqghcpnUZFRywrwcb2fUyXuhoCFqomTSMS1LI8yyjmkXpUTzBiV4jIxIy7SSNM28HEQuSQJeQMVMxCIdRfB+BtFAEfieP9Ya3W337P2smbVnr9n7/X699mvPen6/51mfgdder/36zPP8fgDARGZ+187uviTJIUvM2bjAsW8mecb4CwAAAAB2ykzfkQYAAAAAs0KRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAAWa+SKuqu1XV2VV1TVVdWVUvqqo9Jzh/j6q6qKq6qh6znFkBAAAAWL3WrXSAxVTV+iRnJbkkyRFJ7pjkFRkVgMcOvMzTk9x+WQICAAAAsGbM+h1pxyTZK8mR3X1md5+S5IVJnl1V+y518riIe0mS5y9vTAAAAABWu1kv0g5PckZ3Xz3n2GkZlWsHDzj/D5Ocn+TsZcgGAAAAwBoy60XaXZNcOvdAd38uyTXjse2qqnsl+aUkz1m2dAAAAACsGTO9RlqS9UmuWuD41vHYYv4kycndfVlVbVzqjarq6CRHJ8mGDRty8cUXT5Z0RjzxoBtWOgJr1O76MwMAAABDzXqRtkOq6meT3CXJTw09p7tPTXJqkmzevLk3bdq0TOmW1+NOu2KlI7BGnXD07vkzAwAAAEPN+qOdW5Pst8Dx9eOx71NVN0nyR0mOT7JHVe2fZNvGBHtX1T7LERQAAACA1W3Wi7RLM28ttKo6MMnNM2/ttDn2TnL7JCdmVLZtTfKx8dhpST66LEkBAAAAWNVm/dHO05M8t6r26e5vjI8dleTaJOdu55xvJnnYvGMHJHljkuclOWc5ggIAAACwus16kXZKkmcleWtVHZ/koCTHJTmxu6/eNqmqLktybnc/rbuvT/LeuReZs9nAx7v7g8sfGwAAAIDVZqaLtO7eWlWHJjk5yTsy2sHzpIzKtLnWJdlz16YDAAAAYC2Z6SItSbr7kiSHLDFn4xLjW5LU9FIBsNOOW2gvGSZy3NdXOgFMj8+EneczAQCW3axvNgAAAAAAM0GRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAAWa+SKuqu1XV2VV1TVVdWVUvqqo9lzjnx6rqr6rqsvF5n6qqF1TVD+yq3AAAAACsLutWOsBiqmp9krOSXJLkiCR3TPKKjArAYxc59ajx3OOT/EeSeyX5w/H3n17GyAAAAACsUjNdpCU5JsleSY7s7quTnFlV+yY5rqpOGB9byMu7+6tzXr+3qr6V5M+r6g7d/dllzg0AAADAKjPrj3YenuSMeYXZaRmVawdv76R5Jdo2Hx1/v+304gEAAACwVsx6kXbXJJfOPdDdn0tyzXhsEg9McmOS/5xONAAAAADWkll/tHN9kqsWOL51PDZIVR2Q0Zpqb+juL29nztFJjk6SDRs25OKLL5487Qx44kE3rHQE1qjd9WeGFXTgU1c6we7Pzx2ric+EneczAQCW3awXaTutqm6a5E1Jvpnkf29vXnefmuTUJNm8eXNv2rRp1wScsseddsVKR2CNOuHo3fNnhhX0ttetdILd39P+z0ongOnxmbDzfCYAwLKb9SJta5L9Fji+fjy2qKqqJK9PcvckD+7uJc8BAAAAgIXMepF2aeathVZVBya5eeatnbYdr0xyRJJHdPeQ+QAAAACwoFnfbOD0JIdV1T5zjh2V5Nok5y52YlX9XpJfS/Lk7j5v+SICAAAAsBbMepF2SpJvJ3lrVT18vCHAcUlO7O6rt02qqsuq6rVzXj8pyUszeqzziqp6wJyvW+/avwIAAAAAq8FMP9rZ3Vur6tAkJyd5R0Y7eJ6UUZk217oke855/ZPj708df831i0leN92kAAAAAKx2M12kJUl3X5LkkCXmbJz3+qn5/gINAAAAAHbYrD/aCQAAAAAzQZEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAA61Y6AADLZ+PvvmulI2zXlh9Y6QS7v5n+//vyR690BAAAmDp3pAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADGDXTnaZLT/wpJWOsNvb+K2/W+kIAACw/I7bb6UT7P6O+/pKJ4BVyR1pAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYICZL9Kq6m5VdXZVXVNVV1bVi6pqzwHn7VdVf1VVW6vq61X1t1V1y12RGQAAAIDVZ91KB1hMVa1PclaSS5IckeSOSV6RUQF47BKnvynJnZM8PcmNSY5P8rYkP75ceQEAAABYvWa6SEtyTJK9khzZ3VcnObOq9k1yXFWdMD72farqgUl+MsnB3f0v42NXJPlgVT28u8/aRfkBAAAAWCVmvUg7PMkZ8wqz0zK6u+zgJO9Y5LwvbSvRkqS7P1RVnxmPKdIAgDVn4+++a6UjbNeWH1jpBLu/mf7/+/JHr3QEAJiKWV8j7a5JLp17oLs/l+Sa8djg88Y+ucR5AAAAALCgWS/S1ie5aoHjW8dj0z4PAAAAABY064927jJVdXSSo8cvv1lVn1rJPKtRrXSApd0qyVdXOsTiHrPSAbarjl/pBOxufCZMg88EVg+fCdPgMwF2sdn+XHjhbvDJunu6w0oHYGXNepG2Ncl+CxxfPx5b7LxbT3Jed5+a5NRJA7J6VNVF3b15pXMAs8FnAjCXzwRgPp8LsDbN+qOdl2bemmZVdWCSm2fhNdC2e97Y9tZOAwAAAIBFzXqRdnqSw6pqnznHjkpybZJzlzjvgKp6yLYDVbU5yUHjMQAAAACYyKwXaack+XaSt1bVw8frmB2X5MTuvnrbpKq6rKpeu+11d78/yXuSvL6qjqyqxyX52yTndfdZu/RvwO7Eo73AXD4TgLl8JgDz+VyANai6e6UzLKqq7pbk5CQPzGgnztckOa67b5gzZ0uS93b3U+cc2z/JSUken1Fh+M4kz+ru2V0MEgAAAICZNfNFGgAAAADMgll/tBMAAAAAZoIiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAy6yqjquqrqqHrnQWAAB2nCINAFjVqmrfqnplVb2vqq6sqm9V1Zer6kNV9ZtVtfdKZ1xJNXLmuOjrqlq30pkAAGaVIg0AWO1+MMnRSW5I8q4kJyZ5c5J9kpyU5ENVte/KxVtxv5bkYUm+tdJBAABm3cT/4lhVt0zy+CT/I8ne3X3MnON3SHJJd/tFDACYFZcn2a+7vzN/oKr+JsnPJTkmyQm7OthKq6q7JDk+yR8n+dmMfpcDAGA7Jrojrap+IcmWJH+e5H8n+eU5w7dLcmGSJ00rHAAwG6rqFlV1XVWdP+/4XuNHJbuqnjJv7Bnj47+0a9P+d919w0Il2tibx9/vNI33qqr7VtW7q+obVXV1VZ1VVQ+cxrWnbfwI5xuSfDrJC1Y4DgDAbmFwkVZVhyb5yySfSfIzGZVp39Xd/5rkk0keN82AAMDK6+5vJvlQkvtV1T5zhh6c5GbjPx8677Rtr89e5ng746fG3/91Zy9UVQ9K8r4kD09yepKTk1yX5L1J7r+z118Gxya5d5Kndve3VzoMAMDuYJJHO38nyReT/Hh3f72q7rnAnIuTPGAqyQCAWXNORsXZT2S01lgyKstuSHJu5hRpVbVHRutufbq7P7vUhatq/yS/OWGet3X3xUMnj+/AOnb88geT/HiSTUn+OclfTPje869dGf2D415JHtfd/zRn7DeSvHLC623K5P84+cruvmrg9X8syfOTvLy7L5rwfQAA1qxJirQfS/Km7v76InM+n+SAnYsEAMyos5P8fkaF2dwi7cNJ3prk5Kq6c3f/e0YF1Q8mecvAa++fyR8v3JLRP+INtW6B93hDkl+dwvquD0pylyT/MrdEGzs5ya8nueME19uUyf97vC7JkkVaVe2V0d/7E0leNOF7AACsaZOskfYDSb6xxJz9k9y443EAgBn2/iTXZnznWVXtl+Q+GRVs54znbLsr7ZDx93MyQHdv6e6a8Ot1k4Tv7m91d2X0+8/tkzw1o8cwL6qqjZNcawH3GX8/d4H3vSHJeZNcrLtftwP/PbYMvPwJSQ5K8guLrB0HAMACJinStiS57xJz7pfk33c4DQAws7r7uowKoXtW1a2TPDTJnknO7u5PJvlCvlekHZqkM7BI25V65Iru/uskR2Z0J9nJO3nZ/cbfv7Sd8S/u5PWnoqoOTvLMJC/u7o+tdB4AgN3NJI92vj3Jc6rqyO5+6/zBqvr5JP8zo0c+AIDV6Zwkj8ioKHtQkm8lOX/O2OFVdbOM1h/7RHd/echFd8UaaQvp7g9U1VUZlYI7Y9vSF7fZzvhES18s4xpp905SSV5YVS/czpzvjJZ8y7139r8vAMBqM0mRdnySo5K8qar+Psn6JKmqYzL6ZfmJSS5L8qpphwQAZsa2HTgPTfLAJBfMWV/s7CQ/l+QZSfbOZLt17oo10r7PeAfSfbP08hVL+cj4+8ELvMeeSR4y4fWWa420f0vy2u2MHZXkFhltmtBJvjbh+wMArHrV3cMnj9YP+ZuM/gV6vvcn+dnuvnwqyQCAmTMuhb6W5Lokt07y/O5+6XjsDhmVW19O8kNJjujut69Q1O8a7zT+H/M3FKiqm2a0W+fPJ/m77v65eeOdJON11ZZ6j0ryyYweE11s186Hdfd7d/xvs3yqakuSOyS5SXdfv8JxAABm0kRF2ndPqrpPRv8KfcuMHmX4QHd/cMrZAIAZVFVvS3LE+OUD5v4OUFWXZbQ75Q1JbrnEbt+7RFW9MskvZvQI6mczumvrtkl+MqNHLj+VUcH1hTnn7JHR3+GG7h50B39VPTjJmUlumtEuppdldGfZoRk99vrIKNIAAHZrkzza+V3d/ZF87xEGAGBtOTujIu3qJBctMHbHJB+ehRJt7M0ZPbL4wPHXPhllvyTJK5K8uruvmXfOPcffTxv6Jt19flX9eJKXJDl8fPiDGa2/dlhGRRoAALuxwXekjRcOvmWSryy0Vfr48YhbJflad397KuGqfjTJczP6pffuSd7X3Q8dcN5+GT1C8biMdiZ9Z5Jndbe1PgCAJVXVszL6XeKe3f2Jlc4DAMBs2GOCuX+Q5D8zWpB3IfuMx5+3s6HmuHuSR2X0yMW/T3DemzL619+nJ3lqkh9L8rYp5gIAVreDk7xdiQYAwFyT3JH20SRXdPdjFpnz9iS36+77TiVc1R7dfeP4z/+Q5FZL3ZFWVQ9MckGSg7v7X8bH7pfRoxWP6O6zppENAAAAgLVlkjvSfiSjO8MW8+9JNu5wmnm2lWgTOjzJl7aVaOPrfCjJZ/K99UoAAAAAYCKTFGk3yWj3qsXcmGSvHY8zFXdNcukCxz85HgMAAACAiU2ya+dnMlovZDEHJ/ncjseZivUZbWs/39YkB23vpKo6OsnRSbLXXnvdd+PGjcsSDgAAANg9ffKTn/xqd996pXOwciYp0t6e5Heq6tndfeL8wap6TpLNSf54WuF2pe4+NcmpSbJ58+a+6KKLVjgRAAAAMEuq6rMrnYGVNUmR9sdJnpzkj6rqiUnek+SKJLdLclhGJdrnk5ww7ZAT2ppkoXZ4/XgMAAAAACY2uEjr7v9XVQ9N8sYk9xt/dZIaT/lQkid199emHXJClyb58QWO3zXJ23ZxFgAAAABWiUnuSEt3fzrJ/avqfkkekGT/jNYj+8B4Z8xZcHqS36+qh3T3eUlSVZszWh/t9BVNBgAAAMBua6IibZtxabbsxVlV3TzJo8Yvb5dk36p6wvj1/+3ua6rqsiTndvfTxtneX1XvSfL68bptNyY5Psl53X3WcmcGAAAAYHXaoSJtF/qhJG+ed2zb6x9JsiWjv8Oe8+YcleSkJH+ZZI8k70zyrGVLCQAAAMCqN1GRVlXrkjwmo/XR1uf7C6wk6e7+lSlkS3dvyffWYNvenI0LHLsqyS+OvwAAAABgpw0u0qrqgCRnJrlbFi+3OslUijQAAAAAmBWT3JH2iiR3z+jRyr9IcnmS65cjFAAAAADMmkmKtMMyWrD/qOUKAwAAAACzao8J5u6V5P3LFQQAAAAAZtkkRdonkvzwcgUBAAAAgFk2SZH2iiSPraq7LlcYAAAAAJhVk6yRdnmSdyZ5f1WdmOTDSa5aaGJ3XzCFbAAAAAAwMyYp0s5L0kkqyXFLzN1zRwMBAAAAwCyapEh7aUZFGgAAAACsOYOLtO4+djmDAAAAAMAsm2SzAQAAAABYsyZ5tDNJUlXrkjw0yf9Icovuftn4+E2T3CLJ1u72CCgAAAAAq8pEd6RV1cOTfDrJGUn+T5IXzxm+b5KvJDlqaukAAAAAYEYMLtKq6j5J3pnRXWzPTXLa3PHufn+SLUkeP8V8AAAAADATJrkj7Q+SXJtkc3efmORTC8y5MMmmaQQD+Jy6SgAAIABJREFUAAAAgFkySZH2kCT/2N1XLjLnc0k27FwkAAAAAJg9kxRpt8hoDbTF7DXhNQEAAABgtzBJ6XVFkrsvMWdTks/seBwAAAAAmE2TFGlnJHlkVT1wocGq+skkD85oQwIAAAAAWFUmKdJemuTrSc6qqpckuWuSVNVh49dvSfKlJCdOPSUAAAAArLB1Qyd29+er6rAkb0rye0k6SSX5v+PvW5Ic2d1LraMGAAAAALudwUVaknT3RVV15yRHJHlAkltmdJfaBzLa0fO66UcEAAAAgJU3uEirqtsm+c74jrO3jL8AAAAAYE2YZI20y5OcsFxBAAAAAGCWTVKkXZXky8sVBAAAAABm2SRF2geT3Hu5ggAAAADALJukSHthkoOr6qnLlAUAAAAAZtYku3YemuScJK+tqmOSXJjki0l63rzu7pdNKR8AAAAAzIRJirQXz/nz/cZfC+kkijQAAAAAVpVJirRHLFsKAAAAAJhxg4u07j57OYMAAAAAwCwbvNlAVb2nqo5bxiwAAAAAMLMm2bXzIUluulxBAAAAAGCWTVKkXZbkwOUKAgAAAACzbJIi7bVJHlVVt1+uMAAAAAAwqybZtfMtSQ5Ncn5VvSzJhUm+mKTnT+zuK6cTDwAAAABmwyRF2ucyKs0qyZ8uMq8nvC4AAAAAzLxJCq+/ywJ3nwEAAADAWjC4SOvuJy9nEAAAAACYZZNsNgAAAAAAa5a1zFaZjb/7rpWOwBq15eWPXukIAAAAsKwGF2lVderAqd3dv7KDeQAAAABgJk1yR9rTlxjftqNnJ1GkAQAAALCqTFKk3Wk7x/dP8mNJjk3yvvF3AAAAAFhVJtm18z8XGf5wVZ2e5F+TnJFksbkAAAAAsNuZ2q6d3f3ZJP+U5Dendc0kqaq7VdXZVXVNVV1ZVS+qqj0HnLe5qt5TVf9v/HVWVd1/mtkAAAAAWDumVqSNfSnJnad1sapan+SsjNZdOyLJi5L8VpIXLnHegePz1iV5yvhrXZIzq+oO08oHAAAAwNoxyRppi6qqPZI8LMnV07pmkmOS7JXkyO6+OqMibN8kx1XVCeNjC3l0kn2SPL67vz7Od0GSryZ5VJI/m2JGAAAAANaAwUVaVT1okWscmOSXktw7yWunkGubw5OcMa8wOy3J8UkOTvKO7Zx3kyTXJ/mvOce+OT5WU8wHAAAAwBoxyR1p52X0iOX2VJILkvz2TiX67+6a5Jy5B7r7c1V1zXhse0XaWzJ6DPQVVfWS8bE/SLI1yZunmA8AAACANWKSIu2lWbhIuzGjgupD3X3BVFJ9z/okVy1wfOt4bEHdfWVVPSzJO5M8a3z4C0kO6+6vTDkjAAAAAGvA4CKtu49dziDTVFUbMrrz7MNJnj4+/Mwk76qqB3X35xY45+gkRyfJhg0bcvHFF++quFP1xINuWOkIrFG7688MAAAADDW1zQaWydYk+y1wfP14bHuem9E6aU/o7u8kSVWdk+Q/kjwn37tL7bu6+9QkpybJ5s2be9OmTTuXfIU87rQrVjoCa9QJR++ePzMAAAAw1B5DJ1bVvavqeVV1m+2M32Y8fq/pxculGa2FNvd9Dkxy8/HY9tw1ySe2lWhJ0t3XJflEkjtOMR8AAAAAa8TgIi2jO7mekeTL2xn/SpJjkjx7Z0PNcXqSw6pqnznHjkpybZJzFznvs0nuUVU33Xagqm6W5B5JtkwxHwAAAABrxCRF2oOS/HN3L7hzZ3ffmNEOmw+ZRrCxU5J8O8lbq+rh43XMjktyYndfvW1SVV1WVa+dc95rktw2yT9W1aOr6jFJ3pZkQ8aPbwIAAADAJCYp0g5IcvkSc67IqKyaiu7emuTQJHsmeUeSFyY5KckL5k1dN56z7bwPJ3lkkn2SvCHJ6zN6HPQR3f2xaeUDAAAAYO2YZLOBa5Lceok5t05y3Y7H+X7dfUmSQ5aYs3GBY2cnOXuaWQAAAABYuya5I+1jSR5bVXsvNDhex+yx43kAAAAAsKpMUqT9RZIfSnJGVd197kBV3SPJuzO6I+0104sHAAAAALNh8KOd3f3Gqnp0kicl+VhVXZnRmmi3y2hh/z2S/G13/82yJAUAAACAFTTJGmnp7idX1QVJfj3JXZLcfjx0aZJXdfcpU84HAAAAADNhoiItSbr71UleXVX7Jtk/yVXdffXUkwEAAADADJm4SNtmXJ4p0AAAAABYEwZvNlBVm6rqeVV1m+2M32Y8fq/pxQMAAACA2TDJrp3PTfKMJF/ezvhXkhyT5Nk7GwoAAAAAZs0kRdqDkvxzd/dCg919Y5JzkjxkGsEAAAAAYJZMUqQdkOTyJeZckWTDjscBAAAAgNk0SZF2TZJbLzHn1kmu2/E4AAAAADCbJinSPpbksVW190KDVbVPkseO5wEAAADAqjJJkfYXSX4oyRlVdfe5A1V1jyTvzuiOtNdMLx4AAAAAzIZ1Qyd29xur6tFJnpTkY1V1ZUZrot0uyW0zKuX+trv/ZlmSAgAAAMAKGlykJUl3P7mqLkjy60nukuT246FLk7yqu0+Zcj4AAAAAmAkTFWlJ0t2vTvLqqto3yf5Jruruq6eeDAAAAABmyMRF2jbj8kyBBgAAAMCaMFGRVlUPTvLgjNZES5Irk5zf3edPOxgAAAAAzJJBRVpVPSTJnyW527ZD4+89Hv9Ekmco1AAAAABYrZYs0qrq8UlOS3KTJF9Kcm6Sy8fDByY5OMk9kpxTVU/s7n9apqwAAAAAsGIWLdKqakOS1ye5MaOdOv+8u6+fN2ddkl9O8ookb6iqu3T3F5YpLwAAAACsiD2WGP/NJHsneUp3/+n8Ei1Juvv67v6zJE9JcoskvzH9mAAAAACwspYq0h6Z5MLu/oelLtTdb0nyoSSHTyMYAAAAAMySpYq0jUnOm+B654/PAQAAAIBVZaki7SZJrpvgeteNzwEAAACAVWWpIu0LGe3IOdTdk3xxx+MAAAAAwGxaqkh7X5JHVNWdl7pQVd0lyWFJ/mUawQAAAABglixVpP1pkpsmeee4KFvQuGh7R5J1SV49vXgAAAAAMBvWLTbY3RdW1YlJnp3k4qp6c5Kzk1w+nnJgkocneUKSmyV5ZXd/aBnzAgAAAMCKWLRIG3tukmuS/F6SJyf5uXnjleTGJC9LcuxU0wEAAADAjFiySOvuTvIHVfW6JE9L8uAkG8bDX0xyXpK/6u7LliskAAAAAKy0IXekJUm6+9NJnr+MWQAAAABgZi212QAAAAAAEEUaAAAAAAyiSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAA2y3SqurLVfWcOa+fV1UP2TWxAAAAAGC2LHZH2q2S3HzO6xcnOWR54wAAAADAbFqsSPtSktvtqiAAAAAAMMvWLTL2oSRPqarrknxhfOwnqup5S1yzu/tlU0kHAAAAADNisSLtuUn+Kckz5xw7JEs/3tlJFGkAAAAArCrbLdK6+9+r6h5JfjSjRzzPSvL6JG/YRdkAAAAAYGYsdkdauvuGJJ9K8qmqSpJPd/fZuyIYAAAAAMySxTYbmO8mSf5wuYJsT1XdrarOrqprqurKqnpRVe058Nwjq+rCqrq2qr5WVe+uqr2XOzMAAAAAq8+id6TNNb47LUlSVRuSbEqyf5KvJ/lod39he+fuqKpan9EjpZckOSLJHZO8IqMC8Nglzn16kpOTnJDRem/rM1rfbfDfGQAAAAC2mahUqqrbJzklyeELjJ2e5Fe7+3NTypYkxyTZK8mR3X11kjOrat8kx1XVCeNjC+W8VZKTkvx6d//FnKF/nGI2AAAAANaQwY92VtVtkpyf5FFJPp/kjUlOHH//3Pj4eeN503J4kjPmFWanZVSuHbzIeU8cf//rKWYBAAAAYA2bZI20Y5McmOT5Se7Y3U/u7ud295OT3CnJ85LcPks8cjmhuya5dO6B8R1v14zHtuf+GW2S8LSq+nxVfaeqPlhVD5piNgAAAADWkEmKtMckOau7X9bd188d6O7ru/vlSc4cz5uW9UmuWuD41vHY9hyQ5C4ZlXq/k+SnkvxXkndP+Y45AAAAANaISdZI25Dk75aYc1EWf+RyV6kkt0jyM9397iSpqguSfDbJryX5/e87oeroJEcnyYYNG3LxxRfvurRT9MSDblh6EiyD3fVnBgAAAIaapEi7OskPLzHnwPG8admaZL8Fjq8fjy12Xid577YD3X11VX04yd0WOqG7T01yapJs3ry5N23atIORV9bjTrtipSOwRp1w9O75MwMAAABDTfJo5/lJnlBV919osKo2J/mZJOdNI9jYpZm3FlpVHZjk5pm3dto8n8zorrSaHzPJjVPMBwAAAMAaMUmR9pLx/PdV1V9V1c9X1SOq6ilV9dqMirY9krxsivlOT3JYVe0z59hRSa5Ncu4i571z/P1h2w5U1X5J7pvkY1PMBwAAAMAaMfjRzu6+qKqOSvJXSX4hyc/PGa6MNgV4WndfOMV8pyR5VpK3VtXxSQ5KclySE7v7u4+QVtVlSc7t7qfNyfpPSV5bVb+b5KtJfjvJd5L86RTzAQAAALBGTLJGWrr7bVV1dpLHJ7lPRuuXfT3JR5O8tbu/Mc1w3b21qg5NcnKSd2RU1p2UUZk217oke8479uQkf5TkxIweBT0/ySHdvdjaagAAAACwoImKtCQZl2WvH38tu+6+JMkhS8zZuMCxbyZ5xvgLAAAAAHbKJGukAQAAAMCapUgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhgcJFWVbdaziAAAAAAMMsmuSPt8qr626r6iWVLAwAAAAAzapIi7TNJ/leSf66qS6rqN6pq/TLlAgAAAICZMrhI6+67JXlokjcm+ZEkJyW5oqr+uqoetDzxAAAAAGA2TLTZQHf/S3c/Ocltk/xWki1JnpLkfVX18ap6ZlXtO/2YAAAAALCydmjXzu7e2t0nzblL7e+S/GiSVyW5sqpeU1X3nl5MAAAAAFhZO1SkzXNFki8k+WaSSrJXkl9KclFV/UNV7T+F9wAAAACAFbVDRVpV7VlVT6iqM5N8Kslzknw9yW8n+aEkP5nkrCRHJnn1lLICAAAAwIpZN8nkqvqRJL+c5BczKsw6ybuSvLq7z5gz9awkZ1XVW5M8ckpZAQAAAGDFDC7SquqMJIdmdBfbl5K8LMmfd/fli5x2YZIjdiohAAAAAMyASe5Ie0SS92X0qOZbu/s7A855Z5Iv70gwAAAAAJglkxRp9+zuT0xy8e7+eJKPTxYJAAAAAGbP4M0GJi3RAAAAAGA1GVykVdVPV9V7qup22xm/7XjcmmgAAAAArDqDi7SMduu8dXdfsdBgd1+Z5JZJjp5GMAAAAACYJZMUaffMaBfOxVyY5H/ueBwAAAAAmE2TFGm3ytI7cH5tPA8AAAAAVpVJirSvJvnRJebcMclVOx4HAAAAAGbTJEXa+UkeW1V3Xmiwqu6S5IjxPAAAAABYVSYp0k5MctMk51XVr1bVQVV1s/H3ZyY5L8m6JH+8HEEBAAAAYCWtGzqxuz9QVb+W5E/GX/PdmOTXu/v90woHAAAAALNicJGWJN19SlWdn+RXk9w/yf4ZrYn2gSSv7u5/m35EAAAAAFh5ExVpSdLdH0/yjGXIAgAAAAAza5I10gAAAABgzZr4jrSqqiR3SrI+yZ4LzenuC3YyFwAAAADMlImKtKr6vSS/lVGJtpgFCzYAAAAA2F0NLtKq6reSvCTJN5K8McnlSa5fplwAAAAAMFMmuSPtV5JcmeS+3f2lZcoDAAAAADNpks0GfjjJPyrRAAAAAFiLJinSvhRrnwEAAACwRk1SpP1DkkdU1c2WKwwAAAAAzKpJirTfT/KVJH9fVQcuUx4AAAAAmEmTbDZwcZKbJrl/kp+qqq8luWqBed3dd5lGOAAAAACYFZMUaTdP0hnt3LnNXtONAwAAAACzaXCR1t23X84gAAAAADDLJlkjDQAAAADWrB0u0qpqn6raMM0wAAAAADCrJirSqurmVXV8VX0+o40GLp8zdr+qentVbZp2SAAAAABYaYPXSKuqfZK8L8m9kvxbkquTzN2d8xNJDklyaUY7fAIAAADAqjHJHWnHZlSiPb2775XkTXMHu/u/kpyb5NDpxQMAAACA2TBJkfbTSd7T3X85ft0LzNmSZKq7e1bV3arq7Kq6pqqurKoXVdWeE5y/R1VdVFVdVY+ZZjYAAAAA1o7Bj3ZmVJC9ZYk530yy347H+e+qan2Ss5JckuSIJHdM8oqMCsBjB17m6ZlyuQcAAADA2jPJHWnfTHLrJeb8SJKv7nic73NMkr2SHNndZ3b3KUlemOTZVbXvUiePi7iXJHn+FDMBAAAAsAZNUqRdmOQxVXWLhQar6oAkhye5YBrBxg5PckZ3Xz3n2GkZlWsHDzj/D5Ocn+TsKWYCAAAAYA2apEh7VZJbJXlnVd1p7sD49d9nVHC9anrxcteMdgH9ru7+XJJrxmPbVVX3SvJLSZ4zxTwAAAAArFGD10jr7tOr6sUZrU12aZJvJ0lVfTGjRz4ryfO7+7wp5luf5KoFjm8djy3mT5Kc3N2XVdXGpd6oqo5OcnSSbNiwIRdffPFkSWfEEw+6YaUjsEbtrj8zAAAAMNQkmw2ku/+gqt6X5FlJHpDkZuOv9yQ5sbvPnH7EyVXVzya5S5KfGnpOd5+a5NQk2bx5c2/atGmZ0i2vx512xUpHYI064ejd82cGAAAAhpqoSEuScVm2qwqzrVl4F9D147HvU1U3SfJHSY5PskdV7Z9k28YEe1fVPt39jeUICwAAAMDqNckaaSvh0sxbC62qDkxy88xbO22OvZPcPsmJGZVtW5N8bDx2WpKPLktSAAAAAFa1ie9I28VOT/LceXeRHZXk2iTnbuecbyZ52LxjByR5Y5LnJTlnOYICAAAAsLoNLtKq6jtJesDU7u6b7Xik/+aUjNZje2tVHZ/koCTHZbQe29Vzsl2W5Nzuflp3X5/kvfOybxz/8ePd/cEpZQMAAABgDZnkjrQPZuEibf8kP5rRpgMfT3L1AnN2SHdvrapDk5yc5B0Z7eB5UkZl2lzrkuw5rfcFAAAAgPkGF2nd/ZDtjVXVvklelWRzJtgpc+D7XpLkkCXmbFxifEuSml4qAHbacQvtJcNEjvv6SieA6fGZsPN8JgDAspvKZgPjxyyfltEday+ZxjUBAAAAYJZMbdfO7r4hyT8nefy0rgkAAAAAs2JqRdrYTZOsn/I1AQAAAGDFTa1Iq6o7JfmZJP85rWsCAAAAwKwYvNlAVZ26yDUOTPIT4z//zhRyAQAAAMBMGVykJXn6EuOXJfmj7n7NTuQBAAAAgJk0SZF2p+0cvzHJ1u6+agp5AAAAAGAmDS7SutvaZwAAAACsWdPetRMAAAAAVqVJNht40I6+SXdfsKPnAgAAAMAsmGSNtPOS9A6+z547eB4AAAAAzIRJirSXJrlvksOSbElyfpIvJjkgyYOTbEzy7iQfnmpCAAAAAJgBkxRpb0/yW+OvV3X3DdsGqmrPJL+Z5A+TvKC7L5xqSgAAAABYYZMUaS9Ock53nzR/YFyqvaKqDs2oTHvklPIBAAAA7HY+8pGPHLZu3boXdPcBsdnj7uDGqvri9ddf/8L73Oc+Z2xv0iRF2v2SnLzEnI8meeYE1wQAAABYVT7ykY8cdrOb3ezkjRs3XrfXXntt3WOPPXZ0zXl2kRtvvLGuvfba/bZs2XLyRz7ykV/bXpk2SSO6R5KDlphz0ITXBAAAAFhV1q1b94KNGzdet/fee1+rRNs97LHHHr333ntfu3HjxuvWrVv3gu3Om+Ca70/yhKpa8LHNqnpUkickueD/s3fv0XaX9Z34358AIQFDiFyDCBFQUVARUqi2goJ4ww4KVVocKwqDMuPgaK2V36gE7ShQFVDrBW94QbEqpVVGraDgpVZFCnhDQQ0UgihOAIEESPL8/tj76CGe5Hx3ck7OzsnrtdZeO9/n8t2fQ9bZK+vN832ewUoFAAAAmD5aazvPnj17+VTXweBmz569vP847pgGebTzdUkuT3JxVV2a5GtJbk2yU5JDkhya5N4k/3vdywUAAADY6M2wEm3j1P97W+PCs85BWmvtu1X19CQfSvLU/qslqf6QnyV5SWvte+teLgAAAAAMp0FWpKW19vWqekSSJyXZP8ncJHckuTLJ11tr0lYAAAAApqWBDwZoPV9rrZ3dWjut//41IRoAAADA9PXd7353VlUd8PnPf35O1zlvfetbt//Yxz627WTWtSENtCJtRFXNTrJXkge11r41sSUBAAAATD8LXnvxAVPxuYtPP2LKtuE677zzdnjkIx+57IUvfOHtU1XDRBpoRVpVza+qTyW5PclVSb4+qu9Pquqaqjp4gmsEAAAAgCnXOUirqp2TfCfJ0Um+lOTb+f1BA+n3PSTJ8yeyQAAAAAA2vNNPP32HnXfe+bGzZ89+/KGHHrrXTTfdNHN0/6mnnrrTvvvu+6g5c+bst9122z3u0EMP3esHP/jBliP9Bx544CN/+MMfbnXhhRduV1UHVNUB73jHO7ZLkne9613bHXDAAY+cO3fufttss81+Bx100CO+9rWvbbWhf8ZBDfJo56lJ5id5Rmvtkqo6NclBI52ttfur6utJrEgDAAAA2Ih9/OMf3/aUU07Z7dhjj/31UUcddftXv/rVOSeddNKC0WNuuummmS996Ut/9bCHPey+O+64Y8a55567w8EHH7z3dddd94Pttttu5Xve854bnve85+2522673fv617/+liR51KMedW+SLF68eOZf/uVf/ubhD3/4vffee2998pOffPDTnva0va+88sofPPrRj75vCn7kTgYJ0o5I8i+ttUvWMubGJH+6fiUBAAAAMJXOOOOM+U960pPuPP/8829MkqOPPvrO2267bfNPfepT24+M+eAHP/ifI39esWJFjjzyyDt32mmn/T75yU9u+/KXv/w3BxxwwPKtttpq1XbbbbfisMMOu3v0/d/61rfeMvLnlStX5rnPfe6dj3jEI7b+0Ic+tN3ovmEzyB5pOyX56Thj7k2y9bqXAwAAAMBUuv/++/PjH/94q2c/+9kPOCDgqKOOWjr6+tJLL936iU984sO33Xbb/bbYYosD5syZs/8999wz46c//emWGceVV1456/DDD99zu+22e9zmm29+wMyZMw9YvHjxrOuuu27WRP88E2mQFWlLk+w6zpiHJ/nlupcDAAAAwFS65ZZbNl+5cmV22mmn+0e3z58/f8XIn6+77rqZRx555CMe+9jH3n3WWWfdsOuuu9635ZZbtuc+97kPX758+VoXbi1dunTGs571rEdsv/329//d3/3df+6xxx73zZ49e9WJJ5644N577621zZ1qgwRp30zyX6pqx9bar1bvrKo9kzwzyScmqjgAAAAANqz58+ev2GyzzXLrrbduMbr9lltu+V2O9M///M/bLF++fMYXv/jF67fZZptVSW8l2x133LHZePf/6le/+qBbb711iy984Qs/ffzjH798pP23v/3tuHOn2iCPdr41yVZJLquqw5PMSpKq2rJ//bkkLcnbJ7xKAAAAADaILbbYInvvvfc9n//857cd3X7hhRfOG/nzsmXLZlRV22KLLdpI2wc/+MEHr1y5sla7V7v33nsfkD/dc889M5Jk9uzZq0bavvzlL2+9ZMmSB5wKOow6r0hrrX2rqk5K8q4kXxzVdU//fWWS41tr35/A+gAAAADYwF7zmtfc8qIXvWjPF7zgBbsdffTRt3/1q1+dc9lll80d6X/605/+20WLFtXzn//8BSeccMJt3//+92f/wz/8w05z5sxZOfo+e+211/LLL798m89+9rPb7LDDDise8YhH3HvIIYfctdVWW616yUtesuDVr371L2+88cYtzjjjjF123HHH+/+wkuEyyKOdaa29v6q+nuR/JPnjJNsluSPJvyd5Z2vtRxNfIgAAAMDGb/HpR3xvqmvo6q/+6q9uv+mmm24855xz5l944YXbHXjggb9997vfvfjoo49+eJIceOCBy97xjnf84vTTT9/lmGOOmffIRz7ynvPPP//nL3zhC/cYfZ/TTjttyQknnDDzuOOO2+Ouu+7a7Jxzzll88skn/+YjH/nIz0455ZSHHnvssXvttttuy88+++wb3/a2t+08NT9td9VaG3/UJmbhwoXtiiuumOoy1smC11481SWwiVp8+hFTXQIbm0Vzxx/D2i26Y6orgInjO2H9+U4AmHRV9b3W2sLxxl199dWLH/e4x922IWpi4l199dXbP+5xj1swVl/nPdKq6qdV9Y4JqwoAAAAANiKDHDYwP8ldk1UIAAAAAAyzQYK0HyXZY9xRAAAAADANDRKkvSvJn1XVvpNVDAAAAAAMq0FO7fxZkkuT/FtVvTvJd5P8MskfnFbQWvu3iSkPAAAAAIbDIEHaN9ILzSrJazJGgDbKZutTFAAAAAAMm0GCtDdn7eEZAAAAAExbnYO01trrJrMQAAAAABhmgxw2AAAAAACbrLUGaVX1hqo6eEMVAwAAAADDarxHOxf1X18baaiqVyR5RWttj8krCwAAAGCaWTT3gKn53Du+NyWfO6A77rhjxrbbbvv4c845Z/HJJ5/8m6muZyzr8mjntkl2n+hCAAAAAGCYDf0eaVX16Kq6tKruqaolVfXGqtpsnDl/VFUfrqrr+/N+UlWnVtWsDVU3AAAAwHSxYsWKLF++vKa6jqk21EFaVc1LckmSluTIJG9M8tdJThtn6jFJ9kxyRpJnJfmHJK9Kcv6kFQsAAAAwTRx99NEL9t1330d97GMf23avvfbaZ9asWftfdtllWz/vec9bsOuuuz5m1qxZ+y9YsGDfk08+eZfRAdtPfvKTmVV1wAc+8IF5xx577O5z5szZb6eddnrsK1/5yl1Wrlz5gM8477zztl2wYMG+s2bN2n/hwoWPvPpXsTlkAAAgAElEQVTqq/9gAdSKFSvyqle9apf58+c/ZubMmfvvtdde+7z3ve998Fi1XnDBBXP33HPPfWbPnv34Jz/5yXvdeuutm/3gBz/Y8qCDDnrE7NmzH7/vvvs+6tvf/vbs9fnvMt4eaVPtZUlmJzmqtXZnki9X1TZJFlXVmf22sZzeWrtt1PVlVbU8yfuqavfW2g2TXDcAAADARu3mm2+e+frXv37X17zmNUt22WWX+5Nk3rx5K97ylrf854Mf/OAV11577awzzjhjl9tuu22LT3ziEw/IWk499dRdn/WsZy396Ec/+vMvf/nLc84+++z5++yzz7ITTjhhaZJ84xvf2OqEE07Y8/DDD1965pln3vj9739/9rHHHrvn6jW88pWvfMh73vOenV71qlfdctBBB939mc98Zt5JJ530sKrKS1/60v83Mm7JkiUz3/SmN+3yhje84ea77757xmtf+9rdXvSiF+1+0003bfmiF73o13/913/9yze84Q27HnvssXtcd911P5wxY93WlnUJ0ratqt1GXydJVT00yZhL+lprN65TNX/omUm+tFpgdkF6K80OSfK5NXz+bWM0/0f/fZckgjQAAACAtbj99ts3v/jii3/6xCc+cdlI2zOe8Yy7Rv78tKc97a6tt9561Ste8YoFy5cvv3HWrFltpO/AAw/87fvf//6bkuS5z33unV/5ylfmXnTRRfNGgrQ3v/nNO+++++7LL7744p/PmDEjz3/+8++877776swzz3zIyD1uvfXWzT7wgQ/s+IpXvOKWM88885YkOfroo+9csmTJFm95y1t2GR2k3XnnnZt//etfv3afffa5N0muueaard73vvft9M53vnPxy1/+8t8kSWvt5r/4i7/Y66qrrpq1//77L1+X/yZd4rdXJPnFqNfJ/fbFq7WPvH6+LoWswd5Jrh3d0A/p7un3DeIJSVYl+dnElAYAAAAwfe244473jw7RVq1alTe+8Y077rnnnvvMmjVr/5kzZx5w0kknPey+++6r66+/fubouYcffvgDniJ8+MMfvuyWW27ZYuT66quv3vrpT3/67aNXhh1zzDG3j55z5ZVXzl6+fPmMY489duno9j//8z9fesMNN2y5ZMmS3y0Q22WXXe4dCdGSZK+99lqeJM985jN/V8ejHvWo5Uly4403bpF1NN6KtBvT259sqsxLcvsY7Uv7fZ1U1c5JXpfkY621X61hzIlJTkyS+fPn56qrrhq82iHw/D1Wjj8IJsHG+jvDFHrocVNdwcbP7x3Tie+E9ec7AYAJtv32298/+vpNb3rTjm9605seetJJJ/3yKU95ym+32267Fd/61re2PuWUU3ZbtmzZA55anDdv3gMCipkzZ7Z77733d6nZbbfdtsWOO+64YvSYkcdHR9x0001bJMlDHvKQB7TPnz///iT59a9/vdkuu+yyIkm22WabP/i8/s/wu/Ytt9yyJcmyZcvW+cyAtQZprbUF63rjYVFVM5P8Y5K7krxyTeNaa+cmOTdJFi5c2Pbbb78NU+AEe84FN091CWyizjxx4/ydYQpddN5UV7DxO/6cqa4AJo7vhPXnOwGACVb1wB29Lrroogc/4xnPWPrOd77zd+HDNddcs06b92+//fb3/+pXv3pALrVkyZIHrBTbdddd7x9p33nnnX8XiI2sbNthhx02+GqioT61M72VZ3PHaJ/X71ur6v2NfzTJPkme1Vobdw4AAAAAf2j58uUzZs6cuWp02wUXXPDgNY1fm8c+9rF3f+lLX9p21arf3+5Tn/rUtqPH7L///stmzZq16hOf+MQDnkr87Gc/O2/33Xe/d2Q12oY07Kd2XpvV9kLrH3KwVVbbO20Nzk5yZJLDW2tdxgMAAAAwhkMOOeTOD3/4wzuefvrpdz/84Q+/9+Mf//iDb7jhhlnrcq9TTjnll095ylMedcQRR+xx/PHH33bNNdfMPv/883cYPWannXZaecIJJ/zqnHPOmb/55pu3Aw888J7PfOYz215++eVz3/e+903kHv2dDXuQ9oUkf1NVc1prv+23HZNkWZLL1zaxqk5J8vIkz2+tfWNyywQAAAAYx6I7vjfVJayPM844Y8ltt922+Vve8paHJMkznvGMpX//939/47HHHrvXoPc6+OCD73n/+9//80WLFj3kBS94wV777rvv3eeff/7PnvzkJz9q9Lizzjrr5s0337ydd955O77tbW/bfLfddrv33e9+9y9OPPHEKXnqsFqbyrME1q6q5iX5UZIfJDkjyR5J3p7k7Nba60aNuz7J5a214/vXxyY5P8l5Sd632m1/1lr79do+d+HChe2KK66YqB9jg1rw2ounugQ2UYtPP2KqS2Bjs2isJ/cZyKI7proCmDi+E9af7wSASVdV32utLRxv3NVXX734cY973G0boiYm3tVXX7394x73uAVj9Q31irTW2tKqOizJu5J8Lr0TPM9Ksmi1oZsn2WzU9dP678f1X6O9OL2ADQAAAAA6G+ogLUlaaz9Kcug4Yxasdn1c/jBAAwAAAIB1NuyndgIAAADAUBCkAQAAAEAHAz/aWVU7JDk6yaOSbN1aO2FU+8OSfL+1tmxCqwQAAADYeKxatWpVzZgxY3hPeGRMq1atqiSr1tQ/0Iq0qjo+yeIk/5Dkf6a3cf+InZJ8K8mxA1cJAAAAME1U1S+XLVs2a6rrYHDLli2bVVW/XFN/5yCtqg5Pcm6SnyZ5bpL3jO5vrf0gyQ+TPGfdSgUAAADY+K1YseK0xYsXz7z77rtn91c4MeRWrVpVd9999+zFixfPXLFixWlrGjfIo51/m+SWJIe01u6sqsePMeaaJE8YsFYAAACAaWP//ff/0pVXXvnyn/3sZ6e21naOPeo3Bquq6pcrVqw4bf/99//SmgYNEqQtTHJBa+3OtYy5KcnOA9wTgEm04LUXT3UJa7TYQvf1NtR/v6cfMdUlAABMqX4Ys8ZAho3TIInozCR3jzNm2yQr170cAAAAABhOgwRpi5McMM6Yg5L8ZJ2rAQAAAIAhNUiQ9s9JnlRVzxurs6penOSxST47EYUBAAAAwDAZZI+0M5P8RZJPVtWfJ5mbJFX18iRPSnJUkuuSvHOiiwQAAACAqdY5SGutLa2qQ5J8NMnoVWnv6L9/PcmxrbXx9lEDAAAAgI3OICvS0lq7McmTq+qxSZ6QZLskdyT599ba9yahPgAAAAAYCgMFaSNaa9ckuWaCawEAAACAodU5SKuqM5N8uLX240msh2ls8axjp7qEjd6C5Z+Y6hIAAGDyLZo71RVs/BbdMdUVwLQ0yKmdr07yg6r6TlX9j6p68GQVBQAAAADDZpAg7S+TfCnJ49M7YGBJVX2mqv6sqjablOoAAAAAYEh0DtJaa59qrT0rya5J/jbJdUmOSnJReqHa26tqv8kpEwAAAACm1iAr0pIkrbVbW2tvba09JskBSd6VpJL8ryTfq6qrJrhGAAAAAJhyAwdpo7XW/qO19ookuyT5myQrkjxmIgoDAAAAgGHS+dTOsVTV3CTHJHlRkj9Ob2Wao0EAAAAAmHYGDtKqakaSp6cXnv2XJFsmaUkuTfKRJBdOZIEAAAAAMAw6B2lV9Zgkf5XkBUl2Sm/12U+TfDTJR1trN01KhQAAAAAwBAZZkXZ1//2OJB9Icl5r7VsTXxIAAAAADJ9BgrR/TXJekn9qrd07OeUAAAAAwHDqHKS11p4xmYUAAAAAwDCbMdUFAAAAAMDGYI0r0qrqQ+mdxvn/tdZu7V930Vprx09IdQAAAAAwJNb2aOdx6QVpZyS5tX/dRUsiSAMAAABgWllbkPaw/vvNq10DAAAAwCZnjUFaa+2GtV0DAAAAwKak82EDVfWGqjp4nDFPqqo3rH9ZAAAAADBcBjm1c1GSJ48z5uAkp65rMQAAAAAwrAYJ0rrYIsmqCb4nAAAAAEy5iQ7S9k9y2wTfEwAAAACm3NpO7UxVfWW1puOq6sljDN0syUOT7J7kkxNTGgAAAAAMj7UGaXngnmgtyYL+a3WrkvwmyaeSvHIC6gIAAACAobLWIK219rtHP6tqVZJFrbU3TnpVAAAAADBkxluRNtqLk/zHZBUCAAAAAMOsc5DWWvvIZBYCAAAAAMNskBVpv1NVuyZ5SJItx+pvrX1tfYoCAAAAgGEzUJBWVU9LclaSvccZutk6VwQAAAAAQ2jG+EN6quqPk3w+ybZJ3pWkknwtyfuTXNu//lwShxEAAAAAMO10DtKSnJJkeZI/aq29ot/21dbay5Lsm+Tvkjw1yWcmtkQAAAAAmHqDBGlPSPIvrbUlq89vPW9I8uMkp01gfQAAAAAwFAYJ0uYmuXHU9X1Jtl5tzDeTHLy+RY1WVY+uqkur6p6qWlJVb6yqcfdgq6q5VfXhqlpaVXdU1flVtd1E1gYAAADApmOQwwZ+lWTeatd7rjZmiySz17eoEVU1L8klSX6U5Mj+570tvQDwdeNM/8ckj0hyQpJVSc5IclGSJ01UfQAAAABsOgYJ0n6aBwZn/57kmVX1iNbaT6tq5yRHJ7luAut7WXrB3FGttTuTfLmqtkmyqKrO7Lf9gap6QpKnJTmktfa1ftvNSb5dVU9trV0ygTUCAGwUFrz24qkuYY0Wz5rqCjZ+Q/33e/oRU10CAEyIQR7t/GKSQ6rqwf3rc9ILuf6jqr6b3smdOyQ5ewLre2aSL60WmF3Q/9xDxpl360iIliStte8k+UW/DwAAAAAGMkiQ9r709j+7P0laa99M8rz0wql9k9yS5KTW2kcnsL690wvofqe1dmOSe/p9nef1/XiceQAAAAAwps6PdvZXhX17tbZ/SvJPE13UKPOS3D5G+9I8cL+2QebtMQF1AQAAbNSG+nFgj3uvt6H++/W4NxuxQfZIm9aq6sQkJ/Yv76qqn0xlPdNRTXUB49s+yW1TXcTaPXuqC1ijOmOqK2Bj4zthIvhOYPrwnTARfCcwfWwE3wnJ0H8v+E6YJLtPdQFMrWEP0pYmmTtG+7x+39rm7TDIvNbauUnOHbRApo+quqK1tnCq6wCGg+8EYDTfCcDqfC/ApmmNQVpV/Xwd79laa3uOP6yTa7PanmZV9dAkW2XsPdBGz3vSGO17J7logmoDAAAAYBOytsMGZqS3onbQ1yAHGIznC0meXlVzRrUdk2RZksvHmbdzVf3pSENVLUxvf7QvTGB9AAAAAGwi1rgirbW2YAPWsSbvTXJykgur6oz0grBFSd7eP/wgSVJV1ye5vLV2fJK01r5VVf+a5KNV9eokq5KckeQbrbVLNvDPwMbDo73AaL4TgNF8JwCr870Am6BqrU11DWtVVY9O8q4kT0jvJM4PJFnUWls5asziJJe11o4b1bZtkrOSPDe9VXKfT3Jya22IN4MEAAAAYFitc5BWVfOSPKi19p8TWxIAAAAADJ+B9jOrqgdV1duq6pfpHfP7i1F9B1XV/62q/Se6SAAAAACYap2DtKqam+RbSV6ZZEmSH6d3uMCI76d3UuZfTmSBAAAAADAMBlmR9r+T7JPkuNba/kk+PbqztXZPeidpHjZx5QEAAADAcBgkSDsqyZdaax9dy5gbkjxk/UoCAAAAgOEzSJC2a5JrxhlzV5K5614OAAAAAAynQYK03ybZcZwxD0vvEAIAAAAAmFYGCdK+m+TZVTVnrM6qmp/kWUm+MRGFAQAAAMAwGSRIOyfJdkn+b1U9anRH//rTSWYlecfElQcAAAAAw6Faa90HV52a5NQkLcn9SbZIsjTJvCSV5G9ba38/CXUCAAAAwJQaKEhLkqp6SpKTk/xxeivU7kjy70nOaq19ZcIrBAAAAIAhMHCQBgAAAACbokH2SOukqnaY6HsCAAAAwFSbsCCtquZW1ZuT/Gyi7gkAAAAAw2LzLoOqavckB6R3wMB3Wmu3juqbleSVSV6d3qED90xCnQAAAAAwpcZdkVZV70hvldmnk1yUZHFV/fd+35OT/CTJ3yXZKsk5SfaYrGIBAAAAYKqs9bCBqnpRkg8nWZXk2n7z3v3345O8L8lmSd6f5O9aa0smr1QAAAAAmDrjrUg7Lsl9SZ7UWtu3tbZvkkOTrEzywSS/TLJ/a+2/C9EAAMZWVYuqqvVX8wMAsJEaL0h7bJJ/aq19a6Shtfa19B7xrCQvaa19fxLrAwBYL1W1TVWdXVVfr6olVbW8qn5VVd+pqv9VVVtPdY0bUlU9uR/qrel1+lTXCAAwrMY7bGBukuvHaL+u//6tMfoAAIbJg5OcmOQ7SS5O8uv0/o1zaJKzkvy3qnpCa+3OqStxSlye5LIx2r+xgesAANhojBekzUjvpM7V3Z8krbVlE14RAMDE+s8kc1trf/Bvmqr6eJIXJHlZkjM3dGFT7LLW2qKpLgIAYGMy7qmdSdZ8GgEAsEmoqgdV1X1V9c3V2mf3H5VsVfXC1fpO6re/ZMNW+0CttZVjhWh9n+6/P3wiPquqDqiqL1bVb6vqzqq6pKqeMBH3BgBg6o23Ii1JFlXVorE6qmrlGM2ttdblvgDARqK1dldVfSfJQVU1p7X2237XnyTZsv/nw5J8bNS0w/rvl26gMtfFn/Xfr1nfG1XVE5NckmRmkgvT2x5jv/Qen/zK+t5/EuxVVS9Psk16B0h9vbV23ThzAAA2aV0CrxrwnoOOBwA2Dl9JLzg7OL29xpJeWLYyvf22RoKzVNWMJE9J8vPW2g3j3biqtk3yvwas56LW2lVdB1fV5kle1798cJInpRd0fTXJ+wf87NXvXUk+lGR2kue01v55VN8rkpw94P32S/KcAcs4u7V2+wDjX9B/jf7czyb5b621pQN+NgDAJqFa8+QmADC+qjokvdVVZ7XWXtVv+05620B8NMm7kjyytfbTqto/yfeSvL+1dmKHey9I8osBS3pxa+28AeqflWT1/V0/luS/t9buGvCzV7/3n6S3Sf/XWmuHrNa3WZKfJNkzyVNaa5d1uN9xST48YBkPa60t7nDvfZI8O70wdHGSWUkWJnlzkscn+WaSg1trqwb8fACAaa/LHmkAAEnvtO5l6a88q6q5SfZP79HNkUcXR1alHdp/7/RIY2ttcWutBnydN0jxrbXlrbVK798/uyY5LslTk1zRD/LWx/7998vH+NyVGfAkzNbaeevw32Nxx3v/sLV2RmvtB621u1prt7XWvpjkyemFmX+S3z/yCgDAKII0AKCT1tp96QVCj6mqHdILXjZLcmlr7cdJbsnvg7TD0lupNnR7g7Wem1trH0lyVJJHpreabn3M7b/fuob+X67n/Sdda+3OJJ/oXx48lbUAAAwrhwIAAIP4SpLD0wvKnphkeXqPAo70PbOqtkxv/7EfttZ+1eWmG2KPtLG01v69qm5PLxRcH3f033daQ//Og9xsA+2RNpZf99+3Xs/7AABMS4I0AGAQIydwHpbkCUn+rbW2fFTfC5KclF4QM8hpndsmOXXAWhYnWa8grarmpHdq5W/HGzuOK/vvh6ze0d8j7U8HvN9+Gfy/x3lJ1jdI++P++8/X8z4AANOSRzsBgEFcmd7qqyOT7JMHhmUjj3Gestr1uCZzj7Sqekz/oIHV22em90jnjPz+FNLR/a2qup7K9G/pHShwcFUduVrfy9M7aKCzydwjraoWrqH9vyY5Jsl9Sf5xkHoBADYVVqQBAJ211lZW1WXpBWnJqCCttXZDVf0svdBoZcbYeH+KHJ/kxVX1zSQ3pLdqa5ckT0vvkcufJHn16AlVNfI/G1d2+YDWWquq45N8Oclnq+rCJNent7LssCRfTPKM9f9RJsRnqmpFkiuS3JTeqZ1/lOTAJCuSvLRrKAcAsKkRpAEAg7o0vSDtzvTCmNX79kzyvdbaHatPnCKfTvKg9B5FfUKSOenV/qMkb0vy7tbaPavNeUz//YKuH9Ja+2ZVPSnJ/0nyzH7zt9Pbf+3pGZ4g7T3pnVb6J0m2T1JJbk7v0dCzW2tXT11pAADDrVrr+sTChldVeyX5m/T+0btPkq+31p7cYd7cJGent0nvjCSfT3Jya+03k1ctADBdVNXJ6f1b4jGttR9OdT0AAAyHYV+Rtk+SZyX59yRbDDDvH5M8IskJSVYlOSPJRemdIAYAMJ5DkvyLEA0AgNGGfUXajNbaqv6fP5Nk+/FWpFXVE9Lb8PeQ1trX+m0HpvdoxeGttUsmt2oAAAAApqOhPrVzJEQb0DOT3DoSovXv850kv8jv9ysBAAAAgIEMdZC2jvZOcu0Y7T/u9wEAAADAwIZ9j7R1MS+9Y+1XtzTJHmuaVFUnJjkxSWbPnn3AggULJqU4AAAAYOP04x//+LbW2g5TXQdTZzoGaeuktXZuknOTZOHChe2KK66Y4ooAAACAYVJVN0x1DUyt6fho59Ikc8don9fvAwAAAICBTccg7dqMvRfamvZOAwAAAIBxTccg7QtJdq6qPx1pqKqF6e2P9oUpqwoAAACAjdpQ75FWVVsleVb/8iFJtqmqP+9f/9/W2j1VdX2Sy1trxydJa+1bVfWvST5aVa9OsirJGUm+0Vq7ZAP/CAAAAABME0MdpCXZMcmnV2sbuX5YksXp/QybrTbmmCRnJflQeqvuPp/k5EmrEgAAAIBpb6iDtNba4iQ1zpgFY7TdnuTF/RcAAAAArLfpuEcaAAAAAEw4QRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoY+iCtqh5dVZdW1T1VtaSq3lhVm3WYt7Cq/rWq/l//dUlVHbQhagYAAABg+hnqIK2q5iW5JElLcmSSNyb56ySnjTPvof15myd5Yf+1eZIvV9Xuk1kzAAAAANPT5lNdwDhelmR2kqNaa3emF4Rtk2RRVZ3ZbxvLEUnmJHlua+2OJKmqf0tyW5JnJXnP5JcOAAAAwHQy1CvSkjwzyZdWC8wuSC9cO2Qt87ZIsiLJ3aPa7uq31UQXCQAAAMD0N+wr0vZO8pXRDa21G6vqnn7f59Yw77PpPQb6tqr6P/22NyRZmuTTk1TrUFjw2ounugQ2UYtPP2KqSwAAAIBJNexB2rwkt4/RvrTfN6bW2pKqekqSzyc5ud98S5Knt9Z+PdacqjoxyYlJMn/+/Fx11VXrU/eUef4eK6e6BDZRG+vvDAAAAHQ17EHaOqmq+emtPPtekhP6zf8jycVV9cTW2o2rz2mtnZvk3CRZuHBh22+//TZUuRPqORfcPNUlsIk688SN83cGAAAAuhr2IG1pkrljtM/r963J36S3T9qft9buT5Kq+kqS65K8Or9fpQYAAAAAnQz7YQPXprcX2u9U1UOTbNXvW5O9k/xwJERLktbafUl+mGTPSagTAAAAgGlu2IO0LyR5elXNGdV2TJJlSS5fy7wbkuxbVTNHGqpqyyT7Jlk8CXUCAAAAMM0Ne5D23iT3Jrmwqp7aPxBgUZK3t9buHBlUVddX1QdHzftAkl2S/FNVHVFVz05yUZL56e+DBgAAAACDGOogrbW2NMlhSTZL8rkkpyU5K8mpqw3dvD9mZN73kjwjyZwkH0vy0fQeBz28tXb15FcOAAAAwHQz7IcNpLX2oySHjjNmwRhtlya5dJLKAgAAAGATM9Qr0gAAAABgWAjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQwdAHaVX16Kq6tKruqaolVfXGqtqs49yjquq7VbWsqn5TVV+sqq0nu2YAAAAApp+hDtKqal6SS5K0JEcmeWOSv05yWoe5JyT5RJIvJHlmkhOSXJdk88mqFwAAAIDpa9hDpZclmZ3kqNbanUm+XFXbJFlUVWf22/5AVW2f5Kwk/7O19v5RXf806RUDAAAAMC0N9Yq09FaSfWm1wOyC9MK1Q9Yy7/n9949MVmEAAAAAbFqGPUjbO8m1oxtaazcmuafftyYHJflJkuOr6qaqur+qvl1VT5y8UgEAAACYzoY9SJuX5PYx2pf2+9Zk5ySPTPK6JH+b5M+S3J3ki1W100QXCQAAAMD0N+x7pK2rSvKgJM9rrX0xSarq35LckOTlSV7/BxOqTkxyYpLMnz8/V1111YardgI9f4+VU10Cm6iN9XcGAAAAuhr2IG1pkrljtM/r961tXkty2UhDa+3OqvpekkePNaG1dm6Sc5Nk4cKFbb/99lvHkqfWcy64eapLYBN15okb5+8MAAAAdHw0ny8AACAASURBVDXsj3Zem9X2QquqhybZKqvtnbaaH6e3Kq1Wa68kqyayQAAAAAA2DcMepH0hydOras6otmOSLEty+Vrmfb7//pSRhqqam+SAJFdPdJEAAAAATH/DHqS9N8m9SS6sqqf29zFblOTtrbU7RwZV1fVV9cGR69baFUn+OckHq+pFVXVEkn9Jcn+Sf9iQPwAAAAAA08NQB2mttaVJDkuyWZLPJTktyVlJTl1t6Ob9MaP91yQXJXl7ks+kF6Id2r8nAAAAAAxk2A8bSGvtR0kOHWfMgjHa7kpyUv8FAAAAAOtlqFekAQAAAMCwEKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdDD0QVpVPbqqLq2qe6pqSVW9sao2G2D+jKq6oqpaVT17MmsFAAAAYPrafKoLWJuqmpfkkiQ/SnJkkj2TvC29APB1HW9zQpJdJ6VAAAAAADYZw74i7WVJZic5qrX25dbae5OcluRVVbXNeJP7Qdz/SfK/J7dMAAAAAKa7YQ/SnpnkS621O0e1XZBeuHZIh/lvSvLNJJdOQm0AAAAAbEKGPUjbO8m1oxtaazcmuafft0ZV9dgkL0ny6kmrDgAAAIBNxlDvkZZkXpLbx2hf2u9bm3cmeVdr7fqqWjDeB1XViUlOTJL58+fnqquuGqzSIfH8PVZOdQlsojbW3xkAAADoatiDtHVSVX+R5JFJ/qzrnNbauUnOTZKFCxe2/fbbb5Kqm1zPueDmqS6BTdSZJ26cvzMAAADQ1bA/2rk0ydwx2uf1+/5AVW2R5O+TnJFkRlVtm2TkYIKtq2rOZBQKAAAAwPQ27EHatVltL7SqemiSrbLa3mmjbJ1k1yRvTy9sW5rk6n7fBUn+Y1IqBQAAAGBaG/ZHO7+Q5G+qak5r7bf9tmOSLEty+Rrm3JXkKau17Zzkk0n+vyRfmYxCAQAAAJjehj1Ie2+Sk5NcWFVnJNkjyaIkb2+t3TkyqKquT3J5a+341tqKJJeNvsmowwa+31r79uSXDQAAAMB0M9RBWmttaVUdluRdST6X3gmeZ6UXpo22eZLNNmx1AAAAAGxKhjpIS5LW2o+SHDrOmAXj9C9OUhNXFQDrbdFYZ8kwkEV3THUFMHF8J6w/3wkAMOmG/bABAAAAABgKgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQw9EFaVT26qi6tqnuqaklVvbGqNhtnzh9V1Yer6vr+vJ9U1alVNWtD1Q0AAADA9LL5VBewNlU1L8klSX6U5MgkeyZ5W3oB4OvWMvWY/tgzklyX5LFJ3tR/P3oSSwYAAABgmhrqIC3Jy5LMTnJUa+3OJF+uqm2SLKqqM/ttYzm9tXbbqOvLqmp5kvdV1e6ttRsmuW4AAAAApplhf7TzmUm+tFpgdkF64doha5q0Wog24j/677tMXHkAAAAAbCqGPUjbO8m1oxtaazcmuaffN4gnJFmV5GcTUxoAAAAAm5Jhf7RzXpLbx2hf2u/rpKp2Tm9PtY+11n61hjEnJjkxSebPn5+rrrpq8GqHwPP3WDnVJbCJ2lh/Z5hCDz1uqivY+Pm9YzrxnbD+fCcAwKQb9iBtvVXVzCT/mOSuJK9c07jW2rlJzk2ShQsXtv3222/DFDjBnnPBzVNdApuoM0/cOH9nmEIXnTfVFWz8jj9nqiuAieM7Yf35TgCASTfsQdrSJHPHaJ/X71urqqokH02yT5I/aa2NOwcAAAAAxjLsQdq1WW0vtKp6aJKtstreaWtwdpIjkxzeWusyHgAAAADGNOyHDXwhydOras6otmOSLEty+domVtUpSV6e5L+21r4xeSUCAAAAsCkY9iDtvUnuTXJhVT21fyDAoiRvb63dOTKoqq6vqg+Ouj42yZvTe6zz5qr641GvHTbsjwAAAADAdDDUj3a21pZW1WFJ3pXkc+md4HlWemHaaJsn2WzU9dP678f1X6O9OMl5E1spAAAAANPdUAdpSdJa+1GSQ8cZs2C16+PyhwEawCZnwWsvnuoS1mjxrKmuYOM31H+/px8x1SUAAMCEG/ZHOwEAAABgKAjSAAAAAKADQRoA/z97dx/leV3Xffz1ZlcFDNZVLDDJFdI4mMWp7QaVUFAJtQOSSVmdTD179MroRu2yLjoudHOELm6uI1cRpZKVF2kppYYkS2JKapDolbAq5sIVmIQurLjgDXyuP37fyWmY2fnM7sz8fjPzeJwz5zfzvfnNe3bPfFmffm8AAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQYf24B2Dt2LH/i8Y9woq36b63jnsEAABYels3jHuClW/r3eOeAFYlZ6QBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKDD+nEPMJ+qOjrJG5Icm+SuJH+c5KzW2v3z7LchyYVJTs0oGL47yRmttS8u7cQAAJNp02vfM+4R5rRj/3FPsPJN9N/v65877hEAYFFMdEirqo1JrkpyY5JTkhyZ5LyMwtiZ8+z+tiRPTPKyJA8kOSfJ5UmOW6p5AQAAAFi9JjqkJXl5kgOSnNZa25XkfVV1cJKtVXXusOxBqurYJM9Ocnxr7QPDstuSfKSqntlau2qZ5gcAAABglZj0e6SdnOTKGcHssozi2vHz7PeFqYiWJK21jyb53LAOAAAAABZk0kPaUUm2T1/QWrs1ye5hXfd+g5vm2Q8AAAAAZjXpl3ZuzOgBAzPtHNbtzX5HzLZDVW1JsmX48p6q+tQC5qRDjXuA+R2S5M5xD7Fnzxv3AHOqc8Y9ASuNY8JicExg9XBMWAyOCbDMJvu4cNYKOLKuTI8b9wCM16SHtGXTWrskySXjnoPxqarrWmubxz0HMBkcE4DpHBOAmRwXYG2a9Es7dybZMMvyjcO6xd4PAAAAAGY16SFte2bc06yqDk9yYGa/B9qc+w3muncaAAAAAOzRpIe0K5KcVFUHTVt2epJ7k1wzz36HVtXTphZU1eaM7o92xVIMyqrg0l5gOscEYDrHBGAmxwVYg6q1Nu4Z5lRVG5PcmORfkpyTUQg7P8mFrbUzp213c5JrWmsvnbbsyiRPSPLqJA8M+9/RWjtu+X4CAAAAAFaLiT4jrbW2M8mJSdYleVeSs5JckOR1MzZdP2wz3ekZnbX2piRvSXJ9kucv5bwAAAAArF4TfUYaAAAAAEyKiT4jDQAAAAAmhZAGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAABZBVW2tqlZVTx/3LAAALA0hDQBY8arq4Kq6sKr+oapur6r7quqOqvpoVf1yVT183DMup6p6RFW9pqr+vKpurKpvDJHvmfPst66qfqWqPlFV91bVl6rqb6vqKcs1OwDAJBPSAIDV4JFJtiS5P8l7kpyf5O1JDkpyQZKPVtXB4xtv2W1Kcm6SF2X0Z3DnfDtUVSW5LKM/u4cmuSjJO5P8SJIPVNUpSzUsAMBKsX7cAwAALIL/l2RDa+3rM1dU1Z8l+ekkL88oLq0FtyR5ZpKPtda+VFWXJvm5efb5ySQvSHJtkhNba/clSVVdnOSDSf6oqq5urX156cYGAJhszkgDAJIkVfUtVfW1qvrQjOUHDJdKtqr62RnrXjEsf8nyTvtftdbuny2iDd4+vD5hMb5XVX1/Vb23qr5cVbuq6qqqOnYx3nuxtNZ2tta2tda+tIDdXjG8njkV0Yb3+qckf5Hk0RmFNgCANUtIAwCSJK21e5J8NMkPVtVB01Y9NcnDhs9PnLHb1Nfblni8ffFjw+sn9vWNhnuF/UNGZ3tdkdHlj19L8v4kP7Sv7z8uVbV/kqck2Z3RzzfTFcPrCcs2FADABHJpJwAw3dUZhbMfyeheY8kolt2f5JpMC2lVtV+SZyT519baLfO9cVU9IskvL3Cey1trN/RuXFXrk5w5fPnIJMclOSbJ3yf5owV+75nvXUnelOSAJKe21v562rpfSnLhAt/vmCSnLnCMC1trdy1wnx5HJlmX0d/lN2ZZ/5nh9YlL8L0BAFYMIQ0AmG5bkt/MKJhND2nXJ3lHkouq6omttU9nFKgemeSvOt/7EUlet8B5diTpDmkZ/dtm5vf40yT/bfrlinvpKUm+K8kHpke0wUVJfjGjINXrmCz8z+PSJEsR0jYMr3fPsX5q+SOW4HsDAKwYLu0EAKb7xyT3ZjjzrKo2JPm+jALb1cM2U2elTV3md3U6tNZ2tNZqgR+XLmT41tp9rbXK6N84j03y4owuw7yuqjYt5L1m8X3D6zWzfN/7M7ohf7fW2qV78eexYx9/BgAA9oGQBgD8p9ba1zIKQk+uqkcneXpGl/xta63dlOTz+WZIOzFJS2dIW05t5LbW2p8kOS2jM8ku2se3nTpr6wtzrP/3fXz/cZo642zDHOunli/F2XAAACuGSzsBgJmuTvKsjELZU5Lcl+RD09adXFUPy+j+Y59srd3R86bLcY+02bTWPlxVd2UUBffFVGz6tjnWH7qQN5uwe6R9NqP74B1RVetnuU/a1BNPP70E3xsAYMUQ0gCAmaaewHlikmOTXDvt/mLbkvx0klckeXgW9rTO5bhH2oMMTyA9OMmX9+V9kvzz8Hr8LN9jXZKnLfD9JuYeaa21+6rq2ozi6HEZPZxhupOH14k7+xAAYDm5tBMAmOmfMzr76pQkT8p/jWVTIeXXZ3w9r6W8R1pVPbmq9p9l+UMzuqRzv3zz4QnT17eqap0/wrVJPpXkR6rqlBnrXpmFPWhgEu+R9gfD629P/7Osqh9IcnqS/0j/gyUAAFalaq33344AwFpRVZdnFNKS5Idbax+Ztu7mjKLR/Uke1Vqb60mPy6aqLkzy8xldgnpLRmdtPSbJszO65PJTSZ7RWvv8tH32y+hnuL+11nWWflU9Ncn7kjw0o6eY3pzRmWUnZhQVf3T4Pu9flB9sH1TV/0xyyPDl0zL6O/u7jO5zl4wum7182vaV5G1JXpBke5J3JXlURhFt/yQ/PsvTSgEA1hSXdgIAs9mWUUjbleS6WdYdmeT6SYhog7cn+ZaMLkU9NslBGc1+Y5Lzkvx+a233jH2ePLxe1vtNWmsfqqrjkvxOvnm540cyuv/aSRmFtEnxgiSPm7Hs2dM+35HkP0Naa61V1U9ldObdS5L8Ykb3x/tAkt9urV27pNMCAKwAE31GWlV9Z5LXZPQP4icl+YfW2tM79tuQ5MKMbuC7X5J3JzmjtfbFpZsWAFhJquqMjP698OTW2ifHPQ8AAJNv0s9Ie1KS5yT5cJKHLGC/tyV5YpKXJXkgyTkZ/T+uxy32gADAinV8kr8R0QAA6DXpZ6Tt11p7YPj8L5McMt8ZaVV1bEaXJBzfWvvAsOwHM7rs4lmttauWdmoAAAAAVqOJfmrnVERboJOTfGEqog3v89Ekn8s372UCAAAAAAsy0SFtLx2V0ZOmZrppWAcAAAAACzbp90jbGxszeuT9TDuTHDHXTlW1JcmWJDnggAO+f9OmTUsyHAAAALAy3XTTTXe21h497jkYn9UY0vZKa+2SJJckyebNm9t111035okAAACASVJVt4x7BsZrNV7auTPJhlmWbxzWAQAAAMCCrcaQtj2z3wttrnunAQAAAMC8VmNIuyLJoVX1tKkFVbU5o/ujXTG2qQAAAABY0Sb6HmlVdWCS5wxffnuSg6vqBcPXf9ta211VNye5prX20iRprf1jVf1dkrdU1auTPJDknCQfbK1dtcw/AgAAAACrxESHtCTfmuTtM5ZNff34JDsy+hnWzdjm9CQXJHlTRmfdvTvJGUs2JQAAAACr3kSHtNbajiQ1zzabZll2V5KfHz4AAAAAYJ+txnukAQAAAMCiE9IAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKDD+nEPwOLa9Nr3jHsE1qgdr3/uuEcAAACAJeWMNAAAAADoIKQBAAAAQIeJD2lVdXRVbauq3VV1e1WdXVXrOvbbXFV/V1VfGj6uqqofWo6ZAQAAAFh9JjqkVdXGJFclaUlOSXJ2klclOWue/Q4f9luf5GeHj/VJ3ldVj1vKmQEAAABYnSb9YQMvT3JAktNaa7syCmEHJ9laVecOy2bz3CQHJXl+a+3uJKmqa5PcmeQ5Sf5g6UcHAAAAYDWZ6DPSkpyc5MoZweyyjOLa8XvY7yFJvpHkK9OW3TMsq8UeEgAAAIDVb9JD2lFJtk9f0Fq7NcnuYd1c/mrY5ryq+taq+tYkFyTZmeTtSzQrAAAAAKvYpF/auTHJXbMs3zmsm1Vr7faqekaSdyc5Y1j8+SQntdb+Y7Z9qmpLki1Jcthhh+WGG27Yl7nH5oVH3D/uEVijVurvDAAAAPSa9JC2V6rqsIzOPLs+ycuGxb+Q5D1V9ZThrLb/orV2SZJLkmTz5s3tmGOOWa5xF9Wpl9027hFYo87dsjJ/ZwAAAKDXpIe0nUk2zLJ847BuLq/J6D5pL2itfT1JqurqJJ9J8up88yw1AAAAAOgy6fdI254Z90KrqsOTHJgZ906b4agkn5yKaEnSWvtakk8mOXIJ5gQAAABglZv0kHZFkpOq6qBpy05Pcm+Sa/aw3y1JvruqHjq1oKoeluS7k+xYgjkBAAAAWOUmPaRdnOSrSd5RVc8cHgiwNcn5rbVdUxtV1c1V9cZp+/1xksckeWdVPbeqnpfk8iSHZbgPGgAAAAAsxESHtNbaziQnJlmX5F1JzkpyQZLXzdh0/bDN1H7XJ/nRJAcl+dMkb8noctBntdY+vvSTAwAAALDaTPrDBtJauzHJCfNss2mWZduSbFuisQAAAABYYyb6jDQAAAAAmBRCGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOgw8SGtqo6uqm1Vtbuqbq+qs6tqXee+p1XVP1XVvVX1xap6b1U9fKlnBgAAAGD1meiQVlUbk1yVpCU5JcnZSV6V5KyOfV+W5K1JrkhycpKXJflMkvVLNS8AAAAAq9ekR6WXJzkgyWmttV1J3ldVByfZWlXnDssepKoOSXJBkl9srf3RtFXvXPKJAQAAAFiVJvqMtIzOJLtyRjC7LKO4dvwe9nvh8PonSzUYAAAAAGvLpIe0o5Jsn76gtXZrkt3Durn8UJJPJXlpVf1bVX29qj5SVU9ZulEBAAAAWM0mPaRtTHLXLMt3DuvmcmiS70pyZpL/nuTHknwlyXur6tsWe0gAAAAAVr9Jv0fa3qok35LkJ1pr702Sqro2yS1JXpnkNx+0Q9WWJFuS5LDDDssNN9ywfNMuohcecf+4R2CNWqm/MwAAANBr0kPaziQbZlm+cVi3p/1akvdPLWit7aqq65McPdsOrbVLklySJJs3b27HHHPMXo48Xqdedtu4R2CNOnfLyvydAQAAgF6Tfmnn9sy4F1pVHZ7kwMy4d9oMN2V0VlrNWF5JHljMAQEAAABYGyY9pF2R5KSqOmjastOT3Jvkmj3s9+7h9RlTC6pqQ5LvT/LxxR4SAAAAgNVv0kPaxUm+muQdVfXM4T5mW5Oc31rbNbVRVd1cVW+c+rq1dl2Sv07yxqr6uap6bpK/SfL1JP97OX8AAAAAAFaHiQ5prbWdSU5Msi7Ju5KcleSCJK+bsen6YZvpfibJ5UnOT/KXGUW0E4b3BAAAAIAFmfSHDaS1dmOSE+bZZtMsy+5J8orhAwAAAAD2yUSfkQYAAAAAk0JIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoMPEhraqOrqptVbW7qm6vqrOrat0C9t+vqq6rqlZVz1vKWQEAAABYvdaPe4A9qaqNSa5KcmOSU5IcmeS8jALgmZ1v87Ikj12SAQEAAABYMyb9jLSXJzkgyWmttfe11i5OclaSX62qg+fbeQhxv5PkfyztmAAAAACsdpMe0k5OcmVrbde0ZZdlFNeO79j/t5J8KMm2JZgNAAAAgDVk0kPaUUm2T1/QWrs1ye5h3Zyq6nuSvCTJq5dsOgAAAADWjIm+R1qSjUnummX5zmHdnrwhyUWttZuratN836iqtiTZkiSHHXZYbrjhhoVNOiFeeMT94x6BNWql/s4AAABAr0kPaXulqn4yyXcl+bHefVprlyS5JEk2b97cjjnmmCWabmmdetlt4x6BNercLSvzdwYAAAB6TfqlnTuTbJhl+cZh3YNU1UOS/F6Sc5LsV1WPSDL1YIKHV9VBSzEoAAAAAKvbpIe07ZlxL7SqOjzJgZlx77RpHp7ksUnOzyi27Uzy8WHdZUk+tiSTAgAAALCqTfqlnVckeU1VHdRa+/Kw7PQk9ya5Zo597knyjBnLDk3yf5L8RpKrl2JQAAAAAFa3SQ9pFyc5I8k7quqcJEck2Zrk/NbarqmNqurmJNe01l7aWvtGkvdPf5NpDxv4v621jyz92AAAAACsNhMd0lprO6vqxCQXJXlXRk/wvCCjmDbd+iTrlnc6AAAAANaSiQ5pSdJauzHJCfNss2me9TuS1OJNBcA+2zrbs2RYkK13j3sCWDyOCfvOMQEAltykP2wAAAAAACaCkAYAAAAAHYQ0AAAAAOggpAEAJa2uUAAAGkhJREFUAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6THxIq6qjq2pbVe2uqtur6uyqWjfPPj9QVW+uqpuH/T5VVa+rqv2Xa24AAAAAVpf14x5gT6pqY5KrktyY5JQkRyY5L6MAeOYedj192PacJJ9J8j1Jfmt4/fElHBkAAACAVWqiQ1qSlyc5IMlprbVdSd5XVQcn2VpV5w7LZvP61tqd075+f1Xdl+QPq+pxrbVblnhuAAAAAFaZSb+08+QkV84IZpdlFNeOn2unGRFtyseG18cs3ngAAAAArBWTHtKOSrJ9+oLW2q1Jdg/rFuLYJA8k+ezijAYAAADAWjLpl3ZuTHLXLMt3Duu6VNWhGd1T7U9ba3fMsc2WJFuS5LDDDssNN9yw8GknwAuPuH/cI7BGrdTfGcbo8BePe4KVz+8dq4ljwr5zTACAJTfpIW2fVdVDk7wtyT1JfmWu7VprlyS5JEk2b97cjjnmmOUZcJGdetlt4x6BNercLSvzd4YxuvzScU+w8r30f417Alg8jgn7zjEBAJbcpIe0nUk2zLJ847Buj6qqkrwlyZOSPLW1Nu8+AAAAADCbSQ9p2zPjXmhVdXiSAzPj3mlzuDDJKUme1Vrr2R4AAAAAZjXpDxu4IslJVXXQtGWnJ7k3yTV72rGqfj3JK5P8TGvtg0s3IgAAAABrwaSHtIuTfDXJO6rqmcMDAbYmOb+1tmtqo6q6uareOO3rFyX53Ywu67ytqn542sejl/dHAAAAAGA1mOhLO1trO6vqxCQXJXlXRk/wvCCjmDbd+iTrpn397OH1xcPHdD+f5NLFnRQAAACA1W6iQ1qStNZuTHLCPNtsmvH1i/PggAYAAAAAe23SL+0EAAAAgIkgpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA4T/9ROAPbepte+Z9wjzGnH/uOeYOWb6L/f1z933CMAAMCic0YaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAO68c9AGvHjv1fNO4RVrxN97113CMAAMDS27ph3BOsfFvvHvcEsCo5Iw0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB0mPqRV1dFVta2qdlfV7VV1dlWt69hvQ1W9uap2VtXdVfXnVfWo5ZgZAAAAgNVn/bgH2JOq2pjkqiQ3JjklyZFJzssoAJ45z+5vS/LEJC9L8kCSc5JcnuS4pZoXAGCSbXrte8Y9wpx27D/uCVa+if77ff1zxz0CACyKiQ5pSV6e5IAkp7XWdiV5X1UdnGRrVZ07LHuQqjo2ybOTHN9a+8Cw7LYkH6mqZ7bWrlqm+QEAAABYJSb90s6Tk1w5I5hdllFcO36e/b4wFdGSpLX20SSfG9YBAAAAwIJMekg7Ksn26Qtaa7cm2T2s695vcNM8+wEAAADArCb90s6NSe6aZfnOYd3e7HfEbDtU1ZYkW4Yv76mqTy1gTjrUuAeY3yFJ7hz3EHv2vHEPMKc6Z9wTsNI4JiwGxwRWD8eExeCYAMtsso8LZ62AI+vK9LhxD8B4TXpIWzattUuSXDLuORifqrqutbZ53HMAk8ExAZjOMQGYyXEB1qZJv7RzZ5INsyzfOKxb7P0AAAAAYFaTHtK2Z8Y9zarq8CQHZvZ7oM2532Cue6cBAAAAwB5Neki7IslJVXXQtGWnJ7k3yTXz7HdoVT1takFVbc7o/mhXLMWgrAou7QWmc0wApnNMAGZyXIA1qFpr455hTlW1McmNSf4lyTkZhbDzk1zYWjtz2nY3J7mmtfbSacuuTPKEJK9O8sCw/x2tteOW7ycAAAAAYLWY6DPSWms7k5yYZF2SdyU5K8kFSV43Y9P1wzbTnZ7RWWtvSvKWJNcnef5SzgsAAADA6jXRZ6QBAAAAwKSY6DPSYDlU1dFVta2qdlfV7VV1dlXNPMMRWAOq6jur6g+r6hNVdX9VvX/cMwHjU1U/UVV/U1W3VdU9VXV9Vf3UuOcCxqOqXlBV11bVF6vqvqr6VFWdWVUPHfdswPJZP+4BYJyG+/BdldG9+E5JcmSS8zKKzGfuYVdgdXpSkuck+XCSh4x5FmD8fjXJ55L8SpI7Mzo+vLWqDmmtvWGskwHj8KgkVyf5vSR3JfnBJFuTHJrkleMbC1hOLu1kTauqX0/ya0ke11rbNSz7tQz/QZxaBqwNVbVfa+2B4fO/THJIa+3p450KGJchmN05Y9lbkxzbWnv8mMYCJkhV/U6SX0iysfkf17AmuLSTte7kJFfOCGaXJTkgyfHjGQkYl6mIBpAkMyPa4GNJHrPcswAT64tJXNoJa4iQxlp3VJLt0xe01m5NsntYBwAw3bFJPj3uIYDxqap1VXVgVT0tyRlJ/sDZaLB2uEcaa93GjO5vMNPOYR0AQJKkqk5McmqSl4x7FmCsvpLkYcPnb0nymjHOAiwzZ6QBAMA8qmpTkrcm+evW2qVjHQYYt6ckOS7JqzJ6YNlF4x0HWE7OSGOt25lkwyzLNw7rAIA1rqoemeSKJLck+ekxjwOMWWvtn4dPP1hVdyb5k6o6r7X22XHOBSwPZ6Sx1m3PjHuhVdXhSQ7MjHunAQBrT1UdmOTdGd1M/Hmttd1jHgmYLFNRzZN8YY0Q0ljrrkhyUlUdNG3Z6UnuTXLNeEYCACZBVa1P8vYkT0jyo621O8Y8EjB5njq8fm6sUwDLxqWdrHUXZ/SknXdU1TlJjkiyNcn5rbVd4xwMWH7DmSfPGb789iQHV9ULhq//1pkosOb8fkbHhF9K8qiqetS0dR9rrX11PGMB41BV701yVZJPJrk/o4j2qiR/4bJOWDvKU3pZ66rq6IxuEHpsRk/w/OMkW1tr9491MGDZDTcTn+v/UX58a23Hsg0DjF1V7UjyuDlWOybAGlNVv5Xk+Uk2JflGkn9N8uYkF7fWvj7G0YBlJKQBAAAAQAf3SAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AKBbVb24qlpVvXjcs0ySqvq3qrp5Ed7nz4Y/38cuxlyLrao2VNVFVbWjqr4xzPrd454LAGC5CGkA0GEIBm2ebXYM221anqmoqkOq6oGq+vc51h879XdXVc+YY5tbhvXfsbTTLo3FinidzkvyC0k+nuR3k5yV5I497VBVH5z2dzDXx5nLMDsAwD5bP+4BAIAV5Z1JPpzk8+MeJElaa3dW1SeSfG9VPam19skZm5w4tWmSE5L8/fSVVfWdSb4jyWdaa7fuwyjHD99jtXtekhtba6fsxb5vTjLXn/EH9n4kAIDlI6QBAN1aa3cnuXvcc8xwdZLvzSiUzQxpJyT5bJJdw+e/Ocv6JNm2LwO01j67L/uvBFW1Lsm3JfmXvXyLN7XWPriIIwEALDuXdgLAEquqU4d7X326qr4yfFxfVWdU1YP+W1xVlw6Xuz2+ql5ZVTdW1X3DpaO/UVU1bPcTVfXR4f3uGO5ddcD/b+/eg62syjiOf38aeIlAMVA0RYcYtdtopmJ4VC6OWWZpg0VmXobRYFLMpPJSig2OphZaeUvMIbWsIdFM1JCLoHlBmHFycEALFRRtIEREufn0x1qvvGzefc4+cOCo5/eZYfactd533fZhBh/XelZFeyFpmqRdJd0q6bX8zmOSmvIzH5V0VT7muErSs5KGVLRVmSMtj21BqZ2XcjvPS/pxMeaadyRpZGl+i/IcuhXtNbjERRBsYLlQ0vbAYaRdaFOBgyV1qXm3biBN0rGSJklakufygqRfSOpa8Wzl8UpJO0m6Ls/tHUlzJZ0rqW9ex1vqzEmSRkj6V35vsaQby31LGpyPG+8B9Kk5Klmv3dpOdpd0Q+l7f13SBEkH1jw3E1ibfxxU6mdyI/20RjEvSRdL6ifpfklLVcodV6x3/l0Zm8e/RqUjonntr5Q0P6/hUkkPSBq4KX2amZmZgXekmZmZbQ1XAO8CTwCLgG6kAM61wMHAKXXeuxo4Cvgb8BBwPDAG6CxpaW53IjADOJqUu2pbYHhFWzsBjwJvAn8EugPfAh6UdBhwUy67D+gEDAXukvRyRDze4Dw7AQ8CuwOTSIGXr+dxbk/Kp1X22zzWV4CbgdV5jofkttY02O8jua+jJG0TEe/m8v653yl53ucBRwD3Q4pUAQNIRzJrj3xeRtq9toS0/v8l7XobBXxJ0hcjYkVzg5K0Y273AGA28AdgZ+AS0lHQ5lxD+k7vI63pIOAsoE8uB/g3aU3Py/O/rvT+7BbaR1IfYCawGzAZuJN0zHUI8BVJJ0TEpPz4raR1/CnwH2B8aQxbyuHAz0jf7zigJxv+TmwPTAO6Ag+QvuMFAJK6k37f9wOeBCYAPYCTgMmSzoyIqmBjS32amZlZB6eIjpDOw8zMbPNo/UUDtcGgsnNJQbJ9ImJB6d0+tUf/lHai/R74LtAvIp4o1d0GnAq8CPSPiEW5fCfgeWAHYCVwRETMzXXbAXNIgZY9I+L1UnvF2G8CRhSBJkmnkAIi/yMFHYZExDu5rokUTJgYESeU2jotj/v0iLitVL4A6E0KoH0jIt7O5T2BefmxHhGxpqb9ecChEbEsl3cmBXWagBcjYu/6y73Bej5G2n12cETMymVjgAuBXnm9lgJjI+L8XP9Z4BlgTkR8vtTW0aTA5UzguHyctagbBvwOuDoiRpXKFwLvRMQnS2WjSUGZO4BTIv+jS1JvUqCrOzAuIoaV3rkdOJkUEGqKiIW5vBMwPc/xoIiYXXpno74bXLOHSQHdn0TElaXyJlKAainQOyJW5vKPkIJKD0fE4Fb0M5MU1GwuR9r1xe+spMHAP3L5sIgYV9HmQtJOvAeBE4sxlurHAWcAN0TEiFL5fsBTpEBt34h4udE+zczMzMBHO83MzFrrkmb+dKt6oSp/Vg5mXZt/PKZOXz8vgmj5nWXAvcCOpADB3FLdKuAuoDOwf0VbK4FRpd1akHYgrSXtkhpZBNFyezNIwZwD6oytnnOKIFpu53XgHtLa7Ft67tT8OaYIouXnVwMXtLJPqD7eORCYGxGLI2I5KXhVW19+97055M9h5SBaHt8tpBxhJzcwplOBdcAFRRAtt/EiG+4eqzK6CKLld9aQAlGQduxtFqWbZQeSdpddU67L3/2fgY+TdhS2ldOp/3enZ8XzsxoIaP2wIoi2HfBtUl68C8t1EfEc8BtgO6p3gjbSp5mZmXVgDqSZmZm1QkSo3h/SDrKNSNpF0hWSnpG0osgvBTydH9mjTnezKspeyZ9PV9QVQbeqnE7zIuLNmrmsA14DlkVE1RG9RXXaqueNiNgoTxjwcv7cuVRW5OCqSj7/OOvzcTVqSv4cCCDpY8AX2PDI5lTS7Z7dy8+ycSDtMGAVMFTSpbV/SKkxekmqDJzm/ncm7dB7qdj1VKOlpPtV333VOm6qYv0fiYiqtZ5S81xbaGrm70/VBQZPttDeWxW3tAJ8inTsc045SFvS3Nxa6tPMzMw6OOdIMzMz24LyccyngH1I/5E+nnRkbi0pb9lI0u6YKlW3Y65toK5Tg20V7zRX15p/K1QFLcrj2rZUVgShXqt9OCLWSVrSin4BHgPeBpryMcgjSWOfUnpmGvAjYICkifmZ1aQjpmXdAZF2SjWnC/XXru78WigvVK1l1TpuqmJ8r9apL8p3aoO+NtXiFurrreHmzK2lPs3MzKyDcyDNzMxsyxpGCqKNjohLyxU5yf/I9hjU+8Dy/LkrNQnrJW0L7ML6HXYtiohVOU/aIKAfabdZkIJnhRmkYNRA0u6ubqQdWSs3bI3lwOqIqDpu2Kjy/KrUK99aigDgbnXqe9U81x5aSuRbr35z5ubkwWZmZtYsH+00MzPbsooE8BMq6lq6ufHDbE7+PLyirh+b9j/7ynnSBgLPRMR7O9vyLZuzSvXld8oeB3pI2reiriERsZSUWH8vSXtWPFI17021jtbvUivWvykHLmsNyJ8t3v75PjSXdDT3QEldK+o/yHMzMzOzduZAmpmZ2Za1IH8eVS6UdCCbllT/w2J8/ryonGss39p5+Sa2WRzjHAJ8jg3zoxWmAvux/rKAqkDaL/PnLZJ61VZK6iLp0AbGM54U4Lpckkrv78X6Cw3awhKgZ06y35B8q+xU0i2vZ5frJPUHvpnbvafthrl15Esz7iTtOLysXCepL/B90pHe27f+6MzMzOyDzkc7zczMtqzxwChgrKQBwHygL3Ac8FdSwKLDiYjpkm4GzgSelTQBWAN8lXTk7hXg3WaaqDIrv/vp/POUimemkgKYnwFWUJFcPiIeknQx8HNgvqRJpNstuwB7k3YSTiV9h825Avga8B1gf0mTSXm5TgKmk27EbO0cqzxMSpz/gKQZpCDRnIj4ewvvnUW69OBXko4lXWCxFykQuRY4LSLeaoPxFc6QNLhO3eyIuLcN+xpF2vU3UtIhpPXuQVr7LsDwiHipDfszMzOzDsKBNDMzsy0oIl6R1EQKqhwOHAM8B4wAJtNBA2nZcNJanAV8j7QD6m7gQmAh8EJrGsuXFEwHjicdd6y9RADgUVKgqTMpP9qaOm2NyUGpc4D+pIDYG3lcNwJ3NDCetyQdSQrInQj8gJQP7jLgCVIgbXn9Fho2GuhKCuw1kXbBjQOaDaRFxHxJBwEXA18mHXlcnt+7PCKqbg7dHKc3UzcOaLNAWkQsybsGLwROAM4DVgL/BK6KiMlt1ZeZmZl1LIpwTlUzMzN7/8jH7+YBf4qIoe09ni1B0nDgemBYRIxr7/GYmZmZWWOcI83MzMzahaTdJG1TU7YjMDb/ePfWH1XbkrR7RVlv4CLSUdaWjl+amZmZ2fuIj3aamZlZezkXGCppGvAqsBswCPgEMAn4S/sNrc3ck+8ZmA0sA/YhHcHcARgVEYvbcWxmZmZm1ko+2mlmZmbtQtIg4HzgAKA7KcH9PNKNi2Pr5S/7IJF0NumG0L6kPGYrSEG1X0fExPYcm5mZmZm1ngNpZmZmZmZmZmZmDXCONDMzMzMzMzMzswY4kGZmZmZmZmZmZtYAB9LMzMzMzMzMzMwa4ECamZmZmZmZmZlZAxxIMzMzMzMzMzMza4ADaWZmZmZmZmZmZg34P+VXYaTHZ8kMAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -1239,7 +1319,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1482,12 +1562,12 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1518,7 +1598,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1553,23 +1633,23 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 119, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1580,36 +1660,30 @@ ], "source": [ "success_threshold = .8\n", - "successes = determine_successes_from_ckt_success_probs(avg_pr_succ_arr, success_threshold)\n", + "ckt_success_probs = get_single_target_success_probabilities(noisy_results, ideal_results)\n", + "successes = determine_successes(ckt_success_probs, num_shots)\n", "plot_success(successes, f\"Volumetric Benchmark\\n Random Classical Circuits\\n Pr[Success] > {success_threshold}\")" ] }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 41, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[7, 3, 1, 0, None]\n" - ] - }, { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 134, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1620,8 +1694,6 @@ ], "source": [ "fake_successes = successes\n", - "fake_successes[4][2] = True\n", - "fake_successes[3][5] = False\n", "plot_pareto_frontier(successes, 'Pareto Frontier', widths=[2,3,4,5,6], depths = [2,3,4,5,7,10,15])" ] }, @@ -1644,12 +1716,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1680,12 +1752,12 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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pM8siYHsXBvHqlX5KOkdHxHpJewNXSLo1Iq4qbiBpGbAMYNrcWWOVYTYp1Pnwqr7psEFErM9/NwKXku7J07jNiohYFBGLhmbvUnaIZrUw2qbTbqpKXyQdSbtKmjn6GHg5cHO1UZnVV4TaTlXpl8OrpwCXSoIU84UR8d1qQzKrLzckT1C+rekhVcdh1g8i6t2m0xdJx8zGQwz77JWZlanKNpt2nHTMBoyvvTKzckVq16krJx2zAeSzV2ZWmnBDspmVzYdXZlYqn70ys9JEOOmYWcl8ytzMSuU2HTMrTSBGfPbKzMpU44pOf4ynY2bjEN0bT0fSEkm3SVor6fQm27xW0hpJqyVd2K5M13TMBlEXqjqSpgLLgWOBdcB1klZGxJrCNguBM4CjIuL+PJxwS67pmA2gLtV0FgNrI+L2iHgcuAhY2rDNW4DlEXF/2m9sbFfowNZ0RkI8unWnqsPoyD4zt1QdwrjsNm1r1SF0bIpGqg6hdAGMjHSUVOZIWlWYX9Fwl5V5wF2F+XXAEQ1lPAtA0k+AqcCH243qObBJx2zSCqCzmsy9XbhT7hCwEHgxMB+4StJzI+KBZv/gwyuzARTRfurAemBBYX5+Xla0DlgZEdsi4rfA/5KSUFNOOmaDKDqY2rsOWCjpAEk7AScCKxu2+QaploOkOaTDrdtbFerDK7OB051bzETEdkmnAZeT2mvOi4jVks4GVkXEyrzu5ZLWAMPA+yJic6tynXTMBlGXegdGxGXAZQ3Lzio8DuDdeeqIk47ZoAmIzs5eVcJJx2wgOemYWZlqfPGVk47ZIJqMSUfSdODVwP7F/UTE2b3ap5kxns6BlehlTeebwIPA9UD/9Js3GwCTdRCv+RGxpIflm1kzNT571cseyddIem4PyzezJhTtp6p0vaYj6SbSUeUQ8DeSbicdXonUl+h53d6nmRV0fplDJXpxeHV8D8o0s45pcjUkR8QdAJIuiIiTi+skXQCcPOY/mln3TLKazqiDizN56MM/7eH+zGxUjccu63pDsqQzJG0BnifpIUlb8vxG0ml0M+ul0X467aaKdD3pRMS/RMRM4JMRMSsiZuZpr4g4YyJlS5oq6QZJ3+pSuGYDaVKdvSo4U9JfAUeTcu//RMQ3JljmO4BbgFkTDc5soNW4TaeX/XSWA6cCNwE3A6dKWr6jhUmaD7wSOLc74ZlZFXpZ0zkGODAP8oOk84HVEyjv34H3AzObbSBpGbAMYGjO7Ansyqy/VXn41E4vazprgX0L8wvysnGTdDywMSKub7VdRKyIiEURsWjq7F12ZFdm/S9Il0G0myrSy5rOTOAWST8nvQyLgVWSVgJExAnjKOso4ARJxwEzgFmSvhQRb+h20GYDocY1nV4mnbPab9KZfNbrDABJLwbe64Rj1lydD696lnQi4seS9gMWRsT3Je0MDEVEf93O0qwf1Tjp9KxNR9JbgEuAz+VF80n3yJmQiPhRRPj6LrNWunPfq57oZUPy20ltMQ8BRMSvgb17uD8zo7OOgYPaOXBrRDwupVZySUPUutJnNkAm6SBeP5Z0JrCzpGOBrwL/3cP9mVlW55pOL5PO6cAmUo/kt5LuEvjBHu7PzEbVuE2nl2evRiR9A/hGRGzq1X7MrEHFNZl2ejG0hSR9WNK9wG3AbZI2Sepavx0za6PGNZ1eHF69i3TW6vCI2DMi9gSOAI6S9K4e7M/MGmik/VSVXiSdk4GTIuK3owsi4nbgDcAbe7A/M+sjvWjTmRYR9zYujIhNkqb1YH9m1qjGbTq9SDqP7+A6M+uGmjck9yLpHCLpoTGWi3SFuJn12mRKOhExtdtlmtk4TaakY2bVEtWenWqnlz2SzawKXbzgU9ISSbdJWivp9BbbvVpSSFrUrkwnHbNB1IXOgfkGmcuBVwAHASdJOmiM7WaS7tTys05Cc9IxG0Td6ZG8GFgbEbdHxOPARcDSMbb7KPBx4LFOCh3YNh0BQ0PDVYfRkaE6H4CP4ZHtO1UdQsdGdpqcv6sdHj7NkbSqML8iIlYU5ucBdxXm15GuLnhiP9LzgQUR8W1J7+tkpwObdMwmtc6Szr0R0bYNphlJU4BPAW8ez/856ZgNmuja2av1pFtHjZqfl42aCTwH+FEerO+pwEpJJ0REsQb1JE46ZoOoO/10rgMWSjqAlGxOBF73h11EPAjMGZ2X9CPSnVqaJhxwQ7LZQOrGKfOI2A6cBlwO3AJcHBGrJZ0taTz3rXsS13TMBlGXeiRHxGWkUT+Ly8YcGysiXtxJmU46ZoOm4kG62nHSMRswYvJdZW5mFXPSMbNyOemYWamcdMysNJNw5EAzq5qTjpmVqc7XEDvpmA0gH16ZWXncOdDMSuekY2ZlcY/kLpA0A7gKmE6K+ZKI+FC1UZnVl0bqm3X6IukAW4FjIuLhfGviqyV9JyKurTows9pxm87ERUQAD+fZaXmq8ctqVq06H171zSBekqZK+iWwEbgiIjq63YXZpNSdu0H0RN8knYgYjohDSeO0Lpb0nMZtJC2TtErSquGHHik/SLOa6NbN9nqhb5LOqIh4ALgSWDLGuhURsSgiFk2dtUv5wZnVhWs6EyNprqTd8+OdgWOBW6uNyqym8t0g2k1V6YuGZGAf4Px8m9MppAGiv1VxTGa15H46XRARNwKHVR2HWd+I+madvkg6ZjY+rumYWXncOdDMyubxdMysVE46ZlaewA3JZlYuNySbWbmcdMysLO4caGblivAgXmZWsvrmHCcds0HkwyszK08APrwys1LVN+f0x3g6ZjY+3Ro5UNISSbdJWivp9DHWv1vSGkk3SvqBpP3alemkYzaANBJtp7ZlpPGrlgOvAA4CTpJ0UMNmNwCLIuJ5wCXAJ9qV66RjNmg6Gaq0s5rOYmBtRNweEY8DFwFLn7SriCsjYnRA8mtJY5i3NNBtOsPD/ZFTt2ybXnUI47LH9P4Z9H5oynDVIZQudQ7sKKvMkbSqML8iIlYU5ucBdxXm1wFHtCjvFOA77XY60EnHbNLq7CrzeyNiUTd2J+kNwCLgRe22ddIxG0Ad1nTaWQ8sKMzPz8uevC/pZcAHgBdFxNZ2hfbH8YeZda57bTrXAQslHSBpJ+BEYGVxA0mHAZ8DToiIjZ0U6pqO2cDpzrVXEbFd0mnA5cBU4LyIWC3pbGBVRKwEPgnsBnxVEsCdEXFCq3KddMwGUZcG8YqIy4DLGpadVXj8svGW6aRjNmjCw5WaWdk8XKmZlaq+OcdJx2wQaaS+x1dOOmaDJui0c2AlnHTMBoyIbnUO7AknHbNB5KRjZqVy0jGz0rhNx8zK5rNXZlai8OGVmZUocNIxs5LV9+iqP8bTkbRA0pV51PnVkt5RdUxmdaaItlNV+qWmsx14T0T8QtJM4HpJV0TEmqoDM6slH15NTETcDdydH2+RdAtp0GgnHbNGETBc3+Orvkg6RZL2Bw4DfjbGumXAMoChubNLjcusVmpc0+mLNp1RknYDvga8MyIealwfESsiYlFELBqatUv5AZrVRUT7qSJ9U9ORNI2UcL4cEV+vOh6z2gqgC2Mk90pfJB2lEZ+/ANwSEZ+qOh6zeguI+rbp9Mvh1VHAycAxkn6Zp+OqDsqsloLUkNxuqkhf1HQi4mrS3VLNrBM1bkjui6RjZuPkpGNm5fEFn2ZWpgA8tIWZlco1HTMrjy+DMLMyBUSN++k46ZgNIvdINrNSuU3HzEoT4bNXZlYy13TMrDxBDA9XHURTTjpmg8ZDW5hZ6Wp8yrxfhrYwsw4FECPRduqEpCWSbpO0VtLpY6yfLum/8vqf5eGEW3LSMRs0kQfxaje1IWkqsBx4BXAQcJKkgxo2OwW4PyKeCXwa+Hi7cp10zAZQDA+3nTqwGFgbEbdHxOPARcDShm2WAufnx5cAL80jfTalqPGptYmQtAm4owdFzwHu7UG5vdBPsUJ/xdurWPeLiLkTKUDSd0nxtTMDeKwwvyIiVhTKeQ2wJCL+Ls+fDBwREacVtrk5b7Muz/8mb9P0tRnYhuSJvnHNSFoVEYt6UXa39VOs0F/x1jnWiFhSdQyt+PDKzJpZDywozM/Py8bcRtIQMBvY3KpQJx0za+Y6YKGkAyTtBJwIrGzYZiXwpvz4NcAPo02bzcAeXvXQivab1EY/xQr9FW8/xbpDImK7pNOAy4GpwHkRsVrS2cCqiFhJujXUBZLWAveRElNLA9uQbGb15MMrMyuVk46ZlcpJpwOSFki6UtIaSaslvaPqmFqRNEPSzyX9Ksf7kapjakfSVEk3SPpW1bG0I+l3km7Kd5pdVXU8/cYNyZ3ZDrwnIn4haSZwvaQrImJN1YE1sRU4JiIeljQNuFrSdyLi2qoDa+EdwC3ArKoD6dBLWnWAs+Zc0+lARNwdEb/Ij7eQvhzzqo2quUgezrPT8lTbMwaS5gOvBM6tOhbrPSedccpX0R4G/KzaSFrLhyu/BDYCV0REneP9d+D9QH3HY3iyAL4n6XpJy6oOpt846YyDpN2ArwHvjIiHqo6nlYgYjohDSb1IF0t6TtUxjUXS8cDGiLi+6ljG4eiIeD7p6uu3S3ph1QH1EyedDuW2ka8BX46Ir1cdT6ci4gHgSqCu1+McBZwg6Xekq5iPkfSlakNqLSLW578bgUtJV2Nbh5x0OpAv1f8CcEtEfKrqeNqRNFfS7vnxzsCxwK3VRjW2iDgjIuZHxP6k3qw/jIg3VBxWU5J2zScTkLQr8HLg5mqj6i8+e9WZo4CTgZtyOwnAmRFxWYUxtbIPcH4ehGkKcHFE1P5UdJ94CnBpHjJmCLgwIr5bbUj9xZdBmFmpfHhlZqVy0jGzUjnpmFmpnHTMrFROOmZWKiedSUzSpyW9szB/uaRzC/P/JulMSZc0+f8fSVqUH59ZWL5/vkuA2R9x0pncfgIcCSBpCum2JQcX1h9J6qz3mg7KOrP9JmZOOpPdNcAL8uODST1rt0jaQ9J04EDgvtFai6SdJV0k6RZJlwI75+XnADvn8WW+nMubKunzeTyf7+W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\n", 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" ] @@ -1891,8 +1963,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0111\n", - "The estimated product of the one and two qubit fidelity is F = 0.9889\n" + "The estimated error is p = 0.0114\n", + "The estimated product of the one and two qubit fidelity is F = 0.9886\n" ] } ], @@ -1914,12 +1986,12 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1950,12 +2022,12 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2018,7 +2090,7 @@ { "data": { "text/plain": [ - "array([0.05850703, 0.00244478])" + "array([0.05860858, 0.00311172])" ] }, "execution_count": 58, @@ -2039,11 +2111,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.88208017 0.83047228 0.78188381 0.73613811]\n", - " [0.87992368 0.82844195 0.77997228 0.73433842]\n", - " [0.87777246 0.8264166 0.77806542 0.73254312]\n", - " [0.8756265 0.82439619 0.77616322 0.73075222]\n", - " [0.86497514 0.81436802 0.76672176 0.72186315]]\n" + "[[0.88071106 0.82909383 0.78050182 0.73475771]\n", + " [0.87797053 0.82651392 0.77807311 0.73247135]\n", + " [0.87523853 0.82394204 0.77565196 0.7301921 ]\n", + " [0.87251503 0.82137816 0.77323835 0.72791995]\n", + " [0.85902413 0.80867795 0.76128248 0.71666479]]\n" ] } ], @@ -2055,12 +2127,12 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index e43432cd..80f101d3 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -66,6 +66,11 @@ class CircuitTemplate: Blume-Kohout and Young. arXiv:1904.05546v2 (2019) https://arxiv.org/pdf/1904.05546.pdf + + .. [QVol] Validating quantum computers using randomized model circuits. + Cross et al. + arXiv:1811.12926v1 (2018). + https://arxiv.org/abs/1811.12926 """ generators: List[Callable] = field(default_factory=lambda : []) sequence_transforms: List[Callable] = field(default_factory=lambda : []) @@ -612,10 +617,48 @@ def get_single_target_success_probabilities(noisy_results, ideal_results, for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} -def determine_successes_from_ckt_success_probs(ckt_success_probs, - threshold_probability: float = 2/3): - return {w: {d: prob > threshold_probability for d, prob in d_ckt_succ_probs.items()} - for w, d_ckt_succ_probs in ckt_success_probs.items()} +def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_shots: int) \ + -> Tuple[float, float]: + """ + Helper to calculate the estimate for the probability of sampling a successful output at a + particular depth as well as the 2 sigma one-sided confidence interval on this estimate. + + :param num_success: total number of successful outputs sampled at particular depth across all + circuits and shots + :param num_circuits: the total number of model circuits of a particular depth and width whose + output was sampled + :param num_shots: the total number of shots taken for each circuit + :return: estimate for the probability of sampling a successful output at a particular depth as + well as the 2 sigma one-sided confidence interval on this estimate. + """ + total_sampled_outputs = num_circuits * num_shots + prob_sample_heavy = num_success / total_sampled_outputs + + # Eq. (C3) of [QVol]. Assume that num_heavy/num_shots is worst-case binomial with param + # num_circuits and take gaussian approximation. Get 2 sigma one-sided confidence interval. + one_sided_confidence_interval = prob_sample_heavy - \ + 2 * np.sqrt(num_success * (num_shots - num_success / num_circuits)) / total_sampled_outputs + + return prob_sample_heavy, one_sided_confidence_interval + + +def determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt): + return {w: + {d: + calculate_success_prob_est_and_err( + sum(np.asarray(succ_probs) * num_shots_per_ckt), + len(succ_probs), + num_shots_per_ckt + )[1] for d, succ_probs in d_ckt_succ_probs.items() + } for w, d_ckt_succ_probs in ckt_success_probs.items() + } + + +def determine_successes(ckt_success_probs, num_shots_per_ckt, + success_threshold: float = 2 / 3): + lower_bounds = determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt) + return {w: {d: lb > success_threshold for d, lb in d_lower_bounds.items()} + for w, d_lower_bounds in lower_bounds.items()} def average_distributions(distrs): @@ -851,10 +894,10 @@ def plot_pareto_frontier(successes, title, widths=None, depths=None): ax.set_xlabel('Width') ax.set_ylabel('Depth') - min_depth_failure_at_width = [] + min_depth_idx_failure_at_width = [] for w_idx, w in enumerate(widths): if w not in successes.keys(): - min_depth_failure_at_width.append(None) + min_depth_idx_failure_at_width.append(None) continue depth_succ = successes[w] @@ -865,23 +908,33 @@ def plot_pareto_frontier(successes, title, widths=None, depths=None): if not depth_succ[d]: min_depth_failure = d_idx break - min_depth_failure_at_width.append(min_depth_failure) + min_depth_idx_failure_at_width.append(min_depth_failure) - for idx, depth in enumerate(min_depth_failure_at_width): - if depth is None: - continue # the depth was not determined, so leave this boundary open + for w_idx, failure_idx in enumerate(min_depth_idx_failure_at_width): + if failure_idx is None: + continue # this width was not measured, so leave the boundary open # horizontal line for this width - if depth < len(depths): - ax.plot((idx - margin, idx + margin), (depth - margin, depth - margin), color='black') + if failure_idx < len(depths): # measured a failure + ax.plot((w_idx - margin, w_idx + margin), (failure_idx - margin, failure_idx - margin), + color='black') # vertical lines - if idx < len(min_depth_failure_at_width) - 1: + if w_idx < len(widths) - 1: # check not at max width for d_idx in range(len(depths)): - if depths[d_idx] not in [d for d in successes[widths[idx]].keys()]: + # check that the current depth was measured for this width + if depths[d_idx] not in [d for d in successes[widths[w_idx]].keys()]: continue # do not plot line if this depth was not measured - if depth > d_idx >= min_depth_failure_at_width[idx + 1]: - ax.plot((idx + margin, idx + margin), (d_idx - margin, d_idx + margin), + + # if the adjacent width is not measured leave the boundary open + if min_depth_idx_failure_at_width[w_idx + 1] is None: + continue + + # check if in the interior but adjacent to exterior + # or if in the exterior but adjacent to interior + if failure_idx > d_idx >= min_depth_idx_failure_at_width[w_idx + 1] \ + or failure_idx <= d_idx < min_depth_idx_failure_at_width[w_idx + 1]: + ax.plot((w_idx + margin, w_idx + margin), (d_idx - margin, d_idx + margin), color='black') ax.set_title(title) From e1e6e0de00480086c1f0fd2b8d84014a17d50cc3 Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 16 Aug 2019 15:34:53 -0400 Subject: [PATCH 32/49] Get quantum volume functional. --- forest/benchmarking/volumetrics.py | 156 ++++++++++++++++++++++++++--- 1 file changed, 140 insertions(+), 16 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 80f101d3..66c3cc79 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -8,6 +8,7 @@ from scipy.special import comb from dataclasses import dataclass, field import matplotlib.pyplot as plt +from statistics import median from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate from pyquil.quilatom import QubitPlaceholder @@ -15,12 +16,14 @@ from pyquil.api import QuantumComputer, BenchmarkConnection from pyquil.gates import * from pyquil.paulis import exponential_map, sX, sZ +from pyquil.numpy_simulator import NumpyWavefunctionSimulator from rpcq.messages import TargetDevice from rpcq._utils import RPCErrorError from forest.benchmarking.randomized_benchmarking import get_rb_gateset from forest.benchmarking.distance_measures import total_variation_distance as tvd from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary +from forest.benchmarking.utils import bit_array_to_int def make_default_pattern(num_generators): @@ -503,17 +506,30 @@ def func(graph, qc, sequence, **kwargs): # ================================================================================================== # Data acquisition # ================================================================================================== +def sample_random_connected_graphs(graph: nx.Graph, widths: List[int], num_ckts_per_width): + samples = {w: [] for w in widths} + for w in widths: + connected_subgraphs = generate_connected_subgraphs(graph, w) + random_indices = np.random.choice(range(len(connected_subgraphs)), size=num_ckts_per_width) + samples[w] = [connected_subgraphs[idx] for idx in random_indices] + return samples + + def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, - graph: nx.Graph = None, pattern = None): - if graph is None: - graph = qc.qubit_topology() + graphs: Dict[int, List[nx.Graph]] = None, pattern = None): + if graphs is None: + graphs = sample_random_connected_graphs(qc.qubit_topology(), widths, + len(depths)*num_circuit_samples) programs = {width: {depth: [] for depth in depths} for width in widths} for width, depth_array in programs.items(): + circuit_number = 0 for depth, prog_list in depth_array.items(): for _ in range(num_circuit_samples): + graph = graphs[width][circuit_number] + circuit_number += 1 prog = ckt.sample_program(graph, repetitions=depth, width=width, qc=qc, pattern=pattern) prog_list.append(prog) @@ -522,8 +538,9 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = 500, - use_active_reset: bool = False, - use_compiler: bool = False): + measure_qubits: Dict[int, List[int]] = None, + use_active_reset: bool = False, + use_compiler: bool = False): reset_prog = Program() if use_active_reset: reset_prog += RESET() @@ -533,11 +550,13 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = for width, depth_array in program_array.items(): for depth, prog_list in depth_array.items(): - for program in prog_list: + for idx, program in enumerate(prog_list): prog = program.copy() - # TODO: provide some way to ensure spectator qubits measured when relevant. - qubits = sorted(list(program.get_qubits())) + if measure_qubits is not None: + qubits = measure_qubits[width][depth][idx] + else: + qubits = sorted(list(program.get_qubits())) ro = prog.declare('ro', 'BIT', len(qubits)) for idx, q in enumerate(qubits): @@ -554,6 +573,54 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = return results + +def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, + measure_qubits: Dict[int, List[int]] = None): + """ + Collects and returns those 'heavy' bitstrings which are output with greater than median + probability among all possible bitstrings on the given qubits. + + The method uses the provided wfn_sim to calculate the probability of measuring each bitstring + from the output of the circuit comprised of the given permutations and gates. + + :param wfn_sim: a NumpyWavefunctionSimulator that can simulate the provided program + :return: a list of the heavy outputs of the circuit, represented as ints + """ + heavy_output_array = {w: {d: [] for d in d_arr.keys()} for w, d_arr in program_array.items()} + + for w, d_progs in program_array.items(): + for d, ckts in d_progs.items(): + for idx, ckt in enumerate(ckts): + wfn_sim.reset() + for gate in ckt: + wfn_sim.do_gate(gate) + + if measure_qubits is not None: + qubits = measure_qubits[w][d][idx] + else: + qubits = sorted(list(ckt.get_qubits())) + + # Note that probabilities are ordered lexicographically with qubit 0 leftmost. + # we need to restrict attention to the subset `qubits` + probs = abs(wfn_sim.wf)**2 + probs = probs.reshape([2]*wfn_sim.n_qubits) + marginal = probs + for q in reversed(range(wfn_sim.n_qubits)): + if q in qubits: + continue + marginal = np.sum(marginal, axis=q) + + probabilities = marginal.reshape(-1) + + median_prob = median(probabilities) + + # store the integer indices, which implicitly represent the bitstring outcome. + heavy_outputs = [idx for idx, prob in enumerate(probabilities) if prob > median_prob] + heavy_output_array[w][d].append(heavy_outputs) + + return heavy_output_array + + # TODO: # def do_volumetric_measurements(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], # depths: List[int], @@ -573,10 +640,10 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = # ================================================================================================== def get_error_hamming_weight_distributions(noisy_results, ideal_results): - # allow for ideal result to depend only on width (pass in a list) - if not isinstance(ideal_results, dict): - ideal_results = {width: {depth: ideal_results[width] for depth in depth_array.keys()} - for width, depth_array in noisy_results.items()} + # allow for ideal result to depend only on width (pass in a dict {w: result}) + # if not isinstance(ideal_results.values()[0], dict): + # ideal_results = {width: {depth: ideal_results[width] for depth in depth_array.keys()} + # for width, depth_array in noisy_results.items()} distrs = {width: {depth: [] for depth in depth_array.keys()} for width, depth_array in noisy_results.items()} @@ -617,6 +684,63 @@ def get_single_target_success_probabilities(noisy_results, ideal_results, for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} +def get_success_probabilities(noisy_results, ideal_results): + prob_success = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in noisy_results.items()} + + for width, depth_array in prob_success.items(): + for depth, samples in depth_array.items(): + + noisy_ckt_sample_results = noisy_results[width][depth] + ideal_ckt_sample_results = ideal_results[width][depth] + + for noisy_shots, ideal_results in zip(noisy_ckt_sample_results, + ideal_ckt_sample_results): + targets = ideal_results + if not isinstance(ideal_results[0], int): + targets = [bit_array_to_int(res) for res in ideal_results] + + pr_success = 0 + # determine if each result bitstring is a success, i.e. matches an ideal_result + for result in noisy_shots: + # convert result to int for comparison with heavy outputs. + output = bit_array_to_int(result) + if output in targets: + pr_success += 1 / len(noisy_shots) + prob_success[width][depth].append(pr_success) + + return prob_success + + +def count_heavy_hitters_sampled(noisy_results, heavy_hitters): + """ + Simple helper to count the number of heavy hitters sampled given the sampled results for a + number of circuits along with the the actual heavy hitters for each circuit. + + :param noisy_results: results from running each circuit on a quantum computer. + :param heavy_hitters: the heavy hitters for each circuit (presumably calculated through + simulating the circuit classically) + :return: the number of samples which were heavy for each circuit. + """ + num_sampled = {w: {d: [] for d in depth_array.keys()} + for w, depth_array in noisy_results.items()} + + for w, d_results in noisy_results.items(): + for d, ckts_results in d_results.items(): + ckts_hh = heavy_hitters[w][d] + for ckt_results, ckt_hh in zip(ckts_results, ckts_hh): + num_hh = 0 + # determine if each result bitstring is a heavy output, as determined from simulation + for result in ckt_results: + # convert result to int for comparison with heavy outputs. + output = bit_array_to_int(result) + if output in ckt_hh: + num_hh += 1 + num_sampled[w][d].append(num_hh) + + return num_sampled + + def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_shots: int) \ -> Tuple[float, float]: """ @@ -958,18 +1082,18 @@ def basement_function(number: float): # ================================================================================================== # Graph tools # ================================================================================================== -def generate_connected_subgraphs(G: nx.Graph, n_vert: int): +def generate_connected_subgraphs(graph: nx.Graph, n_vert: int): """ Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all subgraphs with n_vert connect vertices. :params n_vert: number of vertices of connected subgraph. - :params G: networkx Graph + :params graph: networkx graph :returns: list of subgraphs with n_vert connected vertices """ subgraph_list = [] - for sub_nodes in itertools.combinations(G.nodes(), n_vert): - subg = G.subgraph(sub_nodes) + for sub_nodes in itertools.combinations(graph.nodes(), n_vert): + subg = graph.subgraph(sub_nodes) if nx.is_connected(subg): subgraph_list.append(subg) return subgraph_list From 9d6f2e0ee32c126e795fdfb461eefde6fbea9048 Mon Sep 17 00:00:00 2001 From: Kyle Date: Fri, 16 Aug 2019 21:17:22 -0400 Subject: [PATCH 33/49] Update notebook --- examples/volumetrics.ipynb | 1135 ++++++++++++++++++------------------ 1 file changed, 572 insertions(+), 563 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index 71686196..d828df00 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -76,7 +76,7 @@ }, { "data": { - "image/png": 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cP36c77//Xv77sxJSfGbgzJkz+mHyTZs24ebmpg9/vvfee1XHsygjR47knnvuYc6cOaqjCCtRUFCAu7s7r732Go888ojqOMIIpPgUOXDggH6+LjU1lX79+umHyW+77TbV8SzWyZMncXZ2Zt26dbi5uamOI6zE9u3bCQwMZPv27XJm1ApI8dUQTdPYsWOHfr/uxIkTDBkyhJCQEPr16ydLc0a0bNkyPv74Y7Zu3SrTN4TRvPPOO0RHR7N+/XoZk2fhpPhMqKioiNjYWMLCwggPD6dBgwb6/TpPT0+rO0xuLjRNo3///gQGBvLiiy+qjiOsRElJCX369GHkyJE8//zzquOIapDiM7Lz58+zdu1aQkNDWbt2LR06dNDLrlOnTnJzvIbs27ePnj17sm3bNrlPKoxm7969eHp6EhcXJ08GsWBSfEZw7NgxwsPDCQ0NJSEhAW9vb/0wuTzYUp0PPviADRs2sG7dOvmDQxjNZ599xpIlS0hMTJQjRRZKiu8maJrGb7/9pm9O2bt3L0FBQYSEhDBgwAAaNWqkOqLAsDTVo0cPpkyZwuOPP646jrASmqYxZMgQXF1d+c9//qM6jrgJUnxVVFpaSkJCgr45paioiODgYEJCQujTpw9169ZVHVGUY8eOHQQGBpKZmcntt9+uOo6wEidOnMDV1ZVff/2VXr16qY4jbpAUXyXy8/PZsGEDYWFhrF69mrvuukufnOLq6irLZxbi5Zdf5siRI3z77beqowgr8ssvv/Dyyy+TlpZGw4YNVccRN8Byii8nB5Ytg4wMyM2FJk3AxQXGjgUjPkT19OnTrF69mtDQUKKjo3F3d9cPk99zzz1G+z6i5uTn5+Ps7MzChQsJCgpSHUdYkTFjxlC/fn2WLFmiOoq4AeZffMnJMHs2REYa/rmw8O/P2duDpkFgIEyfDjf57Kx9+/bp9+vS09Pp378/ISEhBAUFyXw+K7Fx40aefPJJdu7cKfdghdHk5ubStWtXPvnkEwYNGqQ6jqgi8y6+RYtg6lQoKDAUXEXs7Awl+OGHMGHCdd9W0zRSUlL0+3WnTp1i6NChBAcH069fPxo0aGDEH0KYi7Fjx9K4cWM++ugj1VGEFYmNjeXhhx8mPT2dFkZcfRKmY77Fd7n08vOr/jUODhWWX1FREdHR0fph8oYNG+rn63r27CmTGGzAmTNncHJyIjQ0lJ49e6qOI6zISy+9xL59+1i5cqXc+7cA5ll8ycng61tu6X0PvAkcAloBy4A+V77AwQFiY6F7d3Jzc4mIiCAsLIx169bh6Oiol13Hjh1N/3MIs/P999/zzjvvsH37djmDJYzm0qVLeHh48OKLLzJ69GjVccR1mGfxDR8OoaH/WN7cADwF/AD0AI7/9fErnzeu2dmx38WFCbffztatW/Hx8dEPk7ds2bJG4gvzdfkMlqenJ6+99prqOMKKpKen88ADD5CcnCwb4cyc+RVfTg60bXv1Jpa/9Aae/Ot/lSmqVYt1n3+O34gRss1Y/MOhQ4dwd3cnPj6eTp06qY4jrMj777/PmjVriIqKklm8Zsz8bmwtW1buh0uBFOAUcB/QGngOKCjntfXq12fImTNSeqJcd999NzNnzmTcuHGUlZWpjiOsyIsvvoimacybN091FFEJ8yu+jIxyr/ZOAsXAz0A8kAakAuUODCoogMxME4YUlm7ixIkUFxfz+eefq44irEjt2rVZvnw57733HhkZGarjiAqYX/Hl5pb74ctPq5sE3AE0B14AIip6n7NnjZ1MWJHatWvzxRdf8Nprr3Hs2DHVcYQVadeuHe+//z6jRo3i0qVLquOIcphf8TVpUu6Hm2JY3rxyo3Clm4abNjVeJmGVunTpwsSJE3nuuedURxFWZsyYMbRv356ZM2eqjiLKYX7F5+ICFRwgHwssBHKAs8A8YHB5L7S3B2dnUyUUVmTGjBlkZWXxyy+/qI4irIidnR2fffYZX331FXFxcarjiGtY1K7OYuB54FugATACeP+v/3+VBg3g0CGjzvAU1mvz5s2MHDmSnTt3cuutt6qOI6zIqlWrmDx5Munp6TRu3Fh1HPEX8ys+qPAcX5XY2cGwYbBypfFzCas1ceJESktLZdiwMLpx48ZRUlLCl19+afhADQ3cFxUzz+KrZHLLdV0xuUWIqsrNzcXJyYkVK1bQt29f1XGEFcnLy6Nr1658MX48flu3mnTgvqga8yw+MPqsTiGuJywsjJdffpn09HQZVC6Mau/Uqdw5dy72dnbYGXHgvrg55lt8UOWnM5QCtRwcsJN/WUQ1PfTQQ3Ts2JH//KfcE6JC3Dj5I97smHfxAaSkGJ7HFxFh+Guo4IpZLfb2aJpGdIMGXHrhBQJff11dTmEVjh8/TteuXdm4cSMuLi6q4whLV85tm48xDNfPBB7+6/+XS27bmIz5F99lp04ZbghnZhoOpzdtajiyMGYMiXv38tBDD5GVlSUPGRXV9sUXX/DZZ5+RmJgo8xZF9ZSzUe8XDOfI1mEYubisoq+VjXomYznFdx2jR4/mjjvuYM6cOaqjCAunaRr+/v6EhITw/PPPq44jLFUlR7MAXgOOUEnxgRzNMhHzO8B+k+bMmcMXX3zBnj17VEcRFu7y4eO3336bgwcPqo4jLFUFA/dviJ2dcd5HXMVqiu+OO+5g2rRpTJkyRXUUYQXuv/9+XnzxRSZMmICVLIqImlbBwP0bIgP3TcJqig9g8uTJ7Nu3jzVr1qiOIqzA1KlTOXbsGN99953qKMISVTBw/4bJwH2js6riq1evHvPnz2fKlCkyFV1UW926dfniiy944YUXOH36tOo4woKUlJSQU1RknDeTgftGZ1XFBzBw4EA6d+4sD4IURuHh4cEjjzzCCy+8oDqKMHMHDhxgyZIlPPjgg7Ro0YJvMjMprlPnH68rAQoxnD8u/ev/l1T0pjJw3ySsZlfnlfbv30+PHj1IT0/nrrvuUh1HWLiLFy/i5OTEkiVLCAgIUB1HmInz588TExPDunXrWL9+PRcuXCAgIICAgAAeeOABWtWqVe6uzjeAN695r1l/ffwfZFenSVhl8QG89tprHDhwgG+++UZ1FGEF1q1bxzPPPMPOnTu55ZZbVMcRCpSWlrJjxw696FJTU/H09NTLztnZmVq1rllEk4H7Zslqi+/ixYs4OjryzTff0KdPH9VxhBV47LHHuP3225k7d67qKKKGHD58mPXr17N+/Xo2bdpEq1at9KLz8fHBwcGh8jeQgftmyWqLD+CHH35g9uzZbN++XSZwiGo7ffo0Tk5OrF69mu7yy8gqXbx4kdjYWL3scnJy6N+/PwEBAfTv35/WrVvf+JvKrE6zY9XFp2kafn5+/Pvf/2aC/AskjOCbb77hgw8+IDk5mbp166qOI6qprKyM9PR0vei2bduGu7s7AQEBDBgwADc3t38uX94MGbhvVqy6+AAyMjLo378/v/32G7fddpvqOMLCaZpGYGAgvr6+TJs2TXUccROOHz/Ohg0bWL9+PRs2bODWW2/Vi65v376mm/d7nYH7aBqJTZuSPWwYYz/5xDQZBGADxQcwadIkSktL+fTTT1VHEVbg4MGDdO/encTERO6//37VccR1FBQUsHnzZv2q7vDhw/j7+zNgwAD69+/PPffcU7OBKhm4v//CBTw8PEhPT7+5ZVVRJTZRfH/++SeOjo6sW7cOV1dX1XGEFZg/fz5hYWFERUVhZ2enOo64gqZp7Nq1Sy+6LVu24OLiwoABAwgICKB79+7UKed8nbl47bXX2L9/P99++63qKFbLJooP4LPPPmPFihXExsbKLypRbaWlpfTq1Yvx48fz5JNPqo5j806dOsXGjRv1sqtfv75edH5+ftx6662qI1aZ7Eg3PZspvtLSUnr06MHUqVN5+OGHVccRViA9PZ3+/fuTkZFBq1atVMexKUVFRSQkJOhn6vbt24evr69+1KB9+/YW/Qeu7Eg3LZspPoAtW7bw73//m+zsbBo2bKg6jrACr776Knv27OHHH39UHcWqaZrG77//rhddXFwcjo6OetF5enpa1S5bTdPw9fVl5MiRsiPdBGyq+MBwCLlNmza8++67qqMIK1BYWIiLiwsffPABwcHBquNYlT///JNNmzbpy5dlZWX68mW/fv2sfpf25RWFrKwsq/9Za5rNFd+xY8dwcXFh69at3HfffarjCCsQGxvLqFGj2LVrF40bN1Ydx2IVFxeTlJSkF91vv/1Gnz599Ku6Tp06WfTy5c147rnn0DSNT+R4g1HZXPEBvP/++8THx7Nq1SrVUYSVGDduHHXr1pVfUDdo3759etFFR0dz77336mfqevfuTf369VVHVOryjvT169fTtWtX1XGshk0W36VLl3B2dmb+/PkEBQWpjiOswLlz5+jSpQs//vgjXl5equOYrdzcXKKjo/Wyu3jxol50DzzwALfffrvqiGZn8eLFfPvtt7Ij3YhssvgAIiMjef7558nMzLT5vyqFcaxcuZLXXnuNtLQ0+XfqL6WlpaSkpOhFl5aWRq9evfR7dU5OTvLL/DpKS0txd3dn2rRpjBw5UnUcq2CzxQcwZMgQvL29eeWVV1RHEVZA0zSGDx9O165deeONN1THUebQoUNXPdHgzjvv1IuuT58+2Nvbq45oceLj43nkkUfIzs6Wx2IZgU0X3969e/H09CQjI4M777xTdRxhBY4ePYqrqysxMTF06dJFdZwakZeXR2xsrH7U4M8//7zqiQby35ZxPProo7Rr147//Oc/qqNYPJsuPoAZM2Zw6NAhVqxYoTqKsBKLFy/mq6++YvPmzcaZ7G9mysrKSE1N1a/qUlJS8PDw0Hdfurq6WuXPrdrRo0fp2rUrSUlJtG/fXnUci2bzxZeXl4ejoyPff/+9bEoQRlFWVkbfvn0ZOXIkzz77rOo4RnH06NGrnmjQvHlzvej69u0rAyFqyOzZs9m6dSthYWGqo1g0my8+gO+++47333+flJQUGQ8kjCI7O5s+ffqwY8cO2rRpozrODSsoKCAuLk6/qjt27Bj9+vXTly/btm2rOqJNunTpEl26dOHjjz9m4MCBquNYLCk+DJsS+vbty6OPPsr48eNVxxFW4u2332bbtm2Eh4eb/c5FTdPIzMzUiy4xMRFXV1f9qIG7u7v8UWgmVq9ezdSpU8nIyKBevXqq41gkKb6/pKenExAQQFZWFs2aNVMdR1iBoqIiunXrxsyZMxkxYoTqOP9w8uTJq55ocMstt+hF5+vrS5MmTVRHFOXQNI1BgwbRr18/XnzxRdVxLJIU3xWeffZZ7Ozs+Pjjj1VHEVZi69atDB8+nJ07dxr+oMrJMTyENCMDcnOhSRNwcYGxY6FFC5NmuXTpElu2bGH9+vWsW7eOAwcO4O/vry9fyoYJy7F79268vLzIzMzkjjvuUB3H4kjxXeHMmTN07txZxgMJo5o8eTLNDxxgZt26EBlp+GBh4d8vsLcHTYPAQJg+HTw8jPJ9NU0jOztbL7rNmzfTpUsXfVNKjx49rOqJBrbm5ZdfJicnh2XLlqmOYnGk+K6xePFivvvuO2JiYsz+voywDIXz5lH24os0AGpV9p+bnZ2hBD/8EG7yUTRnzpy5avmyVq1a+uFxf39/Wca3IhcuXKBTp06sXLkST09P1XEsihTfNUpLS+nevTuvvPKKjAcS1bdoEUydCvn5Vf8aB4cql19xcTGJiYl60e3evRsfHx/9qq5Dhw7yB5wV+/rrr1mwYAFJSUlydvIGSPGVY/PmzTz88MMyHkhUT3Iy+PpWWHp7AGfgX8A/xic4OEBsLHTvftWHNU1j7969etHFxMRw//3360XXu3dv2elnQ8rKyvD29ubJJ5/kySefVB3HYkjxVeDRRx/lnnvu4Z133lEdRViq4cMhNNRw/64cAUAB0JZyis/ODoYNg5UrOXfuHFFRUXrZXbp0SS+6Bx54gBYm3hQjzFtKSgqDBw8mOzubW2+9VXUciyDFVwEZDySqJScH2ra9ehPLFb4HfgE6A3spp/iA4jp1CHFzIy4rCy8vL/1eXefOnWX5Ulzl6aefpmHDhsybN091FIsgxVeJOXPmkJCQQHh4uOoowtK8/z7MmlVu8Z0HugNRwBdUXHxFdepwcOxY7ikDS5EAACAASURBVF6wgAYNGpg0rrBsp06donPnzsTGxtK5c2fVccye3A2txP/93/+RlZVF5OUt6EJUVUZGhVd7rwNPAq2v8xb1SkroUFgopSeuq0WLFrz++utMnjwZuZa5Pim+StSvX5/58+czZcoUioqKVMcRliQ3t9wPpwEbgf+r6vucPWukQMLaTZgwgRMnTvDrr7+qjmL2pPiuY9CgQdx///189NFHqqMIS1LBuK8Y4CBwN9AK+BBYCXSr6H2aNjV6NGGd6taty4IFC3jhhRcoKChQHcesSfFVwbx583jvvfc4duyY6ijCzO3du5cFCxbwRXIy5f3qGQfsw3DllwY8AwwC1pX3Zvb24OxssqzC+vj7++Ph4cEHH3ygOopZk80tVTR9+nSOHj3KV199pTqKMCOFhYXExcURERFBREQEFy5cICgoiGFeXgyaOBG7S5cq/fo3qHhzCw0awKFDJp/hKazLH3/8Qbdu3dixY4c8PqoCUnxVlJeXR6dOnfjxxx/p3bu36jhCoYMHDxIZGUlERASxsbG4uLgQFBREYGAgrq6ufx81uM45vkpdcY5PiBv15ptvsnPnTn766SfVUcySFN8N+Pbbb5k7dy7btm2TZ5PZkKKiIjZv3kxERASRkZGcOnWKgQMHEhQUREBAQMXzL68zuaVSFUxuEaIqCgoKcHR05Msvv8Tf3191HLMjxXcDNE3Dx8eHxx57jHHjxqmOI0zo6NGj+lVdVFQUHTt2JCgoiKCgINzd3as+F9HEszqFqMgvv/zCzJkzSUtLo06dOqrjmBUpvhuUlpbGgAEDyM7OpqnsuLMaJSUlJCYm6vfqjhw5woABAwgKCmLAgAHVGwt2ufwKCipd9iwFaNCA2v/9r5SeqDZN0+jfvz/BwcFMmjRJdRyzIsV3EyZMmECdOnVYuHCh6iiiGk6cOMHatWuJiIhg48aNtGvXjsDAQIKCgujRo4dx/0pOSYHZsyEiwnD/7srt5n89j++IiwsvnDrFiuxsGTQtjGLXrl34+vry22+/yUzXK0jx3YQzZ87g6OjIpk2bcJbt5hajtLSUbdu26UuY+/bt44EHHiAoKIiBAwfWzJOsT50yPIE9M9NwOL1pU8ORhTFj0Jo3JyQkBHd3d2bOnGn6LMImTJkyhfz8fD777DPVUcyGFN9N+vTTT/nxxx+Jjo6WgcFm7PTp06xbt46IiAjWrVvHnXfeqd+r69Wrl9k9gfzIkSO4ubnJzEVhNOfOnaNTp06sWbMGd3d31XHMghTfTSotLcXd3Z0ZM2YwYsQI1XHEX8rKytixY4d+ry4rKws/Pz/9uEGbNm1UR7yuTz/9lG+++Yb4+Hh5uKgwiqVLl7J06VK2bNkif6gjxVctcXFxjBo1iqysLHlgrUJnz55lw4YN+nGDZs2a6Vd13t7e1K9fX3XEG1JWVkafPn149NFHmThxouo4wgqUlZXRs2dPnn/+eUaNGqU6jnJSfNX0yCOP0L59e95++23VUWyGpmlkZGToV3Xp6en4+PgQGBhIYGAg9957r+qI1fbbb7/Rt29fUlNTad36es9xEOL6EhMT+de//kV2djaNGjVSHUcpKb5qOnLkCK6urmzbts0qfuGaq/Pnz7Nx40Z9Y4q9vb1+Vde3b1/s7e1VRzS6t956i5SUFMLCwmR5ShjF6NGjadWqFe+9957qKEpJ8RnB7NmzSUpKIjQ0VHUUq6FpGllZWfpVXXJyMr1799bL7v7771cd0eSKioro1q0bs2bN4qGHHlIdR1iB48eP4+zsTEJCAh06dFAdRxkpPiO4dOkSXbp04ZNPPmHAgAGq41isixcvEhUVpZcdGB4LFRgYiL+/v03eR01MTOTBBx9k586dFY9GE+IGfPjhh0RHR7NmzRrVUZSR4jOS1atXM3XqVDIyMuTwcRVpmsaePXv0TSkJCQl4eHjoV3WOjo6yxAdMmjSJ/Px8li5dqjqKsAJFRUU4Ozszd+5cBg8erDqOElJ8RqJpGoMGDcLf35+pU6eqjmO2CgoKiI2N1a/qCgoK9KLr168fjRs3Vh3R7Fy4cIEuXbrwv//9j379+qmOI6zA2rVree6559i1a5fF7Xo2Bik+I/r999/p3bs3mZmZNTMFxEIcOHBAL7r4+HhcXV31c3UuLi5yVVcFq1evZsqUKWRmZlrlRh5R84KDg+nVqxfTpk1THaXGSfEZ2SuvvMKJEydYvny56ijKXLp0ifj4eH0J888//9RnYPbv31+Ge9+khx9+mLZt2zJnzhzVUYQV2LdvHz179iQ9PZ277rpLdZwaJcVnZBcuXKBTp078/PPP9OrVS3WcGnP48GH9qEF0dDSdO3fWlzDd3NxkAokR5OTk4OzszNq1a3Fzc1MdR1iBV199lYMHD/LNN9+ojlKjpPhMYMWKFcyfP5+kpCSrfWBtcXExCQkJ+hLm8ePHr3qMT/PmzVVHtErLly9nwYIFJCUlyTPWRLXl5eXh6OjId999h7e3t+o4NUaKzwQ0TcPb25uxY8fy1FNPqY5jNMePH7/qMT7t27fXr+o8PDystuTNiaZpBAQEMGDAANlEJYziu+++4/333yclJcVm/huW4jORHTt2EBQURFZWlsXe0yotLSUpKUm/qjt48CD9+/fXr+patWqlOqJNunxvJikpifbt26uOIyycpmn07duXRx99lPHjx6uOUyOk+EzomWeeoX79+nz00Ueqo1TZqVOn9Ku69evX06ZNG31jSq9evWR5zUx8+OGHrF27lg0bNsiuWFFtaWlpDBgwgKysLJsYlCDFZ0KnT5+mc+fOREVF4eTkpDpOucrKykhJSdE3pmRnZ9OvXz/94awyINk8lZSU0LNnTyZNmsSYMWNUxxFWYOLEidSqVYuPP/5YdRSTk+IzsY8//phffvmFTZs2mc1f5n/++Sfr168nIiKCtWvX0qJFC/1enZeXl0yesRCpqakMHDiQjIwMWrZsqTqOsHBnzpzB0dGRjRs34uLiojqOSUnxmVhJSQndunXj9ddfNwwazsmBZcsgIwNyc6FJE3BxgbFjoUULk2TQNI20tDT9Xl1mZia+vr76Y3zuuecek3xfYXrTpk3jjz/+4LvvvlMdRViBRYsW8cMPPxAdHW02f6ibghRfDYiNjeXDf/+bX3v0oM6GDYYPFhb+/QJ7e9A0CAyE6dPBw6Pa3zM3N5cNGzYQGRlJZGQkDRs21K/qfHx8aNCgQbW/h1CvoKAAFxcX5s2bZ7NzF4XxlJaW4u7uzowZMxgxYoTqOCYjxVcTFi3i0qRJ1C0tpdJj3HZ2hhL88EOYMOGGvoWmaezatUu/qtu+fTve3t76aLD77ruvWj+CMF9RUVGMGTOGXbt22fwDRkX1xcXFMWrUKLKysqz2iShSfKa2aBFMnQr5+VX/GgeHKpVfXl4emzZt0suuTp06+lWdn58fDg4O1QwvLMWTTz6Jg4MDCxcuVB1FWIGHH36Y++67j7ffflt1FJOQ4jOl5GTw9b2q9Bpe85ICYCLwj19XDg4QGwvdu+sf0jSN3bt36zswt27dSs+ePfWy69ixo1Wvy4uK/fnnnzg5ObFy5UqbGpUnTOPIkSN07dqV5ORk7r33XtVxjE6Kz5SGD4fQUMP9u3LkAa2ACMDn2k/a2cGwYeR//TUxMTH6VV1xcbFedP7+/rK0JXQ//fQTb7zxBqmpqbIzV1Tbu+++S3JyMr/++qvqKEYnxWcqOTnQtu3Vm1iusRx4E9gHlHedVlSrFh3t7Wnbvbt+iNzJyUmu6kS5NE0jJCQEd3d3Zs6cqTqOsHCFhYV06dKFRYsWERAQoDqOUUnxmcr778OsWZUWnz+GK703Kvh8Sb16FL/6KvbyS0xU0ZEjR3BzcyM2NpbOnTurjiMsXHh4OC+//DIZGRlWtYogz4oxlYyMSkvvDyAWGF3JW9QpKsJ+715jJxNWrHXr1rz11ls8/fTTlJWVqY4jLNyQIUO45557rG6aixSfqeTmVvrprwFvoN313ufsWSMFErZi/Pjx2NnZsXjxYtVRhIWzs7Pjo48+4t133+XEiROq4xiNFJ+pNGlS6ae/ovKrPZ2FPtlBqFOrVi0+++wzZs2axeHDh1XHERauY8eOjB07lhkzZqiOYjRSfKbi4gIVTEdJAI4CD13vPeztwdnZyMGELejcuTOTJk3i2WefRW7ji+p6/fXXWbt2LUlJSaqjGIUUn6lUMjF/OTAcuO5BBE2r9H2EqMy0adPYv38/P//8s+oowsI1btyY2bNnM2nSJKu4dyy7Ok3pOuf4KvXXOT5WrjR+LmEzEhMTefDBB9m5c6dNPGdNmE5ZWRleXl6MGzeOsWPHKhm4byxSfKZUzuSWKitncosQN2Py5MlcvHiRpUuXqo4iLFxycjKvBwYS7ulJvU2bDB808cB9U5ClTlPy8DDM3LzRmZmXZ3VK6QkjeOedd9i4cSObLv+iEuImeaSkEHbuHHXWrDEU3rVHtgoKDB8LDTX80b9okZKc1yPFZ2oTJvxdfteZuFIGXKpT56aeziBERRo1asSnn37K+PHjyb+Z1QchQB+4X/96T5kBw1Vffr5hQL8Zlp8UX02YMMGwbDlsmGGnp7391Z+3t4cGDSgZPJgHb7uNiLZt1eQUVmvQoEF4eHjw5ptvqo4iLFFy8j+eMnMJeBJoi2GjnisQee3XXS6/lJSaSlolco+vpp06ZbghnJlpOJzetKnhyMKYMdCiBTExMTzyyCOkp6fTwsxvEAvLkpOTg7OzM2vXrsXNzU11HGFJytmodxH4ABgD3I1h2P7DQCZwz5Vfa4Yb9aT4zNDLL7/Mnj17+OWXX2QgtTCq5cuXs2DBApKSkqhTp47qOMISVGHg/mUuwCzgwWs/0aABHDpkNrs9ZanTDL399tscOHCA//3vf6qjCCvz+OOP06xZM+bPn686irAUy5ZV6WUngd+BLuV90s6uyu9TE6T4zFD9+vVZsWIFr7zyCvv27VMdR1gROzs7lixZwpw5c+TfLVE11xm4D1AMPIphDGOn8l5QUGC4vWMmpPjMlJOTE6+++iqPPfYYJSUlquMIK3Lvvfcyffp0xo8fL+PMxPVdZ+B+GfAYUA+o9BkOZjRwX4rPjE2ePJlbbrmFOXPmqI4irMzzzz/PuXPnWL58ueoowsydr1VxTWgYdnaeBFYCdSt7IzMauC/FZ8Zq1arFsmXLWLhwIcnJyarjCCtSp04dPv/8c15++WVOnjypOo4wI2VlZSQmJjJ9+nQ6d+7M/KgoimrXLve1E4AsYBVgX+4r/mJmA/dlV6cF+PHHH3n99dfZsWMHt9xyi+o4wopMmzaNgwcP8v3336uOIhTKz89n06ZNhIWFsXr1apo3b05wcDBDhw7Fo21barVr94/7fH9gOLZQH7hyf/ASDPf7rmJmuzql+CzE448/TsOGDfn0009VRxFWpKCgABcXF+bNm8fgwYNVxxE1KCcnh9WrVxMeHk5UVBTu7u4MHTqUoUOH0r59+6tfbGUD96X4LERubi5du3blk08+YdCgQarjCCsSHR3N6NGj2bVrF40aXfdhWcJCaZrG7t27CQsLIzw8nF27dhEQEMDQoUMJCgqq/OkdVjZwX4rPgsTFxTFy5EjS0tK4/fbbVccRVuSpp57C3t6ehQsXqo4ijKi0tJSEhATCw8MJCwsjPz+foUOHEhwcjK+vL/Xr16/6m/01q/OGyu/ywH0zmz0sxWdhpk2bRlZWFqGhoTLVRRjN2bNn6dKlCz///DO9e/dWHUdUQ15eHuvXryc8PJw1a9bQunVrfQmzW7du1fu9cbn8CgoqX/a0szNsaDHD0gMpPotTVFREz549efbZZ3nqqadUxxFW5KeffmLWrFmkpqbe2JWAUO748eOsWrWKsLAw4uPj6dmzJ8HBwQwZMoS2xh56n5ICs2dDRISh4AoK/v7c5efxBQUZnsdnRsubV5Lis0C7du3C19eXxMRE7rvvPtVxhJXQNI2QkBC6devGrFmzVMcRldA0jV27dun3637//XcCAwMZOnQogYGBNGnSxPQhrjNw35xJ8VmoBQsW8O2337J582YZNiyM5siRI7i5uREbG0vnzp1VxxFXKC4uZvPmzXrZlZWV6UcO+vTpQ7169VRHtBhSfBaqrKyMgQMH4u3tzcyZM1XHEVZk0aJFrFixgvj4eGpVMrVDmN758+dZu3Yt4eHhREZGcu+99+qbU5ydneU+/02S4rNgR48epVu3bqxatYoePXqojiOsRFlZGT4+PjzyyCNMnDhRdRybc/jwYcLDwwkPDychIQFvb2+Cg4MZPHgwrVu3Vh3PKkjxWbiff/6ZGTNmkJqaKlNdhNFkZWXh4+PDjh07aNOmjeo4Vk3TNNLS0vQjB3/88QeDBg0iODiYgIAAOVtpAlJ8VmD06NHY29uzePFi1VGEFXnrrbdITk4mPDxcltSMrKioiNjYWP1+Xb169fT7dV5eXnLf3sSk+KzA+fPn6dq1KwsXLpSxU8JoioqK6NatGzNnzmTEiBGq41i8s2fPEhkZSVhYGOvXr6dTp076/TpHR0f546IGSfFZifj4eEaMGEF6erpMdRFGk5iYyIMPPsjOnTsrH2klynXgwAH9fl1ycjK+vr4MHTqUwYMH06pVK9XxbJYUnxWZPn26frZH/noUxjJ58mQuXrzI0qVLVUcxe2VlZWzfvl2/X3fixAkGDx5McHAwDzzwgNyHNxNSfFakqKgIT09PnnnmGcaNG6c6jrASFy5cwMnJiS+//JJ+/fqpjmN2CgsLiYqKIjw8nFWrVtGoUSP9fp2npye1K3iWnVBHis/KXN6Nl5CQwP333686jrASa9as4fnnnycjIwMHBwfVcZQ7ffo0ERERhIWFsXHjRlxcXPQRYR07dlQdT1yHFJ8VWrhwIStWrGDz5s3UrVtXdRxhJR555BHatGnDe++9pzqKEnv27NHv16WlpdGvXz+GDh3KoEGDaGHmI7rE1aT4rFBZWRlBQUF4enryxhtvqI4jrEROTg7Ozs5ERkbSrVs31XFMrqysjKSkJP3IwdmzZ/WnHPj7+2Nvb686orhJUnxW6tixY3Tr1o2wsDB69uypOo6wEsuXL+ejjz5i27ZtVnnWLD8/n40bNxIeHs7q1atp0aKFfuSge/fuMsLNSkjxWbGVK1cybdo0UlNTadiwoeo4wgpomkZAQAABAQG89NJLquMYxcmTJ1m9ejXh4eFER0fj7u6u369r37696njCBKT4rNzYsWOpW7cun332meoowkrs37+fHj16kJSUZJHFoGka2dnZ+pGD3377jYCAAIYOHUpQUJCcV7QBUnxW7vz587i6ujJ//nyGDh2qOo6wEnPnziUyMpINGzZYxJnRkpISEhMT9ft1BQUF+v06X19fefCujZHiswGbN2/moYceIi0tjZYtW6qOI6xASUkJnp6ePPfcc4wZM0Z1nHLl5eWxfv16wsPDWbNmDa1bt9bv17m5uVlEYQvTkOKzEa+++irp6emsWrVK/oMXRpGWlsaAAQPIyMgwmz+ojh07xqpVqwgPDyc+Ph5PT0/9yu7uu+9WHU+YCSk+G1FUVESvXr0YN24c48ePVx1HWIlp06Zx8OBBvv/+eyXfX9M0du7cqd+v27t3LwMHDiQ4OJiBAwfSpEkTJbmEeZPisyFZWVn06dOHhIQEOnTooDqOsAIFBQW4uLjw3//+lyFDhkBODixbBhkZkJsLTZqAiwuMHQtGOuRdXFxMfHy8fpi8rKxMHxHm4+MjQxvEdUnx2ZhPPvmE5cuXs2XLFvkFIYwiOjqauSNH8muPHtTduNHwwcLCv19gbw+aBoGBMH06eHjc8Pc4f/48a9euJSwsjMjISNq3b6/fr3N2dpble3FDpPhsjKZpBAUF0aNHD958803VcYQ1WLSIS5MmUbe0lEqPd9vZGUrwww9hwoTrvu3hw4f1q7rExES8vb0ZOnQoQ4YM4a677jJafGF7pPhs0PHjx3Fzc+PXX3+lV69equMIS7ZoEUydCvn5+od8ga3A5bkudwG7r/waB4dyy0/TNNLS0vT7dYcOHSIoKIjg4GACAgJo1KiRSX8UYTuk+GzUr7/+yksvvURaWppMdRE3JzkZfH2vKj0wFN8o4KnKvtbBAWJjKXJxISYmRr+yq1evHsHBwQQHB9O7d2+rHIsm1JPis2FPPPEEtWvX5vPPP1cdRVii4cMhNNRw/+4Kvly/+Mrs7Ei+804G5OXh6Oiob05xdHSU+3XC5KT4bNiFCxdwdXVl7ty5hISEqI4jLElODrRte/Umlr/4ArsADegIvPPXx65VUqcOZ1JTaenkZMKgQvyTjBq3YY0aNeLrr7/mmWee4cSJE6rjCEuybFmFn3oP2A8cBcYBQ4B95byuTt26tIyMNEU6ISolxWfjevfuzVNPPcUTTzyBXPyLKsvIKPdqD6An0AioD4wGvICI8l5YUACZmaZKKESFpPgEs2bN4tSpUyxevFh1FGEpcnOr/FI7DMue5Tp71hhphLghsmVKULduXVasWIG3tzf+/v507NhRdSRhho4fP050dDRRUVEMjo6mvLvC54AkoC+GXy4/AHHARxW9adOmJskqRGWk+AQAHTt25M0332TUqFEkJCTIVBfB6dOniYmJISoqiujoaE6ePImvry9+fn54NmmC9umn2F2z3FkMvAZkA7WBTkAoUO6APHt7cHY28U8hxD/Jrk6h0zSNQYMG4e7uzttvv606jqhh586dIy4uTr+qO3jwIH369MHPzw9/f39cXFyoXbu24cWV7OqssgYN4NAho83wFKKqpPjEVU6cOIGrqyu//PILvXv3Vh1HmFBeXh6bN2/Wiy47OxtPT0/8/f3x8/PD3d298iv/Cs7xVYmdHQwbBitX3vwPIMRNkuIT/xAaGsqLL75IWlqajImyIoWFhSQmJhIVFUVUVBTp6em4u7vrRdezZ88bexJ5BZNbquSvyS10737jXytENUnxiXI99dRTaJrG0qVLVUcRN6moqIjk5GT9Ht22bdtwdnbWly579+6Ng4ND9b5JObM6r6uCWZ1C1BQpPlGuCxcu4ObmxgcffMCwYcNUxxFVUFJSQmpqql50CQkJ3H///XrReXt707hxY+N/48vlV1BQ+bLnDT6dQQhTkeITFUpMTGTYsGGkpqZyxx13qI4jrlFWVkZmZqZ+jy4uLo42bdroRefj40OzZs1qJkxKCsyeDRERhoIrKPj7c5efxxcUZHgenyxvCsWk+ESlZs6cSXJyMhERETI8WDFN08jOztaLLiYmhmbNmun36Hx9fWnZsqXakKdOGcaZZWYaDqc3bWo4sjBmjOzeFGZDik9Uqri4GC8vL0aPHs2zzz6rOo5N0TSN/fv360UXHR1NvXr16NevH35+fvj5+dG6dWvVMYWwOFJ84rp+//13vLy8iI+Pp1OnTqrjWLXDhw8THR2tl11xcbG+dOnn50e7du3kyluIapLiE1WyePFiPv/8cxITE6lXr57qOFbj5MmTVxXduXPn8PX11YuuY8eOUnRCGJkUn6gSTdMYMmQIXbt25Z133lEdx2L9+eefxMTE6EV37NgxfHx89KJzcnKiVi2ZHS+EKUnxiSo7efIkrq6u/Pzzz3h5eamOYxHOnz9PfHy8fo9u7969eHl56cuXbm5uf48BE0LUCCk+cUPCw8OZMmUKaWlppjkTZuHy8/PZsmWLXnQ7d+6kZ8+eetF5eHjIAHAhFJPiEzfs6aefpqSkhP/973+qoyh36dIltm7dqi9d7tixA1dXV33pslevXjRo0EB1TCHEFaT4xA3Ly8vDzc2NOXPm8OCDD6qOU6OKi4tJSUnRiy4pKQlHR0e96Ly8vGjYsKHqmEKISkjxiZuydetWgoODSU1N5c4771Qdx2RKS0tJS0vTi27Lli20a9dOLzofHx+aNGmiOqYQ4gZI8YmbNmvWLJKSkoiMjLSaLfeaprFr1y79Hl1sbCytWrXS79H17duX5s2bq44phKgGKT5x04qLi/H29uaxxx7jueeeUx3npmiaxp49e/Sii46OpnHjxnrR+fr6ypxSIayMFJ+olj179tCrVy/i4uLo3Lmz6jhVcvDgQX3pMioqilq1auHv768vX959992qIwohTEiKT1TbkiVLWLJkCVu3bjXLqS7Hjh27at7lxYsX9ZLz9/enffv2VrNUK4S4Pik+UW2apjF06FCcnZ159913DR/MyTFM6c/IgNxcaNIEXFxg7FiTT+k/deoUMTExetGdOnUKX19fvegcHR2l6ISwYVJ8wiguT3WJePNN3NauhchIwycKC/9+0eXnsgUGGp7L5uFhlO997tw5YmNj9au6P/74gz59+uhXdV27dpUxYEIInRSfMJqMiRO5b/Fi7AE7Ez6JOy8vj/j4eL3odu/eTa9evfSic3d3p06dOjf/gwghrJoUnzCORYtg6lTIz6/61zg4VKn8CgoKSEhI0IsuIyOD7t2760XXo0cP6tevX80fQAhhK6T4RPUlJ4Ov7z9K7yAwEUgE6gP/AuYDV12LOThAbCx0765/qKioiG3btun36JKTk3FxcdHv0fXu3Rt7e3vT/kxCCKslxSeqb/hwCA013L+7QhBwO7AYOAf0B54GJl/5Ijs7yoKDSZk+XS+6hIQEOnTooB8x8Pb2plGjRjX0wwghrJ0Un6ienBxo2/bqTSx/cQTmYihAgJeA88CSa15XCAR06oRbQAB+fn707duXpk2bmjK1EMKGyQ4AUT3LllX4qSnA94AvcBaIBN4u53X1GjQg7okn4KWXTBBQCCGuJnu8RfVkZJR7tQfgA+wCGgOtge5ASDmvq1VYCJmZJosohBBXkuIT1ZObW+6Hy4CBwHDgInAaw1XfKxW9z9mzJggnhBD/JMUnqqeCR/L8CRwCnsOwo/M2YCwQUdH7yD09IUQNkeIT1ePiAuU8Ybw50A5YBJRg2NW526fGGQAAAXBJREFUHHAp7z3s7cHZ2YQhhRDib7KrU1RPJbs60zBscEkHagP+wEKg5bUvbNAADh0y+QxPIYQAueIT1XX77YbZm+UMfXYFYjDc2zsN/Eg5pWdnB0FBUnpCiBojV3yi+iqY3FIl5UxuEUIIU5IrPlF9Hh6GmZsODjf2dZdndUrpCSFqkBxgF8ZxedD01KlQUPCP8WVXqebTGYQQojpkqVMYV0oKzJ4NERGGgiso+Ptzl5/HFxRkeB6fXOkJIRSQ4hOmceqUYZxZZqbhcHrTpoYjC2PGyEYWIYRSUnxCCCFsimxuEUIIYVOk+IQQQtgUKT4hhBA2RYpPCCGETZHiE0IIYVOk+IQQQtgUKT4hhBA2RYpPCCGETZHiE0IIYVOk+IQQQtgUKT4hhBA2RYpPCCGETZHiE0IIYVOk+IQQQtgUKT4hhBA2RYpPCCGETZHiE0IIYVOk+IQQQtgUKT4hhBA2RYpPCCGETZHiE0IIYVP+H2YOHGVJN+BqAAAAAElFTkSuQmCC\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -214,31 +214,31 @@ "text": [ "X 0\n", "X 1\n", - "Z 2\n", - "I 3\n", - "Z 4\n", - "Z 5\n", - "Z 6\n", + "X 2\n", + "Z 3\n", + "I 4\n", + "I 5\n", + "X 6\n", "I 7\n", - "Z 8\n", - "CZ 0 3\n", + "X 8\n", + "I 0\n", + "I 3\n", "I 0\n", "I 1\n", "CZ 1 4\n", - "I 1\n", - "I 2\n", + "CZ 1 2\n", "CZ 2 5\n", - "CZ 3 6\n", "I 3\n", - "I 4\n", + "I 6\n", + "CZ 3 4\n", "I 4\n", "I 7\n", "I 4\n", "I 5\n", "CZ 5 8\n", - "CZ 6 7\n", + "I 6\n", "I 7\n", - "I 8\n", + "CZ 7 8\n", "\n" ] } @@ -258,22 +258,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 0\n", - "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(-pi/2) 2\n", - "RZ(-pi/2) 3\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 5\n", - "RX(-pi) 5\n", + "RZ(pi/2) 0\n", + "RX(-pi/2) 0\n", + "RX(-pi/2) 1\n", + "RZ(-pi) 1\n", + "RX(pi/2) 2\n", + "RZ(pi/2) 2\n", + "RZ(pi/2) 3\n", + "RX(-pi) 3\n", + "RX(-pi/2) 4\n", + "RZ(-pi) 4\n", + "RX(pi/2) 5\n", + "RZ(pi/2) 5\n", + "RX(-pi/2) 6\n", "RZ(-pi/2) 6\n", "RX(-pi/2) 6\n", "RX(-pi/2) 7\n", + "RX(pi/2) 8\n", "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", "\n" ] } @@ -299,10 +303,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 1\n", "I 4\n", - "I 1\n", + "X 7\n", "I 4\n", + "I 7\n", "\n" ] } @@ -321,9 +325,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "CNOT 0 3\n", - "I 0\n", - "I 3\n", + "I 4\n", + "I 7\n", + "CNOT 4 7\n", "\n" ] } @@ -342,10 +346,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 4\n", - "H 6\n", - "H 7\n", - "H 8\n", + "H 0\n", + "H 1\n", + "H 2\n", + "H 5\n", "\n" ] } @@ -364,17 +368,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "CZ 6 7\n", + "CZ 7 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 8\n", "RX(-pi/2) 7\n", - "RX(pi/2) 6\n", - "CZ 6 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 6\n", - "RZ(-pi) 6\n", - "CZ 6 7\n", - "RX(pi/2) 6\n", - "CZ 6 7\n", - "RX(pi/2) 7\n", + "CZ 7 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 7 8\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi) 7\n", "\n" ] } @@ -394,35 +399,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-0.9972031830559858) 2\n", - "RX(pi/2) 2\n", - "RZ(2.499986143155603) 2\n", - "RX(-pi/2) 2\n", - "RZ(2.774737393757615) 2\n", - "RZ(2.4545623393205607) 5\n", - "RX(pi/2) 5\n", - "RZ(0.6293682455256661) 5\n", - "RX(-pi/2) 5\n", - "RZ(-2.793139262913046) 5\n", - "CZ 5 2\n", - "RZ(-0.4929538141728491) 2\n", - "RX(pi/2) 2\n", - "RZ(2.648638839416944) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 2\n", - "RX(-pi/2) 2\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(2.195187298908836) 2\n", - "RX(pi/2) 2\n", - "RZ(1.4859323114149499) 2\n", - "RX(-pi/2) 2\n", - "RZ(2.3749224706830834) 2\n", - "RZ(1.117192567208857) 5\n", - "RX(pi/2) 5\n", - "RZ(1.100128590553038) 5\n", - "RX(-pi/2) 5\n", - "RZ(-1.3228472411340935) 5\n", "\n" ] } @@ -442,40 +418,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-2.340746467015041) 4\n", + "RZ(-0.07485140215683961) 4\n", "RX(pi/2) 4\n", - "RZ(1.7938386866903413) 4\n", + "RZ(2.018484028959887) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.6990997351796275) 4\n", - "RZ(2.0274187165352697) 7\n", + "RZ(2.189582454518943) 4\n", + "RZ(2.4546383713704456) 7\n", "RX(pi/2) 7\n", - "RZ(1.5585146643016898) 7\n", + "RZ(0.5128477331264145) 7\n", "RX(-pi/2) 7\n", - "RZ(0.7513186312249509) 7\n", + "RZ(1.3692666388679378) 7\n", "CZ 7 4\n", "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(2.569499219983433) 4\n", + "RZ(1.7840235069198425) 4\n", "RX(-pi/2) 4\n", "RZ(-pi/2) 7\n", "RX(-pi/2) 7\n", "CZ 7 4\n", "RX(pi/2) 4\n", - "RZ(-1.6555312195555723) 4\n", + "RZ(-1.6262546737923005) 4\n", "RX(-pi/2) 4\n", - "RZ(1.163901379855961) 7\n", + "RZ(1.26924235270498) 7\n", "RX(pi/2) 7\n", "CZ 7 4\n", - "RZ(-0.2642123127790188) 4\n", + "RZ(-2.4561083762331126) 4\n", "RX(pi/2) 4\n", - "RZ(1.5763311584646886) 4\n", + "RZ(0.3578106670331639) 4\n", "RX(-pi/2) 4\n", - "RZ(-2.995763740841851) 4\n", - "RZ(-1.9563592134079801) 7\n", + "RZ(-1.5286697079172273) 4\n", + "RZ(-0.4386947993359078) 7\n", "RX(-pi/2) 7\n", - "RZ(0.9008098742333037) 7\n", + "RZ(1.1436356557642386) 7\n", "RX(-pi/2) 7\n", - "RZ(-1.6152828259033303) 7\n", + "RZ(-2.442546010449126) 7\n", "\n" ] } @@ -502,23 +478,20 @@ "output_type": "stream", "text": [ "I 1\n", - "X 4\n", - "X 5\n", - "I 8\n", - "I 1\n", + "I 2\n", "I 4\n", - "CNOT 4 5\n", - "CNOT 5 8\n", + "X 7\n", + "CNOT 1 4\n", + "CNOT 1 2\n", + "CNOT 4 7\n", "X 1\n", + "X 2\n", "X 4\n", - "I 5\n", - "X 8\n", + "I 7\n", + "CNOT 1 4\n", "I 1\n", - "I 4\n", - "I 4\n", - "I 5\n", - "I 5\n", - "I 8\n", + "I 2\n", + "CNOT 4 7\n", "\n" ] } @@ -544,23 +517,23 @@ "name": "stdout", "output_type": "stream", "text": [ + "H 4\n", "H 7\n", - "H 8\n", + "I 4\n", + "Z 7\n", + "I 4\n", "I 7\n", - "I 8\n", - "H 7\n", - "CZ 7 8\n", - "H 7\n", + "I 4\n", "I 7\n", - "I 8\n", + "H 4\n", + "CZ 4 7\n", + "H 4\n", + "Z 4\n", "I 7\n", - "I 8\n", - "Z 7\n", - "I 8\n", + "I 4\n", "I 7\n", - "I 8\n", + "H 4\n", "H 7\n", - "H 8\n", "\n" ] } @@ -585,44 +558,68 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RX(pi/2) 1\n", - "CZ 1 2\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "RX(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RZ(-pi) 2\n", - "RZ(-pi) 2\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RX(pi/2) 2\n", - "RZ(pi/2) 2\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RX(pi/2) 1\n", - "CZ 1 2\n", - "RZ(-pi/2) 2\n", - "RX(pi/2) 1\n", - "RZ(1.1027544334978108) 2\n", - "RX(pi) 2\n", - "CZ 1 2\n", - "RX(pi) 1\n", - "RZ(-2.038838220091982) 2\n", - "RX(pi) 2\n", + "RX(-pi) 6\n", + "RZ(-pi) 7\n", + "RZ(-pi) 7\n", + "RX(-pi) 7\n", + "RZ(-pi/2) 6\n", + "RX(pi/2) 6\n", + "RZ(pi/2) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi) 7\n", + "RZ(-pi) 7\n", + "CZ 6 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 7\n", + "RZ(pi/2) 6\n", + "RX(-pi/2) 6\n", + "CZ 6 7\n", + "RX(-pi/2) 7\n", + "RX(-pi/2) 6\n", + "CZ 6 7\n", + "RX(-pi/2) 7\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "RX(-pi) 7\n", + "RX(pi/2) 7\n", + "RX(-pi/2) 6\n", + "CZ 6 7\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 6\n", + "RX(-pi/2) 6\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RZ(-pi/2) 6\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RZ(pi/2) 7\n", + "DAGGER RX(pi/2) 7\n", + "DAGGER CZ 6 7\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RX(pi/2) 7\n", + "DAGGER RX(-pi) 7\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RZ(-pi/2) 6\n", + "DAGGER RX(-pi/2) 7\n", + "DAGGER CZ 6 7\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RX(-pi/2) 7\n", + "DAGGER CZ 6 7\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RZ(pi/2) 6\n", + "DAGGER RX(-pi/2) 7\n", + "DAGGER RZ(pi/2) 7\n", + "DAGGER CZ 6 7\n", + "DAGGER RZ(-pi) 7\n", + "DAGGER RZ(-pi) 7\n", + "DAGGER RX(-pi/2) 6\n", + "DAGGER RZ(pi/2) 6\n", + "DAGGER RX(pi/2) 6\n", + "DAGGER RZ(-pi/2) 6\n", + "DAGGER RX(-pi) 7\n", + "DAGGER RZ(-pi) 7\n", + "DAGGER RZ(-pi) 7\n", + "DAGGER RX(-pi) 6\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -631,10 +628,8 @@ } ], "source": [ - "clifford_sandwich = clifford_1q_layer + clifford_2q_layer + get_dagger_all_template()\n", - "# here we demonstrate another simple use of a pattern. We want to do some Clifford layers n=reps\n", - "# number of times and then dagger the result of all those reps. \n", - "clifford_sandwich.pattern = [([0, 1], 'n'), -1]\n", + "clifford_sandwich = clifford_1q_layer + clifford_2q_layer\n", + "clifford_sandwich.sequence_transforms.append(dagger_sequence)\n", "prog = clifford_sandwich.sample_program(G, repetitions=3, width=2, qc=noisy_qc)\n", "print(prog)\n", "\n", @@ -659,19 +654,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 2\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", "RZ(-pi/2) 3\n", "RX(pi) 3\n", - "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RX(pi) 3\n", "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", + "CZ 3 4\n", "RZ(pi/2) 5\n", "RX(-pi/2) 5\n", + "RX(pi) 3\n", + "RX(pi/2) 4\n", + "RX(pi) 7\n", + "CZ 7 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", "CZ 4 5\n", - "RZ(pi) 4\n", + "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", "RZ(pi/2) 5\n", "RX(pi/2) 5\n", @@ -680,424 +680,368 @@ "RX(pi/2) 4\n", "RX(-pi/2) 5\n", "CZ 4 5\n", - "RX(pi) 5\n", - "CZ 2 5\n", - "RZ(-pi/2) 2\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 5\n", - "RX(pi/2) 5\n", - "CZ 5 2\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", - "RX(-pi/2) 5\n", - "CZ 2 5\n", - "RZ(-pi/2) 8\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", "RZ(pi) 3\n", - "RX(pi) 3\n", + "RX(pi/2) 3\n", + "RZ(2.2708339550000107) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(-2.29399624460067) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RZ(pi) 3\n", + "RX(pi/2) 3\n", "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "CZ 3 4\n", + "RX(pi/2) 4\n", + "RZ(-0.7000376282051146) 4\n", + "RX(pi/2) 4\n", + "CZ 4 7\n", "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(pi/2) 1\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", "RX(pi/2) 3\n", - "CZ 0 3\n", + "RZ(1.7286650484424175) 4\n", "RX(pi) 4\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RX(pi/2) 3\n", "CZ 4 3\n", - "RX(pi) 1\n", + "RZ(pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RZ(0.8475964089891228) 3\n", + "RZ(-2.9837239319422726) 4\n", "RX(pi/2) 4\n", - "CZ 1 4\n", + "RZ(-pi/2) 7\n", + "RX(pi) 7\n", + "CZ 4 7\n", "RX(-pi/2) 4\n", + "CZ 4 5\n", + "RZ(pi) 7\n", "CZ 4 3\n", - "RZ(pi/2) 1\n", - "RX(-pi/2) 1\n", + "RZ(pi/2) 5\n", + "RX(-pi/2) 5\n", "RZ(pi/2) 4\n", "RX(pi) 4\n", - "CZ 1 4\n", - "RX(-pi/2) 3\n", + "RZ(pi) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 4\n", "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", - "RZ(pi/2) 2\n", - "RZ(pi) 3\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", + "CZ 4 5\n", "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RZ(2.696717311558566) 1\n", - "RX(-pi/2) 1\n", - "RZ(-0.566587165683357) 2\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RZ(pi) 2\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "CZ 4 3\n", - "RX(pi/2) 1\n", - "RZ(2.575005487906436) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 4\n", - "RX(pi) 4\n", - "CZ 1 4\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RX(pi/2) 2\n", - "CZ 1 2\n", + "RZ(-pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 4 7\n", + "CZ 4 5\n", + "CZ 4 7\n", + "RZ(pi) 3\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(pi) 5\n", - "RX(pi/2) 5\n", - "CZ 5 8\n", - "RX(-pi/2) 5\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "CZ 8 5\n", - "RZ(pi) 5\n", + "RZ(-pi/2) 4\n", + "RZ(pi/2) 5\n", "RX(pi/2) 5\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RZ(-pi/2) 0\n", - "RX(pi) 0\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RZ(0.44487534203122825) 2\n", - "RZ(-pi/2) 3\n", + "RZ(pi/2) 5\n", + "RZ(0.9781997183417529) 3\n", "RX(pi/2) 3\n", + "RZ(0.3830531979820055) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.48467006037794125) 3\n", + "RZ(-3.1252189336617793) 4\n", + "RX(pi/2) 4\n", + "RZ(0.627338448032136) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.07778716285087062) 4\n", + "CZ 4 3\n", "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(2.694501844223513) 3\n", + "RX(-pi/2) 3\n", "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", - "RZ(-pi/2) 5\n", - "RX(pi) 5\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "RZ(-0.9420657351918189) 0\n", - "RX(pi/2) 0\n", - "RZ(1.64332552049446) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.428808686378022) 0\n", - "RZ(2.7792681094301965) 3\n", + "CZ 4 3\n", "RX(pi/2) 3\n", - "RZ(1.1631859231446993) 3\n", + "RZ(-1.6076937413025152) 3\n", "RX(-pi/2) 3\n", - "RZ(-2.363464482159524) 3\n", - "CZ 3 0\n", - "RZ(-pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.438314082991957) 0\n", - "RX(-pi/2) 0\n", + "RZ(1.5999848757758146) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(-0.2480811331587236) 5\n", + "RX(pi/2) 5\n", + "RZ(1.5259819542726336) 5\n", + "RX(-pi/2) 5\n", + "RZ(-1.6652628620457273) 5\n", + "RX(pi/2) 3\n", + "RZ(-0.393362566007039) 6\n", + "RX(pi/2) 6\n", + "RZ(2.1511327141667) 6\n", + "RX(-pi/2) 6\n", + "RZ(-0.023829283401341383) 6\n", + "CZ 3 6\n", "RZ(-pi/2) 3\n", "RX(-pi/2) 3\n", - "CZ 3 0\n", - "RX(pi/2) 0\n", - "RZ(-2.172735112541817) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.401167176798845) 3\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 6 3\n", + "RZ(pi) 3\n", "RX(pi/2) 3\n", - "CZ 3 0\n", - "RZ(-2.233562388363687) 4\n", + "RX(-pi/2) 6\n", + "CZ 3 6\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(0.8900537274511487) 4\n", + "CZ 4 5\n", + "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", - "RZ(-2.265901978436181) 4\n", - "RZ(-0.41550158831996953) 5\n", + "RZ(pi/2) 5\n", "RX(pi/2) 5\n", - "RZ(1.8617925473150376) 5\n", - "RX(-pi/2) 5\n", - "RZ(-3.077175788094706) 5\n", "CZ 5 4\n", - "RZ(-pi/2) 4\n", + "RZ(pi) 4\n", "RX(pi/2) 4\n", - "RZ(2.5752204269167738) 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 5\n", "RX(-pi/2) 5\n", - "CZ 5 4\n", + "CZ 4 5\n", + "RZ(-pi/2) 3\n", + "RX(pi) 3\n", + "RZ(-pi/2) 4\n", + "RX(pi) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", "RX(pi/2) 4\n", - "RZ(-2.270131820384229) 4\n", + "RZ(2.4520024836736334) 4\n", "RX(-pi/2) 4\n", - "RZ(1.553043119679467) 5\n", - "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(-2.69885777150156) 0\n", - "RX(pi/2) 0\n", - "RZ(1.966977955679435) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.5602311124224046) 0\n", - "RZ(-2.0739104588584127) 3\n", + "CZ 3 4\n", + "RZ(1.7564637723835403) 3\n", "RX(pi/2) 3\n", - 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"RZ(-2.3196466239952036) 5\n", + "RZ(0.4403458943095204) 4\n", + "RZ(-2.7096040826668073) 5\n", "RX(pi/2) 5\n", - "RZ(0.4886584408701164) 5\n", + "RZ(0.9297381181608476) 5\n", "RX(-pi/2) 5\n", - "RZ(-0.24925992869056524) 5\n", + "RZ(-0.49630797881517097) 5\n", + "RZ(-1.7069805516849792) 6\n", + "RX(pi/2) 6\n", + "RZ(0.5586233876167076) 6\n", + "RX(-pi/2) 6\n", + "RZ(-1.954346632603958) 6\n", "\n" ] } @@ -1111,19 +1055,84 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Acquire data for ranges of (width, depth)" + "## Run quantum volume for one width and depth\n", + "\n", + "1. Generate the programs\n", + "2. Determine the heavy outputs\n", + "3. Collect experimental data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, + "outputs": [], + "source": [ + "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", + "wfn_sim = NumpyWavefunctionSimulator(9)\n", + "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, widths=[2], depths=[2], num_circuit_samples=200)\n", + "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", + "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: [0.8140000000000006, 0.8720000000000007, 0.7680000000000006, 0.7380000000000005, 0.7900000000000006, 0.9580000000000007, 0.8860000000000007, 0.7940000000000006, 0.6300000000000004, 0.6340000000000005, 0.7740000000000006, 0.8040000000000006, 0.7040000000000005, 0.8900000000000007, 0.9420000000000007, 0.6460000000000005, 0.7840000000000006, 0.7400000000000005, 0.7040000000000005, 0.9360000000000007, 0.8460000000000006, 0.8100000000000006, 0.9380000000000007, 0.7680000000000006, 0.7940000000000006, 0.7900000000000006, 0.8440000000000006, 0.7980000000000006, 0.8460000000000006, 0.6920000000000005, 0.7580000000000006, 0.7960000000000006, 0.6780000000000005, 0.9540000000000007, 0.6420000000000005, 0.7660000000000006, 0.7520000000000006, 0.7860000000000006, 0.7640000000000006, 0.6760000000000005, 0.8340000000000006, 0.9640000000000007, 0.7840000000000006, 0.6860000000000005, 0.7360000000000005, 0.9260000000000007, 0.7540000000000006, 0.7200000000000005, 0.7460000000000006, 0.8980000000000007, 0.8760000000000007, 0.8800000000000007, 0.7340000000000005, 0.7680000000000006, 0.8280000000000006, 0.7660000000000006, 0.8480000000000006, 0.8180000000000006, 0.5580000000000004, 0.8000000000000006, 0.7340000000000005, 0.6520000000000005, 0.8780000000000007, 0.7000000000000005, 0.7600000000000006, 0.8000000000000006, 0.6860000000000005, 0.7220000000000005, 0.9560000000000007, 0.9660000000000007, 0.7020000000000005, 0.8160000000000006, 0.7560000000000006, 0.7960000000000006, 0.8740000000000007, 0.6940000000000005, 0.8040000000000006, 0.7920000000000006, 0.8280000000000006, 0.7920000000000006, 0.7820000000000006, 0.9740000000000008, 0.7940000000000006, 0.7240000000000005, 0.8120000000000006, 0.7320000000000005, 0.9460000000000007, 0.8280000000000006, 0.9020000000000007, 0.8780000000000007, 0.9740000000000008, 0.8380000000000006, 0.7900000000000006, 0.7720000000000006, 0.9260000000000007, 0.8440000000000006, 0.7540000000000006, 0.6740000000000005, 0.6600000000000005, 0.6620000000000005, 0.7040000000000005, 0.7960000000000006, 0.9100000000000007, 0.8820000000000007, 0.8100000000000006, 0.6380000000000005, 0.8380000000000006, 0.7920000000000006, 0.7800000000000006, 0.7500000000000006, 0.8440000000000006, 0.7880000000000006, 0.7820000000000006, 0.7940000000000006, 0.7560000000000006, 0.7440000000000005, 0.6400000000000005, 0.8200000000000006, 0.8900000000000007, 0.6180000000000004, 0.8220000000000006, 0.8440000000000006, 0.8880000000000007, 0.8140000000000006, 0.9300000000000007, 0.5920000000000004, 0.7120000000000005, 0.9620000000000007, 0.8280000000000006, 0.6820000000000005, 0.7380000000000005, 0.7280000000000005, 0.8220000000000006, 0.7400000000000005, 0.7860000000000006, 0.8680000000000007, 0.5660000000000004, 0.8340000000000006, 0.8320000000000006, 0.6780000000000005, 0.8780000000000007, 0.7740000000000006, 0.7080000000000005, 0.7340000000000005, 0.9120000000000007, 0.6260000000000004, 0.6880000000000005, 0.6860000000000005, 0.8480000000000006, 0.8160000000000006, 0.9640000000000007, 0.7740000000000006, 0.9200000000000007, 0.7880000000000006, 0.9800000000000008, 0.8080000000000006, 0.8460000000000006, 0.9580000000000007, 0.6780000000000005, 0.6400000000000005, 0.6560000000000005, 0.8480000000000006, 0.7260000000000005, 0.7780000000000006, 0.8100000000000006, 0.9200000000000007, 0.9400000000000007, 0.8840000000000007, 0.8100000000000006, 0.8920000000000007, 0.9040000000000007, 0.7060000000000005, 0.5820000000000004, 0.7140000000000005, 0.9260000000000007, 0.6060000000000004, 0.6880000000000005, 0.6820000000000005, 0.9300000000000007, 0.6580000000000005, 0.8040000000000006, 0.6220000000000004, 0.8560000000000006, 0.6920000000000005, 0.6280000000000004, 0.8260000000000006, 0.5980000000000004, 0.8440000000000006, 0.7440000000000005, 0.8680000000000007, 0.8620000000000007, 0.8060000000000006, 0.7840000000000006, 0.8680000000000007, 0.8860000000000007, 0.8120000000000006, 0.8480000000000006, 0.6300000000000004, 0.6320000000000005, 0.7680000000000006]}}\n", + "0.7885500000000006\n" + ] + } + ], + "source": [ + "# num_hh_sampled = count_heavy_hitters_sampled(experimental_data, heavy_outputs)\n", + "# print(num_hh_sampled)\n", + "qvol_success_probs = get_success_probabilities(experimental_data, heavy_outputs)\n", + "print(qvol_success_probs)\n", + "print(np.average(qvol_success_probs[2][2]))\n", + "# calculate_success_prob_est_and_err(prob_success, 50, 500)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: True}}\n" + ] + } + ], + "source": [ + "qvol_successes = determine_successes(qvol_success_probs, 500)\n", + "print(qvol_successes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acquire data for ranges of (width, depth)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" ] } ], @@ -1137,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1147,14 +1156,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [array([[0, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[0, 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array([0.881 , 0.1115, 0.0075])}, 3: {2: array([8.274e-01, 1.588e-01, 1.320e-02, 6.000e-04]), 3: array([8.434e-01, 1.461e-01, 1.010e-02, 4.000e-04]), 4: array([8.238e-01, 1.624e-01, 1.330e-02, 5.000e-04]), 5: array([8.329e-01, 1.518e-01, 1.450e-02, 8.000e-04]), 10: array([0.8341, 0.1496, 0.0131, 0.0032])}, 4: {2: array([7.715e-01, 2.075e-01, 1.970e-02, 1.100e-03, 2.000e-04]), 3: array([7.775e-01, 1.990e-01, 2.240e-02, 1.000e-03, 1.000e-04]), 4: array([7.680e-01, 2.059e-01, 2.340e-02, 2.400e-03, 3.000e-04]), 5: array([7.833e-01, 1.944e-01, 1.930e-02, 2.900e-03, 1.000e-04]), 10: array([0.7646, 0.2009, 0.028 , 0.0051, 0.0014])}, 5: {2: array([7.467e-01, 2.239e-01, 2.700e-02, 1.900e-03, 4.000e-04, 1.000e-04]), 3: array([7.239e-01, 2.377e-01, 3.480e-02, 3.100e-03, 5.000e-04, 0.000e+00]), 4: array([7.305e-01, 2.320e-01, 3.310e-02, 3.700e-03, 7.000e-04, 0.000e+00]), 5: array([7.129e-01, 2.424e-01, 3.700e-02, 6.200e-03, 1.000e-03, 5.000e-04]), 10: array([7.021e-01, 2.375e-01, 4.520e-02, 1.100e-02, 3.900e-03, 3.000e-04])}}\n" ] } ], @@ -1208,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1223,12 +1232,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1260,12 +1269,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1287,12 +1296,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1314,12 +1323,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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" ] @@ -1341,16 +1350,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: 0.8813000000000001, 3: 0.8879999999999999, 4: 0.8794999999999998, 5: 0.8702000000000002, 10: 0.8788}, 3: {2: 0.8305, 3: 0.8680999999999998, 4: 0.8341, 5: 0.8156000000000001, 10: 0.8131999999999999}, 4: {2: 0.7840999999999999, 3: 0.7713, 4: 0.7678999999999999, 5: 0.7870999999999999, 10: 0.7247}, 5: {2: 0.7336, 3: 0.7263, 4: 0.7278, 5: 0.7222999999999999, 10: 0.7094000000000001}}\n", - "{2: {2: 0.9964999999999999, 3: 0.9964000000000001, 4: 0.9953, 5: 0.9934, 10: 0.9922000000000001}, 3: {2: 0.9895999999999999, 3: 0.9912000000000001, 4: 0.9856, 5: 0.9841000000000001, 10: 0.9789}, 4: {2: 0.9979000000000001, 3: 0.9968, 4: 0.9974000000000001, 5: 0.9964000000000001, 10: 0.9928000000000001}, 5: {2: 0.9960999999999999, 3: 0.9959, 4: 0.9932000000000001, 5: 0.9940999999999999, 10: 0.9865999999999999}}\n", - "{2: {2: 0.6313, 3: 0.6379999999999999, 4: 0.6295, 5: 0.6202000000000001, 10: 0.6288}, 3: {2: 0.7055, 3: 0.7430999999999999, 4: 0.7091, 5: 0.6906, 10: 0.6881999999999999}, 4: {2: 0.7215999999999999, 3: 0.7088, 4: 0.7053999999999999, 5: 0.7246, 10: 0.6621999999999999}, 5: {2: 0.7765000000000001, 3: 0.7732, 4: 0.7694999999999999, 5: 0.7694, 10: 0.7530000000000001}}\n" + "{2: {2: 0.8835, 3: 0.8930999999999998, 4: 0.8768999999999998, 5: 0.8802999999999997, 10: 0.881}, 3: {2: 0.8273999999999999, 3: 0.8434000000000001, 4: 0.8238000000000001, 5: 0.8329000000000001, 10: 0.8341}, 4: {2: 0.7715000000000001, 3: 0.7774999999999999, 4: 0.7680000000000001, 5: 0.7833000000000001, 10: 0.7646}, 5: {2: 0.7467000000000001, 3: 0.7239000000000001, 4: 0.7304999999999999, 5: 0.7129000000000001, 10: 0.7021}}\n", + "{2: {2: 0.9955, 3: 0.9955999999999998, 4: 0.9946000000000002, 5: 0.9962000000000002, 10: 0.9924999999999999}, 3: {2: 0.9862, 3: 0.9894999999999998, 4: 0.9861999999999999, 5: 0.9846999999999999, 10: 0.9836999999999996}, 4: {2: 0.9987, 3: 0.9989000000000001, 4: 0.9973000000000001, 5: 0.9970000000000001, 10: 0.9935}, 5: {2: 0.9975999999999999, 3: 0.9963999999999997, 4: 0.9956000000000002, 5: 0.9923, 10: 0.9847999999999999}}\n", + "{2: {2: 0.6335, 3: 0.6430999999999999, 4: 0.6268999999999999, 5: 0.6302999999999999, 10: 0.631}, 3: {2: 0.7023999999999999, 3: 0.7184000000000001, 4: 0.6988000000000001, 5: 0.7079000000000001, 10: 0.7091}, 4: {2: 0.7090000000000001, 3: 0.715, 4: 0.7055, 5: 0.7208, 10: 0.7020999999999998}, 5: {2: 0.7831, 3: 0.7741000000000001, 4: 0.7749999999999999, 5: 0.7678000000000001, 10: 0.7521}}\n" ] } ], @@ -1407,12 +1416,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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RcRaSh5gwswbRvXt3evbsyZ577slOO+1ERUXFBre56qqrGDRoEF26dGH//fdn5513BmDvvfdm8ODB9O7dG4CzzjqLnj171tj0syHnnXcep59+OqWlpey555506dKF1q1bA3DDDTdw9NFH0759e8rLy1mxYgUAv/3tbzn//PPp1q0bq1evpl+/ftx2223ccsstTJgwgSZNmtClSxeOOOII7r33Xm688UaaN29Oy5Yt+ctf/pJ3jIXW6J5ZXF5eHn4wjdmGzZo1i7322qvYYWz21qxZw6pVqygpKeGNN97gkEMOYc6cOWy11VbFDm2j1fS3lzQ1IsprKu8rAjPLtJUrV3LggQeyatUqIoJbb721USeBjeFEYGaZ1qpVq8w//tadxWZmGedEYGaWcU4EZmYZ50RgZpZxTgRmVjDXXXcdXbp0oVu3bvTo0YNJkyYVOyTmzZtHixYt6NGjB6WlpZxzzjl5jYw6b948ysrK8tpn//79a+yQHj16NDfccAMAV199NTfddBMAV155JePHjwfglltuKfj4RL5ryMwK4rnnnuPRRx9l2rRpbL311ixZsoTPPvus2GEBsOuuuzJ9+nRWr17NQQcdxMMPP1w11hHA6tWradas8B+PAwcOZODAgV9Yfu2111ZN33LLLZx66ql86UtfKlgcviIwMwAefnEhFTc8SeehY6i44UkefnHTRoVftGgR7dq1qxrrp127dnTo0AFY9wEzU6ZMoX///gCsWLGC7373u3Tt2pVu3brx97//HYDHH3+c/fbbj7333psTTjih6hu+Q4cOpbS0lG7dunHppZcC8MADD1BWVkb37t3p169frTE2a9aM/fffn7lz5/LPf/6TAw44gIEDB1JaWgrUPNQ1JInilFNOYa+99uJb3/pW1Rn7tddeS69evSgrK2PIkCHrDJt99913Vw1FXTkc9l133cUFF1zwhbgGDx7MqFGj+O1vf8u7777LgQceyIEHHsidd97JRRddVFXu9ttv5+KLL67Ln6NWTgRmxsMvLuQnD77CwuWfEMDC5Z/wkwdf2aRkcNhhhzF//nx23313zjvvPJ566qkNbvPzn/+c1q1b88orr/Dyyy9z0EEHsWTJEn7xi18wfvx4pk2bRnl5OTfffDNLly7loYceYsaMGbz88stcccUVQPJhPG7cOF566SVGjx5d6/5WrlzJE088UTVa6bRp0/jNb37Da6+9ts5Q188//zy33347L774IgBz5szhvPPOY9asWWy77bbceuutAFxwwQVMnjyZV199lU8++YRHH310nX1Nnz6dW2+9lTPOOKNOr+EPfvADOnTowIQJE5gwYQInnngijzzyCKtWrQLgz3/+c53rqo0TgZlx47g5fLJqzTrLPlm1hhvHzdnoOlu2bMnUqVMZPnw47du356STTuKuu+6qdZvx48dz/vmfD0rctm1bnn/+eWbOnElFRQU9evRgxIgRvP3227Ru3ZqSkhLOPPNMHnzwwaqmk4qKCgYPHsztt9/OmjVratzPG2+8QY8ePaioqOCoo47iiCOOAKB379507twZWHeo65YtW1YNdQ2sM1bSqaeeysSJEwGYMGECffr0oWvXrjz55JPMmDGjap+DBiUDMvfr148PP/yQ5cuX5/uS0rJlSw466CAeffRRZs+ezapVq6qS2KZwH4GZ8e7yT/JaXldNmzalf//+9O/fn65duzJixAgGDx5Ms2bNqjpoc4d3rklEcOihh3LPPfd8Yd0LL7zAE088wahRo/j973/Pk08+yW233cakSZMYM2YM++yzD1OnTmX77bdfZ7vKPoLqcofNrk3u8NSV859++innnXceU6ZMYaedduLqq69e59hq2mZjnHXWWVx//fXsueeefPe79fMYF18RmBkd2rTIa3ldzJkzh9dff71qfvr06eyyyy5A0kcwdepUgKp+AIBDDz2UYcOGVc0vW7aMfffdl2effZa5c+cC8PHHH/Paa6+xYsUKPvjgA4488kh+/etf89JLLwHJ2X6fPn249tprad++PfPnz9+o+Gsb6vqdd97hueeeA2DkyJH07du36kO/Xbt2rFixglGjRq1T33333QckVxqtW7euGuF0Q1q1asVHH31UNd+nTx/mz5/PyJEjq64yNpUTgZlx2YA9aNG86TrLWjRvymUD9tjoOlesWFE1vHO3bt2YOXMmV199NZAML33hhRdSXl5O06af7/eKK65g2bJlVZ29EyZMoH379tx1110MGjSIbt26sd9++zF79mw++ugjjj76aLp160bfvn25+eabk2O57DK6du1KWVkZ+++/P927d9+o+HOHuu7Tp0/VUNcAe+yxB8OGDWOvvfZi2bJlnHvuubRp04azzz6bsrIyBgwYQK9evdapr6SkhJ49e3LOOedUPXKzLoYMGcLhhx/OgQceWLXsxBNPpKKiouqJbZvKw1AX2cMvLuTGcXN4d/kndGjTgssG7MGxPXcsdli2Bch3GGq/FxuPo48+mosvvpiDDz64xvUehroRqbxTo7KTrvJODcD/gNbgju25o993m7nly5fTu3dvunfvvt4ksDGcCIqotjs1/A9pZtW1adOG1157rd7rdR9BERXqTg2zSo2t6dc23cb8zZ0IiqgQd2qYVSopKWHp0qVOBhkSESxdupSSkpK8tnPTUBFdNmCPdfoIYNPv1DCr1LFjRxYsWMDixYuLHYo1oJKSEjp27JjXNk4ERVTZD+A7NawQmjdvXvUtWbPaOBEU2eZ6p4ZvJTTLjswkAn+w1Z1vazXLlkx0FhdiZMUtWSEGIDOzzVcmEoE/2PLj21rNsiUTTUP+YMtPhzYtWFjDa7M53NbqJj7LmoZ4z2fiisD36+enEAOQ1Qc38VnWNNR7PhOJYHP9YNtcHdtzR/77+K7s2KYFAnZs04L/Pr5r0c+83cRnWdNQ7/mCNg1JOhz4DdAU+FNE3FBt/c7ACKBNWmZoRIyt7zh8v37+NsfbWt3EZ1nTUO/5giUCSU2BYcChwAJgsqTRETEzp9gVwP0R8QdJpcBYoFMh4tkcP9gsP5tz34VZITTUe76QTUO9gbkR8WZEfAbcCxxTrUwA26bTrYF3CxiPNXJu4rOsaaj3fCGbhnYEcp8RtwDoU63M1cDjkr4PbAMcUlNFkoYAQwB23nnneg/UGgc38VnWNNR7vmBPKJP0LeDwiDgrnf8O0CciLsgpc0kaw68k7QfcAZRFxNr11bulPaHMzKwh1PaEskI2DS0EdsqZ75guy3UmcD9ARDwHlADtChiTmZlVU8hEMBnYTVJnSVsBJwOjq5V5BzgYQNJeJInAY+aamTWggiWCiFgNXACMA2aR3B00Q9K1kgamxX4InC3pJeAeYHD4KRpmZg2qoN8jSL8TMLbasitzpmcCFYWMwczMapeJbxabmdn6ORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY1q2tBSU2A7kAH4BPg1Yh4v1CBmZlZw9hgIpC0K/Bj4BDgdWAxUALsLmkl8EdgRESsLWSgZmZWGHW5IvgF8AfgexERuSskfRn4NvAdYET9h2dmZoW2wT6CiBgUEU9XTwLpuvcj4paIqDEJSDpc0hxJcyUNXU+ZEyXNlDRD0sj8D8HMzDZFnTuLJZ0gqVU6/TNJD0rau5byTYFhwBFAKTBIUmm1MrsBPwEqIqILcNFGHIOZmW2CfO4a+llEfCSpL3AwcAdJk9H69AbmRsSbEfEZcC9wTLUyZwPDImIZJFcYecRjZmb1IJ9EsCb9fRQwPCLGAFvVUn5HYH7O/IJ0Wa7dSTqdn5X0vKTDa6pI0hBJUyRNWbx4cR4hm5nZhuSTCBZK+iNwEjBW0tZ5bl+TZsBuQH9gEHC7pDbVC0XE8Igoj4jy9u3bb+IuzcwsVz4f5CcC44ABEbEc2A64rJbyC4GdcuY7pstyLQBGR8SqiHgLeI0kMZiZWQPJJxF8BRgTEa9L6g+cALxQS/nJwG6SOkvaCjgZGF2tzMMkVwNIakfSVPRmHjGZmdkmyicR/B1YI+lrwHCSs/313u4ZEauBC0iuImYB90fEDEnXShqYFhsHLJU0E5gAXBYRSzfiOMzMbCOphq8H1FxQmhYRe0v6EfBJRPxO0osR0bOwIa6rvLw8pkyZ0pC7NDNr9CRNjYjymtblc0WwStIg4DTg0XRZ800NzszMiiufRPBdYD/guoh4S1Jn4O7ChGVmZg2lzqOPRsRMST8Gdk7n3wJ+WajAzMysYeQzxMQ3gOnA/6bzPSRVvwvIzMwamXyahq4mGTZiOUBETAe+WoCYzMysAeXVWRwRH1Rb5mcQmJk1cnXuIwBmSPo20DQdNfQHwL8KE5aZmTWUfK4Ivg90Af5D8kWyD/Cw0WZmjV4+dw2tBC5Pf8zMbAuRz11D/8gdGVRSW0njChOWmZk1lHyahtqlo44CkD5M5sv1H5KZmTWkfBLBWkk7V85I2gWo20BFZma22crnrqHLgYmSngIEHAAMKUhUZmbWYPLpLP7f9GH1+6aLLoqIJYUJy8zMGko+ncXHkXyp7NGIeBRYLenYwoVmZmYNIZ8+gqtyv1mcdhxfVf8hmZlZQ8onEdRUNp8+BjMz2wzlkwimSLpZ0q7pz83A1EIFZmZmDSPfISY+A+5Lf/4DnF+IoMzMrOHkc9fQx8DQAsZiZmZFUOdEIGkCNXyBLCIOqteIzMysQeXT2XtpznQJ8E1gdf2GY2ZmDS2fpqHqHcPPSnqhnuMxM7MGlk/T0HY5s02AfYDW9R6RmZk1qHyahqaS9BGIpEnoLeDMQgRlZmYNJ5+moc6FDMTMzIojn7GGTpDUKp2+QtKD6SB0ZmbWiOXzhbKfRcRHkvoChwB3AH8oTFhmZtZQ8kkEa9LfRwHDI2IMsFX9h2RmZg0pn0SwUNIfgZOAsZK2znN7MzPbDOXzQX4iMA4YkA5BvR1wWUGiMjOzBrPBRCCpJUBErIyIByPi9XR+UUQ8nlumhm0PlzRH0lxJ6x2nSNI3JYWk8o07DDMz21h1uSL4H0m/ktRP0jaVCyV9VdKZksYBh1ffSFJTYBhwBFAKDJJUWkO5VsCFwKSNPQgzM9t4G0wEEXEw8ATwPWCGpA8kLQX+CvwXcHpEjKph097A3Ih4MyI+A+4Fjqmh3M+BXwKfbuQxmJnZJqjrF8oeA16JiPl51L0jkFt+AdAnt0D6PYSdImKMpPX2N0gaAgwB2HnnnfMIwczMNqROncUREcDY+tyxpCbAzcAP67D/4RFRHhHl7du3r88wzMwyL5+7hqZJ6pVH+YXATjnzHdNllVoBZcA/Jc0D9gVGu8PYzKxh5TPoXB/g1PRD+2OSweciIrqtp/xkYDdJnUkSwMnAtytXRsQHQLvKeUn/BC6NiCn5HICZmW2afBLBgHwqjojVki4g+e5BU+DOiJgh6VpgSkSMzqc+MzMrjA0mAkklwDnA14BXgDsiok5PJouIsVTrW4iIK9dTtn9d6jQzs/pVlz6CEUA5SRI4AvhVQSMyM7MGVZemodKI6Aog6Q7Aj6c0M9uC1OWKYFXlRF2bhMzMrPGoyxVBd0kfptMCWqTzlXcNbVuw6MzMrOA2mAgiomlDBGJmZsXh5wmYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWVcQROBpMMlzZE0V9LQGtZfImmmpJclPSFpl0LGY2ZmX1SwRCCpKTAMOAIoBQZJKq1W7EWgPCK6AaOA/1eoeMzMrGaFvCLoDcyNiDcj4jPgXuCY3AIRMSEiVqazzwMdCxiPmZnVoJCJYEdgfs78gnTZ+pwJPFbTCklDJE2RNGXx4sX1GKKZmW0WncWSThqRLgQAAAbwSURBVAXKgRtrWh8RwyOiPCLK27dv37DBmZlt4ZoVsO6FwE458x3TZeuQdAhwOfD1iPhPAeMxM7MaFPKKYDKwm6TOkrYCTgZG5xaQ1BP4IzAwIt4vYCxmZrYeBUsEEbEauAAYB8wC7o+IGZKulTQwLXYj0BJ4QNJ0SaPXU52ZmRVIIZuGiIixwNhqy67MmT6kkPs3M7MN2yw6i83MrHicCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMq6gXyjb3FzzyAxmvvthscMwM8tbaYdtueobXQpSt68IzMwyLlNXBIXKpmZmjZmvCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws4wqaCCQdLmmOpLmShtawfmtJ96XrJ0nqVMh4zMzsiwqWCCQ1BYYBRwClwCBJpdWKnQksi4ivAb8GflmoeMzMrGaFvCLoDcyNiDcj4jPgXuCYamWOAUak06OAgyWpgDGZmVk1hUwEOwLzc+YXpMtqLBMRq4EPgO2rVyRpiKQpkqYsXry4QOGamWVTo+gsjojhEVEeEeXt27cvdjhmZluUQiaChcBOOfMd02U1lpHUDGgNLC1gTGZmVk0hE8FkYDdJnSVtBZwMjK5WZjRwejr9LeDJiIgCxmRmZtU0K1TFEbFa0gXAOKApcGdEzJB0LTAlIkYDdwB3S5oL/B9JsjAzswZUsEQAEBFjgbHVll2ZM/0pcEIhYzAzs9o1is5iMzMrHCcCM7OMcyIwM8s4JwIzs4xTY7tbU9Ji4O1NqKIdsKSewqlPjis/m2tcZoWyqe/5XSKixm/kNrpEsKkkTYmI8mLHUZ3jys/mGpdZoRTyPe+mITOzjHMiMDPLuCwmguHFDmA9HFd+Nte4zAqlYO/5zPURmJnZurJ4RWBmZjmcCMzMMi4TiUDSTpImSJopaYakC4sdE4CkEkkvSHopjeuaYseUS1JTSS9KerTYsVSSNE/SK5KmS5pS7HjMCkHSnZLel/RqzrLtJP1D0uvp77b1tb9MJAJgNfDDiCgF9gXOl1Ra5JgA/gMcFBHdgR7A4ZL2LXJMuS4EZhU7iBocGBE9/D0C24LdBRxebdlQ4ImI2A14Ip2vF5lIBBGxKCKmpdMfkXy4VX9+coOLxIp0tnn6s1n03kvqCBwF/KnYsZhlTUQ8TfKMllzHACPS6RHAsfW1v0wkglySOgE9gUnFjSSRNr9MB94H/hERm0VcwC3Aj4C1xQ6kmgAelzRV0pBiB2PWgHaIiEXp9L+BHeqr4kwlAkktgb8DF0XEh8WOByAi1kRED5JnOveWVFbsmCQdDbwfEVOLHUsN+kbE3sARJE18/YodkFlDSx/pW2+tB5lJBJKakySBv0XEg8WOp7qIWA5M4IvtgsVQAQyUNA+4FzhI0l+LG1IiIhamv98HHgJ6FzciswbznqSvAKS/36+vijORCCSJ5PnIsyLi5mLHU0lSe0lt0ukWwKHA7OJGBRHxk4joGBGdSJ4j/WREnFrksJC0jaRWldPAYcCrtW9ltsUYDZyeTp8O/E99VVzQZxZvRiqA7wCvpO3xAD9Nn6lcTF8BRkhqSpKU74+IzeZWzc3QDsBDSV6nGTAyIv63uCGZ1T9J9wD9gXaSFgBXATcA90s6k2Qo/hPrbX8eYsLMLNsy0TRkZmbr50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYFaNpDXp6KYz0pFhfyhpo/9XJP00Z7pT7oiSZpsDJwKzL/okHd20C8mX/I4guY97Y/10w0XMiseJwKwW6VAWQ4ALlGgq6UZJkyW9LOl7AJL6S3pa0hhJcyTdJqmJpBuAFukVxt/SaptKuj294ng8/Va5WdE4EZhtQES8CTQFvgycCXwQEb2AXsDZkjqnRXsD3wdKgV2B4yNiKJ9fYZySltsNGJZecSwHvtlwR2P2RU4EZvk5DDgtHapkErA9yQc7wAsR8WZErAHuAfqup463IqJyqJOpQKcCxmu2QVkZa8hso0n6KrCGZLRHAd+PiHHVyvTni8MCr2/8lv/kTK8B3DRkReUrArNaSGoP3Ab8Ph0DfhxwbjqsOZJ2T0dCheR5Ep3TO4xOAiamy1dVljfbHPmKwOyLWqRNP81Jnnd9N1A5fPmfSJpypqXDmy/m80cGTgZ+D3yN5NkSD6XLhwMvS5oGXN4QB2CWD48+alYP0qahSyPi6GLHYpYvNw2ZmWWcrwjMzDLOVwRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ9/8BdNg5I78mTtsAAAAASUVORK5CYII=\n", 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ppzFz5swGxVlI7mLCzJpEnz596NevH7vuuivbbbcdlZWVG1zm8ssvZ/jw4fTs2ZO9996b7bffHoDdd9+dESNGMGDAAABOO+00+vXrV2vVz4acddZZnHzyyZSVlbHrrrvSs2dPOnbsCMA111zD4YcfTmlpKRUVFaxcuRKAX//615x99tn07t2bNWvWMHjwYG6++WZuuOEGpkyZQqtWrejZsyeHHHIId911F9deey1t27alffv2/OlPf8o7xkJrce8srqioCL+YxmzD5s6dy2677VbsMJq9tWvXsnr1akpKSnjttdc44IADmD9/PptttlmxQ2uw2v72kmZEREVt5X1FYGaZtmrVKvbdd19Wr15NRHDTTTe16CTQEE4EZpZpHTp0yPzrb91YbGaWcU4EZmYZ50RgZpZxTgRmZhnnRGBmBXPVVVfRs2dPevfuTd++fXn22WeLHRILFy6kXbt29O3bl7KyMs4444y8ekZduHAh5eXleW1zyJAhtTZIjx8/nmuuuQaAUaNGcd111wFw2WWXMXnyZABuuOGGgvdP5LuGzKwgnn76aR566CFmzpzJ5ptvznvvvcenn35a7LAA2HHHHZk1axZr1qxhv/3244EHHqju6whgzZo1tGlT+MPjsGHDGDZs2BemX3nlldXDN9xwAyeeeCJf+tKXChaHrwjMDIAHnl9C5TWP0ePiCVRe8xgPPL9xvcK/8847dOnSpbqvny5dutC1a1dg3RfMTJ8+nSFDhgCwcuVKvvOd79CrVy969+7NX//6VwAeeeQR9tprL3bffXeOOeaY6id8L774YsrKyujduzcXXnghAPfeey/l5eX06dOHwYMH1xljmzZt2HvvvVmwYAF///vf2WeffRg2bBhlZWVA7V1dQ5IoTjjhBHbbbTe+9a1vVZ+xX3nllfTv35/y8nJGjhy5TrfZd9xxR3VX1FXdYd9+++2cc845X4hrxIgRjBs3jl//+te8/fbb7Lvvvuy7777cdtttnHfeedXlbrnlFs4///z6/Dnq5ERgZjzw/BJ+fN9LLFnxMQEsWfExP77vpY1KBgcddBCLFi1i55135qyzzuLxxx/f4DI/+9nP6NixIy+99BIvvvgi++23H++99x4///nPmTx5MjNnzqSiooLrr7+eZcuWcf/99zN79mxefPFFLr30UiA5GE+aNIkXXniB8ePH17m9VatW8eijj1b3Vjpz5kx+9atf8corr6zT1fUzzzzDLbfcwvPPPw/A/PnzOeuss5g7dy5bbrklN910EwDnnHMO06ZN4+WXX+bjjz/moYceWmdbs2bN4qabbuKUU06p12f4/e9/n65duzJlyhSmTJnCsccey4MPPsjq1asB+OMf/1jvddXFicDMuHbSfD5evXadaR+vXsu1k+Y3eJ3t27dnxowZjB49mtLSUo477jhuv/32OpeZPHkyZ5/9eafEnTt35plnnmHOnDlUVlbSt29fxowZw5tvvknHjh0pKSnh1FNP5b777quuOqmsrGTEiBHccsstrF27ttbtvPbaa/Tt25fKykoOO+wwDjnkEAAGDBhAjx49gHW7um7fvn11V9fAOn0lnXjiiUydOhWAKVOmMHDgQHr16sVjjz3G7Nmzq7c5fHjSIfPgwYP54IMPWLFiRb4fKe3bt2e//fbjoYceYt68eaxevbo6iW0MtxGYGW+v+Div6fXVunVrhgwZwpAhQ+jVqxdjxoxhxIgRtGnTprqBNrd759pEBAceeCB33nnnF+Y999xzPProo4wbN47f/va3PPbYY9x88808++yzTJgwgT322IMZM2aw9dZbr7NcVRtBTbndZtclt3vqqvFPPvmEs846i+nTp7PddtsxatSodfattmUa4rTTTuPqq69m11135TvfaZzXuPiKwMzo2qldXtPrY/78+bz66qvV47NmzWKHHXYAkjaCGTNmAFS3AwAceOCB3HjjjdXjy5cvZ8899+Spp55iwYIFAHz00Ue88sorrFy5kvfff59DDz2UX/7yl7zwwgtAcrY/cOBArrzySkpLS1m0aFGD4q+rq+u33nqLp59+GoCxY8cyaNCg6oN+ly5dWLlyJePGjVtnfXfffTeQXGl07NixuofTDenQoQMffvhh9fjAgQNZtGgRY8eOrb7K2FhOBGbGRUN3oV3b1utMa9e2NRcN3aXB61y5cmV19869e/dmzpw5jBo1Cki6lz733HOpqKigdevPt3vppZeyfPny6sbeKVOmUFpayu23387w4cPp3bs3e+21F/PmzePDDz/k8MMPp3fv3gwaNIjrr78+2ZeLLqJXr16Ul5ez995706dPnwbFn9vV9cCBA6u7ugbYZZdduPHGG9ltt91Yvnw5Z555Jp06deL000+nvLycoUOH0r9//3XWV1JSQr9+/TjjjDOqX7lZHyNHjuTggw9m3333rZ527LHHUllZWf3Gto3lbqjNNlH5dkP9wPNLuHbSfN5e8TFdO7XjoqG7cGS/bQsYoTXU4Ycfzvnnn8/+++9f63x3Q21mDXJkv2194G/mVqxYwYABA+jTp896k0BDOBGYmbUQnTp14pVXXmn09bqNwGwT1tKqfm3jNeRv7kRgtokqKSlh2bJlTgYZEhEsW7aMkpKSvJZz1VCRuYHOCqVbt24sXryYpUuXFjsUa0IlJSV069Ytr2WcCIqo6rH+qic6qx7rB5wMbKO1bdu2+ilZs7o4ERRRXY/1OxHUzldQljVN8Z3PTCJojgeQQj3Wv6nyFZRlTVN95zPRWFyInhUbQyEe628sjd0lcWMoRMdoZs1ZU33nM5EImusBpBCP9TeG5po4fQVlWdNU3/lMJILmegA5st+2/NfRvdi2UzsEbNupHf91dK+iV3M018TZnK+gzAqhqb7zmWgj6NqpHUtqOeg3hwNIc3ysv7kmzouG7rJOfSk0jysos0Jpqu98Jq4ImmsVTHPVXM+8m+sVlFmhNNV3vqC9j0o6GPgV0Br4Q0RcU2P+9sAYoFNa5uKImFjXOhva+2hzvGuouap5pwIkidMHXbOWq67eRwuWCCS1Bl4BDgQWA9OA4RExJ6fMaOD5iPidpDJgYkR0r2u97oa6aThxmm1aitUN9QBgQUS8ngZxF3AEMCenTABbpsMdgbcLGI/loTm2XZhZYRSyjWBbIPcdcYvTablGASdKWgxMBL5X24okjZQ0XdJ095tiZta4it1YPBy4PSK6AYcCd0j6QkwRMToiKiKiorS0tMmDNDPblBUyESwBtssZ75ZOy3UqcA9ARDwNlABdChiTmZnVUMhEMA3YSVIPSZsBxwPja5R5C9gfQNJuJInAdT9mZk2oYIkgItYA5wCTgLnAPRExW9KVkoalxX4AnC7pBeBOYET4LRpmZk2qoE8Wp88ETKwx7bKc4TlAZSFjMDOzuhW7sdjMzIrMicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMq5NfQtKagX0AboCHwMvR8S7hQrMzMyaxgYTgaQdgR8BBwCvAkuBEmBnSauA3wNjIuKzQgZqZmaFUZ8rgp8DvwO+GxGRO0PSl4FvA/8JjGn88MzMrNA22EYQEcMj4omaSSCd925E3BARtSYBSQdLmi9pgaSL11PmWElzJM2WNDb/XTAzs41R78ZiScdI6pAO/1TSfZJ2r6N8a+BG4BCgDBguqaxGmZ2AHwOVEdETOK8B+2BmZhshn7uGfhoRH0oaBOwP3EpSZbQ+A4AFEfF6RHwK3AUcUaPM6cCNEbEckiuMPOIxM7NGkE8iWJv+PgwYHRETgM3qKL8tsChnfHE6LdfOJI3OT0l6RtLBta1I0khJ0yVNX7p0aR4hm5nZhuSTCJZI+j1wHDBR0uZ5Ll+bNsBOwBBgOHCLpE41C0XE6IioiIiK0tLSjdykmZnlyudAfiwwCRgaESuArYCL6ii/BNguZ7xbOi3XYmB8RKyOiDeAV0gSg5mZNZF8EsFXgAkR8aqkIcAxwHN1lJ8G7CSph6TNgOOB8TXKPEByNYCkLiRVRa/nEZOZmW2kfBLBX4G1kr4GjCY521/v7Z4RsQY4h+QqYi5wT0TMlnSlpGFpsUnAMklzgCnARRGxrAH7YWZmDaRaHg+ovaA0MyJ2l/RD4OOI+I2k5yOiX2FDXFdFRUVMnz69KTdpZtbiSZoRERW1zcvnimC1pOHAScBD6bS2GxucmZkVVz6J4DvAXsBVEfGGpB7AHYUJy8zMmkq9ex+NiDmSfgRsn46/AfyiUIGZmVnTyKeLiW8As4D/Tcf7Sqp5F5CZmbUw+VQNjSLpNmIFQETMAr5agJjMzKwJ5dVYHBHv15jmdxCYmbVw9W4jAGZL+jbQOu019PvAPwoTlpmZNZV8rgi+B/QE/k3yINn7uNtoM7MWL5+7hlYBl6Q/Zma2icjnrqG/5fYMKqmzpEmFCcvMzJpKPlVDXdJeRwFIXybz5cYPyczMmlI+ieAzSdtXjUjaAahfR0VmZtZs5XPX0CXAVEmPAwL2AUYWJCozM2sy+TQW/2/6svo900nnRcR7hQnLzMyaSj6NxUeRPFT2UEQ8BKyRdGThQjMzs6aQTxvB5blPFqcNx5c3fkhmZtaU8kkEtZXNp43BzMyaoXwSwXRJ10vaMf25HphRqMDMzKxp5NvFxKfA3enPv4GzCxGUmZk1nXzuGvoIuLiAsZiZWRHUOxFImkItD5BFxH6NGpGZmTWpfBp7L8wZLgG+Caxp3HDMzKyp5VM1VLNh+ClJzzVyPGZm1sTyqRraKme0FbAH0LHRIzIzsyaVT9XQDJI2ApFUCb0BnFqIoMzMrOnkUzXUo5CBmJlZceTT19Axkjqkw5dKui/thM7MzFqwfB4o+2lEfChpEHAAcCvwu8KEZWZmTSWfRLA2/X0YMDoiJgCbNX5IZmbWlPJJBEsk/R44DpgoafM8lzczs2YonwP5scAkYGjaBfVWwEUFicrMzJrMBhOBpPYAEbEqIu6LiFfT8Xci4pHcMrUse7Ck+ZIWSFpvP0WSvikpJFU0bDfMzKyh6nNF8D+S/lvSYElbVE2U9FVJp0qaBBxccyFJrYEbgUOAMmC4pLJaynUAzgWebehOmJlZw20wEUTE/sCjwHeB2ZLel7QM+DPwH8DJETGulkUHAAsi4vWI+BS4CziilnI/A34BfNLAfTAzs41Q3wfKHgZeiohFeax7WyC3/GJgYG6B9DmE7SJigqT1tjdIGgmMBNh+++3zCMHMzDakXo3FERHAxMbcsKRWwPXAD+qx/dERURERFaWlpY0ZhplZ5uVz19BMSf3zKL8E2C5nvFs6rUoHoBz4u6SFwJ7AeDcYm5k1rXw6nRsInJgetD8i6XwuIqL3espPA3aS1IMkARwPfLtqZkS8D3SpGpf0d+DCiJiezw6YmdnGyScRDM1nxRGxRtI5JM8etAZui4jZkq4EpkfE+HzWZ2ZmhbHBRCCpBDgD+BrwEnBrRNTrzWQRMZEabQsRcdl6yg6pzzrNzKxx1aeNYAxQQZIEDgH+u6ARmZlZk6pP1VBZRPQCkHQr4NdTmpltQupzRbC6aqC+VUJmZtZy1OeKoI+kD9JhAe3S8aq7hrYsWHRmZlZwG0wEEdG6KQIxM7Pi8PsEzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyrqCJQNLBkuZLWiDp4lrmXyBpjqQXJT0qaYdCxmNmZl9UsEQgqTVwI3AIUAYMl1RWo9jzQEVE9AbGAf+vUPGYmVntCnlFMABYEBGvR8SnwF3AEbkFImJKRKxKR58BuhUwHjMzq0UhE8G2wKKc8cXptPU5FXi4thmSRkqaLmn60qVLGzFEMzNrFo3Fkg/uqS0AAAbwSURBVE4EKoBra5sfEaMjoiIiKkpLS5s2ODOzTVybAq57CbBdzni3dNo6JB0AXAJ8PSL+XcB4zMysFoW8IpgG7CSph6TNgOOB8bkFJPUDfg8Mi4h3CxiLmZmtR8ESQUSsAc4BJgFzgXsiYrakKyUNS4tdC7QH7pU0S9L49azOzMwKpJBVQ0TERGBijWmX5QwfUMjtm5nZhjWLxmIzMyseJwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4wr6ANlzc0VD85mztsfFDsMM7O8lXXdksu/0bMg6/YVgZlZxmXqiqBQ2dTMrCXzFYGZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZVxBE4GkgyXNl7RA0sW1zN9c0t3p/GcldS9kPGZm9kUFSwSSWgM3AocAZcBwSWU1ip0KLI+IrwG/BH5RqHjMzKx2hbwiGAAsiIjXI+JT4C7giBpljgDGpMPjgP0lqYAxmZlZDYVMBNsCi3LGF6fTai0TEWuA94Gta65I0khJ0yVNX7p0aYHCNTPLphbRWBwRoyOiIiIqSktLix2OmdkmpZCJYAmwXc54t3RarWUktQE6AssKGJOZmdVQyEQwDdhJUg9JmwHHA+NrlBkPnJwOfwt4LCKigDGZmVkNbQq14ohYI+kcYBLQGrgtImZLuhKYHhHjgVuBOyQtAP6PJFmYmVkTKlgiAIiIicDEGtMuyxn+BDimkDGYmVndWkRjsZmZFY4TgZlZxjkRmJllnBOBmVnGqaXdrSlpKfDmRqyiC/BeI4XTmBxXfpprXGaFsrHf+R0iotYncltcIthYkqZHREWx46jJceWnucZlViiF/M67asjMLOOcCMzMMi6LiWB0sQNYD8eVn+Yal1mhFOw7n7k2AjMzW1cWrwjMzCyHE4GZWcZlIhFI2k7SFElzJM2WdG6xYwKQVCLpOUkvpHFdUeyYcklqLel5SQ8VO5YqkhZKeknSLEnTix2PWSFIuk3Su5Jezpm2laS/SXo1/d25sbaXiUQArAF+EBFlwJ7A2ZLKihwTwL+B/SKiD9AXOFjSnkWOKde5wNxiB1GLfSOir58jsE3Y7cDBNaZdDDwaETsBj6bjjSITiSAi3omImenwhyQHt5rvT25ykViZjrZNf5pF672kbsBhwB+KHYtZ1kTEEyTvaMl1BDAmHR4DHNlY28tEIsglqTvQD3i2uJEk0uqXWcC7wN8iolnEBdwA/BD4rNiB1BDAI5JmSBpZ7GDMmtA2EfFOOvxPYJvGWnGmEoGk9sBfgfMi4oNixwMQEWsjoi/JO50HSCovdkySDgfejYgZxY6lFoMiYnfgEJIqvsHFDsisqaWv9G202oPMJAJJbUmSwF8i4r5ix1NTRKwApvDFesFiqASGSVoI3AXsJ+nPxQ0pERFL0t/vAvcDA4obkVmT+ZekrwCkv99trBVnIhFIEsn7kedGxPXFjqeKpFJJndLhdsCBwLziRgUR8eOI6BYR3UneI/1YRJxY5LCQtIWkDlXDwEHAy3UvZbbJGA+cnA6fDPxPY624oO8sbkYqgf8EXkrr4wF+kr5TuZi+AoyR1JokKd8TEc3mVs1maBvg/iSv0wYYGxH/W9yQzBqfpDuBIUAXSYuBy4FrgHsknUrSFf+xjbY9dzFhZpZtmagaMjOz9XMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjCrQdLatHfT2WnPsD+Q1OD/FUk/yRnuntujpFlz4ERg9kUfp72b9iR5yO8Qkvu4G+onGy5iVjxOBGZ1SLuyGAmco0RrSddKmibpRUnfBZA0RNITkiZImi/pZkmtJF0DtEuvMP6Srra1pFvSK45H0qfKzYrGicBsAyLidaA18GXgVOD9iOgP9AdOl9QjLToA+B5QBuwIHB0RF/P5FcYJabmdgBvTK44VwDebbm/MvsiJwCw/BwEnpV2VPAtsTXJgB3guIl6PiLXAncCg9azjjYio6upkBtC9gPGabVBW+hoyazBJXwXWkvT2KOB7ETGpRpkhfLFb4PX13/LvnOG1gKuGrKh8RWBWB0mlwM3Ab9M+4CcBZ6bdmiNp57QnVEjeJ9EjvcPoOGBqOn11VXmz5shXBGZf1C6t+mlL8r7rO4Cq7sv/QFKVMzPt3nwpn78ycBrwW+BrJO+WuD+dPhp4UdJM4JKm2AGzfLj3UbNGkFYNXRgRhxc7FrN8uWrIzCzjfEVgZpZxviIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLuP8Pzh5F445wR94AAAAASUVORK5CYII=\n", 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" ] @@ -1460,12 +1469,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1498,12 +1507,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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srDzoetjQUtGlpaUlmZmZ+xpaKrq+erV16NDhQHPD2LFjez388MP/Xr58ecmdd965oarqPy0ybdq0ORBbtOsvVVVVJWVkZBzWMtGgJEFERGLlvDvXk5J+8IUpJb2G8+7UUtEN+OKLL5JycnL2VVVV2TPPPNPgGI7zzjtv13PPPdepurqaNWvWpM6fP79D+P7Vq1enn3rqqYe1TDQoSRARkVg5bcw2ht+zhvZd9oJB+y57GX7PGk4bo6WiGzBx4sQNQ4cO7VdYWJjfp0+fyobqX3fddTt69+5dddJJJw349re/nTt48OADAzbXrl2bkp6e7jk5OYd924SWihYRaQW0VHT9WvJS0b/+9a87H3PMMTW33357nd+blooWERGpQ0teKvrYY4/df/PNNx9RYqelokVEpNVryUtFh7pVjoRaEkRERCQiJQkiIiISkZIEERERiUhJgoiIxNy6XesOe7Y/iT8lCSIiElMbdm9Inb1q9nEbdm9olEThW9/6Vm6nTp0G9enTp/+RvH/o0KF9c3NzB/Tt27fg1FNPzV+0aFHEZZRD9f761792BJgzZ067k08+OT8/P7+gd+/e/SdMmHBCpPfFy4wZM47LyckZcMEFF5zUWMdUkiAiIjFVVF7UrrK6MqmovKhdYxzvhhtu2DJr1qxPjuYYTz755KqysrKSUaNGbbn99tt71N4fWq75ySefXBW662HMmDG9pk2btqa0tLRk+fLlS6+55pqYTQp1JG666abtU6dOXdOYx1SSICIiMbNh94bUdbvWpeV0yKlat2tdWmO0Jlx00UW7o5m1MBrDhg3bvWbNmnQ4dLnm2nW3bduWkpOTsw8gJSWFIUOGVAJMmDDhhLvuuqtLqF6fPn36l5WVpQE8/PDDmXl5eQV9+/YtuPzyy3tBYAbEr3zlKyf27du3oG/fvgVvvPFGO4CpU6d2GjhwYL/8/PyCUaNG9ayurqa6uporr7wyt0+fPv3z8vIKfv3rX3cG+K//+q/OJ554Yv+8vLyCiy++uHdj/C0i0TwJIiISM0XlRe0yUjNqzIyM1IyaovKidpeddNmOeMcV8sILL3TMz88/sKZBaLlmgEcffbRzeN2xY8du6tev34DTTz9911e/+tWdP/jBD7ZmZGTUOW1xcXFxm/vvv7/rvHnzSrt27Vq9adOmZIDx48fnnHPOObvuuuuuldXV1ezcuTN54cKFbZ577rlOxcXFpenp6X7ttdfmPPLII5mDBg3as3HjxtRPPvlkKcCWLVuSASZPnnz8mjVrFrdt29ZDZbGglgQREYmJUCtCx7SO+wE6pnXc31itCUdr9OjRvfPz8wvmzZvX/qGHHlobVr69rvfcf//9G+fNm7fswgsv/OzZZ5/NPP/88/PqO8drr712zCWXXLK9a9eu1QBdunTZD/D+++93+PGPf1wBgRaJzMzM/f/85z87LFmyJGPQoEH98vPzC959991jVq1alZ6fn1+1du3a9O985zs9nnvuuWOOO+64/QB9+/bd841vfKPX1KlTO6WmpsZsfQW1JIiISEyEtyIANFVrQnV1NQMGDCgAGDFixI4//OEPG2rXefLJJ1ede+65hywlHb5ccyT9+/ev6t+/f8WECRMqMjMzTykvL09OSUnx0OJQAFVVVXa4Mbu7fetb39o6ZcqUQ1bIXLJkScmLL754zCOPPJI9c+bMTv/7v//76dy5cz/5xz/+0eGll17qeP/993ctKytbmpra+LlXTFsSzGyEmZWZ2Qozmxhhf46ZzTWzj8zsYzP7WizjERGRplG7FSGkKVoTUlJSKC0tLSktLS2JlCAcqWeeeaZjKBlYvHhxm+TkZM/Kytqfm5tb9a9//asdwLvvvpuxfv36dIDhw4d/9vLLLx9XXl6eDBDqbjj77LN33XfffdkQSGi2bt2aPGLEiM9mz5593Pr161NCdZcvX562cePGlP3793P99dfvuOeee9YvXrw4Y//+/axcuTLtkksu2TVlypT1u3fvTt65c2dMuhxi1pJgZsnAFOArwDqgyMxmuXtJWLVfAM+6+x/NrAB4FciNVUwiItI0arcihDRGa8Ill1zSa/78+R22b9+e0qVLl5MnTpy4ob4VDhvLU089lTlx4sQebdq0qUlJSfFHH310dUpKCqNHj97+17/+NfOkk07qP3jw4M979uxZCVBYWFh5xx13bDznnHPyk5KSfMCAAV88//zzn/7xj3/89/XXX98zLy8vKykpiYcffnjNhRde+PkvfvGL9cOGDcurqakhNTXVJ0+e/O+MjIyaMWPG5NbU1BjApEmT1lVXV9uoUaN67dq1K9nd7cYbb9yclZW1v/7oj0wsuxuGAivcfRWAmT0DXAaEJwkOHBN83RFotIxPRETio/zz8pSVO1a2aZPSpqayuvKQFmvHWbljZZvyz8tTjm93/GHfpfDyyy+vPpr4Pvzww7JI5evXr19c3/tmz569KlJ5+/bt/b333ot4S+Ytt9yy9ZZbbjlogaUePXpUz5kzZ2XtujfddNP2m2666ZAxEaGBlOEWLFgQ8TM0tlgmCd2AtWHb64DTa9W5G3jdzG4B2gEXRjqQmY0FxgLk5OQ0eqAiItJ42qW2q/la76812ErQLrVdvf3/8XbsscdWjxkzptfdd9+9rjmsEDljxozj7r333hMGDhx4yFiLIxXvgYvfBv7s7g+Y2ZnAX8xsgLsf9A/H3acD0wEKCwtjNopTREQaVFNTU2NJSUl1/r+4Q1qHmvxO+ZVNGVQsvP7664f82k9kdbVENCTYlRExYYtq4KKZdTezC4Kv080smlmz1gPhs1h1D5aFGwM8C+Du84A2QFY0MYmISFwsqaio6BjqI5fmraamxioqKjoCSyLtb7AlwcxuAG4mMGbgRKAnMJU6ugbCFAF9zKwXgeTgamBUrTr/BoYBfzazfgSShIqGYhIRkfiorq6+sby8/NHy8vIBaK6dlqAGWFJdXX1jpJ3RdDfcSmAQ4gcA7r7czDrX/xZw92ozuxl4DUgGHnf3pWY2CSh291nAHcAMM7udwCDG691d3QkiIglqyJAhm4FL4x2HNI1okoRKd98bNhlGMhBVM5O7v0rgtsbwsrvCXpcAZ0cdrYiIiDSZaJqK3jOznwBtguMSZgKzYxuWiIiIxFs0ScJPgF1AKfBDYA7w81gGJSIiIvEXTXdDKjDN3f8IYGZJQBrQ7G9vERERkbpF05Iwl8BERyHtgDdjE46IiIgkimiShLbuviu0EXydEbuQREREJBFEkyR8YWaDQhtmdgrqahAREWnxohmTcDvwopmtIXDrYw8C0yk3bx8/C3Mmwc510LE7DLsLTh4Z76gkGvruRESaRINJgrt/EJwNsV+wqMTd98Y2rBj7+Fl4+VbYtyewvXNtYBt0sUl0+u5ERJpMtFNqDgLygALgm2ZWe3rl5mXOpP9cZEL27QmUS2LTdyci0mSiWbvhzwSSg38B+4PFDvwtdmHF2M51h1feVBKxGT3RYkrU705EpAWKZkzCGUBB7eWbm7WO3QPN1JHK4yURm9ETMaZE/O5ERFqoaLoblgLZsQ6kSQ27C1LbHlyW2jZQHi+J2IyeiDEl4ncnItJCRdOS0BEoMbP5QFWo0N2viFlUsRb6Faxm9PolYkyJ+N2JiLRQ0SQJ98Q8ing4eWRiXVgSsRk9EWOCxPvuRERaqAa7G9x9TqRHUwTXqiRiM3oixiQiIk2mwSTBzE4zs/lmttPMKs2sysw+a4rgWpWTR8Ilk6FjD8ACz5dMju8v5kSMSUREmoy5e/0VzIqAa4FngKHA9UBPd/9FzKOLoLCw0IuLi+NxahGRZsvMFrh7YbzjkOYlmrsbkty9DEhx933uPgP4eozjEhERkTiLZuDi52aWBiwys/8GNgLJsQ1LRKSVSrQJzKRVi6Yl4fpgvZsJzLjYB7gyhjGJiLROoQnMdq4F/D8TmH38bLwjk1YqmiTha+5e6e473P2X7n4rMDzWgYmItDqJOIGZtGrRJAk3RCgb09iBiIi0eok4gZm0anWOSTCzq4CrgV5m9kLYrmOAHbEOTESk1UnUCcyk1apv4OKHwFagOzAlrHwX8FEsgxIRaZWG3XXwomqgCcwkrupMEtx9NbDazN4H9ri7m9mJQF8CS0WLiEhj0tokkmCiuQXybeBcM+sIvAksJNANMTqWgYmItEpam0QSSLSTKX1B4LbHP7r7N4CTYxuWiIiIxFtUSYKZnQZcA8wOlmkyJRERkRYumiRhAvBrYLa7LzGz3sA7sQ1LRERE4q3BMQnu/iaBsQih7VXA92MZlIiIiMRfffMkPODud5jZi0S4m8Hdr2jo4GY2AniIQPfEo+5+b4Q6I4G7g+dY5O6jog9fREREYqW+loSZweeHj+TAZpZMYH6FrwDrgCIzm+XuJWF1+gA/Bc529+1m1vlIziUiIiKNr755Ej4MPs85wmMPBVYEuycws2eAy4CSsDo3AVPcfXvwXJuP8FwiIiLSyOrrbviIeiZNcvdTGzh2NyB8ftF1wOm16uQFz/UegS6Ju939nxFiGQuMBcjJyWngtCIiItIY6utu+GbweTyBC/hfgtvXEFgyurHO3wc4n8D0z2+b2UB3P2htCHefDkwHKCws1GyPIiIiTaC+7oaVAGY2rFarwUdmthC4s4Fjrwd6hG13D5aFWwd84O77CEwBvZxA0lAUZfwiIiISI9HMk5BsZmeENszsdKKbTKkI6GNmvcwsjcBUzrNq1fk7gVYEzCyLQPfDqiiOLSIiIjEWzdoNNwJ/MrM2we09wA0Nvcndq83sZuA1AknF4+6+1MwmAcXuPiu476tmVkKgC+PH7r71SD6IiIiINC5zj66L38wyAeJ9ES8sLPTi4uJ4hiAi0uyY2QJ3L4x3HNK8RNOSAMQ/ORAREZGmFc2YBBEREWmFlCSIiIhIRFF1N5jZUCA3vL67/y1GMYmIiEgCaDBJMLM/AwXAv/jPJEoOKEkQERFpwaJpSTgDKHD3mlgHIyIiIokjmjEJS4HsWAciIiIiiSWaloSOQImZzQeqQoXufkXMohIREZG4iyZJuCfmUYiIiEjCaTBJcPc5wXUVQjN1Fbv7ltiGJSIiIvHW4JgEM7sSWAhcB4wGis3sG7EOTEREROIrmu6Gu4DT3H0TgJl1AV4HXoxlYCIiIhJf0dzdkBRKEII2R/k+ERERacaiaUl43cxeAZ4Obl9NYIlnERERacGiSRJ+BIwEzg5uPwE8F7OIREREJCFEc3eDAzODDxEREWkl6kwSzOwtdz/PzLYTWKvhwC4CuUOnmEcnIiIicVNfS8IFweespghEREREEkuddymELej0mLvvD38AjzVNeCIiIhIv0dzKeHL4hpklA6fFJhwRERFJFHUmCWZ2Z3A8wslmti342A5UAK82WYQiIiISF/W1JPyewBLR/xN8zgay3L2Tu/+4KYITERGR+Klz4GLw1sdq4Mdm1hE4EWhjZqH97zdJhCIiIhIXDc6TYGY3AHcA3YDFBMYjzAfOj2lkIiIiElfRDFy8ncAy0Z+6+znAEGBrTKMSERGRuIsmSah09z0AZpbm7kuBvrENS0REROItmrUbNprZscDLwGtmtg1YF9uwREREJN6iWbvh0uDLX5rZMKAj8EpMoxIREZG4q2/thnbu/rmZHRNWXBR8TgeqYhqZiIiIxFV9LQnPARcBSwks8GS1nnNiHp2IiIjETX3zJFxkgUkRTnf3DU0Yk4iIiCSAeu9uCE6o9PqRHtzMRphZmZmtMLOJ9dS70szczAqP9FwiIiLSuKK5BfJfZjb4cA8cXAhqCoEuiwLg22ZWEKFeB+CHwAeHew4RERGJnWiShMFAUbBFYKGZfWRmC6N431Bghbuvcve9wDPAZRHq/Qb4HVAZddQiIiISc9HMk3Bpw1Ui6gasDdteB5weXsHMTgV6uPsrZlbnolFmNhYYC5CTo/GSIiIiTaHBlgR3X+nuK4HtwJ6wx1ExsyTgQQLrQjQUw3R3L3T3wuzs7KM9tYiIiEShwSTBzL5uZssJtAR8QKB14M0ojr0e6BG23T1YFtIBGAD8n5l9CpwBzNLgRRERkcQQzZiE3wJnA2Xu3gMYAbwTxfuKgD5m1svM0oCrgVmhne6+08gRgocAAAzjSURBVN2z3D3X3XMJrCx5qbsXH+6HEBERkcYXTZJQ7e4VQJKZmbu/QWBQYr3cvRq4GXgNWAY86+5LzWySmR3pOAcRERFpItEMXNxpZu2Bd4EnzWwzUY5JcPdXgVdrld1VR93zozmmiIiINI1oWhIuJ5AU3Ab8H4FxBZfEMCYRERFJANG0JHyXQFdBOfBYjOMRERGRBBFNS0I2gTsQ5prZeDPLinVQIiIiEn/RzJPwS3fPJzCfQS9gnpn9M+aRiYiISFxF05IQshb4FNiAlokWERFp8aKZTGmsmf0/AnMjdANucfdDFmoSERGRliWagYt9gIma5EhERKR1aTBJcPc6F14SERGRlutwxiSIiIhIK6IkQURERCJSkiAiIiIR1Tkmwcy2Ax5pF+Du3ilmUYmIiEjc1TdwUTMrioiItGJ1dje4+/7wB9AR6BL2EBGRGNn0+aZ4hyAS1WRKXzez5cA64IPg85uxDkxEpLWq+KKC19e8TsUXFfEORVq5aAYu/hY4Gyhz9x7AcAKzL4qISCP7+0fr+dq0p5g0exFfm/YUf/9ofbxDklYsmiSh2t0rgCQzM3d/Axga47hERFqdv3+0np++9B479pbj+zLZsbecn770nhIFiZtokoSdZtYeeBd40sweAPbENiwRkdbnvtfK2JeyGq9JBwyvSWdfymrue60s3qFJKxVNknA5gaTgNuD/gPXAxTGMSUSkVdq4exOWug1q2gYKatpiqdvYuFuDGCU+okkSfhq8w2Gfuz/m7g8CE2IdmIhIa5OZWX6gFSEg0JqQmVkez7CkFYsmSRgRoezrjR2IiEhrVvFFBecXJJNm7Q4qT7N2nF+QrDsdJC7qTBLMbJyZfQT0NbOFYY9PgGVNF6KISMu3qGIRQ3O7Mur0nnRqlwZAp3ZpjDq9J0Nzu7KoYlGcI5TWqL4ZF58F5gD3ABPDyne5++aYRiUJZ9Pnm+jSTnNoicTClj1bWLVjFW1S2tCzs3HLV7LD9u5jT/Vetu/YzpbOW8hqq8lwpenUmSS4+3ZgO/AtM+sPnBPc9Q6gJCGGEu2CHJrYZUTuCLIzsht+g4gcloyUDC7MvTCqeiJNqb6WBADM7AfAD4C/B4ueNbMp7j41ppG1Uol2Qf77R+v57f89y45965mcuoWfnz+Sywd3i3dYIi1KRmoGvTv2jncYIodoMEkAxgFD3X03gJn9N/A+0CKShET61Z5oF+TQxC7Vbcvx6kx2eGBiFzhbiYKISCsQzd0NBuwN297Hf+7PadYSaX70RJxpLdEndtECOCIisVXf3Q2hVoa/AB+Y2S/M7BcEWhGeaIrgYinR5kdPxAtyIk/skkgJnohIS1VfS8KHAO7+ewJdDl8EH+Pd/f4miC1mEvFXeyJekBN1YpdES/BERFqq+pKEA10K7v6huz8YfBQ1QVwxlYi/2hPtgpyoE7skYoInItJS1ZckZJvZhLoe0RzczEaYWZmZrTCziRH2TzCzEjP72MzmmFnPI/4khyHRfrUn4gU5USd2ScQET0Skparv7oZkoD1HOEjRzJKBKcBXgHVAkZnNcveSsGofAYXu/oWZfQ/4PXDVkZzvcGRmlrO9MnF+tYcuyB3T9jNr0Qa2fb6XTu3SuHTQCfQ9IZlFFYu4sGfD91A3lkSe2GXj7k0kd9iGVx8bKAgleLviP05CRKSlqS9J2Ojuk47i2EOBFe6+CsDMngEuAw4kCe4+N6z+fODaozhfVEK/2l/5qB178QPl4b/am3J+gkS8ICfyxC6JluCJiLRk9SUJR3ubYzdgbdj2OuD0euqPAf4RMRCzscBYgJycnKMKKtF+tSfiBTlRJ3ZJtARPRKSlqy9JGNZUQZjZtUAhcF6k/e4+HZgOUFhY6JHqRCMhf7Un6AU5ESVagici0tLVt3bDtqM89nqgR9h292DZQczsQuDnwHnuXnWU56xXIv5ql+gkYoInItLSRTMt85EqAvqYWS8CycHVwKjwCmY2GJgGjGiKlSX1q735UoInItL0YpYkuHu1md0MvEbgTonH3X2pmU0Cit19FnAfgTso/tfMAP7t7pfGKiZpvpTgiYg0vVi2JODurwKv1iq7K+y1OpBFREQSVDQLPImIiEgrpCRBREREIlKSICIiIhEpSRAREZGIlCSIiIhIREoSREREJCIlCSIiIhKRkgQRERGJSEmCiIiIRKQkQURERCJSkiAiIiIRKUkQERGRiJQkiIiISERKEkRERCQiJQkiIiISkZIEERERiUhJgoiIiESkJEFEREQiUpIgIiIiESlJEBERkYiUJIiIiEhEShJEREQkIiUJIiIiEpGSBBEREYlISYKIiIhEpCRBREREIlKSICIiIhEpSRAREZGIlCSIiIhIREoSREREJCIlCSIiIhJRTJMEMxthZmVmtsLMJkbYn25mM4P7PzCz3FjGIyIiItGLWZJgZsnAFOAioAD4tpkV1Ko2Btju7icB/wP8LlbxiIiIyOGJZUvCUGCFu69y973AM8BltepcBjwRfP0cMMzMLIYxiYiISJRimSR0A9aGba8LlkWs4+7VwE4gs/aBzGysmRWbWXFFRUWMwhUREZFwzWLgortPd/dCdy/Mzs6OdzgiIiKtQiyThPVAj7Dt7sGyiHXMLAXoCGyNYUwiIiISpVgmCUVAHzPrZWZpwNXArFp1ZgHfCb7+JvCmu3sMYxIREZEopcTqwO5ebWY3A68BycDj7r7UzCYBxe4+C3gM+IuZrQC2EUgkREREJAHELEkAcPdXgVdrld0V9roS+FYsYxAREZEj0ywGLoqIiEjTU5IgIiIiESlJEBERkYiUJIiIiEhE1tzuODSzCmBNIx4yC9jSiMdrDIopOokYk0hjaex/3z3dXbPRyWFpdklCYzOzYncvjHcc4RRTdBIxJpHGon/fkgjU3SAiIiIRKUkQERGRiJQkwPR4BxCBYopOIsYk0lj071virtWPSRAREZHI1JIgIiIiESlJEBERkYhaZZJgZj3MbK6ZlZjZUjP7YQLE1MbMPjSzRcGYfh3vmELMLNnMPjKz2fGOBcDMPjWzxWb2LzMrjnc8IkfLzB43s81mtiSsrJOZvWFmnwSfj4tnjNI6tcokAagG7nD3AuAM4AdmVhDnmKqAL7v7IOAUYISZnRHnmEJ+CCyLdxC1XODup+g+cmkh/gyMqFU2EZjj7n2AOcFtkSbVKpMEd9/o7guDr3cRuAB2i3NM7u67g5upwUfcR5WaWXfg68Cj8Y5FpKVy97eBbbWKLwOeCL5+Ari8SYMSoZUmCeHMLBcYDHwQ30gONOv/C9gMvOHucY8J+APwE6Am3oGEceB1M1tgZmPjHYxIjHRx943B1+VAl3gGI61Tq04SzKw98Dxwm7t/Fu943H2/u58CdAeGmtmAeMZjZhcDm919QTzjiOBL7n4qcBGBrqJz4x2QSCx54F71uLcsSuvTapMEM0slkCD81d1fiHc84dx9BzCXQ/som9rZwKVm9inwDPBlM3sqviGBu68PPm8GXgSGxjcikZjYZGZdAYLPm+Mcj7RCrTJJMDMDHgOWufuD8Y4HwMyyzezY4Ou2wFeA0njG5O4/dffu7p4LXA286e7XxjMmM2tnZh1Cr4GvAkvqf5dIszQL+E7w9XeAl+IYi7RSKfEOIE7OBq4DFgfHAAD8zN1fjWNMXYEnzCyZQPL2rLsnxC2HCaYL8GIgzyMF+Ju7/zO+IYkcHTN7GjgfyDKzdcCvgHuBZ81sDLAGGBm/CKW10rTMIiIiElGr7G4QERGRhilJEBERkYiUJIiIiEhEShJEREQkIiUJIiIiEpGSBJFazGx/cIXJpcFVOe8wsyP+b8XMfhb2Ojd8pT8RkUSmJEHkUHuCK0z2JzCp1UUE7ls/Uj9ruIqISOJRkiBSj+DUz2OBmy0g2czuM7MiM/vYzMYBmNn5Zva2mb1iZmVm9oiZJZnZvUDbYMvEX4OHTTazGcGWiteDM2yKiCQcJQkiDXD3VUAy0BkYA+x099OA04CbzKxXsOpQ4BagADgRuMLdJ/KflolrgvX6AFOCLRU7gCub7tOIiERPSYLI4fkqMDo4nfcHQCaBiz7Ah+6+yt33A08DX6rjGKvdPTQd+AIgN4bxiogcsda6doNI1MysN7CfwCp8Btzi7q/VqnM+hy7lW9ec51Vhr/cD6m4QkYSklgSRephZNvAI8LAHFjp5DfhecKlxzCwvuBolwFAz6xW8E+Iq4N1g+b5QfRGR5kQtCSKHahvsTkgFqoG/AKElxR8l0D2wMLjkeAVweXBfEfAwcBIwF3gxWD4d+NjMFgI/b4oPICLSGLQKpEgjCHY3/MjdL453LCIijUXdDSIiIhKRWhJEREQkIrUkiIiISERKEkRERCQiJQkiIiISkZIEERERiUhJgoiIiET0/wHNnYhBcEc2CQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -1543,7 +1552,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1552,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1562,12 +1571,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1598,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1633,23 +1642,23 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 40, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1667,17 +1676,17 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 41, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, @@ -1706,7 +1715,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1716,12 +1725,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1752,12 +1761,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1794,7 +1803,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1803,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1827,7 +1836,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1841,7 +1850,7 @@ " 10, 10, 10, 10]])" ] }, - "execution_count": 47, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1876,7 +1885,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1900,7 +1909,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1924,7 +1933,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1946,7 +1955,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -1956,15 +1965,15 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0114\n", - "The estimated product of the one and two qubit fidelity is F = 0.9886\n" + "The estimated error is p = 0.0111\n", + "The estimated product of the one and two qubit fidelity is F = 0.9889\n" ] } ], @@ -1976,7 +1985,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1986,12 +1995,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { - "image/png": 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LY2blRWoHqSsHELOa81UYMysl3IhqZiPhUxgzK81XYcyslAgHEDMbAV/GNbPS3AZiZqUEYtBXYcysrBpXQKoZUMjMOhTdGw9E0gJJd0haJunkJmneJOlWSUslfbNdnq6BmNVdF6ogksYDZwKHAyuBGyUtjohbC2nmAacAB0bE2jzkaEuugZjVXJdqIPsDyyJieURsBC4Cjm5I807gzIhYm/Ybq9tl2hc1kPEaZKfxj1VdjI7sMri+6iJslU3bja+6CNZCAIODHQWImZKWFOYXNTztYBawojC/EjigIY9nAUj6OTAeOL3daIF9EUDMxqwAOqthrOnCEx4nAPOAQ4HZwDWSnhsRDzfbwKcwZjUX0X7qwCpgTmF+dl5WtBJYHBGbIuJO4L9JAaUpBxCzuosOpvZuBOZJeoak7YA3A4sb0lxGqn0gaSbplGZ5q0x9CmNWa915bENEbJb0HuAKUvvGVyNiqaQzgCURsTive42kW0kPvv9QRDzYKl8HELO661JPsoi4HLi8YdlphdcBfCBPHXEAMauzgOjsKkwlHEDMas8BxMzKqvHNMA4gZnXX7wFE0vbAG4G9ittExBm9KZaZAVvTkawSndZAvgM8AtwEbOhdccys0bYwoNDsiFjQ05KY2fBqfBWm056o10p6bk9LYmbDUrSfqtKyBiLpFtJZ2ATg7ZKWk05hROp38rzeF9FsDOu8q3ol2p3CvH5USmFmTah/G1Ej4m4ASedHxHHFdZLOB44bdkMz654+roEM2bc4k4dHe3H3i2NmTzFYdQGaa9mIKukUSeuA50l6VNK6PL+adGnXzHppqB9Iu6kiLQNIRHwyIqYCn4mIaRExNU+7RMQpI9mxpPGSfiXpuyPJx2xb17dXYQpOlfTnwEGkmPj/I+KyEe77fcBtwLQR5mO2batxG0in/UDOBE4AbgF+A5wg6cyyO5U0G3gd8OWyeZhZ9TqtgRwG7J0HHEHSecDSEez3C8CHganNEkhaCCwEePosjxxuY1eVpyjtdFoDWQbsWZifk5dtNUmvB1ZHxE2t0kXEooiYHxHzd5rhAGJjVJC6srebKtJpDWQqcJukG0j/0v7AEkmLASLiqK3Y54HAUZKOBCYB0yRdEBHHbkUeZmNHjWsgnQaQ09on6Uy+enMKgKRDgQ86eJg1V+dTmI4CSET8VNJcYF5E/EjSZGBCRKzrbfHMrM41kI7aQCS9E7gEODsvmk16hsSIRMTVEeH7bcxa6c5zYXqi00bUd5PaLh4FiIjfAm2f3G1mI9NJJ7J+6Ei2ISI2Sqm1V9IEal2xMtuGbAMDCv1U0qnAZEmHA/8O/GfvimVmQ+pcA+k0gJwMPEDqifou0tOtPtqrQplZQY3bQDq9CjMo6TLgsoh4oMdlMrMhFdcw2ml3O78knS5pDXAHcIekByR1rV+ImbVR4xpIu1OY95OuvrwkImZExAzgAOBASe/veenMDA22n6rSLoAcBxwTEXcOLYiI5cCxwFt7WTAzq792bSATI2JN48KIeEDSxB6VycyKatwG0i6AbCy5zsy6oeaNqO0CyPMlPTrMcpHupDWzXuvXABIRHojDrGr9GkDMrFqi2qss7XTaE9XMqtDFm+kkLZB0h6Rlkk5uke6NkkLS/HZ5OoCY1V0XOpLlh8GdCRwB7AMcI2mfYdJNJT0x4fpOiuYAYlZ33emJuj+wLCKWR8RG4CLg6GHSfRz4NPBEJ5n2RRvIOIKp2lR1MTqy64ThLlpZN0zUQNVFqESHpygzJS0pzC+KiEWF+VnAisL8SlKv8if3I70ImBMR35P0oU522hcBxGxM6yyArImItm0WzUgaB3wOeNvWbOcAYlZn0bWrMKtIj2MZMjsvGzIV2A+4Og8c9nRgsaSjIqJYs9mCA4hZ3XWnH8iNwDxJzyAFjjcDb/njLiIeAWYOzUu6mvTEhKbBA9yIalZ73biMGxGbgfcAV5CeSX1xRCyVdIakrXmu0xZcAzGruy71RI2Iy0mjCRaXDTu2T0Qc2kmeDiBmdVbxgEHtOICY1Zjo77txzaxiDiBmVp4DiJmV5gBiZqX0+YhkZlY1BxAzK6vOAwo5gJjVnE9hzKwcdyQzsxFxADGzMtwTtYGkScA1wPZ5/5dExMdGuxxm/UKD9Y0gVdRANgCHRcT6/HjMn0n6fkRcV0FZzOrNbSBbiogA1ufZiXmq8SEyq1adT2EqGVBI0nhJvwZWA1dGREdDyJuNSd0Zlb0nKgkgETEQES8gjcu4v6T9GtNIWihpiaQlax+qcU8asx7r1oOleqHSIQ0j4mHgKmDBMOsWRcT8iJi/8wyPvGhjmGsgT5K0q6Sd8uvJwOHA7aNdDrO+kEdlbzdVpYqrMLsD5+VH7Y0jDe763QrKYVZ77gfSICJuBl442vs161tR3wjinqhmNecaiJmV445kZjYSHg/EzEpzADGzcgI3oppZeW5ENbPyHEDMrAx3JDOz8iI8oJCZjUB944cDiFnd+RTGzMoJwKcwZlZafeNHtQMKmVl73RqRTNICSXdIWibp5GHWf0DSrZJulvRjSXPb5ekAYlZzGoy2U9s80vg7ZwJHAPsAx0japyHZr4D5EfE84BLgn9vl6wBiVmedDGfYWQ1kf2BZRCyPiI3ARcDRW+wq4qqIeCzPXkcas7ilvmgDGU8wdVyN7yjawmPtk9TIpImbqi5Cx6aOe7zqIoy61JGsowgxU9KSwvyiiFhUmJ8FrCjMrwQOaJHfO4Dvt9tpXwQQszGts9/ONRExvxu7k3QsMB84pF1aBxCzmuuwBtLOKmBOYX52XrblvqRXA38PHBIRG9pl6jYQszrrXhvIjcA8Sc+QtB3wZmBxMYGkFwJnA0dFxOpOMnUNxKzWunMvTERslvQe4ApgPPDViFgq6QxgSUQsBj4D7Aj8uySAeyLiqFb5OoCY1V2XBhSKiMuByxuWnVZ4/eqtzdMBxKzOwkMamtlIeEhDMyutvvHDAcSs7jRY33MYBxCzOgs67UhWCQcQsxoT0a2OZD3hAGJWdw4gZlaaA4iZleI2EDMbCV+FMbOSwqcwZlaSH65tZiNS3zOY0R8PRNIcSVfl0Z+XSnrfaJfBrJ8oou1UlSpqIJuBkyLil5KmAjdJujIibq2gLGb151OYJ0XEfcB9+fU6SbeRBnx1ADFrFAED9T2HqbQNRNJewAuB64dZtxBYCDBrlkdetDGsxjWQyr6ZknYEvg2cGBGPNq6PiEURMT8i5s+Y4QBiY1hE+6kildRAJE0kBY9vRMR/VFEGs77gh2tvSWm01q8At0XE50Z7/2b9JSDq2wZSxbnBgcBxwGGSfp2nIysoh1n9BakRtd1UkSquwvyM9MQ+M+tEjRtR3RPVrO4cQMysHN9MZ2ZlBeDb+c2sNNdAzKwcd2U3s7ICosb9QBxAzOrOPVHNrDS3gZhZKRG+CmNmI+AaiJmVE8TAQNWFaMoBxKzOfDu/mY1IjS/jeqgvsxoLIAaj7dQJSQsk3SFpmaSTh1m/vaRv5fXX5yFHW3IAMauzyAMKtZvakDQeOBM4AtgHOEbSPg3J3gGsjYhnAp8HPt0uXwcQs5qLgYG2Uwf2B5ZFxPKI2AhcBBzdkOZo4Lz8+hLgVXkEwab6og3klls2r5k75/67e5D1TGBND/LthX4qK/RXeXtV1rkjzWAda6/4UVwys4OkkyQtKcwviohFhflZwIrC/ErggIY8/pgmIjZLegTYhRbHpi8CSETs2ot8JS2JiPm9yLvb+qms0F/lrXNZI2JB1WVoxacwZmPDKmBOYX52XjZsGkkTgOnAg60ydQAxGxtuBOZJeoak7YA3A4sb0iwGjs+v/wL4SUTrbrB9cQrTQ4vaJ6mNfior9Fd5+6mspeQ2jfcAVwDjga9GxFJJZwBLImIx6XEr50taBjxECjItqU2AMTNryqcwZlaaA4iZlTbmAoikOZKuknSrpKWS3ld1mVqRNEnSDZL+K5f3H6suUzuSxkv6laTvVl2WdiTdJemW/ITEJe23sKKx2Ii6GTgpIn4paSpwk6QrI+LWqgvWxAbgsIhYnx9K/jNJ34+I66ouWAvvA24DplVdkA69MiL6pdNbrYy5GkhE3BcRv8yv15E+6LOqLVVzkazPsxPzVNuWb0mzgdcBX666LNZ7Yy6AFOW7DV8IXF9tSVrLpwS/BlYDV0ZEncv7BeDDQH3vQd9SAD+UdJOkhVUXpt+M2QAiaUfg28CJEfFo1eVpJSIGIuIFpN6D+0var+oyDUfS64HVEXFT1WXZCgdFxItId6m+W9LBVReon4zJAJLbEr4NfCMi/qPq8nQqIh4GrgLqen/EgcBRku4i3e15mKQLqi1SaxGxKv9dDVxKumvVOjTmAki+PfkrwG0R8bmqy9OOpF0l7ZRfTwYOB26vtlTDi4hTImJ2ROxF6sX4k4g4tuJiNSVpSm5IR9IU4DXAb6otVX8Zi1dhDgSOA27J7QoAp0bE5RWWqZXdgfPygDDjgIsjovaXR/vEbsCleciLCcA3I+IH1Rapv7gru5mVNuZOYcysexxAzKw0BxAzK80BxMxKcwAxs9IcQLYBkj4v6cTC/BWSvlyY/6ykUyVd0mT7qyXNz69PLSzfS5L7RVhTDiDbhp8DLweQNI70mIJ9C+tfTurU9Rcd5HVq+yRmiQPItuFa4GX59b6k3pTrJO0saXtgb+ChodqEpMmSLpJ0m6RLgcl5+aeAyXlsjG/k/MZLOiePRfLD3BvWDHAA2SZExL3AZkl7kmobvyDdYfwyYD5wC7CxsMnfAo9FxN7Ax4AX53xOBh6PiBdExF/ltPOAMyNiX+Bh4I2j8C9Zn3AA2XZcSwoeQwHkF4X5nzekPRi4ACAibgZubpHvnREx1OX/JmCv7hXZ+p0DyLZjqB3kuaRTmOtINZCXk4JLWRsKrwcYm/dPWRMOINuOa4HXAw/l8UMeAnYiBZHGAHIN8BaAPLbI8wrrNuXhDszacgDZdtxCuvpyXcOyR4YZ7/MsYEdJtwFnkE5NhiwCbi40opo15btxzaw010DMrDQHEDMrzQHEzEpzADGz0hxAzKw0BxAzK80BxMxK+x8ULRQkFlo1jAAAAABJRU5ErkJggg==\n", 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" ] @@ -2022,12 +2031,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2065,7 +2074,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -2075,7 +2084,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -2084,16 +2093,16 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.05860858, 0.00311172])" + "array([0.06076231, 0.00080728])" ] }, - "execution_count": 58, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2104,18 +2113,18 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.88071106 0.82909383 0.78050182 0.73475771]\n", - " [0.87797053 0.82651392 0.77807311 0.73247135]\n", - " [0.87523853 0.82394204 0.77565196 0.7301921 ]\n", - " [0.87251503 0.82137816 0.77323835 0.72791995]\n", - " [0.85902413 0.80867795 0.76128248 0.71666479]]\n" + "[[0.8807437 0.82722768 0.77696342 0.72975333]\n", + " [0.8800327 0.82655988 0.77633619 0.72916421]\n", + " [0.87932226 0.82589261 0.77570947 0.72857557]\n", + " [0.8786124 0.82522589 0.77508326 0.72798741]\n", + " [0.87507169 0.82190032 0.77195976 0.7250537 ]]\n" ] } ], @@ -2127,12 +2136,12 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2169,7 +2178,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 64, "metadata": {}, "outputs": [ { From dea2adb80073c22703f6922eec6de4aa45544b3d Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 19 Aug 2019 14:59:01 -0400 Subject: [PATCH 34/49] Move compilation from layers to sequence transform at end. --- examples/volumetrics.ipynb | 903 ++++++++++------------------- forest/benchmarking/volumetrics.py | 91 ++- 2 files changed, 342 insertions(+), 652 deletions(-) diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb index d828df00..d664b126 100644 --- a/examples/volumetrics.ipynb +++ b/examples/volumetrics.ipynb @@ -76,7 +76,7 @@ }, { "data": { - "image/png": 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IiKBLly4EBwfj4+ODvb290tGEmUjxWanU1FRGjBjBli1b6CSb/QozOXXqFE5OTixfvpxnnnlG6Th35NKlS6xZs4aQkBDS09MZO3YsQUFB9OjRQ94isDJSfFboxIkTuLi4sHjxYoYPH650HGGlSkpKcHNzY8SIEcyePVvpOCZ14sQJwsLCCAkJoXHjxuUDMbL21TpI8VmZoqIi+vbtS0BAADNmzFA6jrBi06ZN48iRI8TExFjtbil6vZ6kpCRCQkKIjY1lyJAhBAcHM2TIEBmIqcWk+KyIXq9n9OjRNG7cmKVLl8rlGWE2q1at4o033iAjIwNHGzkw9ty5c6xcuZKQkBBOnz7NxIkTCQwM5PHHH1c6mrhFUnxW5K233mLr1q1s3rxZJjiF2fz444/079+fjRs30rVrV6XjKCI3N5eQkBBWrFjBU089RVBQEH5+fjg4OCgdTVSDFJ+V0Gq1vPHGG6SmptKsWTOl4wgrVVhYSM+ePZk5cyaBgYFKx1FcSUkJsbGxLFmyhJSUFEaPHk1wcDDOzs5yxaUGk+KzAmlpaXh5ebF582a6dOmidBxhpQwGA2q1msaNG/Ptt98qHafGOXnyJOHh4YSEhNCgQQOCg4MZN26cLCWqgaT4armTJ0/i4uLCwoULGTlypNJxhBX78ssvCQ0NJTk5WTZ9roLBYGD79u2EhISwZs0annnmGYKCghg6dCh33XWX0vEEUny1WlFREf3792fMmDG8/vrrSscRVmz37t14e3uTkpIiu5vcgr/++otVq1axZMkSTpw4wYQJEwgMDOSJJ55QOppNk+KrpfR6PWPHjsXBwYHQ0FB5P0GYTV5eHj169GDRokWyLvQO/PDDDyxdupRly5bRvn17goODUalUNGzYUOloNkeKr5aaM2cOiYmJbNmyRS47CbMpKyvD3d2d3r1789577ykdxyqUlJSwbt06QkJC2LlzJyqViqCgIFxcXOQXWAuR4quFVq9ezcyZM0lNTaV58+ZKxxFW7M033yQ1NZUNGzbIgm0zOH36NMuWLSMkJIS6desSFBTE+PHj5e+1mUnx1TIZGRl4enqyceNGnn76aaXjCCsWGxvLlClTyMzMlMlEMzMYDCQnJxMSEkJ0dDQDBgwgODgYDw8PGYgxAym+WuTUqVO4uLjw5Zdf4u3trXQcYcWOHDlC7969WbNmDS4uLkrHsSmFhYWsXr2akJAQjhw5gkajISgoiPbt2ysdzWpI8dUUeXkQGgo5OVBQAE2aQJcuEBgITZty4cIFBgwYgK+vr9VtCCxqluLiYvr06UNwcDAvvfSS0nFs2oEDBwgJCSE8PJy2bdsSFBTEmDFjaNSokdLRajUpPqWlp8O8ebB+vfHPFy/+c5u9PRgMGDw8eLOwkOPNm7Ns2TJ5A1yYVXBwMMXFxaxYsUK+12qI0tJS1q9fz5IlS9i+fTu+vr4EBQXRp08f+W90G6T4lLRoEUyfDsXFUMV/Br2dHZfs7Ljrs8+4+9//tmBAYWuWLFnCJ598QlpamryqqKF+++03li1bxpIlSwDKB2JatGihcLLaQ4pPKVdK78KF6n+OgwPMnw+TJpkvl7BZe/bswd3dnR07dtChQwel44ibMBgM7N69m5CQECIjI+nXrx9BQUF4eXlx9913Kx2vRpPiU0J6Ori53VB644DNQBHwIDATeO76z3VwgKQkcHKyQFBhK/Lz83FycmLevHmMGTNG6TjiFp0/fx6dTseSJUv4+eefGT9+PEFBQXTs2FHpaDWSFJ8SfH0hJuaGy5s/AG2B+sABwA1YB/S4+k52duDjA5GRlskqrJ5er2fUqFG0bduWzz77TOk44g4dPHiQpUuXEhYWRuvWrQkODmbMmDHcc889SkerMaT4LC0vD1q3vnaIpQI/YSy+L4Abfv9u0ACOHwdZWyVM4IMPPiA+Pp6tW7fKJTIrcvnyZRISEggJCWHLli14e3sTFBREv379TDMQc5NJ9JpMis/SPvoI3n670uKbDIQCxUA3YDtww4iBvT288w7MmGHGoMIWbNq0CY1GQ3p6Oi1btlQ6jjCTvLw8li9fzpIlSygpKSEoKAiNRnN7/82rMYmOhwfMng3OzqZ5AiZWR+kANicnp8pXewuBQmAH4IvxsucNioshN9cs8YTtOHnyJOPHj2fFihVSelauWbNmvPrqq+zbt4/ly5dz9OhROnfujJeXF1FRUZSUlFTvgRYtMs4nxMQYf45d/7OsuNj4sZgY4/0WLTL1UzEJKT5LKyi46V3qAn2Bk0Cl3zb5+abLJGxOSUkJo0ePZtq0aQwcOFDpOMJC7Ozs6NWrF4sXL+bEiROMHTuWBQsW0KpVK1577TV++OGHyj/56kn0m10oNBiM95s+vUaWnxSfpTVpUu27XgYOV3ajo6Mp0ggbNX36dJo1a8bMmTOVjiIU0rBhQzQaDdu2bSs/XNjd3b28GAuu/iU9Pb3C5VdHAU/AEeMk+ksYf26Vu1J+GRlmfja3RorP0rp0MQ6nXCcPWAmcB8qADYAWeKaChyi9+26K2rQxZ0phxVauXEl8fDxhYWGy64cAoG3btrz//vscP36cOXPmsHHjRlq3bl1ejIYPPjBexrzOZKAZ8CuQDSRhfLvmGsXFxvcEaxAZbrG0SqY6zwIqYC+gB1oD/waer+AhSurUoWPDhrRzdUWtVuPt7S2jyqJa9u/fz4ABA9i4cSNdu3ZVOo6owc6ePcuKFSuIXryYDT/9RIMKqqIj8AnGV30AM4C/gMXX37GGTaLLKz5La9bMOPF03W/aTTH+tnQO4zdOLhWXHnZ21PP2Zu/p02g0GnQ6Ha1atUKlUhEZGcnFmyyTELarsLAQPz8/Pv74Yyk9cVNNmzZl2rRpbAsMpF4ly1ymYbxSdQE4BawHhlV0Rzs749KHGkKKTwmzZxvHfm+HvT3Mnk2jRo1Qq9WsXbuWX375hWHDhrFw4UJatGjBhAkTSEhIoLS01LS5Ra1lMBh47rnn6NevHxMnTlQ6jqhF7HJyqFPJ1Gd/jBtv3AM8DDgBFR6YVsMm0aX4lODsDPPnU3qri4Wv7NV53XZl9913H8899xybN29m//79dO/enTlz5tCyZUsmT57Mjh070Ov1JnwCorZZsGABhw4dYsGCBUpHEbVNJZPoeoyv7nwxbrP4O5APvF7Z49SgSXQpPoVk9erF7Hr10Nvb33DZ8wZ2dtXeoLpFixZMnTqVlJQUUlJSePjhh5k8eTKPPvooM2bMICsrC3lb17YkJyfzwQcfoNPpaFDBYJUQVapkEv1P4DjGSc76wP1AIBBf2ePUoEl0KT4FXLhwgWeffZZuixdTZ/t2496bDRrcePnT3t74cR8f48bUt3gqQ5s2bXjjjTfIzc0lPj6eevXqoVKp6NChA3PmzOGnn34y4bMSNdGZM2fw9/dn6dKlPPbYY0rHEbVRJZPoDwCPYVxrfBnjfEIY0KWix7C3h86dzRjy1shUpwKmTJlCfn4+ERER/3zw7Fnjm7+5ucZLAo6Oxm+UiRNNOgllMBhIS0tDq9WyatUqWrRogVqtxt/fn1atWpns6wjlXb58GXd3d1xdXZk7d67ScURtVcX+wtkYB1z2Ytx4YxDwJdD8+jvWsKlOKT4LW7duHZMnT2bv3r3ce++9imYpKysjKSkJrVZLVFQUTz75JGq1mtGjR9O0hnyDits3e/ZsMjIySEhIoG7dukrHEbVZJSfKVEsNPFFGis+C8vLy6Nq1K1qtlgEDBigd5xolJSVs2LABrVZLfHw8Li4uqNVqfHx8ZI1gLbR27VpeeuklMjMz5ZcYcecqOUO0WmrgGaJSfBZiMBgYMWIEnTt3Zl4N28XgekVFRcTGxqLVatm2bRuDBw9GrVbj5eWF/e0uwxAWc/jwYXr37s3atWtxcXFROo6wFosWUfbqq9S9lbXC1RzKszQpPgv55ptv+Pbbb9m9ezf16tVTOk615efnExUVhVarJTMzkxEjRqBWqxk8eLCc3VYDFRcX07t3b55//nmmTJmidBxhRQoKCvioTRveKSrirpKSqi972tkZB1pqYOmBFJ9FHDhwgH79+rFjxw46dOigdJzb9ttvv7F69Wq0Wi2HDh1CpVKhVqvp27cvderIgLDSDAYDQUFBlJSUsHz5ctmHU5jUuHHjaNy4MYuCg417b8bHGwvu6j08r5zH5+lp3KijBl3evJoUn5mVlJSU/wb+r3/9S+k4JvPLL7+wcuVKtFot+fn5jB07FrVaTffu3eUHrkK+++47Pv/8c1JTU2nYsKHScYQVWbFiBe+99x6ZmZk4ODgYP2ihSXRzkOIzs9mzZ7Nv3z7Wrl1rtYXwww8/oNVq0Wq11K1bt3x5RMeOHZWOZjMyMzPx8PBgx44dtG/fXuk4woocPXoUZ2dnEhMT6datm9JxTEKKz4ySkpJQq9VkZ2fTrFkzpeOYncFgID09vXyNYLNmzcpLsHXr1krHs1p//vknPXr04OOPP0alUikdR1iRy5cv4+bmhre3N9OnT1c6jslI8ZnJuXPnePrpp1m0aBGenp43/wQrU1ZWxvbt21m5ciWRkZG0b9++fI1g8+Y3LG8Vt0mv1zNixAjat2/Pp59+qnQcYWXee+89tm7dysaNG63qfXwpPjMJCAjA0dGRr7/+WukoiispKWHjxo1ERESwbt06evbsWb5GUOlF/LXde++9R0JCAlu3bpUpW2FSqampjBw5kqysLFq2bKl0HJOS4jODCt8IFoBxn9K4uDi0Wi1btmxh0KBBqNVqhg8fLv+ubtHGjRuZMGECGRkZPPTQQ0rHEVaksLCQbt268eGHH+Ln56d0HJOT4jMxa3wj2FzOnTtHdHQ0Wq2WtLQ0hg8fjlqtxt3dXV693MSJEyfo2bMnWq0WNzc3peMIKxMYGEjdunX57rvvlI5iFlJ8JlRWVsbAgQMZPnw4M2fOVDpOrXLmzBm+//57tFotP/30E35+fqjVavr3729V7y2YQklJCf3798fX11e+z4TJrV69mv/85z9kZWXRqFEjpeOYhRSfCc2bN4/ExEQ2bdokmwLfgaNHj7Jq1Sq0Wi1nz54tXyPo5ORktUtCbsXLL7/MyZMniYqKkn8fwqROnDhBjx49WLduHc7OzkrHMRspPhPJyMjA09OTzMxMOd7HhPbv31++UB7A398ftVrNk08+qXAyZURERPD222+TkZFBk0oOCBXidpSVlfHMM88wdOhQZs+erXQcs5LiM4GioiK6d+/OO++8g7+/v9JxrJLBYCAzM7N8jeD9999fvkbw0UcfVTqeRfzwww+4ubmxefNmunSp8LhPIW7bhx9+SHx8PFu2bLH6K1ZSfCbwr3/9i6KiIpYtW6Z0FJug1+vZsWMHWq2WyMhInnjiCdRqNWPGjLHaNYJ//fUXzs7OvPHGG0yYMEHpOMLKXNn5JyMjg0ceeUTpOGYnxXeH1q5dy9SpU8nOzpZLTwooLS1l48aNaLVaYmNjcXZ2Rq1W4+vrazVrBA0GA2PGjOG+++5j8eLFSscRVubKFat3332XsWPHKh3HIqT47sBvv/1G165d0el09O3bV+k4Nu/ChQusW7cOrVbL5s2bGThwIGq1mhEjRtTqNYKfffYZK1asYOfOnTRo0EDpOMLKvPDCC1y6dImwsDClo1iMFN9tMhgMeHp60qNHD9577z2l44jrFBQUlK8RTE1NxcvLq3yNYG06D3Hnzp34+fmRmppqM+9lCsuJjo5m+vTp7Nmzh3vuuUfpOBYjxXebvvrqK8LDw0lOTpbF1jVcXl5e+RrBAwcO4OvrW75GsCa/iX/mzBl69OjBt99+i4eHh9JxhJU5ffo03bp1IyYmht69eysdx6Kk+G7D/v376d+/P7t27aJdu3ZKxxG34NixY+VrBM+cOVO+RtDZ2blGrYm7fPkyQ4YMoX///rzzzjtKxxFWRq/XM3ToUPr168d///tfpeNYnBTfLbp06RK9evViypQpPP/880rHEXfgwIED5ecIlpWVla8R7NSpk9LRmDVrFnv27CE+Pr5GvyoVtdOnn35KZGQkSUlJ3HXXXUrHsTgpvls0c+ZMDh48SHR0dI16hSBun8FgICsrq3yN4L333otarUatVvPYY49ZPE9MTAxTp34vFWoAACAASURBVE4lMzOTBx54wOJfX1i37OxshgwZQlpamiLf3zWBFN8t2Lp1K+PGjSM7O5umTZsqHUeYgV6vZ+fOnWi1WnQ6HY8//nj5GsEWLVrc+RfIy4PQUMjJgYICaNIEunSBwEBo2pRDhw7Rp08f4uLi6Nmz551/PSGucuHCBZycnHjjjTcYN26c0nEUI8VXTfn5+Tz99NP873//Y9iwYUrHERZQWlrKpk2bWLlyJWvXrqV79+6o1Wr8/PxwdHS8tQdLT4d582D9euOfL1785zZ7ezAYuOzujmb/fvq+8gqTJ0823RMR4m9Tpkzh3LlzrFixQukoipLiqwaDwYC/vz/NmzdnwYIFSscRCiguLiY+Pp6IiAg2bdrEgAEDUKvVjBw5koYNG1b9yYsWwfTpUFwMVfx10wOldetS78svsZs0ybRPQNi8uLg4XnrpJbKzs61mc4fbJcVXDeHh4Xz00Uekp6djb2+vdByhsIKCAtasWYNWq2X37t14eHigVqsZNmzYjWsEr5TehQvV/wIODjB/Pkj5CRP57bff6NatG6tXr6Zfv35Kx1GcFN9N/PLLL/Ts2ZNNmzbx9NNPKx1H1DBnz55Fp9Oh1Wr54Ycf8PHxQa1W4+bmRt2sLHBzq7T0fgY6Aypg+fU3OjhAUhI4OZk1v7B+VzbbcHJyYu7cuUrHqRGk+Kpw+fJlBgwYgK+vL6+99prScUQNd+LEifI1gqdPnybBwYEuv/yCXSV/xdyBYqA1FRSfnR34+EBkpHlDC6v35Zdfsnz5cnbu3CmbbfxNiq8Kc+fOJSkpicTERDkFXNySQ7t20XrAAO6+fLnC21cCUcCTwCEqKD6ABg3g+HGQCWJxm/bt28fAgQPZvXs3bdu2VTpOjSE/zSuRmprKV199RWhoqJSeuGVtd+7k7koWBv8F/Bf49GYPYmdnXPogxG24ePEiAQEBfPTRR1J615Gf6BU4f/4848aN4+uvv+bhhx9WOo6ojXJyrl2ycJW3gGDgpt9ZxcWQm2viYMJWzJo1iw4dOjBx4kSlo9Q4trdXTTW88soruLq6olKplI4iaquCggo/nA1sAvZU93Hy800USNiShIQEoqKiyM7Olh2mKiDFd53o6Gi2bNnCnj3V/tEkxI0qOZR4G3AUuHLG9XmgDNgPZFX0Cbe6UF7YvLNnzxIcHMzy5cu57777lI5TI8mlzqucPn2aSZMmsWzZMps6m0qYQZcuxuGU67wAHMb4yi8b+BfgBWyo4CEuANoffiAhIYHS0lIzhhXWwmAwEBwczPjx4xk4cKDScWosKb6/6fV6AgMDefHFF+nTp4/ScURtV8n7Kg7Ag1f90whoAFQ0t2lfvz7n/fyYM2cOLVu2ZMqUKSQnJ6PX680UWtR2ixcv5tSpU7z77rtKR6nRZDnD37744gu0Wi07d+60yWM6hBn4+kJMTJXblFXqunV8hw8fZuXKlaxYsYILFy7g7+9PQEAAnTt3lvdwBAA//vgj/fv3Z+fOnbRv317pODWaFB+Qm5vLoEGDZK2LMK309Cp3bqlSJTu3GAwGcnJyys8RbNy4cfkRSm3atDFNblHrXLp0CRcXFyZNmsQLL7ygdJwaz+aL7+LFi/Ts2ZNp06YRFBSkdBxhZfQLF1Ly73/ToKys+p9Uzb069Xo9u3btIiIi4oYjlB588ME7TC5qkxkzZnDo0CGioqLkCkA12Hzxvfrqqxw7dgydTiffMMLkPvzwQ/jmG2bm5WF3k9MZsLMzHlF0GxtUXzlCKSIigtjYWHr27IlarcbX15cmlUyYCuuwadMmJk6cSHZ2thxcXE02XXwbN24kMDCQvXv3cv/99ysdR1iZrVu3EhAQQFpaGq3OnDGexxcfbyy44uJ/7vj3eXx4esLs2Xe8MfWFCxeIi4tDq9WyZcsWBg8eTEBAAJ6ennK6iJX5448/ePrpp1m6dClDhgxROk6tYbPFJ98wwpxOnTqFk5MTy5YtY/Dgwf/ccPascRuy3Fzj4nRHR+jc2TgFaoY9OfPz84mKikKr1ZKZmcmoUaMICAhg0KBBMsRVyxkMBvz8/Hjsscf45JNPlI5Tq9hk8RkMBlQqFY888gifffaZ0nGElSkpKcHNzQ0vLy/efPNNpeOUO336NKtXryYiIoJjx44xZswYAgICcHFxkcv8tdCSJUtYsGABaWlp1K9fX+k4tYpNFt/SpUv57LPPSEtLo0EFi4yFuBNTp07lyJEjrFmzpsZucH7o0CG0Wi0RERFcvHgRtVpNQEAAnTp1UjqaqIaDBw/i6upKUlISTz75pNJxah2bK77Dhw/j4uLCli1b6Ny5s9JxhJVZuXIlb775JhkZGTjWgu3GDAYDe/fuJSIiAq1Wy7333ktAQAD+/v489thjSscTFSgtLaVPnz4EBgYyefJkpePUSjZVfJcvX6Zfv36MHTuWadOmKR1HWJn9+/czYMAAEhMT6datm9Jxbpleryc5Obl8ecQTTzxBQEAAY8aMoVmzZkrHE39788032bt3L7GxsXKJ+jbZVPHNmTOHXbt2kZCQUGMvQYnaqbCwkJ49ezJz5kwCAwOVjnPHSktL2bhxIxEREcTFxdGrVy8CAgLw8fGRfWwVlJSUhFqtJjs7W34ZuQM2U3y7d+/Gx8eHrKwsHnroIaXjCCtiMBgYO3YsTZo04dtvv1U6jskVFRURFxdHREQE27ZtY8iQIeXLI+Q9csvJz8+na9eufPPNN3h4eCgdp1azieIrLCyka9euzJ8/Hx8fH6XjCCvz+eefs2zZMpKTk62+CP7880+ioqKIiIggOzu7fHnEwIEDZXmEGRkMBvz9/WnevDkLFixQOk6tZxPFFxgYSN26dfnuu++UjiKszM6dO/Hz8yMlJcXmhkFOnTpVvjzixIkT5csjevXqJe89mVh4eDgfffQR6enpsgmBCVh98el0OmbNmkV2djaNGjVSOo6wIr/99hs9evTg22+/xdPTU+k4ijp48CArV64kIiKCkpISAgICUKvVPPXUU0pHq/WuTKJv3ryZLl26KB3HKlh18Z08eZLu3bsTGxtLr169lI4jrMjly5cZPHgw/fv3l7PPrmIwGNizZ0/56RH3339/+fKI1q1bKx2v1rkyie7v78/UqVOVjmM1rLb49Ho97u7u9O/fn//+979KxxFWZubMmezdu5f4+Hjq1q2rdJwaSa/Xs2PHDrRaLTqdjg4dOhAQEMDo0aNpaobt2azRnDlzSElJIT4+XibRTchqi+/TTz8lMjKSpKQkedNdmFRUVBSvvPIKmZmZsht+NZWUlJCYmIhWq2XdunX07t2bgIAAvL29ady4sdLxaqTk5GT8/PzYs2cPLVq0UDqOVbHK4tu7dy+DBw8mNTVVDucUJnXw4EH69u3LunXrcHZ2VjpOrVRUVMTatWuJiIhg+/btDB06FLVajYeHh9VPxVZXQUEBXbt25YsvvmDkyJFKx7E6Vld8xcXFODs7M2PGDCZMmKB0HGFFioqKcHFxYcqUKfzrX/9SOo5V+OOPP4iMjCQiIoKcnBx8fHwICAjAzc3Npi8hjx8/nkaNGrFo0SKlo1glqyu+qVOn8uuvv7Jq1SoZqRYmYzAY0Gg02NnZERYWJt9bZnDy5ElWrVpFREQEp0+fZuzYsQQEBODs7GxT/74jIiJ49913ycrKwsHBQek4Vsmqim/Dhg08//zzZGdnc9999ykdR1iRhQsX8s0335CSkiI/jCzgp59+Kj89Qq/Xl58e0bFjR6WjmdXRo0fp2bMnGzZsqJX7vdYWVlN8Z8+epWvXrixbtoxBgwYpHUdYkdTUVIYPH86uXbt44oknlI5jUwwGA1lZWURERLBy5UqaNm1avjzikUceUTqeSZWVleHm5saoUaOYPn260nGsWu0pvrw848nVOTlQUABNmkCXLhAYiOGBB/Dx8aFt27bMnz9f6aTCipw9e5YePXqwYMECvL29lY5j08rKytixYwcRERFERkby5JNPli+PsIbp2vfff58tW7awceNGWbpgZjW/+NLTYd48WL/e+OeLF/+5zd4eDAZ+6diR/5w/T0hurpxELEymrKyMYcOG0aNHD/7v//5P6TjiKiUlJWzYsIGIiAji4+NxdXUlICCAUaNG1crlEampqYwcOZKsrCxatmypdByrV7OLb9EimD4diouhiphlgF2DBtT59FOYNMly+YRVe+utt0hOTiYxMVHWgtZg58+fL18esWPHDjw8PFCr1QwbNqxW/CJcWFhIt27d+PDDD/Hz81M6jk2oucV3pfQuXKj+5zg4wPz5Un7ijsXFxTFp0iQyMjJo3ry50nFENf3+++/lyyP27duHr68varWaAQMG1NjlEUFBQdSpU0c20begmll86eng5nZD6f0JBAOJwAPAPCDg+s91cICkJHByskBQYY2OHDmCi4sL0dHRuLq6Kh1H3KYTJ06UL4/47bffypdHODk51ZjlEd9//z1vvvkmWVlZsom+BdXM4vP1hZiYGy5vqgE9sATIBryAXcA1+7/b2YGPD0RGWiissCbFxcW4uroyYcIE2RTYihw4cKB8eQRQfnpEhw4dFMt04sQJnJyciIuLk12ALKzmFV9eHrRufe0QC1AEOAL7gHZ/f2w80BK4YeygQQM4fhxkI1xxi4KDgykqKkKr1daYVwXCdAwGAxkZGURERLBq1SoefPBBAgICGDt2LK1atTLdF6piCp2mTSkrK2Pw4MG4u7sze/Zs031dUS01r/g++gjefvuG4tsDuAJXX/ycDyQBsdc/hr09vPMOzJhhzqTCyixZsoRPPvmEtLQ0uexkA8rKykhKSiIiIoLo6Gg6depEQEAAKpWK+++///YetBpT6Hh4EN6yJUtyctiyZUuNfe/RmtW8xSI5OTeUHsB54J7rPtYEKKzoMYqLITfX9NmE1crKymLWrFlERkZK6dmIunXrMmjQIL777jtOnz7Nq6++ytatW2nTpg3Dhw8nIiKC8+fPV/8BFy0yzibExBh/hl3/c6y4GC5exBATg+qrr4hyd5fSU0jNK76Cggo/3Aj467qP/QVUumInP990mYRV+/PPP1GpVHz99ddWvyWWqFj9+vUZNWoUK1eu5OTJk/j7+7NixQpatmyJWq0mNjaWkpKSyh/g6in0m1xEszMYcADu/+AD4+cJi6t5xdekSYUfbgdcBn6+6mN7uW6w5WqOjiaNJayTXq9n/PjxjBo1ijFjxigdR9QAjRs3Zty4caxbt45Dhw7Rv39/Pv74Y1q0aMELL7zAtm3b0Ov1/3xCenqlS6/cgAYYf3FvBLS/+sYLF4yfl5FhzqcjKlDziq9LF+NwynUaAr7AfzEOuiQDazAOuFzPYG8PnTubM6WwEu+//z4FBQV89NFHSkcRNVDTpk2ZNGkS27dvZ8+ePbRt25Zp06bxyCOP8Nprr5GZmYnhgw+MlzEr8RXGt2rOAz9df2NxsfE9QWFRNW+4pZKpTjCu4wsCNgL3Y5zmvGEdH3AReGvCBHxffBEXFxeZzhMVSkxMZOLEiWRkZPDQQw8pHUfUIvv370er1bJh2TJ2HD9O/Up+jLoB44DnqnowmUK3uJr3iq9ZM/DwMK7Hu859QAzGV3zHqbj0sLNDP2wYju3aERgYSLt27Zg7dy5Hjx41Z2pRyxw/fhyNRkNERISUnrhlTz75JHPnziV18mTuuvvuKu87G+OGG67AtoruYGdnXPogLKbmFR/A7NnG0d/bYW+Pw9y5vPHGG/z444+sWLGCM2fO4OzsjJubGyEhIfz11/VjMsKWXLp0CZVKxWuvvYabm5vScUQtZpeTQ90qhl4+BI4Ap4AXgBHA4evvJFPoFlczi8/Z2bjn5q0e+Hllr86/tyuzs7OjZ8+efPXVV5w6dYpp06YRFxfHI488QkBAAAkJCVy+fNkMT0DUZK+88gotW7aUM8/EnatkCv2KXhgnz+sDEzC+6ouv6I4yhW5RNbP4wLjR9JXyu9l7dHZ2N92gul69enh7exMVFcXhw4dxdXXl7bffplWrVkyfPp1c+Y3LJixbtoxNmzYRGhoq7/2KO1fJFHpl7IAK3w2UKXSLqrnFB8YSS0oy7r3ZoMGNlz/t7Y0f9/Ex3q+apzLcf//9TJkyhdTUVLZu3Ur9+vXx8vKiW7dufPbZZ5w5c8YMT0YoLScnh1dffZXIyEia3OIPLCEqVMkUOsA5YAPGYbvLwApgOzDs+jvKFLrF1bypzsqcPWt8Azg313hZwNHR+M0ycaJJpqH0ej1JSUmEhYURExODq6srGo2GkSNHYn+77zeKGqOgoAAnJyfefvttxo0bp3QcYS2qmEI/C3gCB4C6QAdgLjDk+jvKVKfF1Z7is6CioiKio6MJDw8nIyMDlUqFRqPB1dVVLo/VQgaDAV9fX1q0aMHChQuVjiOsTSWnyVSLnCajCCm+mzh58iQRERGEhYVx8eJFxo8fz/jx43n88ceVjiaq6aOPPiIyMpLt27fXihO5RS1Tyfmh1SLnhypCiq+aDAYDe/bsISwsDK1WS7t27dBoNIwZM4Z7771X6XiiEtu2bcPf35+0tDQeeeQRpeMIa7VoEWWvvkrdCi55VuomA3nCfKT4bkNpaSkJCQmEh4ezceNGhg4dikajwd3dnbtvsphVWM6pU6dwdnYmLCyMIUNueGdFCJM5duwYC7t04f1Ll7irpKTqy552dsaBFik9xUjx3aH8/HxWr15NeHg4hw8fRq1Wo9Fo6Nq1q7wfqKDS0lLc3Nzw8PDgP//5j9JxhBUrLCzE1dWVoKAgpvXta9x7Mz7eWHBX7+F55Tw+T0/jJh1yeVMxUnwm9PPPP7Ns2TKWLVtGo0aNmDBhAgEBAbIllgJeeeUVfv75Z9auXUudOjV71Y6ovfR6PT4+PjRr1oz//e9///yya+YpdHFnpPjMQK/Xs3PnTsLDw4mKiqJnz55oNBq8vb1xuNXdaMQtW716NbNmzSIzMxNHWRgszGjWrFmkpKSQmJhIvXr1lI4jqkmKz8wuXLjA2rVrCQ8PZ/fu3fj4+KDRaOjfv7+8EjGDH3/8kf79+7Nhwwa6d++udBxhxcLDw3nnnXdIS0vj/vvvVzqOuAVSfBb066+/li+NKCgoYPz48Wg0Gtq1a6d0NKtQWFhIz549mT59OsHBwUrHEVZs165deHt7s23bNp588kml44hbJMWnkL179xIeHs6KFSt47LHH0Gg0jB07lvvuu0/paLWSwWDA39+fxo0b89133ykdR1ixY8eO0bt3b5YsWYKHh4fSccRtkOJT2OXLl0lMTCQ8PJz169czePBgNBoNHh4e8p7BLfjiiy8ICwsjOTlZtpgTZnPNBOe0aUrHEbdJiq8GOXfuHDqdjvDwcH788Uf8/f2ZMGECPXr0kKURVUhOTsbX15eUlBQee+wxpeMIK3VlgrN58+YsXrxY/k7WYlJ8NdSRI0dYvnw54eHh1KtXD41Gw7PPPkurVq2UjlajnDlzhh49erB48WK8vLyUjiOsmExwWg8pvhrOYDCwe/duwsLC0Ol0dOvWDY1Gg6+vL40aNVI6nqIuX77MkCFD6Nu3L3PnzlU6jrBiYWFhvPvuuzLBaSWk+GqRixcvEhsbS3h4ODt27GDUqFFoNBrc3NyoW7eu0vEs7vXXX2fPnj2sX7/eJp+/sAyZ4LQ+Uny1VF5eHlqtlrCwMM6ePcu4cePQaDR07NhR6WgWER0dzdSpU8nKyuKBBx5QOo6wUlcmOENCQhg27IYjZEUtJcVnBXJzc1m2bBnLly+nZcuWaDQa/P39aWqlWyP9/PPPuLq6EhsbS69evZSOI6zUlQnO4OBgpk6dqnQcYUJSfFakrKyMzZs3Ex4eTlxcHG5ubmg0Gry8vKzmHLqioiJ69+7NpEmTmCQ72wszKSsrw9fXVyY4rZQUn5UqLCwkMjKS8PBwcnJyGDNmDBqNhl69etXav8QGgwGNRgMYt4uqrc9D1Hyvv/46qampMsFppaT4bMCxY8fKl0ZcKY9x48bx6KOPKh3tlnzzzTd8/fXXpKSk0LBhQ6XjCCsVFhbG3LlzSU1NlQlOKyXFZ0MMBgNpaWmEh4ezatUqOnfujEajwc/Pj3vuuUfpeFVKS0tj+PDhJCcn88QTTygdR1ip5ORkfHx8SEpKsplBMVskxWejLl26RHx8POHh4WzduhUvLy80Gg2DBw+ucUsDfv/9d3r06MHnn3+Oj4+P0nGElTp69Ci9e/dm6dKlMsFp5aT4BL///jsrV64kPDyckydP8uyzzzJhwgQ6deqkdDTKysrw8PCgW7dufPjhh0rHEVZKJjhtixSfuMaPP/5Yfop806ZN0Wg0qNVqmjdvrkie//73v2zfvp1NmzZx1113KZJBWLeysjJ8fHx48MEHZYLTRkjxiQqVlZWxbds2wsPDWbNmDX379kWj0TBy5EgaNGhgmi+SlwehoZCTAwUF0KQJdOkCgYHQtCnr1q3jxRdfJCMjgwcffNA0X1OI67z++uukpaWxYcMGmeC0EVJ84qbOnz9PdHQ04eHhZGZmolKpmDBhAn369Lm9347T02HePFi/3vjnixf/uc3eHgwGivr3xzc9nf/GxuLq6mqaJyLEdWSC0zZJ8YlbcvLkSVasWEFYWBiXLl1Co9Ewfvx42rRpU70HWLQIpk+H4mKo4luvDNDffTd3f/EFyEJ1YQYywWm7pPjEbTEYDGRlZREWFsbKlStp3749Go2GMWPG0KRJk4o/6UrpXbhQ/S/k4ADz50v5CZOSCU7bJsUn7lhJSQkJCQmEh4ezadMmhg0bhkajwd3d/Z+BlPR0cHO7pvS+AkKBXED99/+vkIMDJCWBk5P5noSwGTLBKaT4hEn9+eefrF69mrCwMH755RcCAgLQaDQ8/c472K1Zc83lzSigDrABKKaK4rOzAx8fiIw0d3xh5a5McLZo0YJvvvlGJjhtlBSfMJuDBw+ybNky4kND2XXqFPUr+Vb7D3CSKooPoEEDOH4crPTECWEZVyY4ExMTufvuu5WOIxRSR+kAwnq1a9eOuXPnkv7SS9x1pz9k7OyMSx+EuE2hoaFERkai0+mk9GycrAgWZlcnNxdKSu7sQYqLITfXNIGEzUlOTmbmzJkkJSXJsgUhr/iEBRQUmOZx8vNN8zjCphw9ehSVSkV4eLgsWxCAFJ+whMqWN9wqR0fTPI6wGYWFhYwYMYJZs2bJsgVRTopPmF+XLsbhlOtcBi5iXKxe9vf/v1zJQ1yuV4/LTz5ptojC+pSVlREQEECfPn3497//rXQcUYPIVKcwv7w8aN362q3JgDnAO9fd9e2/P369S3Z2dG7ShD6jRjF69GgGDx5M/fr1zRJXWIeZM2eSnp4uE5ziBvKKT5hfs2bg4WGczLzKHMBw3T9zKvp8Ozvq+/iwJTeX7t278+GHH/Lggw8yfvx41qxZQ3FxsXnzi1onNDSUqKgomeAUFZJXfMIyKti5pdoq2Lnl119/JTo6Gp1OR1ZWFh4eHqhUKjw8PHBwcDBdblHr7Ny5E19fX9mDU1RKik9Yjpn26szLyyMmJgadTkdqairu7u6oVCq8vLxo1KiRCYKL2kL24BTVIcUnLKuapzNgZ2c8ougWN6j+448/WLNmDTqdjuTkZAYNGoRKpWL48OGVb54trEJhYSF9+vTh+eefl2EWUSUpPmF5GRnG8/ji440Fd/V7dH+fx4enJ8yefUcbU+fn57N27Vp0Oh1JSUkMGDAAlUrFyJEjcZSlEValrKwMb29vHnroIdmDU9yUFJ9Qztmzxm3IcnONi9MdHaFzZ5g40eR7chYUFBAXF4dOp2Pz5s24urqiUqkYNWoUDzzwgEm/lrA8meAUt0KKT9icwsJC4uPj0el0JCYm0rNnT0aPHo23tzfNmjVTOp64RaGhobz33ntyirqoNik+YdOKiopISEhAp9Oxfv16unfvjkqlKj+6RtRsMsEpbocUnxB/Ky4uJjExEZ1OR1xcHJ07d0alUuHr68vDDz+sdDxxnSsTnKGhoQwdOlTpOKIWkeITogKXLl1i06ZNfP/996xdu5YOHTqgUqnw8/OjdevWSsezeTLBKe6EFJ8QN1FSUsKWLVvQ6XTExMTQpk0bVCoVKpWKNm3aKB3P5sgEp7hTUnxC3ILS0lKSkpLQ6XRER0fTsmXL8hJs166d0vFsgkxwijslxSfEbSorK2PHjh3odDoiIyNp2rRpeQk+KSdJmEVoaCjvv/8+KSkpMsEpbpsUnxAmUFZWxu7du9HpdOh0Ou655x5Gjx6NSqWiU6dOcjnOBK5McG7fvp0OHTooHUfUYlJ8QpiYXq8nLS2tvATr169f/kqwa9euUoK34coEZ1hYGO7u7krHEbWcFJ8QZmQwGMjMzOT7779Hp9MBlJegk5OTlGA1yASnMDUpPiEsxGAwkJ2djU6n4/vvv+fSpUvlJdirVy/q1JHjMa93ZYKzZcuWLFq0SH5RECYhxSeEAgwGA/v27Su/HFpQUICfnx8qlYo+ffpQt25dpSPWCDNnziQjI4MNGzbIBKcwGSk+IWqA/fv3ExkZiU6nIy8vD19fX1QqFf3797fZEpQJTmEuUnxC1DAHDx4sL8GTJ0/i4+ODSqViwIABNvOqRyY4hTlJ8QlRgx05cqS8BA8fPoy3tzcqlYpBgwZRr149peOZxS+//EKfPn1kglOYjRSfELXEsWPHykvwwIEDjBw5EpVKxZAhQ6hfv77S8UziygTnCy+8wMsvv6x0HGGlpPiEqIVOnjxJVFQUOp2O3NxcvLy8UKlUDB06FHt7e6Xj3RaZ4BSWIsUnRC3366+/Eh0djU6nIysri2HDhqFSqfDw8KBhw4ZKx6u2GTNmkJmZKROcwuyk+ISwInl5ecTExKDT6UhNTWXIkCGoVCq8vLxo3Lix0vEqJROcwpKk+ISwUn/88Qdr1qxBp9ORnJzMoEGDUKlUDB8+nCZNmigdr5xMNiPK/AAAA8hJREFUcApLk+ITwgbk5+ezdu1adDodSUlJDBgwAJVKxciRI3F0dFQsl0xwCiVI8QlhYwoKCoiLi0On07F582ZcXV1RqVSMGjWKBx54wHRfKC8PQkMhJwcKCqBJE+jSBQIDoWlT/vrrL1xdXWWCU1icFJ8QNqywsJD4+Hh0Oh2JiYn07NkTlUqFj48PzZo1u70HTU+HefNg/Xrjny9e/Oc2e3swGNAPG8ZrZ89S3KmTTHAKi5PiE0IAUFRUREJCAjqdjvXr19OtWzdUKhW+vr60aNGieg+yaBFMnw7FxVDFjxY9UFKnDnd98QV3vfSSaZ6AENUkxSeEuEFxcTGJiYnodDri4uLo1KlTeQm2atWq4k+6UnoXLlT/Czk4wPz5MGmSaYILUQ1SfEKIKl26dIlNmzah0+lYu3Yt7dq1Kz9OqXXr1sY7paeDm9s1pXcJmAxsAv4EHgfmAR7XfwEHB0hKAicn8z8ZIZDiE0LcgpKSErZs2YJOpyMmJoY2bdqgUqmYvGkTjTZtuubyZhHwMTAReASIB9RALvDo1Q9qZwc+PhAZaamnIWycFJ8Q4raUlpaSlJTEhmXLmBseToNqfE4X4G3A7/obGjSA48ehaVOT5xTienLksxDittx9990MHjyYj596ivoNbl57Z4CDwFMV3WhnZ1z6IIQFSPEJIe5MTg52Vy9ZqEAp8CwwAahwb5biYsjNNX02ISogxSeEuDMFBVXerAfGA/WAr6q6Y36+6TIJUYW7lA4ghKjlqtj30wAEY7zMGQ9UeeaCglunCdsir/iEEHemSxfjcEoFJgE/ArFAlacE2ttD586mzyZEBWSqUwhxZ/LyoHXra7cmA45hXLZQn2svLS3G+H7fNWSqU1iQvOITQtyZZs3Aw8M4mXmV1hgvdV4Ezl/1zw2lZ2cHnp5SesJi5BWfEOLOVbBzS7XJzi3CwuQVnxDizjk7G/fcdHC4tc+7slenlJ6wIJnqFEKYxpWNpqtxOgN2dsaBFtmgWihALnUKIUwrI8N4Hl98vLHgiov/ue3v8/jw9ITZs+WVnlCEFJ8QwjzOnjVuQ5aba1yc7uhoXLIwcaIMsghFSfEJIYSwKTLcIoQQwqZI8QkhhLApUnxCCCFsihSfEEIImyLFJ4QQwqZI8QkhhLApUnxCCCFsihSfEEIImyLFJ4QQwqZI8QkhhLApUnxCCCFsihSfEEIImyLFJ4QQwqZI8QkhhLApUnxCCCFsihSfEEIImyLFJ4QQwqZI8QkhhLApUnxCCCFsihSfEEIImyLFJ4QQwqb8P29D/LoY5MyCAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -212,33 +212,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 0\n", - "X 1\n", - "X 2\n", - "Z 3\n", - "I 4\n", - "I 5\n", - "X 6\n", - "I 7\n", - "X 8\n", "I 0\n", + "I 1\n", + "I 2\n", "I 3\n", + "I 4\n", + "X 5\n", + "I 6\n", + "I 7\n", + "Z 8\n", + "CZ 0 3\n", "I 0\n", "I 1\n", "CZ 1 4\n", - "CZ 1 2\n", - "CZ 2 5\n", + "I 1\n", + "I 2\n", + "I 2\n", + "I 5\n", "I 3\n", "I 6\n", "CZ 3 4\n", - "I 4\n", - "I 7\n", - "I 4\n", + "CZ 4 7\n", + "CZ 4 5\n", "I 5\n", - "CZ 5 8\n", - "I 6\n", + "I 8\n", + "CZ 6 7\n", "I 7\n", - "CZ 7 8\n", + "I 8\n", "\n" ] } @@ -258,24 +258,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", - "RZ(pi/2) 0\n", - "RX(-pi/2) 0\n", - "RX(-pi/2) 1\n", - "RZ(-pi) 1\n", - "RX(pi/2) 2\n", + "RZ(-pi/2) 0\n", + "RX(-pi) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 2\n", "RZ(pi/2) 2\n", - "RZ(pi/2) 3\n", "RX(-pi) 3\n", + "RZ(pi/2) 4\n", "RX(-pi/2) 4\n", - "RZ(-pi) 4\n", - "RX(pi/2) 5\n", - "RZ(pi/2) 5\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 6\n", + "RX(-pi/2) 5\n", + "RZ(-pi) 5\n", "RX(-pi/2) 6\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 8\n", + "RZ(-pi) 6\n", + "RX(pi/2) 7\n", + "RZ(-pi) 7\n", + "RX(-pi/2) 8\n", "RZ(pi/2) 8\n", "RX(-pi/2) 8\n", "\n" @@ -304,9 +302,9 @@ "output_type": "stream", "text": [ "I 4\n", - "X 7\n", + "I 5\n", "I 4\n", - "I 7\n", + "X 5\n", "\n" ] } @@ -325,9 +323,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "I 7\n", - "CNOT 4 7\n", + "I 2\n", + "I 5\n", + "I 2\n", + "I 5\n", "\n" ] } @@ -349,7 +348,7 @@ "H 0\n", "H 1\n", "H 2\n", - "H 5\n", + "H 4\n", "\n" ] } @@ -368,18 +367,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "CZ 7 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 8\n", - "RX(-pi/2) 7\n", - "CZ 7 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", - "CZ 7 8\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi) 7\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi) 0\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", "\n" ] } @@ -399,6 +395,9 @@ "name": "stdout", "output_type": "stream", "text": [ + "DEFGATE Perm102 AS PERMUTATION:\n", + " 0, 2, 1, 3, 4, 6, 5, 7\n", + "Perm102 1 2 4\n", "\n" ] } @@ -418,40 +417,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-0.07485140215683961) 4\n", - "RX(pi/2) 4\n", - "RZ(2.018484028959887) 4\n", - "RX(-pi/2) 4\n", - "RZ(2.189582454518943) 4\n", - "RZ(2.4546383713704456) 7\n", - "RX(pi/2) 7\n", - "RZ(0.5128477331264145) 7\n", - "RX(-pi/2) 7\n", - "RZ(1.3692666388679378) 7\n", - "CZ 7 4\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(1.7840235069198425) 4\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RX(pi/2) 4\n", - "RZ(-1.6262546737923005) 4\n", - "RX(-pi/2) 4\n", - "RZ(1.26924235270498) 7\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RZ(-2.4561083762331126) 4\n", - "RX(pi/2) 4\n", - "RZ(0.3578106670331639) 4\n", - "RX(-pi/2) 4\n", - "RZ(-1.5286697079172273) 4\n", - "RZ(-0.4386947993359078) 7\n", - "RX(-pi/2) 7\n", - "RZ(1.1436356557642386) 7\n", - "RX(-pi/2) 7\n", - "RZ(-2.442546010449126) 7\n", + "DEFGATE LYR0_RSU4_2_5:\n", + " -0.09969160814430622+0.0902156122395286i, -0.33709519811871885-0.048136235456428166i, 0.7703918235977348+0.48727367053674936i, -0.02107343025834974-0.18598165363223637i\n", + " -0.4675035064037158+0.20523079122648438i, 0.038445207841979606-0.25078075097360014i, -0.05647270554583689+0.019169708299199825i, -0.7802899318958636+0.25008549090639387i\n", + " -0.34089008290794076-0.7272853767121489i, 0.0027518127263039885-0.2069765795381728i, 0.2215611250339229-0.12193916055391807i, 0.20591837586243+0.4534778789801883i\n", + " -0.0036986206424105654+0.27582562028872515i, 0.7923348800993039-0.38605604774942803i, 0.3168040759021317-0.034363504424924196i, 0.21335791201204357-0.002332725678737959i\n", + "\n", + "LYR0_RSU4_2_5 2 5\n", "\n" ] } @@ -477,21 +449,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 1\n", - "I 2\n", - "I 4\n", - "X 7\n", - "CNOT 1 4\n", - "CNOT 1 2\n", - "CNOT 4 7\n", - "X 1\n", - "X 2\n", + "I 0\n", + "X 3\n", "X 4\n", - "I 7\n", - "CNOT 1 4\n", - "I 1\n", - "I 2\n", - "CNOT 4 7\n", + "I 6\n", + "I 0\n", + "I 3\n", + "I 3\n", + "I 6\n", + "I 3\n", + "I 4\n", + "I 0\n", + "I 3\n", + "I 4\n", + "X 6\n", + "CNOT 0 3\n", + "CNOT 3 6\n", + "CNOT 3 4\n", "\n" ] } @@ -517,23 +491,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 4\n", "H 7\n", - "I 4\n", + "H 8\n", "Z 7\n", - "I 4\n", - "I 7\n", - "I 4\n", + "Z 8\n", "I 7\n", - "H 4\n", - "CZ 4 7\n", - "H 4\n", - "Z 4\n", + "I 8\n", + "Z 7\n", + "I 8\n", "I 7\n", - "I 4\n", + "I 8\n", "I 7\n", - "H 4\n", + "Z 8\n", + "H 7\n", + "CZ 7 8\n", + "H 7\n", "H 7\n", + "H 8\n", "\n" ] } @@ -558,68 +532,78 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi) 6\n", - "RZ(-pi) 7\n", - "RZ(-pi) 7\n", - "RX(-pi) 7\n", - "RZ(-pi/2) 6\n", - "RX(pi/2) 6\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi) 7\n", - "RZ(-pi) 7\n", - "CZ 6 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 7\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "CZ 6 7\n", - "RX(-pi/2) 7\n", - "RX(-pi/2) 6\n", - "CZ 6 7\n", - "RX(-pi/2) 7\n", - "RZ(-pi/2) 6\n", - "RX(-pi/2) 6\n", - "RX(-pi) 7\n", - "RX(pi/2) 7\n", - "RX(-pi/2) 6\n", - "CZ 6 7\n", - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 6\n", - "RX(-pi/2) 6\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RZ(-pi/2) 6\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RZ(pi/2) 7\n", - "DAGGER RX(pi/2) 7\n", - "DAGGER CZ 6 7\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RX(pi/2) 7\n", - "DAGGER RX(-pi) 7\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RZ(-pi/2) 6\n", - "DAGGER RX(-pi/2) 7\n", - "DAGGER CZ 6 7\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RX(-pi/2) 7\n", - "DAGGER CZ 6 7\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RZ(pi/2) 6\n", - "DAGGER RX(-pi/2) 7\n", - "DAGGER RZ(pi/2) 7\n", - "DAGGER CZ 6 7\n", - "DAGGER RZ(-pi) 7\n", - "DAGGER RZ(-pi) 7\n", - "DAGGER RX(-pi/2) 6\n", - "DAGGER RZ(pi/2) 6\n", - "DAGGER RX(pi/2) 6\n", - "DAGGER RZ(-pi/2) 6\n", - "DAGGER RX(-pi) 7\n", - "DAGGER RZ(-pi) 7\n", - "DAGGER RZ(-pi) 7\n", - "DAGGER RX(-pi) 6\n", + "RZ(-pi/2) 1\n", + "RX(-pi) 1\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 2\n", + "RZ(-pi/2) 1\n", + "RX(-pi) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 1\n", + "RX(-pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 1\n", + "CZ 1 2\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RZ(-pi/2) 2\n", + "RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER RZ(pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER RZ(pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi) 2\n", + "DAGGER RZ(-pi) 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi) 2\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(-pi/2) 1\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -642,7 +626,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Quantum Volume (unoptimized)" + "### Quantum Volume" ] }, { @@ -654,401 +638,123 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RZ(-pi/2) 3\n", - "RX(pi) 3\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RZ(pi/2) 5\n", - "RX(-pi/2) 5\n", - "RX(pi) 3\n", - "RX(pi/2) 4\n", - "RX(pi) 7\n", - "CZ 7 4\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "CZ 4 5\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "RZ(pi/2) 5\n", - "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 5\n", - "CZ 4 5\n", - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RZ(2.2708339550000107) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(-2.29399624460067) 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RZ(pi) 3\n", - "RX(pi/2) 3\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "CZ 3 4\n", - "RX(pi/2) 4\n", - "RZ(-0.7000376282051146) 4\n", - "RX(pi/2) 4\n", - "CZ 4 7\n", - "RX(-pi/2) 4\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RX(pi/2) 3\n", - "RZ(1.7286650484424175) 4\n", - "RX(pi) 4\n", - "CZ 4 3\n", - "RZ(pi/2) 5\n", - "RX(pi/2) 5\n", - "RZ(-pi/2) 5\n", - "RZ(0.8475964089891228) 3\n", - "RZ(-2.9837239319422726) 4\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 7\n", - "RX(pi) 7\n", - "CZ 4 7\n", - "RX(-pi/2) 4\n", - "CZ 4 5\n", - "RZ(pi) 7\n", - "CZ 4 3\n", - "RZ(pi/2) 5\n", - "RX(-pi/2) 5\n", - "RZ(pi/2) 4\n", - "RX(pi) 4\n", - "RZ(pi) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", - "RX(-pi/2) 4\n", - "CZ 4 5\n", - "RX(-pi/2) 4\n", - "RZ(-pi/2) 7\n", - "RX(pi/2) 7\n", - "CZ 4 7\n", - "CZ 4 5\n", - "CZ 4 7\n", - "RZ(pi) 3\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", - "RZ(pi/2) 5\n", - "RX(pi/2) 5\n", - "RZ(pi/2) 5\n", - "RZ(0.9781997183417529) 3\n", + "RZ(-0.6633765634144329) 0\n", + "RX(pi/2) 0\n", + "RZ(2.1992567304350827) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.213273479007638) 0\n", + "RZ(-2.1790140703661987) 1\n", + "RX(pi/2) 1\n", + "RZ(1.3833680725337012) 1\n", + "RX(-pi/2) 1\n", + "RZ(-1.5430363103535998) 1\n", + "CZ 1 0\n", + "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(2.1382446014645566) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 0\n", + "RZ(-1.6745691134157568) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.8121261481912123) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(1.6380912332362045) 3\n", "RX(pi/2) 3\n", - "RZ(0.3830531979820055) 3\n", + "RZ(1.2911009982026904) 3\n", "RX(-pi/2) 3\n", - "RZ(0.48467006037794125) 3\n", - "RZ(-3.1252189336617793) 4\n", - "RX(pi/2) 4\n", - "RZ(0.627338448032136) 4\n", - "RX(-pi/2) 4\n", - "RZ(-0.07778716285087062) 4\n", - "CZ 4 3\n", + "RZ(2.905707049360048) 3\n", + "RZ(-0.3198967078677877) 0\n", + "RX(pi/2) 0\n", + "RZ(1.9993474339045234) 0\n", + "RX(-pi/2) 0\n", + "RZ(-2.045982310794382) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", "RZ(pi/2) 3\n", "RX(pi/2) 3\n", - "RZ(2.694501844223513) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.6076937413025152) 3\n", - "RX(-pi/2) 3\n", - "RZ(1.5999848757758146) 4\n", - 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3\n", - "RX(pi) 3\n", - "RZ(pi) 4\n", - "RX(pi/2) 4\n", - "CZ 4 3\n", - "RX(pi) 8\n", - "CZ 7 8\n", - "RX(pi) 4\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RZ(pi) 8\n", - "RZ(pi) 4\n", - "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RX(-pi/2) 6\n", - "CZ 7 8\n", - "RX(pi/2) 4\n", - "RX(pi) 7\n", - "CZ 7 4\n", - "RZ(-pi/2) 6\n", - "RX(pi) 6\n", - "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(-pi/2) 5\n", - "RX(pi) 5\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RZ(pi/2) 6\n", - "RZ(pi/2) 7\n", - "RX(pi) 7\n", - "RZ(pi) 8\n", - "RZ(2.3127819710245126) 3\n", - "RX(pi/2) 3\n", - "RZ(0.6536576005477784) 3\n", - "RX(-pi/2) 3\n", - "RZ(-0.06054578131424648) 3\n", - "RZ(-0.5050109085962076) 4\n", - "RX(pi/2) 4\n", - "RZ(2.6155060355641764) 4\n", - "RX(-pi/2) 4\n", - "RZ(-2.5292629348291444) 4\n", - "CZ 4 3\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(2.0428228394385606) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - 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"RZ(0.5586233876167076) 6\n", - "RX(-pi/2) 6\n", - "RZ(-1.954346632603958) 6\n", + "RZ(-1.3152784290894761) 3\n", "\n" ] } ], "source": [ - "qv_template = rand_perm_layer + rand_su4_layer\n", - "print(qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=5))" + "qv_template = rand_su4_layer\n", + "# we want to compile the output sequences with graph-restricted compilation.\n", + "qv_template.sequence_transforms.append(compile_merged_sequence)\n", + "qv_prog = qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=4)\n", + "print(qv_prog)" ] }, { @@ -1064,56 +770,61 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ + "start_time = time.time()\n", "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", "wfn_sim = NumpyWavefunctionSimulator(9)\n", - "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, widths=[2], depths=[2], num_circuit_samples=200)\n", + "d = 2\n", + "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, \n", + " widths=[d], depths=[d], num_circuit_samples=200)\n", "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [0.8140000000000006, 0.8720000000000007, 0.7680000000000006, 0.7380000000000005, 0.7900000000000006, 0.9580000000000007, 0.8860000000000007, 0.7940000000000006, 0.6300000000000004, 0.6340000000000005, 0.7740000000000006, 0.8040000000000006, 0.7040000000000005, 0.8900000000000007, 0.9420000000000007, 0.6460000000000005, 0.7840000000000006, 0.7400000000000005, 0.7040000000000005, 0.9360000000000007, 0.8460000000000006, 0.8100000000000006, 0.9380000000000007, 0.7680000000000006, 0.7940000000000006, 0.7900000000000006, 0.8440000000000006, 0.7980000000000006, 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24, + "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: True}}\n" + "{2: {2: True}}\n", + "35.48560094833374\n", + "{2: {2: 0.7382941716386486}}\n" ] } ], "source": [ "qvol_successes = determine_successes(qvol_success_probs, 500)\n", - "print(qvol_successes)" + "print(qvol_successes)\n", + "end_time = time.time()\n", + "print(end_time - start_time)\n", + "print(determine_prob_success_lower_bounds(qvol_success_probs, 500))" ] }, { @@ -1132,7 +843,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: 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1.900e-03, 2.000e-04]), 10: array([0.7377, 0.2199, 0.0345, 0.0068, 0.0011])}, 5: {2: array([7.399e-01, 2.293e-01, 2.730e-02, 3.000e-03, 5.000e-04, 0.000e+00]), 3: array([7.308e-01, 2.334e-01, 3.220e-02, 3.200e-03, 4.000e-04, 0.000e+00]), 4: array([7.299e-01, 2.288e-01, 3.440e-02, 5.500e-03, 1.000e-03, 4.000e-04]), 5: array([7.182e-01, 2.351e-01, 4.000e-02, 5.400e-03, 1.200e-03, 1.000e-04]), 10: array([0.7197, 0.2264, 0.0403, 0.0098, 0.0027, 0.0011])}}\n" ] } ], @@ -1237,7 +948,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1274,7 +985,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1328,7 +1039,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1649,7 +1360,7 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 43, @@ -1658,7 +1369,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1683,7 +1394,7 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 44, @@ -1730,7 +1441,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1766,7 +1477,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -2099,7 +1810,7 @@ { "data": { "text/plain": [ - "array([0.06076231, 0.00080728])" + "array([0.05929668, 0.00165794])" ] }, "execution_count": 61, @@ -2120,11 +1831,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.8807437 0.82722768 0.77696342 0.72975333]\n", - " [0.8800327 0.82655988 0.77633619 0.72916421]\n", - " [0.87932226 0.82589261 0.77570947 0.72857557]\n", - " [0.8786124 0.82522589 0.77508326 0.72798741]\n", - " [0.87507169 0.82190032 0.77195976 0.7250537 ]]\n" + "[[0.88199086 0.82969173 0.78049376 0.73421307]\n", + " [0.88052857 0.82831615 0.77919975 0.73299579]\n", + " [0.87906871 0.82694285 0.77790788 0.73178053]\n", + " [0.87761126 0.82557182 0.77661815 0.73056728]\n", + " [0.8703602 0.81875073 0.77020153 0.72453113]]\n" ] } ], @@ -2141,7 +1852,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 66c3cc79..f6cfdd13 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -209,7 +209,8 @@ def _do_pattern(patt): def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None, pattern = None): - return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence, pattern)) + # TODO: replace with merge_programs after permutation issue fixed. + return sum(self.sample_sequence(graph, repetitions, qc, width, sequence, pattern), Program()) # ================================================================================================== @@ -326,13 +327,16 @@ def random_qubit_permutation(graph: nx.Graph): return p -def random_su4_pairs(graph: nx.Graph): +def random_su4_pairs(graph: nx.Graph, idx_label, randomly_permute_qubits: bool = True): qubits = list(graph.nodes) + if randomly_permute_qubits: + permutation = list(np.random.permutation(range(len(qubits)))) + qubits = [qubits[idx] for idx in permutation] prog = Program() # ignore the edges in the graph for q1, q2 in zip(qubits[::2], qubits[1::2]): matrix = haar_rand_unitary(4) - gate_definition = DefGate(f"RSU4_{q1}_{q2}", matrix) + gate_definition = DefGate(f"LYR{idx_label}_RSU4_{q1}_{q2}", matrix) RSU4 = gate_definition.get_constructor() prog += gate_definition prog += RSU4(q1, q2) @@ -362,7 +366,7 @@ def graph_restricted_compilation(qc, graph, program): new_2q = {} for key, val in two_qs.items(): q1, q2 = key.split('-') - if int(q1) in qubits and int(q2) in qubits: + if (int(q1), int(q2)) in graph.edges: new_2q[key] = val new_isa = {'1Q': new_1q, '2Q': new_2q} @@ -394,6 +398,22 @@ def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **k new_sequence[::2] = random_paulis new_sequence[1::2] = sequence return new_sequence + + +def compile_individual_sequence_elements(qc, sequence: List[Program], graph: nx.Graph, **kwargs): + compiled_sequence = [] + for prog in sequence: + native_quil = graph_restricted_compilation(qc, graph, prog) + # remove gate definitions and HALT + compiled_sequence.append(Program([instr for instr in native_quil.instructions][:-1])) + return compiled_sequence + + +def compile_merged_sequence(qc, sequence: List[Program], graph: nx.Graph, **kwargs): + # compile all of the sequence at once. + # TODO: replace sum with merge_programs after permutation issue fixed. + native_quil = graph_restricted_compilation(qc, graph, sum(sequence, Program())) + return [Program([instr for instr in native_quil.instructions][:-1])] ### # Templates ### @@ -422,38 +442,26 @@ def func(graph, **kwargs): def get_dagger_all_template(): - def func(qc, sequence, **kwargs): - prog = dagger_previous(sequence, len(sequence)) - native_quil = qc.compiler.quil_to_native_quil(prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) + def func(sequence, **kwargs): + return dagger_previous(sequence, len(sequence)) return CircuitTemplate([func]) def get_dagger_previous(n: int = 1): - def func(qc, sequence, **kwargs): - prog = dagger_previous(sequence, n) - native_quil = qc.compiler.quil_to_native_quil(prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) + def func(sequence, **kwargs): + return dagger_previous(sequence, n) return CircuitTemplate([func]) def get_rand_qubit_perm_template(): - def func(graph, qc, **kwargs): - prog = random_qubit_permutation(graph) - native_quil = qc.compiler.quil_to_native_quil(prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) + def func(graph, **kwargs): + return random_qubit_permutation(graph) return CircuitTemplate([func]) -def get_rand_su4_template(): - def func(graph, qc, **kwargs): - prog = random_su4_pairs(graph) - native_quil = graph_restricted_compilation(qc, graph, prog) - # remove gate definitions and HALT - return Program([instr for instr in native_quil.instructions][:-1]) +def get_rand_su4_template(randomly_permute_qubits: bool = True): + def func(graph, sequence, **kwargs): + return random_su4_pairs(graph, len(sequence), randomly_permute_qubits) return CircuitTemplate([func]) @@ -538,7 +546,7 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = 500, - measure_qubits: Dict[int, List[int]] = None, + measure_qubits: Dict[int, Dict[int, List[int]]] = None, use_active_reset: bool = False, use_compiler: bool = False): reset_prog = Program() @@ -575,7 +583,7 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, - measure_qubits: Dict[int, List[int]] = None): + measure_qubits: Dict[int, Dict[int, List[int]]] = None): """ Collects and returns those 'heavy' bitstrings which are output with greater than median probability among all possible bitstrings on the given qubits. @@ -712,35 +720,6 @@ def get_success_probabilities(noisy_results, ideal_results): return prob_success -def count_heavy_hitters_sampled(noisy_results, heavy_hitters): - """ - Simple helper to count the number of heavy hitters sampled given the sampled results for a - number of circuits along with the the actual heavy hitters for each circuit. - - :param noisy_results: results from running each circuit on a quantum computer. - :param heavy_hitters: the heavy hitters for each circuit (presumably calculated through - simulating the circuit classically) - :return: the number of samples which were heavy for each circuit. - """ - num_sampled = {w: {d: [] for d in depth_array.keys()} - for w, depth_array in noisy_results.items()} - - for w, d_results in noisy_results.items(): - for d, ckts_results in d_results.items(): - ckts_hh = heavy_hitters[w][d] - for ckt_results, ckt_hh in zip(ckts_results, ckts_hh): - num_hh = 0 - # determine if each result bitstring is a heavy output, as determined from simulation - for result in ckt_results: - # convert result to int for comparison with heavy outputs. - output = bit_array_to_int(result) - if output in ckt_hh: - num_hh += 1 - num_sampled[w][d].append(num_hh) - - return num_sampled - - def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_shots: int) \ -> Tuple[float, float]: """ From 45985493cb4a864ef7d2850229d3855b91463a1c Mon Sep 17 00:00:00 2001 From: Kyle Date: Tue, 10 Sep 2019 15:27:36 -0400 Subject: [PATCH 35/49] Move nb --- docs/examples/volumetrics.ipynb | 1954 +++++++++++++++++++++++++++++++ 1 file changed, 1954 insertions(+) create mode 100644 docs/examples/volumetrics.ipynb diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb new file mode 100644 index 00000000..d664b126 --- /dev/null +++ b/docs/examples/volumetrics.ipynb @@ -0,0 +1,1954 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Volumetrics\n", + "\n", + "\n", + "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", + "\n", + "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", + "\n", + "The `depth` is defined in context-dependent way; to avoid confusion with circuit depth we may use the term 'repetitions'." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "import itertools\n", + "import networkx as nx\n", + "import numpy as np\n", + "import time\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", + "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", + "from pyquil.quilbase import Pragma\n", + "\n", + "from forest.benchmarking.volumetrics import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get lattice" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pyquil import *\n", + "# if you want to run on a \"real lattice\"\n", + "#list_quantum_computers()\n", + "#perfect_qc = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", + "#noisy_qc = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", + "\n", + "noisy_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)\n", + "perfect_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:518: MatplotlibDeprecationWarning: \n", + "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n", + " if not cb.iterable(width):\n", + "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:565: MatplotlibDeprecationWarning: \n", + "The is_numlike function was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use isinstance(..., numbers.Number) instead.\n", + " if cb.is_numlike(alpha):\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nx.draw(perfect_qc.qubit_topology(),with_labels=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "G = perfect_qc.qubit_topology()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gate sets\n", + "\n", + "### Classical" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def two_q_id(qb1,qb2):\n", + " prog = Program()\n", + " prog +=I(qb1)\n", + " prog +=I(qb2)\n", + " return prog\n", + "\n", + "one_c_gates = [X,I]\n", + "two_c_gates = [two_q_id, CNOT]\n", + "two_c_toffoli = two_c_gates + [CCNOT]\n", + "\n", + "# x basis gates\n", + "from forest.benchmarking.classical_logic import CNOT_X_basis, CCNOT_X_basis\n", + "one_x_c_gates = [Z, I]\n", + "two_x_c_gates = [two_q_id, CNOT_X_basis]\n", + "two_x_c_toffoli = two_x_c_gates + [CCNOT_X_basis]\n", + "# if you want to do something in the X basis, add Hadamard layers appropriately; see below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Some quantum" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "one_q_gates = [X,Z,I]\n", + "two_q_gates = [two_q_id,CZ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Cliffords\n", + "\n", + "We use a benchmarker for this. Typically we use the native gates from `get_rb_gateset` to implement each clifford." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from forest.benchmarking.randomized_benchmarking import get_rb_gateset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# my config has gone all cattywampus so i need to do this\n", + "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'tcp://127.0.0.1:5555'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bm.client.endpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get random gates on a graph" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 0\n", + "I 1\n", + "I 2\n", + "I 3\n", + "I 4\n", + "X 5\n", + "I 6\n", + "I 7\n", + "Z 8\n", + "CZ 0 3\n", + "I 0\n", + "I 1\n", + "CZ 1 4\n", + "I 1\n", + "I 2\n", + "I 2\n", + "I 5\n", + "I 3\n", + "I 6\n", + "CZ 3 4\n", + "CZ 4 7\n", + "CZ 4 5\n", + "I 5\n", + "I 8\n", + "CZ 6 7\n", + "I 7\n", + "I 8\n", + "\n" + ] + } + ], + "source": [ + "prog1 = random_single_qubit_gates(G, one_q_gates)\n", + "prog2 = random_two_qubit_gates(G, two_q_gates)\n", + "print(prog1+prog2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-pi/2) 0\n", + "RX(-pi) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(-pi) 3\n", + "RZ(pi/2) 4\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 5\n", + "RZ(-pi) 5\n", + "RX(-pi/2) 6\n", + "RZ(-pi) 6\n", + "RX(pi/2) 7\n", + "RZ(-pi) 7\n", + "RX(-pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", + "\n" + ] + } + ], + "source": [ + "progy = random_single_qubit_cliffords(bm, G)\n", + "print(progy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make some circuit templates and sample programs from them\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 4\n", + "I 5\n", + "I 4\n", + "X 5\n", + "\n" + ] + } + ], + "source": [ + "classical_1q_layer = get_rand_1q_template(one_c_gates)\n", + "print(classical_1q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 2\n", + "I 5\n", + "I 2\n", + "I 5\n", + "\n" + ] + } + ], + "source": [ + "classical_2q_layer = get_rand_2q_template(two_c_gates)\n", + "print(classical_2q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H 0\n", + "H 1\n", + "H 2\n", + "H 4\n", + "\n" + ] + } + ], + "source": [ + "switch_basis_layer = get_switch_basis_x_z_template()\n", + "print(switch_basis_layer.sample_program(G, repetitions=1, width=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi) 0\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", + "\n" + ] + } + ], + "source": [ + "clifford_1q_layer = get_rand_1q_cliff_template(bm)\n", + "clifford_2q_layer = get_rand_2q_cliff_template(bm)\n", + "print(clifford_2q_layer.sample_program(G, repetitions=2, width=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEFGATE Perm102 AS PERMUTATION:\n", + " 0, 2, 1, 3, 4, 6, 5, 7\n", + "Perm102 1 2 4\n", + "\n" + ] + } + ], + "source": [ + "rand_perm_layer = get_rand_qubit_perm_template()\n", + "# sometimes this returns an empty program, i.e. no permutation\n", + "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=3))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DEFGATE LYR0_RSU4_2_5:\n", + " -0.09969160814430622+0.0902156122395286i, -0.33709519811871885-0.048136235456428166i, 0.7703918235977348+0.48727367053674936i, -0.02107343025834974-0.18598165363223637i\n", + " -0.4675035064037158+0.20523079122648438i, 0.038445207841979606-0.25078075097360014i, -0.05647270554583689+0.019169708299199825i, -0.7802899318958636+0.25008549090639387i\n", + " -0.34089008290794076-0.7272853767121489i, 0.0027518127263039885-0.2069765795381728i, 0.2215611250339229-0.12193916055391807i, 0.20591837586243+0.4534778789801883i\n", + " -0.0036986206424105654+0.27582562028872515i, 0.7923348800993039-0.38605604774942803i, 0.3168040759021317-0.034363504424924196i, 0.21335791201204357-0.002332725678737959i\n", + "\n", + "LYR0_RSU4_2_5 2 5\n", + "\n" + ] + } + ], + "source": [ + "rand_su4_layer = get_rand_su4_template()\n", + "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compose templates" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I 0\n", + "X 3\n", + "X 4\n", + "I 6\n", + "I 0\n", + "I 3\n", + "I 3\n", + "I 6\n", + "I 3\n", + "I 4\n", + "I 0\n", + "I 3\n", + "I 4\n", + "X 6\n", + "CNOT 0 3\n", + "CNOT 3 6\n", + "CNOT 3 4\n", + "\n" + ] + } + ], + "source": [ + "classical_1q_2q = classical_1q_layer + classical_2q_layer\n", + "print(classical_1q_2q.sample_program(G, repetitions=2, width=4))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Classical Logic in X basis" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H 7\n", + "H 8\n", + "Z 7\n", + "Z 8\n", + "I 7\n", + "I 8\n", + "Z 7\n", + "I 8\n", + "I 7\n", + "I 8\n", + "I 7\n", + "Z 8\n", + "H 7\n", + "CZ 7 8\n", + "H 7\n", + "H 7\n", + "H 8\n", + "\n" + ] + } + ], + "source": [ + "logic_layers = get_rand_1q_template(one_x_c_gates) + get_rand_2q_template(two_x_c_gates)\n", + "classical_x_1q_2q = switch_basis_layer + logic_layers + switch_basis_layer\n", + "# here we demonstrate a simple use of a pattern. We want to do the basis switch at beginning and end \n", + "# while doing the repetitions in between some variable number of times.\n", + "# The pattern says to do the 0 idx generator, do [1,2] idx generators n times, then finish with 3 idx generator\n", + "classical_x_1q_2q.pattern = [0, ([1, 2], 'n'), 3]\n", + "print(classical_x_1q_2q.sample_program(G, repetitions=3, width=2))\n", + "# note that the x basis CNOT(0, 1) is H(0) CZ(0, 1) H(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-pi/2) 1\n", + "RX(-pi) 1\n", + "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 2\n", + "RZ(-pi/2) 1\n", + "RX(-pi) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi) 2\n", + "RX(-pi) 2\n", + "CZ 1 2\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 1\n", + "RX(-pi/2) 2\n", + "RZ(pi/2) 2\n", + "RX(-pi/2) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 1\n", + "CZ 1 2\n", + "RX(pi/2) 2\n", + "RX(-pi/2) 1\n", + "CZ 1 2\n", + "RZ(-pi/2) 2\n", + "RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER RZ(pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(pi/2) 2\n", + "DAGGER RZ(pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi) 2\n", + "DAGGER RZ(-pi) 2\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER RX(-pi/2) 2\n", + "DAGGER CZ 1 2\n", + "DAGGER RX(-pi) 2\n", + "DAGGER RZ(-pi/2) 2\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "\n", + "This program compiles away to nothing: \n", + "HALT\n", + "\n" + ] + } + ], + "source": [ + "clifford_sandwich = clifford_1q_layer + clifford_2q_layer\n", + "clifford_sandwich.sequence_transforms.append(dagger_sequence)\n", + "prog = clifford_sandwich.sample_program(G, repetitions=3, width=2, qc=noisy_qc)\n", + "print(prog)\n", + "\n", + "# We can check that this is the identity by compiling it fully\n", + "print(\"This program compiles away to nothing: \")\n", + "print(noisy_qc.compiler.quil_to_native_quil(prog))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Quantum Volume" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-0.6633765634144329) 0\n", + "RX(pi/2) 0\n", + "RZ(2.1992567304350827) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.213273479007638) 0\n", + "RZ(-2.1790140703661987) 1\n", + "RX(pi/2) 1\n", + "RZ(1.3833680725337012) 1\n", + "RX(-pi/2) 1\n", + "RZ(-1.5430363103535998) 1\n", + "CZ 1 0\n", + "RZ(pi/2) 0\n", + "RX(pi/2) 0\n", + "RZ(2.1382446014645566) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RX(pi/2) 0\n", + "RZ(-1.6745691134157568) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.8121261481912123) 1\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(1.6380912332362045) 3\n", + "RX(pi/2) 3\n", + "RZ(1.2911009982026904) 3\n", + "RX(-pi/2) 3\n", + "RZ(2.905707049360048) 3\n", + "RZ(-0.3198967078677877) 0\n", + "RX(pi/2) 0\n", + "RZ(1.9993474339045234) 0\n", + "RX(-pi/2) 0\n", + "RZ(-2.045982310794382) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 3 0\n", + "RZ(pi) 0\n", + "RX(pi/2) 0\n", + "RX(-pi/2) 3\n", + "CZ 0 3\n", + "RZ(-1.7211797008449619) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.1291752835794742) 1\n", + "RX(-pi/2) 1\n", + "RZ(-0.9663730999073499) 2\n", + "RX(pi/2) 2\n", + "RZ(1.8104685056998722) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.1562967037020901) 2\n", + "CZ 1 2\n", + "RZ(-2.2067063329930843) 1\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 2\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(pi) 1\n", + "RX(pi/2) 1\n", + "RX(-pi/2) 2\n", + "CZ 1 2\n", + "RZ(-1.7823332810518906) 0\n", + "RX(pi/2) 0\n", + "RZ(0.5707386474274007) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.0405476330691377) 0\n", + "RZ(-1.6504916090017687) 1\n", + "RX(pi/2) 1\n", + "RZ(2.578029427303778) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.6934673043744666) 1\n", + "CZ 0 1\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(2.640187735366899) 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(1.3955050168956022) 0\n", + "RX(pi/2) 0\n", + "RX(pi/2) 1\n", + "RZ(-2.0662135365992644) 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(-1.7203952944369068) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.644187513360958) 0\n", + "RX(-pi/2) 0\n", + "RZ(1.4122248717631236) 0\n", + "RZ(0.37149567048520904) 1\n", + "RX(pi/2) 1\n", + "RZ(2.29520538060332) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.599836886240988) 1\n", + "RZ(-1.2615159694384492) 2\n", + "RX(pi/2) 2\n", + "RZ(1.043867674689562) 2\n", + "RX(-pi/2) 2\n", + "RZ(-1.7558737696969433) 2\n", + "RZ(2.681912008883467) 3\n", + "RX(pi/2) 3\n", + "RZ(0.5879267224374873) 3\n", + "RX(-pi/2) 3\n", + "RZ(-1.3152784290894761) 3\n", + "\n" + ] + } + ], + "source": [ + "qv_template = rand_su4_layer\n", + "# we want to compile the output sequences with graph-restricted compilation.\n", + "qv_template.sequence_transforms.append(compile_merged_sequence)\n", + "qv_prog = qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=4)\n", + "print(qv_prog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run quantum volume for one width and depth\n", + "\n", + "1. Generate the programs\n", + "2. Determine the heavy outputs\n", + "3. Collect experimental data" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", + "wfn_sim = NumpyWavefunctionSimulator(9)\n", + "d = 2\n", + "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, \n", + " widths=[d], depths=[d], num_circuit_samples=200)\n", + "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", + "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: [0.6720000000000005, 0.8240000000000006, 0.9260000000000007, 0.8420000000000006, 0.8780000000000007, 0.6560000000000005, 0.5840000000000004, 0.8820000000000007, 0.8220000000000006, 0.8620000000000007, 0.8140000000000006, 0.7160000000000005, 0.8860000000000007, 0.7420000000000005, 0.6640000000000005, 0.8240000000000006, 0.6520000000000005, 0.5800000000000004, 0.7420000000000005, 0.6680000000000005, 0.9320000000000007, 0.8100000000000006, 0.6600000000000005, 0.9280000000000007, 0.6720000000000005, 0.6020000000000004, 0.7580000000000006, 0.7900000000000006, 0.8220000000000006, 0.9260000000000007, 0.6420000000000005, 0.7320000000000005, 0.7480000000000006, 0.9740000000000008, 0.8020000000000006, 0.7740000000000006, 0.7800000000000006, 0.9220000000000007, 0.7720000000000006, 0.8020000000000006, 0.7800000000000006, 0.7340000000000005, 0.8900000000000007, 0.8540000000000006, 0.7160000000000005, 0.8020000000000006, 0.7660000000000006, 0.8700000000000007, 0.7140000000000005, 0.8800000000000007, 0.8880000000000007, 0.8640000000000007, 0.8360000000000006, 0.9620000000000007, 0.9080000000000007, 0.8560000000000006, 0.7820000000000006, 0.6780000000000005, 0.8580000000000007, 0.8080000000000006, 0.8200000000000006, 0.9380000000000007, 0.6060000000000004, 0.6240000000000004, 0.6740000000000005, 0.8200000000000006, 0.7240000000000005, 0.8380000000000006, 0.7840000000000006, 0.8800000000000007, 0.8660000000000007, 0.9720000000000008, 0.9380000000000007, 0.7260000000000005, 0.7280000000000005, 0.8620000000000007, 0.7340000000000005, 0.8660000000000007, 0.6460000000000005, 0.7840000000000006, 0.6600000000000005, 0.8340000000000006, 0.7460000000000006, 0.7000000000000005, 0.8000000000000006, 0.9200000000000007, 0.9020000000000007, 0.8320000000000006, 0.7700000000000006, 0.8160000000000006, 0.8980000000000007, 0.7460000000000006, 0.8280000000000006, 0.8240000000000006, 0.8860000000000007, 0.9220000000000007, 0.7060000000000005, 0.6040000000000004, 0.7160000000000005, 0.7980000000000006, 0.6360000000000005, 0.8920000000000007, 0.6620000000000005, 0.8620000000000007, 0.7440000000000005, 0.8340000000000006, 0.8940000000000007, 0.7200000000000005, 0.6400000000000005, 0.7980000000000006, 0.8940000000000007, 0.6860000000000005, 0.9120000000000007, 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0.9640000000000007, 0.6400000000000005, 0.7680000000000006, 0.8060000000000006, 0.8880000000000007, 0.8400000000000006, 0.8440000000000006, 0.8200000000000006, 0.8560000000000006, 0.9760000000000008, 0.6920000000000005, 0.6440000000000005, 0.7720000000000006, 0.6780000000000005, 0.7480000000000006, 0.6380000000000005, 0.7980000000000006, 0.7860000000000006, 0.7280000000000005, 0.6520000000000005, 0.7980000000000006, 0.7700000000000006, 0.8580000000000007, 0.8960000000000007, 0.6080000000000004, 0.8400000000000006, 0.7900000000000006, 0.8580000000000007, 0.8540000000000006, 0.7140000000000005, 0.9120000000000007, 0.7180000000000005, 0.8040000000000006, 0.6640000000000005, 0.8780000000000007, 0.6980000000000005, 0.8780000000000007]}}\n", + "0.7953500000000006\n" + ] + } + ], + "source": [ + "qvol_success_probs = get_success_probabilities(experimental_data, heavy_outputs)\n", + "print(qvol_success_probs)\n", + "print(np.average(qvol_success_probs[d][d]))" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: True}}\n", + "35.48560094833374\n", + "{2: {2: 0.7382941716386486}}\n" + ] + } + ], + "source": [ + "qvol_successes = determine_successes(qvol_success_probs, 500)\n", + "print(qvol_successes)\n", + "end_time = time.time()\n", + "print(end_time - start_time)\n", + "print(determine_prob_success_lower_bounds(qvol_success_probs, 500))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acquire data for ranges of (width, depth)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" + ] + } + ], + "source": [ + "widths = [2, 3, 4, 5]\n", + "depths = [2, 3, 4, 5, 10]\n", + "ckt = classical_1q_2q\n", + "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=20)\n", + "print(prog_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "num_shots = 500\n", + "noisy_results = acquire_volumetric_data(noisy_qc, prog_array, num_shots)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: [array([[1, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 0]])], 3: [array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 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array([[0, 0, 0, 0, 1]]), array([[1, 1, 0, 0, 1]]), array([[1, 0, 0, 1, 1]])]}}\n" + ] + } + ], + "source": [ + "ideal_results = acquire_volumetric_data(perfect_qc, prog_array, num_shots=1)\n", + "print(ideal_results)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: [array([0.848, 0.15 , 0.002]), array([0.82, 0.17, 0.01]), array([0.884, 0.116, 0. ]), array([0.878, 0.118, 0.004]), array([0.892, 0.104, 0.004]), array([0.9, 0.1, 0. ]), array([0.9 , 0.098, 0.002]), array([0.872, 0.126, 0.002]), array([0.902, 0.098, 0. ]), array([0.872, 0.116, 0.012]), array([0.906, 0.092, 0.002]), array([0.888, 0.108, 0.004]), array([0.898, 0.102, 0. ]), array([0.916, 0.08 , 0.004]), array([0.898, 0.1 , 0.002]), array([0.898, 0.1 , 0.002]), array([0.938, 0.062, 0. ]), array([0.814, 0.172, 0.014]), array([0.836, 0.156, 0.008]), array([0.888, 0.11 , 0.002])], 3: [array([0.948, 0.05 , 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0.254, 0.062, 0.01 , 0.006, 0. ]), array([0.738, 0.228, 0.018, 0.004, 0.01 , 0.002]), array([0.784, 0.18 , 0.02 , 0.014, 0.002, 0. ]), array([0.794, 0.184, 0.018, 0.004, 0. , 0. ]), array([0.65 , 0.258, 0.076, 0.016, 0. , 0. ]), array([0.724, 0.226, 0.038, 0.01 , 0.002, 0. ]), array([0.704, 0.236, 0.046, 0.014, 0. , 0. ]), array([0.732, 0.21 , 0.048, 0.008, 0.002, 0. ]), array([0.846, 0.118, 0.028, 0.006, 0.002, 0. ]), array([0.65 , 0.28 , 0.062, 0.008, 0. , 0. ]), array([0.618, 0.336, 0.04 , 0.006, 0. , 0. ]), array([0.684, 0.254, 0.044, 0.002, 0.012, 0.004]), array([0.668, 0.268, 0.044, 0.002, 0.004, 0.014]), array([0.732, 0.228, 0.03 , 0.008, 0.002, 0. ]), array([0.822, 0.134, 0.028, 0.014, 0.002, 0. ]), array([0.778, 0.176, 0.03 , 0.008, 0.006, 0.002]), array([0.722, 0.23 , 0.032, 0.014, 0.002, 0. ]), array([0.688, 0.24 , 0.046, 0.024, 0.002, 0. ])]}}\n" + ] + } + ], + "source": [ + "err_hamm_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results)\n", + "print(err_hamm_distrs)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: array([0.8824, 0.1139, 0.0037]), 3: array([0.8761, 0.1188, 0.0051]), 4: array([0.8923, 0.1034, 0.0043]), 5: array([0.8821, 0.1147, 0.0032]), 10: array([0.8854, 0.1093, 0.0053])}, 3: {2: array([8.329e-01, 1.566e-01, 9.800e-03, 7.000e-04]), 3: array([8.172e-01, 1.698e-01, 1.250e-02, 5.000e-04]), 4: array([0.8408, 0.1487, 0.0093, 0.0012]), 5: array([8.302e-01, 1.577e-01, 1.160e-02, 5.000e-04]), 10: array([0.8265, 0.1525, 0.0194, 0.0016])}, 4: {2: array([7.818e-01, 1.969e-01, 2.040e-02, 7.000e-04, 2.000e-04]), 3: array([0.7962, 0.1834, 0.0192, 0.0012, 0. ]), 4: array([7.868e-01, 1.910e-01, 2.000e-02, 1.800e-03, 4.000e-04]), 5: array([7.691e-01, 2.042e-01, 2.460e-02, 1.900e-03, 2.000e-04]), 10: array([0.7377, 0.2199, 0.0345, 0.0068, 0.0011])}, 5: {2: array([7.399e-01, 2.293e-01, 2.730e-02, 3.000e-03, 5.000e-04, 0.000e+00]), 3: array([7.308e-01, 2.334e-01, 3.220e-02, 3.200e-03, 4.000e-04, 0.000e+00]), 4: array([7.299e-01, 2.288e-01, 3.440e-02, 5.500e-03, 1.000e-03, 4.000e-04]), 5: array([7.182e-01, 2.351e-01, 4.000e-02, 5.400e-03, 1.200e-03, 1.000e-04]), 10: array([0.7197, 0.2264, 0.0403, 0.0098, 0.0027, 0.0011])}}\n" + ] + } + ], + "source": [ + "avg_err_hamm_distrs = average_distributions(err_hamm_distrs)\n", + "print(avg_err_hamm_distrs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot a particular depth and width" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "w = 3 # width\n", + "d = 4 # depth\n", + "\n", + "avg_distr = avg_err_hamm_distrs[3][4]\n", + "\n", + "# rand data\n", + "rand_distr = get_random_hamming_wt_distr(w)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_labels = np.arange(0, len(avg_distr))\n", + "plt.bar(x_labels, avg_distr, width=0.61, align='center')\n", + "plt.bar(x_labels, rand_distr, width=0.31, align='center')\n", + "plt.xticks(x_labels)\n", + "plt.xlabel('Hamming Weight of Error')\n", + "plt.ylabel('Relative Frequency of Occurrence')\n", + "plt.ylim([0, 1])\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.legend(['data','random'])\n", + "plt.title(f'Width = {w}, Depth = {d}')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using our helper function" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], depths=[d], plot_rand_distr=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For a particular width, plot all depths" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], plot_rand_distr=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot all of the distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=None, depths=None, plot_rand_distr=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{2: {2: 0.8824000000000002, 3: 0.8760999999999999, 4: 0.8922999999999999, 5: 0.8821, 10: 0.8854}, 3: {2: 0.8328999999999999, 3: 0.8171999999999999, 4: 0.8407999999999998, 5: 0.8301999999999998, 10: 0.8265}, 4: {2: 0.7817999999999998, 3: 0.7962, 4: 0.7868, 5: 0.7691, 10: 0.7376999999999999}, 5: {2: 0.7399, 3: 0.7308000000000001, 4: 0.7299, 5: 0.7182, 10: 0.7196999999999999}}\n", + "{2: {2: 0.9963000000000001, 3: 0.9948999999999998, 4: 0.9956999999999999, 5: 0.9967999999999998, 10: 0.9947000000000001}, 3: {2: 0.9894999999999999, 3: 0.9870000000000001, 4: 0.9895000000000002, 5: 0.9879, 10: 0.9789999999999999}, 4: {2: 0.9991, 3: 0.9987999999999999, 4: 0.9978, 5: 0.9978999999999998, 10: 0.9921}, 5: {2: 0.9965000000000002, 3: 0.9964000000000001, 4: 0.9930999999999998, 5: 0.9933, 10: 0.9864000000000003}}\n", + "{2: {2: 0.6324000000000001, 3: 0.6260999999999999, 4: 0.6423, 5: 0.6320999999999999, 10: 0.6354}, 3: {2: 0.7079, 3: 0.6922, 4: 0.7157999999999999, 5: 0.7051999999999999, 10: 0.7015}, 4: {2: 0.7192999999999999, 3: 0.7336999999999999, 4: 0.7243, 5: 0.7066, 10: 0.6751999999999999}, 5: {2: 0.7817, 3: 0.7767000000000002, 4: 0.7712000000000001, 5: 0.7657999999999999, 10: 0.7585999999999999}}\n" + ] + } + ], + "source": [ + "# extract data from avg_err_hamm_distrs\n", + "widths = list(avg_err_hamm_distrs.keys())\n", + "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", + "\n", + "avg_pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "# this is equivalently wrapped up in the following\n", + "assert avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results))\n", + "\n", + "# count as success even if there are log many bits incorrect.\n", + "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", + " ideal_results, \n", + " allowed_errors = basement_log_function))\n", + "\n", + "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", + "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", + "\n", + "pr_succ_rand = {w: 1/2**w for w in widths}\n", + "pr_succ_rand_allow_log_errors = {w: sum(rand_distrs[w][0:basement_log_function(w)+1]) for w in widths}\n", + "\n", + "# total variation distance\n", + "tvd_noisy_ideal = {w: {d: get_total_variation_dist(distr, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "# tvd_noisy_ideal is equivalent to 1 - success probability.\n", + "np.testing.assert_allclose([pr for d_vals in avg_pr_succ_arr.values() for pr in d_vals.values()], \n", + " [1 - val for d_vals in tvd_noisy_ideal.values() for val in d_vals.values()])\n", + "\n", + "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", + " for w, d_distrs in avg_err_hamm_distrs.items()}\n", + "\n", + "print(avg_pr_succ_arr)\n", + "print(avg_pr_succ_allow_log_errors)\n", + "print(tvd_noisy_rand)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Success probablity and success probablity including a small number of errors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will plot the success probablity of a circuit with a certain width as a function of depth. " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w=3\n", + "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Probability')\n", + "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.xticks(depths)\n", + "plt.ylabel('Pr(success)')\n", + "plt.title('Pr(success) vs Depth for Width = {}'.format(w))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Sucess if we allow for a small number of errors**\n", + "\n", + "Some near term algorithms have robustness to noise. In light of that we might want to consider as successes answers that are only a little wrong.\n", + "\n", + "To make this notion formal we allow a logarithmic number of bits to be flipped from the correct answer and call all such instances \"success\".\n", + "\n", + "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", + "\n", + "$\\log_2 ({\\rm number\\ of\\ bits})$\n", + "\n", + "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", + "\n", + "\n", + "Supose we have a circuit of width 4 so that the correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4) = 2$.\n", + "\n", + "So any string with hamming weight zero, one, or two counts as a success.\n", + "\n", + "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w=4\n", + "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Prob')\n", + "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", + "plt.scatter(depths, [avg_pr_succ_allow_log_errors[w][d] for d in depths], label='Sucess Prob w/ log errors')\n", + "plt.plot(depths, [pr_succ_rand_allow_log_errors[w] for _ in depths], label='random guess w/ log errors')\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('Depth')\n", + "plt.xticks(depths)\n", + "plt.ylabel('Pr(success)')\n", + "plt.title('Pr(success) (& w/ log errors) vs Depth for Width = {}'.format(w))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Total variation distance from ideal answer and random distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.scatter(depths, [tvd_noisy_ideal[w][d] for d in depths], label='TVD(data, ideal)')\n", + "plt.scatter(depths, [tvd_noisy_rand[w][d] for d in depths], label='TVD(data, rand)')\n", + "plt.scatter(depths, 1-np.asarray([avg_pr_succ_arr[w][d] for d in depths]),\n", + " label='1 - Pr[Success]', alpha=0.33, marker='^', s=80)\n", + "plt.ylim([-0.05,1.05])\n", + "plt.xlabel('Depth')\n", + "plt.xticks(depths)\n", + "plt.ylabel('Total variation distance')\n", + "plt.title('Width = {}'.format(w))\n", + "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot success probablity landscape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just the success probablity as a function of depth and width." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(widths, depths)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "Zdata = np.reshape([avg_pr_succ_arr[w][d] for d in depths for w in widths], X.shape)\n", + "Zrand = np.reshape([pr_succ_rand[w] for d in depths for w in widths], X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "extent = -0.5, len(widths) - 0.5, -0.5, len(depths) - 0.5\n", + "ax = plt.gca()\n", + "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0, vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Success Probability')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Success Probability of Random Guess')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "success_threshold = .8\n", + "ckt_success_probs = get_single_target_success_probabilities(noisy_results, ideal_results)\n", + "successes = determine_successes(ckt_success_probs, num_shots)\n", + "plot_success(successes, f\"Volumetric Benchmark\\n Random Classical Circuits\\n Pr[Success] > {success_threshold}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
,\n", + " )" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fake_successes = successes\n", + "plot_pareto_frontier(successes, 'Pareto Frontier', widths=[2,3,4,5,6], depths = [2,3,4,5,7,10,15])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot total variation distance landscape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "Ztvd_ideal = np.reshape([tvd_noisy_ideal[w][d] for d in depths for w in widths], X.shape)\n", + "Ztvd_rand = np.reshape([tvd_noisy_rand[w][d] for d in depths for w in widths], X.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Total Variation Distance of Noisy to Ideal')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Total Variation Distance of Noisy to Random')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 20)\n", + "(1, 20)\n" + ] + } + ], + "source": [ + "shape = Zdata.shape\n", + "size = Zdata.size\n", + "width_1d = X.reshape((1,size))\n", + "depth_1d = Y.reshape((1,size))\n", + "data_1d = Zdata.reshape((1,size))\n", + "print(data_1d.shape)\n", + "print(width_1d.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0],\n", + " [ 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5,\n", + " 2, 3, 4, 5],\n", + " [ 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5,\n", + " 10, 10, 10, 10]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims = np.zeros_like(width_1d)\n", + "dims[0,0] = shape[0]\n", + "dims[0,1] = shape[1]\n", + "\n", + "xdata = np.vstack((dims, width_1d, depth_1d))\n", + "xdata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fitting models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two parameter model \n", + "\n", + "\n", + "$f(W,D,p_W,p_D) = (1-p_W)^W * (1-p_D)^D $\n", + "\n", + "The fidelity is proporional to $1 - p$" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param(x, pw, pd):\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = (1-pw)**(widths) * (1-pd)**depths\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One parameter model\n", + "\n", + "$f(W,D,p) = (1-p)^{W * D} $" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param(x,p):\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = (1-p)**(widths * depths)\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Josh: \"From my prior work a better model to fit to is \"\n", + "\n", + "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "def two_param_exp(x,p,a,b):\n", + " num_depths, num_widths = x[0][:2]\n", + " widths = x[1].reshape(num_depths, num_widths)\n", + " depths = x[2].reshape(num_depths, num_widths)\n", + " pcheck = np.exp(-(a*p + b) * widths * depths)\n", + " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", + " return rpcheck.ravel()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Start with one paramter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "pguess = 0.1\n", + "popt, pcov = curve_fit(one_param, xdata, data_1d.ravel(), p0=pguess, bounds=(0, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The estimated error is p = 0.0111\n", + "The estimated product of the one and two qubit fidelity is F = 0.9889\n" + ] + } + ], + "source": [ + "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", + "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", + "#print('The one standard deviation on the estimate is ', str(np.round(np.sqrt(np.diag(pcov)[0]),5)))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "zfit = one_param(xdata, popt)\n", + "Z_fit = zfit.reshape(shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Z_fit, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('One parameter fit to success prob')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", + "\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Success prob')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Two parameter model**" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# pguess2d_exp = [0.0276, 0.01, 0.4]\n", + "# popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d, bounds=(0., 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "popt2d, pcov2d = curve_fit(two_param, xdata, data_1d.ravel(), bounds=(0., 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.05929668, 0.00165794])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "popt2d" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.88199086 0.82969173 0.78049376 0.73421307]\n", + " [0.88052857 0.82831615 0.77919975 0.73299579]\n", + " [0.87906871 0.82694285 0.77790788 0.73178053]\n", + " [0.87761126 0.82557182 0.77661815 0.73056728]\n", + " [0.8703602 0.81875073 0.77020153 0.72453113]]\n" + ] + } + ], + "source": [ + "zfit2d = two_param(xdata, popt2d[0], popt2d[1])\n", + "Z_fit2d = zfit2d.reshape(shape)\n", + "print(Z_fit2d)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.gca()\n", + "img = ax.imshow(Z_fit2d, interpolation='none', extent=extent,\n", + " cmap='viridis', origin='lowerleft', vmin=0, vmax=1.0)\n", + "\n", + "ax.set_xticks(range(len(widths)))\n", + "ax.set_xticklabels(widths)\n", + "\n", + "ax.set_yticks(range(len(depths)))\n", + "ax.set_yticklabels(depths)\n", + "\n", + "ax.set_aspect('equal')\n", + "plt.colorbar(img, ax=ax)\n", + "plt.xlabel('Width')\n", + "plt.ylabel('Depth')\n", + "plt.title('Two parameter fit success prob')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the distribution of sublattice widths" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = perfect_qc.qubit_topology()\n", + "len(perfect_qc.qubit_topology())\n", + "# distribution of graph lengths\n", + "distr = []\n", + "for num_nodes in range(1, len(G.nodes) + 1):\n", + " listg = generate_connected_subgraphs(G, num_nodes)\n", + " distr.append(len(listg))\n", + "\n", + "cir_wid = list(range(1, len(G.nodes) + 1))\n", + "plt.bar(cir_wid, distr, width=0.61, align='center')\n", + "plt.xticks(cir_wid)\n", + "plt.xlabel('sublattice / circuit width')\n", + "plt.ylabel('Frequency of Occurence')\n", + "plt.grid(axis='y', alpha=0.75)\n", + "plt.title('Distribution of sublattice widths')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5dbf466fc13aa1c07c06709ac85adf30bc713c04 Mon Sep 17 00:00:00 2001 From: Kyle Date: Tue, 10 Sep 2019 15:28:13 -0400 Subject: [PATCH 36/49] remove nb --- examples/volumetrics.ipynb | 1954 ------------------------------------ 1 file changed, 1954 deletions(-) delete mode 100644 examples/volumetrics.ipynb diff --git a/examples/volumetrics.ipynb b/examples/volumetrics.ipynb deleted file mode 100644 index d664b126..00000000 --- a/examples/volumetrics.ipynb +++ /dev/null @@ -1,1954 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Volumetrics\n", - "\n", - "\n", - "This module that generates circuits on a graph which represents the QPU or QVM lattice. The basic idea is it will compute error rates of circuits as a function of depth and width.\n", - "\n", - "The `width` of the circuit is the number of connected vertices on a particular subgraph.\n", - "\n", - "The `depth` is defined in context-dependent way; to avoid confusion with circuit depth we may use the term 'repetitions'." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "import itertools\n", - "import networkx as nx\n", - "import numpy as np\n", - "import time\n", - "\n", - "from matplotlib import pyplot as plt\n", - "from pyquil.api import get_qc, QuantumComputer, get_benchmarker\n", - "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", - "from pyquil.quilbase import Pragma\n", - "\n", - "from forest.benchmarking.volumetrics import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get lattice" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyquil import *\n", - "# if you want to run on a \"real lattice\"\n", - "#list_quantum_computers()\n", - "#perfect_qc = get_qc(\"Aspen-1-16Q-A\", as_qvm=True, noisy=False)\n", - "#noisy_qc = get_qc(\"Aspen-1-16Q-A\") #, as_qvm=True, noisy=True)\n", - "\n", - "noisy_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=True)\n", - "perfect_qc = get_qc(\"9q-square-qvm\", as_qvm=True, noisy=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:518: MatplotlibDeprecationWarning: \n", - "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n", - " if not cb.iterable(width):\n", - "/home/kylegulshen/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:565: MatplotlibDeprecationWarning: \n", - "The is_numlike function was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use isinstance(..., numbers.Number) instead.\n", - " if cb.is_numlike(alpha):\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nx.draw(perfect_qc.qubit_topology(),with_labels=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "G = perfect_qc.qubit_topology()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gate sets\n", - "\n", - "### Classical" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def two_q_id(qb1,qb2):\n", - " prog = Program()\n", - " prog +=I(qb1)\n", - " prog +=I(qb2)\n", - " return prog\n", - "\n", - "one_c_gates = [X,I]\n", - "two_c_gates = [two_q_id, CNOT]\n", - "two_c_toffoli = two_c_gates + [CCNOT]\n", - "\n", - "# x basis gates\n", - "from forest.benchmarking.classical_logic import CNOT_X_basis, CCNOT_X_basis\n", - "one_x_c_gates = [Z, I]\n", - "two_x_c_gates = [two_q_id, CNOT_X_basis]\n", - "two_x_c_toffoli = two_x_c_gates + [CCNOT_X_basis]\n", - "# if you want to do something in the X basis, add Hadamard layers appropriately; see below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Some quantum" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "one_q_gates = [X,Z,I]\n", - "two_q_gates = [two_q_id,CZ]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Random Cliffords\n", - "\n", - "We use a benchmarker for this. Typically we use the native gates from `get_rb_gateset` to implement each clifford." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from forest.benchmarking.randomized_benchmarking import get_rb_gateset" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# my config has gone all cattywampus so i need to do this\n", - "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'tcp://127.0.0.1:5555'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bm.client.endpoint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get random gates on a graph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 0\n", - "I 1\n", - "I 2\n", - "I 3\n", - "I 4\n", - "X 5\n", - "I 6\n", - "I 7\n", - "Z 8\n", - "CZ 0 3\n", - "I 0\n", - "I 1\n", - "CZ 1 4\n", - "I 1\n", - "I 2\n", - "I 2\n", - "I 5\n", - "I 3\n", - "I 6\n", - "CZ 3 4\n", - "CZ 4 7\n", - "CZ 4 5\n", - "I 5\n", - "I 8\n", - "CZ 6 7\n", - "I 7\n", - "I 8\n", - "\n" - ] - } - ], - "source": [ - "prog1 = random_single_qubit_gates(G, one_q_gates)\n", - "prog2 = random_two_qubit_gates(G, two_q_gates)\n", - "print(prog1+prog2)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RZ(-pi/2) 0\n", - "RX(-pi) 0\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(-pi) 3\n", - "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 5\n", - "RZ(-pi) 5\n", - "RX(-pi/2) 6\n", - "RZ(-pi) 6\n", - "RX(pi/2) 7\n", - "RZ(-pi) 7\n", - "RX(-pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", - "\n" - ] - } - ], - "source": [ - "progy = random_single_qubit_cliffords(bm, G)\n", - "print(progy)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Make some circuit templates and sample programs from them\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 4\n", - "I 5\n", - "I 4\n", - "X 5\n", - "\n" - ] - } - ], - "source": [ - "classical_1q_layer = get_rand_1q_template(one_c_gates)\n", - "print(classical_1q_layer.sample_program(G, repetitions=2, width=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 2\n", - "I 5\n", - "I 2\n", - "I 5\n", - "\n" - ] - } - ], - "source": [ - "classical_2q_layer = get_rand_2q_template(two_c_gates)\n", - "print(classical_2q_layer.sample_program(G, repetitions=2, width=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H 0\n", - "H 1\n", - "H 2\n", - "H 4\n", - "\n" - ] - } - ], - "source": [ - "switch_basis_layer = get_switch_basis_x_z_template()\n", - "print(switch_basis_layer.sample_program(G, repetitions=1, width=4))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", - "\n" - ] - } - ], - "source": [ - "clifford_1q_layer = get_rand_1q_cliff_template(bm)\n", - "clifford_2q_layer = get_rand_2q_cliff_template(bm)\n", - "print(clifford_2q_layer.sample_program(G, repetitions=2, width=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DEFGATE Perm102 AS PERMUTATION:\n", - " 0, 2, 1, 3, 4, 6, 5, 7\n", - "Perm102 1 2 4\n", - "\n" - ] - } - ], - "source": [ - "rand_perm_layer = get_rand_qubit_perm_template()\n", - "# sometimes this returns an empty program, i.e. no permutation\n", - "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=3))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DEFGATE LYR0_RSU4_2_5:\n", - " -0.09969160814430622+0.0902156122395286i, -0.33709519811871885-0.048136235456428166i, 0.7703918235977348+0.48727367053674936i, -0.02107343025834974-0.18598165363223637i\n", - " -0.4675035064037158+0.20523079122648438i, 0.038445207841979606-0.25078075097360014i, -0.05647270554583689+0.019169708299199825i, -0.7802899318958636+0.25008549090639387i\n", - " -0.34089008290794076-0.7272853767121489i, 0.0027518127263039885-0.2069765795381728i, 0.2215611250339229-0.12193916055391807i, 0.20591837586243+0.4534778789801883i\n", - " -0.0036986206424105654+0.27582562028872515i, 0.7923348800993039-0.38605604774942803i, 0.3168040759021317-0.034363504424924196i, 0.21335791201204357-0.002332725678737959i\n", - "\n", - "LYR0_RSU4_2_5 2 5\n", - "\n" - ] - } - ], - "source": [ - "rand_su4_layer = get_rand_su4_template()\n", - "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compose templates" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I 0\n", - "X 3\n", - "X 4\n", - "I 6\n", - "I 0\n", - "I 3\n", - "I 3\n", - "I 6\n", - "I 3\n", - "I 4\n", - "I 0\n", - "I 3\n", - "I 4\n", - "X 6\n", - "CNOT 0 3\n", - "CNOT 3 6\n", - "CNOT 3 4\n", - "\n" - ] - } - ], - "source": [ - "classical_1q_2q = classical_1q_layer + classical_2q_layer\n", - "print(classical_1q_2q.sample_program(G, repetitions=2, width=4))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Classical Logic in X basis" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H 7\n", - "H 8\n", - "Z 7\n", - "Z 8\n", - "I 7\n", - "I 8\n", - "Z 7\n", - "I 8\n", - "I 7\n", - "I 8\n", - "I 7\n", - "Z 8\n", - "H 7\n", - "CZ 7 8\n", - "H 7\n", - "H 7\n", - "H 8\n", - "\n" - ] - } - ], - "source": [ - "logic_layers = get_rand_1q_template(one_x_c_gates) + get_rand_2q_template(two_x_c_gates)\n", - "classical_x_1q_2q = switch_basis_layer + logic_layers + switch_basis_layer\n", - "# here we demonstrate a simple use of a pattern. We want to do the basis switch at beginning and end \n", - "# while doing the repetitions in between some variable number of times.\n", - "# The pattern says to do the 0 idx generator, do [1,2] idx generators n times, then finish with 3 idx generator\n", - "classical_x_1q_2q.pattern = [0, ([1, 2], 'n'), 3]\n", - "print(classical_x_1q_2q.sample_program(G, repetitions=3, width=2))\n", - "# note that the x basis CNOT(0, 1) is H(0) CZ(0, 1) H(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RZ(-pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RZ(-pi/2) 2\n", - "RZ(-pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RX(-pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(pi/2) 1\n", - "RX(-pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(-pi/2) 2\n", - "RX(pi/2) 2\n", - "RX(pi/2) 1\n", - "CZ 1 2\n", - "RX(pi/2) 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RZ(-pi/2) 2\n", - "RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER RZ(pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(pi/2) 1\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER RZ(pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi) 2\n", - "DAGGER RZ(-pi) 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi) 2\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "\n", - "This program compiles away to nothing: \n", - "HALT\n", - "\n" - ] - } - ], - "source": [ - "clifford_sandwich = clifford_1q_layer + clifford_2q_layer\n", - "clifford_sandwich.sequence_transforms.append(dagger_sequence)\n", - "prog = clifford_sandwich.sample_program(G, repetitions=3, width=2, qc=noisy_qc)\n", - "print(prog)\n", - "\n", - "# We can check that this is the identity by compiling it fully\n", - "print(\"This program compiles away to nothing: \")\n", - "print(noisy_qc.compiler.quil_to_native_quil(prog))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Quantum Volume" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RZ(-0.6633765634144329) 0\n", - "RX(pi/2) 0\n", - "RZ(2.1992567304350827) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.213273479007638) 0\n", - "RZ(-2.1790140703661987) 1\n", - "RX(pi/2) 1\n", - "RZ(1.3833680725337012) 1\n", - "RX(-pi/2) 1\n", - "RZ(-1.5430363103535998) 1\n", - "CZ 1 0\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.1382446014645566) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(pi/2) 0\n", - "RZ(-1.6745691134157568) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.8121261481912123) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(1.6380912332362045) 3\n", - "RX(pi/2) 3\n", - "RZ(1.2911009982026904) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.905707049360048) 3\n", - "RZ(-0.3198967078677877) 0\n", - "RX(pi/2) 0\n", - "RZ(1.9993474339045234) 0\n", - "RX(-pi/2) 0\n", - "RZ(-2.045982310794382) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 3 0\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 3\n", - "CZ 0 3\n", - "RZ(-1.7211797008449619) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.1291752835794742) 1\n", - "RX(-pi/2) 1\n", - "RZ(-0.9663730999073499) 2\n", - "RX(pi/2) 2\n", - "RZ(1.8104685056998722) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.1562967037020901) 2\n", - "CZ 1 2\n", - "RZ(-2.2067063329930843) 1\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RZ(-1.7823332810518906) 0\n", - "RX(pi/2) 0\n", - "RZ(0.5707386474274007) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.0405476330691377) 0\n", - "RZ(-1.6504916090017687) 1\n", - "RX(pi/2) 1\n", - "RZ(2.578029427303778) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.6934673043744666) 1\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(2.640187735366899) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(1.3955050168956022) 0\n", - "RX(pi/2) 0\n", - "RX(pi/2) 1\n", - "RZ(-2.0662135365992644) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(-1.7203952944369068) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.644187513360958) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.4122248717631236) 0\n", - "RZ(0.37149567048520904) 1\n", - "RX(pi/2) 1\n", - "RZ(2.29520538060332) 1\n", - "RX(-pi/2) 1\n", - "RZ(2.599836886240988) 1\n", - "RZ(-1.2615159694384492) 2\n", - "RX(pi/2) 2\n", - "RZ(1.043867674689562) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.7558737696969433) 2\n", - "RZ(2.681912008883467) 3\n", - "RX(pi/2) 3\n", - "RZ(0.5879267224374873) 3\n", - "RX(-pi/2) 3\n", - "RZ(-1.3152784290894761) 3\n", - "\n" - ] - } - ], - "source": [ - "qv_template = rand_su4_layer\n", - "# we want to compile the output sequences with graph-restricted compilation.\n", - "qv_template.sequence_transforms.append(compile_merged_sequence)\n", - "qv_prog = qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=4)\n", - "print(qv_prog)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run quantum volume for one width and depth\n", - "\n", - "1. Generate the programs\n", - "2. Determine the heavy outputs\n", - "3. Collect experimental data" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "start_time = time.time()\n", - "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", - "wfn_sim = NumpyWavefunctionSimulator(9)\n", - "d = 2\n", - "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, \n", - " widths=[d], depths=[d], num_circuit_samples=200)\n", - "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", - "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: [0.6720000000000005, 0.8240000000000006, 0.9260000000000007, 0.8420000000000006, 0.8780000000000007, 0.6560000000000005, 0.5840000000000004, 0.8820000000000007, 0.8220000000000006, 0.8620000000000007, 0.8140000000000006, 0.7160000000000005, 0.8860000000000007, 0.7420000000000005, 0.6640000000000005, 0.8240000000000006, 0.6520000000000005, 0.5800000000000004, 0.7420000000000005, 0.6680000000000005, 0.9320000000000007, 0.8100000000000006, 0.6600000000000005, 0.9280000000000007, 0.6720000000000005, 0.6020000000000004, 0.7580000000000006, 0.7900000000000006, 0.8220000000000006, 0.9260000000000007, 0.6420000000000005, 0.7320000000000005, 0.7480000000000006, 0.9740000000000008, 0.8020000000000006, 0.7740000000000006, 0.7800000000000006, 0.9220000000000007, 0.7720000000000006, 0.8020000000000006, 0.7800000000000006, 0.7340000000000005, 0.8900000000000007, 0.8540000000000006, 0.7160000000000005, 0.8020000000000006, 0.7660000000000006, 0.8700000000000007, 0.7140000000000005, 0.8800000000000007, 0.8880000000000007, 0.8640000000000007, 0.8360000000000006, 0.9620000000000007, 0.9080000000000007, 0.8560000000000006, 0.7820000000000006, 0.6780000000000005, 0.8580000000000007, 0.8080000000000006, 0.8200000000000006, 0.9380000000000007, 0.6060000000000004, 0.6240000000000004, 0.6740000000000005, 0.8200000000000006, 0.7240000000000005, 0.8380000000000006, 0.7840000000000006, 0.8800000000000007, 0.8660000000000007, 0.9720000000000008, 0.9380000000000007, 0.7260000000000005, 0.7280000000000005, 0.8620000000000007, 0.7340000000000005, 0.8660000000000007, 0.6460000000000005, 0.7840000000000006, 0.6600000000000005, 0.8340000000000006, 0.7460000000000006, 0.7000000000000005, 0.8000000000000006, 0.9200000000000007, 0.9020000000000007, 0.8320000000000006, 0.7700000000000006, 0.8160000000000006, 0.8980000000000007, 0.7460000000000006, 0.8280000000000006, 0.8240000000000006, 0.8860000000000007, 0.9220000000000007, 0.7060000000000005, 0.6040000000000004, 0.7160000000000005, 0.7980000000000006, 0.6360000000000005, 0.8920000000000007, 0.6620000000000005, 0.8620000000000007, 0.7440000000000005, 0.8340000000000006, 0.8940000000000007, 0.7200000000000005, 0.6400000000000005, 0.7980000000000006, 0.8940000000000007, 0.6860000000000005, 0.9120000000000007, 0.8880000000000007, 0.7760000000000006, 0.7680000000000006, 0.8300000000000006, 0.6280000000000004, 0.9440000000000007, 0.6440000000000005, 0.7720000000000006, 0.8220000000000006, 0.6800000000000005, 0.8480000000000006, 0.6920000000000005, 0.7540000000000006, 0.8460000000000006, 0.8840000000000007, 0.9520000000000007, 0.9840000000000008, 0.8060000000000006, 0.8140000000000006, 0.7780000000000006, 0.7080000000000005, 0.9120000000000007, 0.6340000000000005, 0.8080000000000006, 0.8120000000000006, 0.9320000000000007, 0.7280000000000005, 0.9640000000000007, 0.8200000000000006, 0.7600000000000006, 0.9380000000000007, 0.8700000000000007, 0.9100000000000007, 0.8100000000000006, 0.8740000000000007, 0.9820000000000008, 0.6940000000000005, 0.7980000000000006, 0.6860000000000005, 0.6800000000000005, 0.8220000000000006, 0.8020000000000006, 0.8140000000000006, 0.8280000000000006, 0.8800000000000007, 0.7660000000000006, 0.7600000000000006, 0.6780000000000005, 0.8420000000000006, 0.9300000000000007, 0.9640000000000007, 0.6400000000000005, 0.7680000000000006, 0.8060000000000006, 0.8880000000000007, 0.8400000000000006, 0.8440000000000006, 0.8200000000000006, 0.8560000000000006, 0.9760000000000008, 0.6920000000000005, 0.6440000000000005, 0.7720000000000006, 0.6780000000000005, 0.7480000000000006, 0.6380000000000005, 0.7980000000000006, 0.7860000000000006, 0.7280000000000005, 0.6520000000000005, 0.7980000000000006, 0.7700000000000006, 0.8580000000000007, 0.8960000000000007, 0.6080000000000004, 0.8400000000000006, 0.7900000000000006, 0.8580000000000007, 0.8540000000000006, 0.7140000000000005, 0.9120000000000007, 0.7180000000000005, 0.8040000000000006, 0.6640000000000005, 0.8780000000000007, 0.6980000000000005, 0.8780000000000007]}}\n", - "0.7953500000000006\n" - ] - } - ], - "source": [ - "qvol_success_probs = get_success_probabilities(experimental_data, heavy_outputs)\n", - "print(qvol_success_probs)\n", - "print(np.average(qvol_success_probs[d][d]))" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: True}}\n", - "35.48560094833374\n", - "{2: {2: 0.7382941716386486}}\n" - ] - } - ], - "source": [ - "qvol_successes = determine_successes(qvol_success_probs, 500)\n", - "print(qvol_successes)\n", - "end_time = time.time()\n", - "print(end_time - start_time)\n", - "print(determine_prob_success_lower_bounds(qvol_success_probs, 500))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acquire data for ranges of (width, depth)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" - ] - } - ], - "source": [ - "widths = [2, 3, 4, 5]\n", - "depths = [2, 3, 4, 5, 10]\n", - "ckt = classical_1q_2q\n", - "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=20)\n", - "print(prog_array)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "num_shots = 500\n", - "noisy_results = acquire_volumetric_data(noisy_qc, prog_array, num_shots)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: [array([[1, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 0]])], 3: [array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 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array([[0, 0, 0, 0, 1]]), array([[1, 1, 0, 0, 1]]), array([[1, 0, 0, 1, 1]])]}}\n" - ] - } - ], - "source": [ - "ideal_results = acquire_volumetric_data(perfect_qc, prog_array, num_shots=1)\n", - "print(ideal_results)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: [array([0.848, 0.15 , 0.002]), array([0.82, 0.17, 0.01]), array([0.884, 0.116, 0. ]), array([0.878, 0.118, 0.004]), array([0.892, 0.104, 0.004]), array([0.9, 0.1, 0. ]), array([0.9 , 0.098, 0.002]), array([0.872, 0.126, 0.002]), array([0.902, 0.098, 0. ]), array([0.872, 0.116, 0.012]), array([0.906, 0.092, 0.002]), array([0.888, 0.108, 0.004]), array([0.898, 0.102, 0. ]), array([0.916, 0.08 , 0.004]), array([0.898, 0.1 , 0.002]), array([0.898, 0.1 , 0.002]), array([0.938, 0.062, 0. ]), array([0.814, 0.172, 0.014]), array([0.836, 0.156, 0.008]), array([0.888, 0.11 , 0.002])], 3: [array([0.948, 0.05 , 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0.254, 0.062, 0.01 , 0.006, 0. ]), array([0.738, 0.228, 0.018, 0.004, 0.01 , 0.002]), array([0.784, 0.18 , 0.02 , 0.014, 0.002, 0. ]), array([0.794, 0.184, 0.018, 0.004, 0. , 0. ]), array([0.65 , 0.258, 0.076, 0.016, 0. , 0. ]), array([0.724, 0.226, 0.038, 0.01 , 0.002, 0. ]), array([0.704, 0.236, 0.046, 0.014, 0. , 0. ]), array([0.732, 0.21 , 0.048, 0.008, 0.002, 0. ]), array([0.846, 0.118, 0.028, 0.006, 0.002, 0. ]), array([0.65 , 0.28 , 0.062, 0.008, 0. , 0. ]), array([0.618, 0.336, 0.04 , 0.006, 0. , 0. ]), array([0.684, 0.254, 0.044, 0.002, 0.012, 0.004]), array([0.668, 0.268, 0.044, 0.002, 0.004, 0.014]), array([0.732, 0.228, 0.03 , 0.008, 0.002, 0. ]), array([0.822, 0.134, 0.028, 0.014, 0.002, 0. ]), array([0.778, 0.176, 0.03 , 0.008, 0.006, 0.002]), array([0.722, 0.23 , 0.032, 0.014, 0.002, 0. ]), array([0.688, 0.24 , 0.046, 0.024, 0.002, 0. ])]}}\n" - ] - } - ], - "source": [ - "err_hamm_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results)\n", - "print(err_hamm_distrs)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: array([0.8824, 0.1139, 0.0037]), 3: array([0.8761, 0.1188, 0.0051]), 4: array([0.8923, 0.1034, 0.0043]), 5: array([0.8821, 0.1147, 0.0032]), 10: array([0.8854, 0.1093, 0.0053])}, 3: {2: array([8.329e-01, 1.566e-01, 9.800e-03, 7.000e-04]), 3: array([8.172e-01, 1.698e-01, 1.250e-02, 5.000e-04]), 4: array([0.8408, 0.1487, 0.0093, 0.0012]), 5: array([8.302e-01, 1.577e-01, 1.160e-02, 5.000e-04]), 10: array([0.8265, 0.1525, 0.0194, 0.0016])}, 4: {2: array([7.818e-01, 1.969e-01, 2.040e-02, 7.000e-04, 2.000e-04]), 3: array([0.7962, 0.1834, 0.0192, 0.0012, 0. ]), 4: array([7.868e-01, 1.910e-01, 2.000e-02, 1.800e-03, 4.000e-04]), 5: array([7.691e-01, 2.042e-01, 2.460e-02, 1.900e-03, 2.000e-04]), 10: array([0.7377, 0.2199, 0.0345, 0.0068, 0.0011])}, 5: {2: array([7.399e-01, 2.293e-01, 2.730e-02, 3.000e-03, 5.000e-04, 0.000e+00]), 3: array([7.308e-01, 2.334e-01, 3.220e-02, 3.200e-03, 4.000e-04, 0.000e+00]), 4: array([7.299e-01, 2.288e-01, 3.440e-02, 5.500e-03, 1.000e-03, 4.000e-04]), 5: array([7.182e-01, 2.351e-01, 4.000e-02, 5.400e-03, 1.200e-03, 1.000e-04]), 10: array([0.7197, 0.2264, 0.0403, 0.0098, 0.0027, 0.0011])}}\n" - ] - } - ], - "source": [ - "avg_err_hamm_distrs = average_distributions(err_hamm_distrs)\n", - "print(avg_err_hamm_distrs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot a particular depth and width" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "w = 3 # width\n", - "d = 4 # depth\n", - "\n", - "avg_distr = avg_err_hamm_distrs[3][4]\n", - "\n", - "# rand data\n", - "rand_distr = get_random_hamming_wt_distr(w)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LMkbQNyK+XlA8ZmZWsCxjBL6qx8ysG8vSNdSUjg/cwPvHCH6TW1RmZlaYLImgF7AC+JeSsgCcCMzMuoEss48eV0QgZmZWH1lmH/0FrcwtFBFfzCUiMzMrVJauod+VLPcCJuAHyJiZdRtZuoZ+Xbou6ZfAnNwiMjOzQmWZfbTccLI9j8DMzLqALGMEK3n/GMFzwDdzi8jMzAqVpWvI00qYmXVj7XYNSZogqV/J+maSDss3LDMzK0qWMYIpEfFqy0pEvAJMyS8kMzMrUpZE0FqdLJedmplZF5AlEcyXdJGkbdPXRcBDeQdmZmbFyJIITgXeAn4FXAesAb6SZ1BmZlacLFcNvU7yAHszM+uGslw1dIekzUrW+0u6Pd+wzMysKFm6hgakVwoBEBEv4zuLzcy6jSyJ4B1JW7esSBpCK7ORmplZ15TlMtBvA3MkzQIE7ANMzjUqMzMrTJbB4tskjQN2T4vOiIiX8g3LzMyKUjERSNoIOAYYmRYtBFbmHZSZmRWnzTECSTsCi4D9gKfT137AwvQ9MzPrBiq1CH4KnBQRd5QWSjoAuAT4WJ6BmZlZMSpdNbRVeRIAiIg7gX/MLyQzMytSpUTQQ9LG5YWSeuFJ58zMuo1KieAq4NfpfQMASBoKXA9cnW9YZmZWlDZ/2UfE9ySdAsyWtGla/DowLSJ+Wkh0ZmaWu4pdPBFxMXCxpL7pui8dNTPrZrJMMUFErKwmCUg6SNISSUsltTqDqaQjJS2StFDStet7DDMz65jcBn0l9SS5zPRAoBmYJ2lmRCwqqTMcOAvYKyJeluTJ7MzMClbphrLPpH8Oq3LfuwJLI2JZRLxF8lCbQ8vqnAhcks5oSkS8UOWxzMysSpVaBGcBNwC/BsZVse+tgL+WrDcDu5XVGQEg6V6gJzA1Im4r35GkyaQT3Q0cOJCmpqYqwoEjt1lX1XadVbXnwcysVKVEsELS74FhkmaWvxkRh9To+MNJpq4YBNwjaafS5x+kx5oOTAcYP358jBkzpqqDHXbdMx0KtrO5YHJ158HMrFSlRHAwSUvgauDCKvb9DDC4ZH1QWlaqGXggItYCT0n6M0limFfF8czMrAqV7iN4C5grac+IeFFSn7R8VcZ9zwOGp2MMzwATgc+W1ZkBHA38QtIAkq6iZev5GczMrAOyXD76D5IeIZmCepGkhySNam+jiHgbOAW4HVgMXB8RCyWdK6mlW+l2ki6oRcBdwDciYkVVn8TMzKqS5fLR6cBXI+IuAEn7pWV7trdhRNwC3FJWdk7JcgBfTV9mZlYHWVoEvVuSAEBE3A30zi0iMzMrVJYWwTJJ/4/3Jpo7Fvfjm5l1G1laBF8EtgB+Q3JPwYC0zMzMuoEsD69/GTitgFjMzKwOMk06Z2Zm3ZcTgZlZg2s3EUjavIhAzMysPrK0COZKukHSpyQp94jMzKxQWRLBCJIbyD4HPCHpPEkj8g3LzMyK0m4iiMQdEXE0yfMDvgA8KGmWpD1yj9DMzHLV7uWj6RjBsSQtgueBU4GZwBiS5xVU++AaMzPrBLLcWXw/yV3Fh0VEc0n5fEmX5hOWmZkVJUsi2C6dHO4DIuL8GsdjZmYFyzJY/HtJm7WsSOov6fYcYzIzswJlSQRblD46Mp1yYsv8QjIzsyJlSQTrJG3dsiJpCNBqV5GZmXU9WcYIvg3MkTQLELAPMDnXqMzMrDBZZh+9TdI4YPe06IyIeCnfsMzMrChZWgQAGwN/T+vvKImIuCe/sMzMrChZbig7HziK5OH176TFATgRmJl1A1laBIeR3EvwZt7BmJlZ8bJcNbQM2DDvQMzMrD6ytAhWA02S/gC82yqICD++0sysG8iSCGamLzMz64ayXD56paRNgK0jYkkBMZmZWYGyPKryX4Em4LZ0fYwktxDMzLqJLIPFU4FdgVcAIqIJ2CbHmMzMrEBZEsHaiHi1rOydVmuamVmXk2WweKGkzwI9JQ0HTgPuyzcsMzMrSpYWwanASJJLR38JvAackWdQZmZWnCxXDa0mmYH02/mHY2ZmRcsy19BdtPL8gYj4l1wiMjOzQmUZI/h6yXIv4Ajg7XzCMTOzomXpGnqorOheSQ/mFI+ZmRUsS9fQh0tWewA7A/1yi8jMzAqV5aqhh4D56Z/3A18Djs+yc0kHSVoiaamkMyvUO0JSSBqfZb9mZlY7WbqGhlWzY0k9gUuAA4FmYJ6kmRGxqKxeX+B04IFqjmNmZh2TpWvo8ErvR8Rv2nhrV2BpRCxL93MdcCiwqKzed4HzgW+0G62ZmdVclquGjgf2BP6Yrn+M5M7iF0kuK20rEWwF/LVkvRnYrbSCpHHA4Ii4WVKbiUDSZGAywMCBA2lqasoQ9gcduc26qrbrrKo9D2ZmpbIkgg2BHSPibwCSBgJXRMRxHTmwpB7ARcCk9upGxHRgOsD48eNjzJgxVR3zsOueqWq7zuqCydWdBzOzUlkGiwe3JIHU88DWGbZ7Bhhcsj4oLWvRFxgF3C1pObA7MNMDxmZmxcrSIviDpNtJ5hkCOAq4M8N284DhkoaRJICJwGdb3kxnNB3Qsi7pbuDrETE/W+hmZlYLWa4aOkXSBGDftGh6RNyUYbu3JZ0C3A70BC6PiIWSzgXmR4QfbmNm1glkaREAPAysjIg7JW0qqW9ErGxvo4i4BbilrOycNurulzEW68qmFnQv4tTyR2h0Yz6n1kFZHlV5InAj8LO0aCtgRp5BmZlZcbIMFn8F2IvkOQRExBPAlnkGZWZmxcmSCN6MiLdaViRtQCvTUpuZWdeUJRHMkvQtYBNJBwI3AL/NNywzMytKlkRwJsldxI8CXyIZ/D07z6DMzKw4Fa8aSieOuyoijgEuKyYkMzMrUsUWQUSsA4ZI2qigeMzMrGBZ7iNYRvJUspnA6y2FEXFRblGZmVlhsiSCJ9NXD5L5gczMrBtpMxFI2iAi3o6I7xQZkJmZFavSGMG7D6iX9NMCYjEzszqolAhUsrxX3oGYmVl9VEoEvnvYzKwBVBos3l7SApKWwbbpMul6RMQ/5R6dmZnlrlIi2KGwKMzMrG7aTAQR8ZciAzEzs/rIMteQmZl1Y04EZmYNLlMikLSJpO3yDsbMzIqX5VGV/wo0Abel62PSeYfMzKwbyNIimArsCrwCEBFNwLAcYzIzswJlSQRrI+LVsjLfbGZm1k1kmX10oaTPAj0lDQdOA+7LNywzMytKlhbBqcBI4E3gWuBV4Iw8gzIzs+JkaRFsHxHfBr6ddzBmZla8LC2CCyUtlvRdSaNyj8jMzArVbiKIiI8BHwNeBH4m6VFJZ+cemZmZFSLTDWUR8VxE/AT4Msk9BefkGpWZmRUmyw1lO0iaKulR4KckVwwNyj0yMzMrRJbB4suBXwGfiIhnc47HzMwK1m4iiIg9igjEzMzqo81EIOn6iDgy7RIqvZPYTygzM+tGKrUITk///D9FBGJmZvXR5mBxRPwtXTw5Iv5S+gJOLiY8MzPLW5bLRw9speyTWXYu6SBJSyQtlXRmK+9/VdIiSQsk/UHSkCz7NTOz2mkzEUg6KR0f2C79om55PQUsaG/HknoCl5AkjR2BoyXtWFbtEWB8Ot5wI3BBtR/EzMyqU2mM4FrgVuAHQOmv+ZUR8fcM+94VWBoRywAkXQccCixqqRARd5XUnwscmzFuMzOrkTYTQfoMgleBowEkbQn0AvpI6hMRT7ez762Av5asNwO7Vah/PEni+QBJk4HJAAMHDqSpqamdQ7fuyG3WVbVdZ1XteairwZOKOU5XPDfV8jm1Dmr3PoL0UZUXAR8BXgCGAItJpqauCUnHAuOBf27t/YiYDkwHGD9+fIwZM6aq4xx23TPVhtgpXTC5uvNQVzOuKOY4x/9HMcfpDHxOrYOyDBZ/D9gd+HNEDAP2J+nGac8zwOCS9UFp2ftIOoBkiutDIuLNDPs1M7MayvqoyhVAD0k90n798Rm2mwcMlzRM0kbAROB9D72XNBb4GUkSeGE9YzczsxrIMtfQK5L6APcA10h6AXi9vY0i4m1JpwC3Az2ByyNioaRzgfkRMRP4EdAHuEESwNMRcUiVn8XMzKqQJREcCqwB/i9wDNAPODfLziPiFuCWsrJzSpYPyBypmZnlIsukc6W//q/MMRYzM6uDSpPOraSVyeZ4b9K5D+Ucm5mZFaDSfQR9iwzEzMzqI9OjKiXtLem4dHmApGH5hmVmZkXJ8qjKKcA3gbPSoo2A/8kzKDMzK06Wq4YmAGOBhwEi4llJ7jYyAIaeefN61V/eK6dAyqxvXADLf3hwDpGYdX5ZuobeioggHTiW1DvfkMzMrEhZEsH1kn4GbCbpROBO4L/zDcvMzIqS5T6CaZIOBF4DtgPOiYg7co/MzMwKkWWMgPSL/w4AST0kHRMR1+QamZmZFaLSE8o+JOksSRdL+rgSpwDLgCOLC9HMzPJUqUVwNfAycD9wAvAtkruKD4uIhnlCxfJeny3kOEPXXFvIccysHVP7FXScV4s5TgaVEsE2EbETgKT/Bv4GbB0RawqJzMzMClHpqqG1LQsRsQ5odhIwM+t+KrUIRkt6LV0WsEm67knnzMy6kUqTzvUsMhAzM6uPTJPOmZlZ9+VEYGbW4JwIzMwanBOBmVmDcyIwM2twTgRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4JwIzMwanBOBmVmDcyIwM2twTgRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4JwIzMwaXK6JQNJBkpZIWirpzFbe31jSr9L3H5A0NM94zMzsg9p8eH1HSeoJXAIcCDQD8yTNjIhFJdWOB16OiI9KmgicDxyVV0xmnd3QM29e722W98ohkFasb2zLf3hwTpFYreXZItgVWBoRyyLiLeA64NCyOocCV6bLNwL7S1KOMZmZWRlFRD47lj4NHBQRJ6TrnwN2i4hTSuo8ltZpTtefTOu8VLavycDkdHU7YEkuQdfOAOCldmtZVj6ftedzWltd4XwOiYgtWnsjt66hWoqI6cD0eseRlaT5ETG+3nF0Fz6ftedzWltd/Xzm2TX0DDC4ZH1QWtZqHUkbAP2AFTnGZGZmZfJMBPOA4ZKGSdoImAjMLKszE/hCuvxp4I+RV1+VmZm1KreuoYh4W9IpwO1AT+DyiFgo6VxgfkTMBH4OXC1pKfB3kmTRHXSZbqwuwuez9nxOa6tLn8/cBovNzKxr8J3FZmYNzonAzKzBORHUWHvTalh2ki6X9EJ6v4l1kKTBku6StEjSQkmn1zumrk5SL0kPSvpTek6/U++YquExghpKp9X4MyXTagBHl02rYRlJ2hdYBVwVEaPqHU9XJ2kgMDAiHpbUF3gIOMz/PquXzoTQOyJWSdoQmAOcHhFz6xzaenGLoLayTKthGUXEPSRXk1kNRMTfIuLhdHklsBjYqr5RdW2RWJWubpi+utyvayeC2toK+GvJejP+j2adUDrT71jggfpG0vVJ6impCXgBuCMiutw5dSIwazCS+gC/Bs6IiNfqHU9XFxHrImIMyewJu0rqct2YTgS1lWVaDbO6Sfuxfw1cExG/qXc83UlEvALcBRxU71jWlxNBbWWZVsOsLtKBzZ8DiyPionrH0x1I2kLSZunyJiQXijxe36jWnxNBDUXE20DLtBqLgesjYmF9o+q6JP0SuB/YTlKzpOPrHVMXtxfwOeBfJDWlr0/VO6gubiBwl6QFJD8E74iI39U5pvXmy0fNzBqcWwRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4JwIrOYkrSpbnyTp4gKP/xFJN9ZgP5L0kqT+6fpASSFp75I6L0ravMI+DmlvFlpJ+0lq9ZJDSWdI2nQ9494nnQmzKb22vfS9dSWXjjZ5hlwDJwLrhiLi2Yj4dA32E8BcYI+0aE/gkfRPJG0HrIiIFRX2MTMiftiBMM4A1isRAMcAP4iIMRHxRtl7b6TlLa8PxJbOolu6numRtlnrWefjRGCFkvSvkh6Q9IikOyX9Q1o+VdKVkmZL+oukwyVdIOlRSbelUyMgabmkH6S/ZudLGifpdklPSvpyWmdoyzMM0tbIb9J9PCHpgpJYjpf053Q++cvaaLXcR/rFn/7577w/Mdyb7msLSb+WNC997VVy/IvT5W0lzcHv11AAAANsSURBVE0/0/fKWk59JN0o6XFJ16StkdOAj5DcsHRXK+dy//Q8Pqrk2Q0bSzoBOBL4rqRr1uPvZbmk8yU9DHxG0t2SfixpPnB6ek7/KGmBpD9I2jrd7gpJl0p6ALig4kGs84oIv/yq6QtYBzSVvJ4GLk7f6897NzKeAFyYLk8lmct9Q2A0sBr4ZPreTSTz5gMsB05Kl/8dWAD0BbYAnk/LhwKPpcuTgGVAP6AX8BeS+aA+ku7rw+kxZ7fEWPZZ/hn4Y7o8G+gDzE/XLwOOT5evBfZOl7cmmcah5fgtn/13JM+nAPgysCpd3g94lWRuqh4kd1PvXfJ5B7QSVy+SmW5HpOtXkUwiB3AF8OmMfzdHlRzn30rq3Q38Z8n6b4EvpMtfBGaUHOt3QM96/7vzq/qXm3KWhzcimY0RSH4VA+PT1UHAr5Q8JGUj4KmS7W6NiLWSHgV6Arel5Y+SfLm3mFlS3ieSufVXSnqzZd6XMn+IiFfTWBYBQ4ABwKyI+HtafgMwopVt5wFjJfUGNozkASTLJH2UpEVwYVrvAGBHSS3bfUjJLJ+l9gAOS5evBaaVvPdgRDSnsTSln3dOK/G02A54KiL+nK5fCXwF+HGFbaDs76bMryqs7wEcni5fzft//d8QEevaOa51Yk4EVrSfAhdFxExJ+5G0BFq8CRAR70haG+lPTuAd3v9v9c2S8jdLysvrldeH5Bdx5n/3EbFa0hMkv4IfTovnAp8CtgSWpGU9gN0jYk3p9iWJoT1Vx1hDr7eznnU762I8RmBF68d7U3N/oY5xzAP+WVL/dJDziAp17yMZtL0/Xb8fOB2YW5Ksfg+c2rKBpNZ+dc8tOc7EjHGuJOn6KrcEGJq2TCCZTG5Wxn1W4z7ei/kYkm4y6yacCKxoU4EbJD0EvFSvICLiGeA84EGSAd/lJP30rbkX2Ib3EsHDJF1c95XUOQ0Ynw6mLiIZAyh3BvBVJTNVfrTC8UpNB24rHyxOWx7HkZzLR0laQ5dm2N8mZZePZr2i6VTguDT2z5EkQusmPPuoNSxJfdI+/w1IBqQvj4ibcjzepiR99CFpIsnAsZ9pbXXnMQJrZFMlHUByBc7vgRk5H29n4GIlAwevkIw7mNWdWwRmZg3OYwRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4P4XpUuUMN4ySAYAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x_labels = np.arange(0, len(avg_distr))\n", - "plt.bar(x_labels, avg_distr, width=0.61, align='center')\n", - "plt.bar(x_labels, rand_distr, width=0.31, align='center')\n", - "plt.xticks(x_labels)\n", - "plt.xlabel('Hamming Weight of Error')\n", - "plt.ylabel('Relative Frequency of Occurrence')\n", - "plt.ylim([0, 1])\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.legend(['data','random'])\n", - "plt.title(f'Width = {w}, Depth = {d}')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using our helper function" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], depths=[d], plot_rand_distr=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### For a particular width, plot all depths" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AAAC9pj2kHahK8rQkf6+1dnOSVNXnktyf5O1Jfnt8h9ba1iRbk2TTpk1t48aNKzftEjr32gcmPQKHqUs3H5o/MwAAANBr2i/t3JVk7QLr64Zt+9qvJfnk3MJwn7W7k5y4hPMBAAAAcJiY9pC2I2P3QquqY5McnbF7p425N6Oz0mpsvZI8vpQDAgAAAHB4mPaQdlOSM6tqzby185M8muT2fez3seHxVXMLVbU2yYuTfGmphwQAAABg9k17SLs6yfeSXF9VZwwfCLAlyeXDpZpJkqq6r6reN/e8tXZXkg8neV9V/cOqOjvJR5L8IMm/WclvAAAAAIDZMNUhrbW2K8npSVYl+WiS9yS5Ism7x166enjNfG9MckOSy5P8SUYR7bThmAAAAACwKFP/qZ2ttXuSnLaf12xYYO27Sd42fAEAAADAQZnqM9IAAAAAYFoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKDD1Ie0qjqxqrZV1e6qerCqLq6qVYvY/4iququqWlW9djlnBQAAAGB2rZ70APtSVeuS3JrkniTnJDk+yWUZBcB3dR7mgiQ/sSwDAgAAAHDYmPYz0i5MclSS81prt7TWrk7yniT/e1Uds7+dhxD3O0n+r+UdEwAAAIBZN+0h7awkH2+tPTJv7dqM4tqpHfv/8ySfTbJtGWYDAAAA4DAy7SHthCQ75i+01r6WZPewba+q6u8k+dUk71i26QAAAAA4bEz1PdKSrEvy0ALru4Zt+/Kvk1zVWruvqjbs742qanOSzUmyfv36bN++fXGTTonXH7dn0iNwmDpUf2YAAACg17SHtANSVX8/yQuS/FLvPq21rUm2JsmmTZvaxo0bl2m65XXutQ9MegQOU5duPjR/ZgAAAKDXtF/auSvJ2gXW1w3bnqCqnpTk/0lySZIjqurpSeY+mOCpVbVmOQYFAAAAYLZNe0jbkbF7oVXVsUmOzti90+Z5apKfSHJ5RrFtV5IvDduuTfIXyzIpAAAAADNt2i/tvCnJO6tqTWvtO8Pa+UkeTXL7Xvb5bpJXja09N8kfJ/k/k9y2HIMCAAAAMNumPaRdneSiJNdX1SVJjkuyJcnlrbVH5l5UVfclub219tbW2g+TfHL+QeZ92MB/bq3dsfxjAwAAADBrpjqktdZ2VdXpSa5K8tGMPsHzioxi2nyrk6xa2ekAAAAAOJxMdUhLktbaPUlO289rNuxn+84ktXRTAQAAAHC4mfqQBsCM2rLQhzKzKFsenvQEsHT8Tjh4ficAwLKb9k/tBAAAAICpIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBh6kNaVZ1YVduqandVPVhVF1fVqv3s83er6t9X1X3Dfl+pqndX1VNWam4AAAAAZsvqSQ+wL1W1LsmtSe5Jck6S45NcllEAfNc+dj1/eO0lSb6a5O8k+efD468s48gAAAAAzKipDmlJLkxyVJLzWmuPJLmlqo5JsqWqLh3WFvLe1tq35j3/ZFU9luT/q6qfbK3dv8xzAwAAADBjpv3SzrOSfHwsmF2bUVw7dW87jUW0OX8xPD5v6cYDAAAA4HAx7SHthCQ75i+01r6WZPewbTFOTvJ4kr9cmtEAAAAAOJxM+6Wd65I8tMD6rmFbl6p6bkb3VPtga+2be3nN5iSbk2T9+vXZvn374qedAq8/bs+kR+Awdaj+zDBBx7550hMc+vzcMUv8Tjh4ficAwLKb9pB20KrqyCQfSvLdJP90b69rrW1NsjVJNm3a1DZu3LgyAy6xc699YNIjcJi6dPOh+TPDBN1wzaQnOPS99f+d9ASwdPxOOHh+JwDAspv2kLYrydoF1tcN2/apqirJB5K8MMnLWmv73QcAAAAAFjLtIW1Hxu6FVlXHJjk6Y/dO24srk5yT5NWttZ7XAwAAAMCCpv3DBm5KcmZVrZm3dn6SR5Pcvq8dq+q3krw9yRtba59ZvhEBAAAAOBxMe0i7Osn3klxfVWcMHwiwJcnlrbVH5l5UVfdV1fvmPf8HSf5FRpd1PlBVL5339eyV/RYAAAAAmAVTfWlna21XVZ2e5KokH83oEzyvyCimzbc6yap5z39heHzz8DXfW5Jcs7STAgAAADDrpjqkJUlr7Z4kp+3nNRvGnr85TwxoAAAAAHDApv3STgAAAACYCkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoMPWf2gnAgdvwmzdOeoS92vmUSU9w6Jvq/37fe/akRwAAgCXnjDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB1WT3oADh87n/IPJj3CIW/DY3806REAAGD5bVk76QkOfVsenvQEMJOckQYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5TH9Kq6sSq2lZVu6vqwaq6uKpWdey3tqr+fVXtqqqHq+oPq+qZKzEzAAAAALNn9aQH2JeqWpfk1iT3JDknyfFJLssoAL5rP7t/KMnzk1yQ5PEklyS5IckrlmteAAAAAGbXVIe0JBcmOSrJea21R5LcUlXHJNlSVZcOa09QVScn+YUkp7bWPjWsPZDkjqo6o7V26wrNDwAAAMCMmPaQdlaSj48Fs2szOrvs1CQf3cd+35iLaEnSWruzqv5q2CakAQCHnQ2/eeOkR9irnU+Z9ASHvqn+7/e9Z096BABYEtN+j7QTkuyYv9Ba+1qS3cO27v0G9+5nPwAAAABY0LSHtHVJHlpgfdewban3AwAAAIAFTfulnSumqjYn2Tw8/W5VfWWS88yimvQA+/esJN+a9BD79tpJD7BXdcmkJ+BQ43fCUvA7gdnhd8JS8DsBVth0/154zyHwm/XQ9JOTHoDJmvaQtivJ2gXW1w3b9rXfsxezX2tta5Ktix2Q2VFVd7XWNk16DmA6+J0AzOd3AjDO7wU4PE37pZ07MnZPs6o6NsnRWfgeaHvdb7C3e6cBAAAAwD5Ne0i7KcmZVbVm3tr5SR5Ncvt+9ntuVb18bqGqNiU5btgGAAAAAIsy7SHt6iTfS3J9VZ0x3MdsS5LLW2uPzL2oqu6rqvfNPW+tfT7JJ5J8oKrOq6pzk/xhks+01m5d0e+AQ4lLe4H5/E4A5vM7ARjn9wIchqq1NukZ9qmqTkxyVZKTM/okzt9LsqW1tmfea3Ym+WRr7c3z1p6e5Iokv5xRMPxYkotaa9N7M0gAAAAAptbUhzQAAAAAmAbTfmknAAAAAEwFIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEALLOq2lJVrapeOelZAAA4cEIaADDTquqYqrqyqj5dVQ9W1WNV9c2qurOqfr2qnjrpGVdSVZ1UVb9XVX9RVX9TVd+rqr+uqlur6ryqqknPCAAwrYQ0AGDWPSPJ5iR7ktyY5PIk1yVZk+SKJHdW1TGTG2/FvTjJuUkeSPKhJJcluSXJ/5bkT5O8f3KjAQBMt9WTHgAAYJn9dZK1rbUfjG+oqj9I8oYkFya5dKUHm5A/bq1dM744xMQvJHlTVV3VWrtzxScDAJhyzkgDAParqp5WVd+vqs+OrR81XCrZqupNY9veNqz/6spO+7e11vYsFNEG1w2PP7MU71VVL66qm6vqO1X1yHC55MlLceyl0lr73l7WH0ny8eHpkvx7AADMGiENANiv1tp3k9yZ5Oeqas28TS9L8uThP58+ttvc823LPN7B+KXh8T8d7IGq6pQkn05yRpKbklyV5PtJPpnkJQd7/OVWVUcnOW14+p8nOQsAwLRyaScA0Ou2jMLZz2d0r7FkFMv2JLk980JaVR2R5FVJ/ltr7f79Hbiqnp7k1xc5zw2tte29L66q1UneNTx9RpJXJNmY5M+T/O4i33v82JXk95McleTc1tqH5237J0muXOTxNmZ0H7PFuLK19tAi3uOnk7wxyaokP5bk7CTPS/IvW2sHHRYBAGaRkAYA9NqW5LczCmbzQ9rdSa5PclVVPb+19l8zClTPyOjm9T2enuTdi5xnZ5LukJbR3z3j7/HBJP+otfbYIt973ClJXpDkU/Mj2uCqJL+W5PhFHG9jFv/vcU2S7pCW5KfH3uP7Sd6Z0YcPAACwAJd2AgC9Pp/k0QxnnlXV2iQvyiiw3Ta8Zu6stLlLBG9Lh9baztZaLfLrmsUM31p7rLVWGf398xNJ3pzRZZh3VdWGxRxrAS8aHm9f4H33JPnMYg7WWrvmAP49di7yPW4e/j2OzCiq/U6Sf5HkI1V15GKOBQBwuBDSAIAurbXvZxSETqqqZyd5ZUaXBW5rrd2b5Ov5UUg7PUlLZ0hbSW3kgdba+5Ocl9GZZFcd5GHXDo/f2Mv2/3GQx182rbUftNb+srV2cZL/O8lrk1w04bEAAKaSSzsBgMW4LcmrMwplpyR5LMln5207q6qenNH9x77cWvtmz0FX4h5pC2mtfaGqHsooCh6Mh4fHH9vL9ucu5mArcY+0vbgpyb/M6N/jXx3ksQAAZo6QBgAsxtwncJ6e5OQkn5t3f7FtSd6Q5G1JnprFfVrnStwj7QmGTyA9Jsl3DuY4Sb44PJ66wHusSvLyRR5vJe6RtpAfHx5/eJDHAQCYSS7tBAAW44sZnX11TpIX5m/HsrnLOH9r7Pl+Lec90qrqpKp6ygLrR2Z0SecR+dGHJ8zf3qqqdX4Ln0vylSQ/X1XnjG17exb3QQPLeo+0qtq0l/VnJ3nv8PQJ/x4AACTVWu/fhwAASVXdkFFIS5KXttbumLftvoyi0Z4kz2ytPbzAIVZUVV2Z5C0ZXYJ6f0ZnbT0vyS9kdMnlV5K8qrX29Xn7HJHR97CntdZ1Bn9VvSzJLRndvP/6JPdldGbZ6RlFxV8c3ueTS/KNHaCq2p7kmUnuTPK1jL7PDUlek+SoJDcked3wIQkAAMzj0k4AYLG2ZRTSHkly1wLbjk9y9zREtMF1SZ6W0aWoJydZk9Hs9yS5LOMkB5IAACAASURBVMm/ba3tHtvnpOHx2t43aa19tqpekdGnX541LN+R0f3GzswopE2Df5XR/ddelNFcRyb5Vkax74NJPtT8P60AAAua6jPSquqnk7wzoz96X5jk0621V3bstzbJlRn9kXhEko8luai19u3lmxYAmBVVdVFGf0uc1Fr78qTnAQBgOkz7GWkvzOgygy8kedIi9vtQkucnuSDJ40kuyegyhVcs9YAAwEw6NclHRDQAAOab9jPSjmitPT785z9J8qz9nZFWVSdndMPfU1trnxrWfi6jSyte3Vq7dXmnBgAAAGAWTfWnds5FtEU6K8k35iLacJw7k/xVfnS/EgAAAABYlKkOaQfohCQ7Fli/d9gGAAAAAIs27fdIOxDrMvpY+3G7khy3t52qanOSzUly1FFHvXjDhg3LMhwAAABwaLr33nu/1Vp79qTnYHJmMaQdkNba1iRbk2TTpk3trrvumvBEAAAAwDSpqvsnPQOTNYuXdu5KsnaB9XXDNgAAAABYtFkMaTuy8L3Q9nbvNAAAAADYr1kMaTcleW5VvXxuoao2ZXR/tJsmNhUAAAAAh7SpvkdaVR2d5DXD0x9PckxVvW54/h9aa7ur6r4kt7fW3pokrbXPV9Unknygqt6R5PEklyT5TGvt1hX+FgAAAACYEVMd0pI8J8l1Y2tzz38qyc6MvodVY685P8kVSX4/o7PuPpbkomWbEgAAAICZN9UhrbW2M0nt5zUbFlh7KMlbhi8AAAAAOGizeI80AAAAAFhyQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoMPUhrapOrKptVbW7qh6sqouralXHfpuq6hNV9T+Hr1ur6iUrMTMAAAAAs2f1pAfYl6pal+TWJPckOSfJ8UkuyygAvmsf+x077PfFJG8alt+Z5JaqOqm1dv9yzj1JG37zxkmPwGFq53vPnvQIAAAAsKymOqQluTDJUUnOa609klEIOybJlqq6dFhbyNlJ1iT55dbaw0lSVZ9L8q0kr0ny75Z/dAAAAABmybRf2nlWko+PBbNrM4prp+5jvycl+WGS/zVv7bvDWi31kAAAAADMvmkPaSck2TF/obX2tSS7h21786fDay6rqudU1XOSXJFkV5LrlmlWAAAAAGbYtIe0dUkeWmB917BtQa21B5O8KsmvJPnG8HVekjNba3+zDHMCAAAAMOOm/R5pB6Sq1md05tndSS4Ylv9xkhur6pThrLbxfTYn2Zwk69evz/bt21dq3CX1+uP2THoEDlOH6s8MAAAA9Jr2kLYrydoF1tcN2/bmnRndJ+11rbUfJElV3Zbkq0nekeSi8R1aa1uTbE2STZs2tY0bNx7c5BNy7rUPTHoEDlOXbj40f2YAAACg17Rf2rkjY/dCq6pjkxydsXunjTkhyZfnIlqStNa+n+TLSY5fhjkBAAAAmHHTHtJuSnJmVa2Zt3Z+kkeT3L6P/e5P8rNVdeTcQlU9OcnPJtm5DHMCAAAAMOOmPaRdneR7Sa6vqjOG+5htSXJ5a+2RuRdV1X1V9b55+/1ekucl+bOqOruqXpvkhiTrM1y+CQAAAACLMdUhrbW2K8npSVYl+WiS9yS5Ism7x166enjN3H53J/nFJGuSfDDJBzK6HPTVrbUvLf/kAAAAAMyaaf+wgbTW7kly2n5es2GBtW1Jti3TWAAAAAAcZqb6jDQAAAAAmBZCGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAh6kPaVV1YlVtq6rdVfVgVV1cVas69z2vqv5jVT1aVd+uqpur6qnLPTMAAAAAs2eqQ1pVrUtya5KW5JwkFyf5jSTv6dj3giR/lOSmJGcluSDJV5OsXq55AQAAAJhd0x6VLkxyVJLzWmuPJLmlqo5JsqWqLh3WnqCqnpXkiiS/1lr73Xmb/mzZJwYAAABgJk31GWkZnUn28bFgdm1Gce3Ufez3+uHx/cs1GAAAAACHl2kPaSck2TF/obX2tSS7h21785IkX0ny1qr671X1g6q6o6pOWb5RAQAAAJhl035p57okDy2wvmvYtjfPTfKCJO9K8s+SfHt4vLmqfqa19o3xHapqc5LNSbJ+/fps3779IEefjNcft2fSI3CYOlR/ZgAAAKDXtIe0A1VJnpbk77XWbk6SqvpckvuTvD3Jb4/v0FrbmmRrkmzatKlt3Lhx5aZdQude+8CkR+AwdenmQ/NnBgAAAHpN+6Wdu5KsXWB93bBtX/u1JJ+cWxjus3Z3khOXcD4AAAAADhPTHtJ2ZOxeaFV1bJKjM3bvtDH3ZnRWWo2tV5LHl3JAAAAAAA4P0x7SbkpyZlWtmbd2fpJHk9y+j/0+Njy+am6hqtYmeXGSLy31kAAAAADMvmkPaVcn+V6S66vqjOEDAbYkuXy4VDNJUlX3VdX75p631u5K8uEk76uqf1hVZyf5SJIfJPk3K/kNAAAAADAbpjqktdZ2JTk9yaokH03yniRXJHn32EtXD6+Z741JbkhyeZI/ySiinTYcEwAAAPj/2bv3eEvrul7gny+MGiKXSU1GJSfMy/F2Rp28FwoaoiZKJh3TsjTCLOuYdlFKNG9QgsfIiLRMK0nTzMtB5JIk4AVUzEQs0lEE72cQDRSB7/ljrdHdds/ez5pZe/aavd/v12u/9qzn93ue9Rl47fXar888z+8HTGTmd+3s7kuSHLLEnI0LHPtmkmeMvwAAAABgp8z0HWkAAAAAMCsUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGGDmi7SqultVnV1V11TVlVX1oqrac4Lz96iqi6qqq+oxy5kVAAAAgNVr3UoHWExVrU9yVpJLkhyR5I5JXpFRAXjswMs8PcntlyUgAAAAAGvGrN+RdkySvZIc2d1ndvcpSV6Y5NlVte9SJ4+LuJckef7yxgQAAABgtZv1Iu3wJGd099Vzjp2WUbl28IDz/zDJ+UnOXoZsAAAAAKwhs16k3TXJpXMPdPfnklwzHtuuqrpXkl9K8pxlSwcAAADAmjHTa6QlWZ/kqgWObx2PLeZPkpzc3ZdV1cal3qiqjk5ydJJs2LAhF1988WRJZ8QTD7phpSOwRu2uPzMAAAAw1KwXaTukqn42yV2S/NTQc7r71CSnJsnmzZt706ZNy5RueT3utCtWOgJr1AlH754/MwAAADDUrD/auTXJfgscXz8e+z5VdZMkf5Tk+CR7VNX+SbZtTLB3Ve2zHEEBAAAAWN1mvUi7NPPWQquqA5PcPPPWTptj7yS3T3JiRmXb1iQfG4+dluSjy5IUAAAAgFVt1h/tPD3Jc6tqn+7+xvjYUUmuTXLuds75ZpKHzTt2QJI3JnleknOWIygAAAAAq9usF2mnJHlWkrdW1fFJDkpyXJITu/vqbZOq6rIk53b307r7+iTvnXuROZsNfLy7P7j8sQEAAABYbWa6SOvurVV1aJKTk7wjox08T8qoTJtrXZI9d206AAAAANaSmS7SkqS7L0lyyBJzNi4xviVJTS8VADvtuIX2kmEix319pRPA9PhM2Hk+EwBg2c36ZgMAAAAAMBMUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGGDmi7SqultVnV1V11TVlVX1oqrac4lzfqyq/qqqLhuf96mqekFV/cCuyg0AAADA6rJupQMspqrWJzkrySVJjkhyxySvyKgAPHaRU48azz0+yX8kuVeSPxx//+lljAwAAADAKjXTRVqSY5LsleTI7r46yZlVtW+S46rqhPGxhby8u7865/V7q+pbSf68qu7Q3Z9d5twAAAAArDKz/mjn4UnOmFeYnZZRuXbw9k6aV6Jt89Hx99tOLx4AAAAAa8WsF2l3TXLp3APd/bkk14zHJvHAJDcm+c/pRAMAAABgLZn1RzvXJ7lqgeNbx2ODVNUBGa2p9obu/vJ25hyd5Ogk2bBhQy6++OLJ086AJx50w0pHYI3aXX9mWEEHPnWlE+z+/NyxmvhM2Hk+EwBg2c16kbbTquqmSd6U5JtJ/vf25nX3qUlOTZLNmzf3pk2bdk3AKXvcaVesdATWqBOO3j1/ZlhBb3vdSifY/T3t/6x0Apgenwk7z2cCACy7WS/StibZb4Hj68dji6qqSvL6JHdP8uDuXvIcAAAAAFjIrBdpl2beWmhVdWCSm2fe2mnb8cokRyR5RHcPmQ8AAAAAC5r1zQZOT3JYVe0z59hRSa5Ncu5iJ1bV7yX5tSRP7u7zli8iAAAAAGvBrBdppyT5dpK3VtXDxxsCHJfkxO6+etukqrqsql475/WTkrw0o8c6r6iqB8z5uvWu/SsAAAAAsBrM9KOd3b21qg5NcnKSd2S0g+dJGZVpc61Lsuec1z85/v7U8ddcv5jkddNNCgAAAMBqN9NFWpJ09yVJDllizsZ5r5+a7y/QAAAAAGCHzfqjnQAAAAAwE2b+jjQAdtzG333XSkfYri0/sNIJdn8z/f/35Y9e6QgAADB17kgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwADrVjoAa8eWH3jSSkfY7W381t+tdAQAAFh+x+230gl2f8d9faUTwKrkjjQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwwMwXaVV1t6o6u6quqaorq+pFVbXngPP2q6q/qqqtVfX1qvrbqrrlrsgMAAAAwOqzbqUDLKaq1ic5K8klSY5Icsckr8ioADx2idPflOTOSZ6e5MYkxyd5W5IfX668AAAAAKxeM12kJTkmyV5Jjuzuq5OcWVX7Jjmuqk4YH/s+VfXAJD+Z5ODu/pfxsSuSfLCqHt7dZ+2i/AAAM2Pj775rpSNs15YfWOkEu7+Z/v/78kevdAQAmIpZf7Tz8CRnzCvMTsuoXDt4ifO+tK1ES5Lu/lCSz4zHAAAAAGAis16k3TXJpXMPdPfnklwzHht83tgnlzgPAAAAABY06492rk9y1QLHt47HduS8g6aQCwAAYLc2048De9x7p830/1+Pe7Mbm/UibZepqqOTHD1++c2q+tRK5lmNaqUDLO1WSb660iEW95iVDrBddfxKJ2B34zNhGnwmsHr4TJgGnwmsHrvBZ0Iy858LPhOWyR1WOgAra9aLtK1J9lvg+Prx2GLn3XqS87r71CSnThqQ1aOqLuruzSudA5gNPhOAuXwmAPP5XIC1adbXSLs089Y0q6oDk9w8C6+Btt3zxra3dhoAAAAALGrWi7TTkxxWVfvMOXZUkmuTnLvEeQdU1UO2HaiqzRmtj3b6cgQFAAAAYHWb9SLtlCTfTvLWqnr4eB2z45Kc2N1Xb5tUVZdV1Wu3ve7u9yd5T5LXV9WRVfW4JH+b5LzuPmuX/g3YnXi0F5jLZwIwl88EYD6fC7AGVXevdIZFVdXdkpyc5IEZ7cT5miTHdfcNc+ZsSfLe7n7qnGP7JzkpyeMzKgzfmeRZ3T3Di0ECAAAAMKtmvkgDAAAAgFkw6492AgAAAMBMUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAGCZVdVxVdVV9dCVzgIAwI5TpAEAq1pV7VtVr6yq91XVlVX1rar6clV9qKp+s6r2XumMK6lGzhwXfV1V61Y6EwDArFKkAQCr3Q8mOTrJDUneleTEJG9Osk+Sk5J8qKr2Xbl4K+7XkjwsybdWOggAwKyb+F8cq+qWSR6f5H8k2bu7j5lz/A5JLuluv4gBALPi8iT7dfd35g9U1d8k+bkkxyQ5YVcHW2lVdZckxyf54yQ/m9HvcgAAbMdEd6RV1S8k2ZLkz5P87yS/PGf4dkkuTPKkaYUDAGZDVd2iqq6rqvPnHd9r/KhkV9VT5o09Y3z8l3Zt2v+uu29YqEQbe/P4+52m8V5Vdd+qendVfaOqrq6qs6rqgdO49rSNH+F8Q5JPJ3nBCscBANgtDC7SqurQJH+Z5DNJfiajMu27uvtfk3wyyeOmGRAAWHnd/c0kH0pyv6raZ87Qg5PcbPznQ+edtu312cscb2f81Pj7v+7sharqQUnel+ThSU5PcnKS65K8N8n9d/b6y+DYJPdO8tTu/vZKhwEA2B1M8mjn7yT5YpIf7+6vV9U9F5hzcZIHTCUZADBrzsmoOPuJjNYaS0Zl2Q1Jzs2cIq2q9sho3a1Pd/dnl7pwVe2f5DcnzPO27r546OTxHVjHjl/+YJIfT7IpyT8n+YsJ33v+tSujf3DcK8njuvuf5oz9RpJXTni9TZn8Hydf2d1XDbz+jyV5fpKXd/dFE74PAMCaNUmR9mNJ3tTdX19kzueTHLBzkQCAGXV2kt/PqDCbW6R9OMlbk5xcVXfu7n/PqKD6wSRvGXjt/TP544VbMvpHvKHWLfAeb0jyq1NY3/VBSe6S5F/mlmhjJyf59SR3nOB6mzL5f4/XJVmySKuqvTL6e38iyYsmfA8AgDVtkjXSfiDJN5aYs3+SG3c8DgAww96f5NqM7zyrqv2S3Cejgu2c8Zxtd6UdMv5+Tgbo7i3dXRN+vW6S8N39re6ujH7/uX2Sp2b0GOZFVbVxkmst4D7j7+cu8L43JDlvkot19+t24L/HloGXPyHJQUl+YZG14wAAWMAkRdqWJPddYs79kvz7DqcBAGZWd1+XUSF0z6q6dZKHJtkzydnd/ckkX8j3irRDk3QGFmm7Uo9c0d1/neTIjO4kO3knL7vf+PuXtjP+xZ28/lRU1cFJnpnkxd39sZXOAwCwu5nk0c63J3lOVR3Z3W+dP1hVP5/kf2b0yAcAsDqdk+QRGRVlD0ryrSTnzxk7vKpultH6Y5/o7i8PueiuWCNtId39gaq6KqNScGdsW/riNtsZn2jpi2VcI+3eSSrJC6vqhduZ853Rkm+5987+9wUAWG0mKdKOT3JUkjdV1d8nWZ8kVXVMRr8sPzHJZUleNe2QAMDM2LYD56FJHpjkgjnri52d5OeSPCPJ3plst85dsUba9xnvQLpvll6+YikfGX8/eIH32DPJQya83nKtkfZvSV67nbGjktwio00TOsnXJnx/AIBVr7p7+OTR+iF/k9G/QM/3/iQ/292XTyUZADBzxqXQ15Jcl+TWSZ7f3S8dj90ho3Lry0l+KMkR3f32FYr6XeOdxv9j/oYCVXXTjHbr/Pkkf9fdPzdvvJNkvK7aUu9RST6Z0WOii+3a+bDufu+O/22WT1VtSXKHJDfp7utXOA4AwEyaqEj77klV98noX6FvmdGjDB/o7g9OORsAMIOq6m1Jjhi/fMDc3wGq6rKMdqe8Icktl9jte5eoqlcm+cWMHkH9bEZ3bd02yU9m9MjlpzIquL4w55w9Mvo73NDdg+7gr6oHJzkzyU0z2sX0sozuLDs0o8deHxlFGgDAbm2SRzu/q7s/ku89wgAArC1nZ1SkXZ3kogXG7pjkw7NQoo29OaNHFh84/tono+yXJHlFkld39zXzzrnn+PtpQ9+ku8+vqh9P8pIkh48PfzCj9dcOy6hIAwBgNzb4jrTxwsG3TPKVhbZKHz8ecaskX+vub08lXNWPJnluRr/03j3J+7r7oQPO2y+jRygel9HOpO9M8qzuttYHALCkqnpWRr9L3LO7P7HSeQAAmA17TDD3D5L8Z0YL8i5kn/H483Y21Bx3T/KojB65+PcJzntTRv/6+/QkT03yY0neNsVcAMDqdnCStyvRAACYa5I70j6a5Irufswic96e5Hbdfd+phKvao7tvHP/5H5Lcaqk70qrqgUkuSHJwd//L+Nj9Mnq04hHdfdY0sgEAAACwtkxyR9qPZHRn2GL+PcnGHU4zz7YSbUKHJ/nSthJtfJ0PJflMvrdeCQAAAABMZJIi7SYZ7V61mBuT7LXjcabirkkuXeD4J8djAAAAADCxSXbt/ExG64Us5uAkn9vxOFOxPqNt7efbmuSg7Z1UVUcnOTpJ9tprr/tu3LhxWcIBAAAAu6dPfvKTX+3uW690DlbOJEXa25P8TlU9u7tPnD9YVc9JsjnJH08r3K7U3acmOTVJNm/e3BdddNEKJwIAAABmSVV9dqUzsLImKdL+OMmTk/xRVT0xyXuSXJHkdkkOy6hE+3ySE6YdckJbkyzUDq8fjwEAAADAxAYXad39/6rqoUnemOR+469OUuMpH0rypO7+2rRDTujSJD++wPG7JnnbLs4CAAAAwCoxyR1p6e5PJ7l/Vd0vyQOS7J/RemQfGO+MOQtOT/L7VfWQ7j4vSapqc0bro52+oskAAAAA2G1NVKRtMy7Nlr04q6qbJ3nU+OXtkuxbVU8Yv/6/3X1NVV2W5Nzufto42/ur6j1JXj9et+3GJMcnOa+7z1ruzAAAAACsTjtUpO1CP5TkzfOObXv9I0m2ZPR32HPenKOSnJTkL5PskeSdSZ61bCkBAAAAWPUmKtKqal2Sx2S0Ptr6fH+BlSTd3b8yhWzp7i353hps25uzcYFjVyX5xfEXAAAAAOy0wUVaVR2Q5Mwkd8vi5VYnmUqRBgAAAACzYpI70l6R5O4ZPVr5F0kuT3L9coQCAAAAgFkzSZF2WEYL9h+1XGEAAAAAYFbtMcHcvZK8f7mCAAAAAMAsm6RI+0SSH16uIAAAAAAwyyYp0l6R5LFVddflCgMAAAAAs2qSNdIuT/LOJO+vqhOTfDjJVQtN7O4LppANAAAAAGbGJEXaeUk6SSU5bom5e+5oIAAAAACYRZMUaS/NqEgDAAAAgDVncJHW3ccuZxAAAAAAmGWTbDYAAAAAAGvWJI92Jkmqal2Shyb5H0lu0d0vGx+/aZJbJNna3R4BBQAAAGBVmeiOtKp6eJJPJzkjyf9J8uI5w/dN8pUkR00tHQAAAADMiMFFWlXdJ8k7M7qL7blJTps73t3vT7IlyeOnmA8AAAAAZsIkd6T9QZJrk2zu7hOTfGqBORcm2TSNt0T+cQAAIABJREFUYAAAAAAwSyYp0h6S5B+7+8pF5nwuyYadiwQAAAAAs2eSIu0WGa2Btpi9JrwmAAAAAOwWJim9rkhy9yXmbErymR2PAwAAAACzaZIi7Ywkj6yqBy40WFU/meTBGW1IAAAAAACryiRF2kuTfD3JWVX1kiR3TZKqOmz8+i1JvpTkxKmnBAAAAIAVtm7oxO7+fFUdluRNSX4vSSepJP93/H1LkiO7e6l11AAAAABgtzO4SEuS7r6oqu6c5IgkD0hyy4zuUvtARjt6Xjf9iAAAAACw8gYXaVV12yTfGd9x9pbxFwAAAACsCZOskXZ5khOWKwgAAAAAzLJJirSrknx5uYIAAAAAwCybpEj7YJJ7L1cQAAAAAJhlkxRpL0xycFU9dZmyAAAAAMDMmmTXzkOTnJPktVV1TJILk3wxSc+b1939sinlAwAAAICZMEmR9uI5f77f+GshnUSRBgAAAMCqMkmR9ohlSwEAAAAAM25wkdbdZy9nEAAAAACYZYM3G6iq91TVccuYBQAAAABm1iS7dj4kyU2XKwgAAAAAzLJJirTLkhy4XEEAAAAAYJZNUqS9Nsmjqur2yxUGAAAAAGbVJLt2viXJoUnOr6qXJbkwyReT9PyJ3X3ldOIBAAAAwGyYpEj7XEalWSX500Xm9YTXBQAAAICZN0nh9XdZ4O4zAAAAAFgLBhdp3f3k5QwCAAAAALPMI5irzMbffddKR2CN2vLyR690BAAAAFhWk+zaCQAAAABr1uA70qrq1IFTu7t/ZQfzAAAAAMBMmuTRzqcvMb5tR89OokgDAAAAYFWZpEi703aO75/kx5Icm+R94+8AAAAAsKpMsmvnfy4y/OGqOj3JvyY5I8licwEAAABgtzO1zQa6+7NJ/inJb07rmklSVXerqrOr6pqqurKqXlRVew44b3NVvaeq/t/466yquv80swEAAACwdkx7184vJbnztC5WVeuTnJXRumtHJHlRkt9K8sIlzjtwfN66JE8Zf61LcmZV3WFa+QAAAABYOyZZI21RVbVHkocluXpa10xyTJK9khzZ3VdnVITtm+S4qjphfGwhj06yT5LHd/fXx/kuSPLVJI9K8mdTzAgAAADAGjC4SKuqBy1yjQOT/FKSeyd57RRybXN4kjPmFWanJTk+ycFJ3rGd826S5Pok/zXn2DfHx2qK+QAAAABYIya5I+28jB6x3J5KckGS396pRP/dXZOcM/dAd3+uqq4Zj22vSHtLRo+BvqKqXjI+9gdJtiZ58xTzAQAAALBGTFKkvTQLF2k3ZlRQfai7L5hKqu9Zn+SqBY5vHY8tqLuvrKqHJXlnkmeND38hyWHd/ZUpZwQAAABgDRhcpHX3scsZZJqqakNGd559OMnTx4efmeRdVfWg7v7cAuccneToJNmwYUMuvvjiXRV3qp540A0rHYE1anf9mQEAAIChprbZwDLZmmS/BY6vH49tz3MzWiftCd39nSSpqnOS/EeS5+R7d6l9V3efmuTUJNm8eXNv2rRp55KvkMeddsVKR2CNOuHo3fNnBgAAAIbaY+jEqrp3VT2vqm6znfHbjMfvNb14uTSjtdDmvs+BSW4+Htueuyb5xLYSLUm6+7okn0hyxynmAwAAAGCNGFykZXQn1zOSfHk7419JckySZ+9sqDlOT3JYVe0z59hRSa5Ncu4i5302yT2q6qbbDlTVzZLcI8mWKeYDAAAAYI2YpEh7UJJ/7u4Fd+7s7hsz2mHzIdMINnZKkm8neWtVPXy8jtlxSU7s7qu3Taqqy6rqtXPOe02S2yb5x6p6dFU9JsnbkmzI+PFNAAAAAJjEJEXaAUkuX2LOFRmVVVPR3VuTHJpkzyTvSPLCJCclecG8qevGc7ad9+Ekj0yyT5I3JHl9Ro+DPqK7PzatfAAAAACsHZNsNnBNklsvMefWSa7b8Tjfr7svSXLIEnM2LnDs7CRnTzMLAAAAAGvXJHekfSzJY6tq74UGx+uYPXY8DwAAAABWlUmKtL9I8kNJzqiqu88dqKp7JHl3RnekvWZ68QAAAABgNgx+tLO731hVj07ypCQfq6orM1oT7XYZLey/R5K/7e6/WZakAAAAALCCJlkjLd395Kq6IMmvJ7lLktuPhy5N8qruPmXK+QAAAABgJkxUpCVJd786yaurat8k+ye5qruvnnoyAAAAAJghExdp24zLMwUaAAAAAGvC4M0GqmpTVT2vqm6znfHbjMfvNb14AAAAADAbJtm187lJnpHky9sZ/0qSY5I8e2dDAQAAAMCsmaRIe1CSf+7uXmiwu29Mck6Sh0wjGAAAAADMkkmKtAOSXL7EnCuSbNjxOAAAAAAwmyYp0q5Jcusl5tw6yXU7HgcAAAAAZtMkRdrHkjy2qvZeaLCq9kny2PE8AAAAAFhVJinS/iLJDyU5o6ruPnegqu6R5N0Z3ZH2munFAwAAAIDZsG7oxO5+Y1U9OsmTknysqq7MaE202yW5bUal3N92998sS1IAAAAAWEGDi7Qk6e4nV9UFSX49yV2S3H48dGmSV3X3KVPOBwAAAAAzYaIiLUm6+9VJXl1V+ybZP8lV3X311JMBAAAAwAyZuEjbZlyeKdAAAAAAWBMmKtKq6sFJHpzRmmhJcmWS87v7/GkHAwAAAIBZMqhIq6qHJPmzJHfbdmj8vcfjn0jyDIUaAAAAAKvVkkVaVT0+yWlJbpLkS0nOTXL5ePjAJAcnuUeSc6rqid39T8uUFQAAAABWzKJFWlVtSPL6JDdmtFPnn3f39fPmrEvyy0lekeQNVXWX7v7CMuUFAAAAgBWxxxLjv5lk7yRP6e4/nV+iJUl3X9/df5bkKUlukeQ3ph8TAAAAAFbWUkXaI5Nc2N3/sNSFuvstST6U5PBpBAMAAACAWbJUkbYxyXkTXO/88TkAAAAAsKosVaTdJMl1E1zvuvE5AAAAALCqLFWkfSGjHTmHunuSL+54HAAAAACYTUsVae9L8oiquvNSF6qquyQ5LMm/TCMYAAAAAMySpYq0P01y0yTvHBdlCxoXbe9Isi7Jq6cXDwAAAABmw7rFBrv7wqo6Mcmzk1xcVW9OcnaSy8dTDkzy8CRPSHKzJK/s7g8tY14AAAAAWBGLFmljz01yTZLfS/LkJD83b7yS3JjkZUmOnWo6AAAAAJgRSxZp3d1J/qCqXpfkaUkenGTDePiLSc5L8lfdfdlyhQQAAACAlTbkjrQkSXd/OsnzlzELAAAAAMyspTYbAAAAAACiSAMAAACAQRRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGGC7RVpVfbmqnjPn9fOq6iG7JhYAAAAAzJbF7ki7VZKbz3n94iSHLG8cAAAAAJhNixVpX0pyu10VBAAAAABm2bpFxj6U5ClVdV2SL4yP/URVPW+Ja3Z3v2wq6QAAAABgRixWpD03yT8leeacY4dk6cc7O4kiDQAAAIBVZbtFWnf/e1XdI8mPZvSI51lJXp/kDbsoGwAAAADMjMXuSEt335DkU0k+VVVJ8unuPntXBAMAAACAWbLYZgPz3STJHy5XkO2pqrtV1dlVdU1VXVlVL6qqPQeee2RVXVhV11bV16rq3VW193JnBgAAAGD1WfSOtLnGd6clSapqQ5JNSfZP8vUkH+3uL2zv3B1VVeszeqT0kiRHJLljkldkVAAeu8S5T09ycpITMlrvbX1G67sN/jsDAAAAwDYTlUpVdfskpyQ5fIGx05P8and/bkrZkuSYJHslObK7r05yZlXtm+S4qjphfGyhnLdKclKSX+/uv5gz9I9TzAYAAADAGjL40c6quk2S85M8Ksnnk7wxyYnj758bHz9vPG9aDk9yxrzC7LSMyrWDFznviePvfz3FLAAAAACsYZOskXZskgOTPD/JHbv7yd393O5+cpI7JXlekttniUcuJ3TXJJfOPTC+4+2a8dj23D+jTRKeVlWfr6rvVNUHq+pBU8wGAAAAwBoySZH2mCRndffLuvv6uQPdfX13vzzJmeN507I+yVULHN86HtueA5LcJaNS73eS/FSS/0ry7infMQcAAADAGjHJGmkbkvzdEnMuyuKPXO4qleQWSX6mu9+dJFV1QZLPJvm1JL//fSdUHZ3k6CTZsGFDLr744l2XdoqeeNANS0+CZbC7/swAAADAUJMUaVcn+eEl5hw4njctW5Pst8Dx9eOxxc7rJO/ddqC7r66qDye520IndPepSU5Nks2bN/emTZt2MPLKetxpV6x0BNaoE47ePX9mAAAAYKhJHu08P8kTqur+Cw1W1eYkP5PkvGkEG7s089ZCq6oDk9w889ZOm+eTGd2VVvNjJrlxivkAAAAAWCMmKdJeMp7/vqr6q6r6+ap6RFU9papem1HRtkeSl00x3+lJDquqfeYcOyrJtUnOXeS8d46/P2zbgaraL8l9k3xsivkAAAAAWCMGP9rZ3RdV1VFJ/irJLyT5+TnDldGmAE/r7gunmO+UJM9K8taqOj7JQUmOS3Jid3/3EdKquizJud39tDlZ/ynJa6vqd5N8NclvJ/lOkj+dYj4AAAAA1ohJ1khLd7+tqs5O8vgk98lo/bKvJ/lokrd29zemGa67t1bVoUlOTvKOjMq6kzIq0+Zal2TPeceenOSPkpyY0aOg5yc5pLsXW1sNAAAAABY0UZGWJOOy7PXjr2XX3ZckOWSJORsXOPbNJM8YfwEAAADATplkjTQAAAAAWLMUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAwwu0qrqVssZBAAAAABm2SR3pF1eVX9bVT+xbGkAAAAAYEZNUqR9Jsn/SvLPVXVJVf1GVa1fplwAAAAAMFMGF2ndfbckD03yxiQ/kuSkJFdU1V9X1YOWJx4AAAAAzIaJNhvo7n/p7icnuW2S30qyJclTkryvqj5eVc+sqn2nHxMAAAAAVtYO7drZ3Vu7+6Q5d6n9XZIfTfKqJFdW1Wuq6t7TiwkAAAAAK2uHirR5rkjyhSTfTFJJ9kryS0kuqqp/qKr9p/AeAAAAALCidqhIq6o9q+oJVXVmkk8leU6Sryf57SQ/lOQnk5yV5Mgkr55SVgAAAABYMesmmVxVP5Lkl5P8YkaFWSd5V5JXd/cZc6aeleSsqnprkkdOKSsAAAAArJjBRVpVnZHk0IzuYvtSkpcl+fPuvnyR0y5McsROJQQAAACAGTDJHWmPSPK+jB7VfGt3f2fAOe9M8uUdCQYAAAAAs2SSIu2e3f2JSS7e3R9P8vHJIgEAAADA7Bm82cCkJRoAAAAArCaDi7Sq+umqek9V3W4747cdj1sTDQAAAIBVZ3CRltFunbfu7isWGuzuK5PcMsnR0wgGAAAAALNkkiLtnhntwrmYC5P8zx2PAwAAAACzaZIi7VZZegfOr43nAQAAAMCqMkmR9tUkP7rEnDsmuWrH4wAAAADAbJqkSDs/yWOr6s4LDVbVXZIcMZ4HAAAAAKvKJEXaiUlumuS8qvrVqjqoqm42/v7MJOclWZfkj5cjKAAAAACspHVDJ3b3B6rq15L8yfhrvhuT/Hp3v39a4QAAAABgVgwu0pKku0+pqvOT/GqS+yfZP6M10T6Q5NXd/W/TjwgAAAAAK2+iIi1JuvvjSZ6xDFkAAAAAYGZNskYaAAAAAKxZE9+RVlWV5E5J1ifZc6E53X3BTuYCAAAAgJkyUZFWVb+X5LcyKtEWs2DBBgAAAAC7q8FFWlX9VpKXJPlGkjcmuTzJ9cuUCwAAAABmyiR3pP1KkiuT3Le7v7RMeQAAAABgJk2y2cAPJ/lHJRoAAAAAa9EkRdqXYu0zAAAAANaoSYq0f0jyiKq62XKFAQAAAIBZNUmR9vtJvpLk76vqwGXKAwAAAAAzaZLNBi5OctMk90/yU1X1tSRXLTCvu/su0wgHAAAAALNikiLt5kk6o507t9lrunEAAAAAYDYNLtK6+/bLGQQAAAAAZtkka6QBAAAAwJq1w0VaVe1TVRumGQYAAAAAZtVERVpV3byqjq+qz2e00cDlc8buV1Vvr6pN0w4JAAAAACtt8BppVbVPkvcluVeSf0tydZK5u3N+IskhSS7NaIdPAAAAAFg1Jrkj7diMSrSnd/e9krxp7mB3/1eSc5McOr14AAAAADAbJinSfjrJe7r7L8eve4E5W5JMdXfPqrpbVZ1dVddU1ZVV9aKq2nOC8/eoqouqqqvqMdPMBgAAAMDaMfjRzowKsrcsMeebSfbb8Tj/XVWtT3JWkkuSHJHkjklekVEBeOzAyzw9Uy73AAAAAFh7Jrkj7ZtJbr3EnB9J8tUdj/N9jkmyV5Iju/vM7j4lyQuTPLuq9l3q5HER95Ikz59iJgAAAADWoEmKtAuTPKaqbrHQYFUdkOTwJBdMI9jY4UnO6O6r5xw7LaNy7eAB5/9hkvOTnD3FTAAAAACsQZMUaa9Kcqsk76yqO80dGL/++4wKrldNL17umtEuoN/V3Z9Lcs14bLuq6l5JfinJc6aYBwAAAIA1avAaad19elW9OKO1yS5N8u0kqaovZvTIZyV5fnefN8V865NctcDxreOxxfxJkpO7+7Kq2rjUG1XV0UmOTpINGzbk4osvnizpjHjiQTesdATWqN31ZwYAAACGmmSzgXT3H1TV+5I8K8kDktxs/PWeJCd295nTjzi5qvrZJHdJ8lNDz+nuU5OcmiSbN2/uTZs2LVO65fW4065Y6QisUSccvXv+zAAAAMBQExVpSTIuy3ZVYbY1C+8Cun489n2q6iZJ/ijJ8Un2qKr9k2zbmGDvqtqnu7+xHGEBAAAAWL0mWSNtJVyaeWuhVdWBSW6eeWunzbF3ktsnOTGjsm1rko+Nx05L8tFlSQoAAADAqjbxHWm72OlJnjvvLrKjklyb5NztnPPNJA+bd+yAJG9M8rwk5yxHUAAAAABWt8FFWlV9J0kPmNrdfbMdj/TfnJLRemxvrarjkxyU5LiM1mO7ek62y5Kc291P6+7rk7x3XvaN4z9+vLs/OKVsAAAAAKwhk9yR9sEsXKTtn+RHM9p04ONJrl5gzg7p7q1VdWiSk5O8I6MdPE/KqEyba12SPaf1vgAAAAAw3+Airbsfsr2xqto3yauSbM4EO2UOfN9LkhyyxJyNS4xvSVLTSwXATjtuob1kmMhxX1/pBDA9PhN2ns8EAFh2U9lsYPyY5dMyumPtJdO4JgAAAADMkqnt2tndNyT55ySPn9Y1AQAAAGBWTK1IG7tpkvVTviYAAAAArLipFWlVdackP5PkP6d1TQAAAACYFYM3G6iqUxe5xoFJfmL859+ZQi4AAAAAmCmDi7QkT19i/LIkf9Tdr9mJPAAAAAAwkyYp0u60neM3Jtna3VdNIQ8AAAAAzKTBRVp3W/sMAAAAgDVr2rt2AgAAAMCqNMlmAw/a0Tfp7gt29FwAAAAAmAWTrJF2XpLewffZcwfPAwAAAICZMEmR9tIk901yWJItSc5P8sUkByR5cJKNSd6d5MNTTQgAAAAAM2CSIu3tSX5r/PWq7r5h20BV7ZnkN5P8YZIXdPeFU00JAAAAACtskiLtxUnO6e6T5g+MS7VXVNWhGZVpj5xSPgAAAIDdzkc+8pHD1q1b94LuPiA2e9wd3FhVX7z++utfeJ/73OeM7U2apEi7X5KTl5jz0STPnOCaAAAAAKvKRz7ykcNudrObnbxx48br9tprr6177LHHjq45zy5y44031rXXXrvfli1bTv7IRz7ya9sr0yZpRPdIctAScw6a8JoAAAAAq8q6detesHHjxuv23nvva5Vou4c99tij995772s3btx43bp1616w3XkTXPP9SZ5QVQs+tllVj0ryhCQXTBYVgP/P3r1H2V3W9+J/f0IICRhC5BpEidxEQUVIodoKiiIi9qBQpcVjReGgnOPBo7VWfsdKvBwFqgJqveANLyhWpbTKUSsoeKn1RgFvKKiBQhDFE4hAAiR5fn/sPTqMk8x3kpnMzuT1Wmuvne9z+e7PkDV7Zb15vs8DAABMH621XebMmbNyqutg/ObMmbOy/zjuqMbzaOdrklyZ5NKqujzJV5PclmTnJIclOTzJvUn+9/qXCwAAALDJm2El2qap//e21oVnnYO01tp3qurIJB9M8tT+qyWp/pCfJXlRa+17618uAAAAAAym8axIS2vta1W1T5InJjkwybwkdya5KsnXWmvSVgAAAACmpXEfDNB6vtpaO7e19rr++1eFaAAAAADT13e+853ZVXXQ5z73ubld57zlLW/Z4aMf/eh2k1nXxjSuFWlDqmpOkr2SPKi19s2JLQkAAABg+ln46ksPmorPXXLm0VO2DdcFF1yw4yMe8YgVz3/+8++Yqhom0rhWpFXVgqr6ZJI7klyd5GvD+v6kqq6tqkMnuEYAAAAAmHKdg7Sq2iXJt5Mcl+SLSb6V3x80kH7fQ5I8dyILBAAAAGDjO/PMM3fcZZddHjNnzpzHHX744XvdfPPNs4b3n3HGGTvvv//+j5w7d+4B22+//WMPP/zwvX7wgx9sNdR/8MEHP+KHP/zh1hdffPH2VXVQVR309re/ffskeec737n9QQcd9Ih58+YdsO222x5wyCGH7PPVr3516439M47XeB7tPCPJgiRPb61dVlVnJDlkqLO1dn9VfS2JFWkAAAAAm7CPfexj251++ukPO+GEE3597LHH3vGVr3xl7qmnnrpw+Jibb7551otf/OJfPfzhD7/vzjvvnHH++efveOihh+57/fXX/2D77bdf/e53v/vG5zznOXs+7GEPu/fv/u7vbk2SRz7ykfcmyZIlS2b95V/+5W/23nvve++99976xCc+8eCnPe1p+1511VU/eNSjHnXfFPzInYwnSDs6yb+01i5bx5ibkvzphpUEAAAAwFQ666yzFjzxiU9cfuGFF96UJMcdd9zy22+/feYnP/nJHYbGfOADH/jPoT+vWrUqxxxzzPKdd975gE984hPbvfSlL/3NQQcdtHLrrbdes/322696ylOecvfw+7/lLW+5dejPq1evzrOf/ezl++yzzzYf/OAHtx/eN2jGs0fazkl+OsaYe5Nss/7lAAAAADCV7r///vz4xz/e+pnPfOYDDgg49thjlw2/vvzyy7d5whOesPd22213wJZbbnnQ3LlzD7znnntm/PSnP90qY7jqqqtmH3HEEXtuv/32j505c+ZBs2bNOmjJkiWzr7/++tkT/fNMpPGsSFuWZLcxxuyd5JfrXw4AAAAAU+nWW2+duXr16uy88873D29fsGDBqqE/X3/99bOOOeaYfR7zmMfcfc4559y422673bfVVlu1Zz/72XuvXLlynQu3li1bNuMZz3jGPjvssMP9b3zjG/9zjz32uG/OnDlrTjnllIX33ntvrWvuVBtPkPaNJP+lqnZqrf1qZGdV7ZnkqCQfn6jiAAAAANi4FixYsGqLLbbIbbfdtuXw9ltvvfV3OdI///M/b7ty5coZX/jCF27Ydttt1yS9lWx33nnnFmPd/ytf+cqDbrvtti0///nP//Rxj3vcyqH23/72t2POnWrjebTzLUm2TnJFVR2RZHaSVNVW/evPJmlJ3jbhVQIAAACwUWy55ZbZd9997/nc5z633fD2iy++eP7Qn1esWDGjqtqWW27Zhto+8IEPPHj16tU14l7t3nvvfUD+dM8998xIkjlz5qwZavvSl760zdKlSx9wKugg6rwirbX2zao6Nck7k3xhWNc9/ffVSU5qrX1/AusDAAAAYCN71atedesLXvCCPZ/3vOc97LjjjrvjK1/5ytwrrrhi3lD/kUce+dvFixfXc5/73IUnn3zy7d///vfn/MM//MPOc+fOXT38PnvttdfKK6+8ctvPfOYz2+64446r9tlnn3sPO+ywu7beeus1L3rRixa+8pWv/OVNN9205VlnnbXrTjvtdP8fVjJYxvNoZ1pr76uqryX5H0n+OMn2Se5M8u9J3tFa+9HElwgAAACw6Vty5tHfm+oauvqrv/qrO26++eabzjvvvAUXX3zx9gcffPBv3/Wudy057rjj9k6Sgw8+eMXb3/72X5x55pm7Hn/88fMf8YhH3HPhhRf+/PnPf/4ew+/zute9bunJJ58868QTT9zjrrvu2uK8885bctppp/3mwx/+8M9OP/30h55wwgl7PexhD1t57rnn3vTWt751l6n5abur1trYozYzixYtat/97nenuoz1svDVl051CWymlpx59FSXwKZm8byxx7Bui++c6gpg4vhO2HC+EwAmXVV9r7W2aKxx11xzzZLHPvaxt2+Mmph411xzzQ6PfexjF47W13mPtKr6aVW9fcKqAgAAAIBNyHgOG1iQ5K7JKgQAAAAABtl4grQfJdljzFEAAAAAMA2NJ0h7Z5I/q6r9J6sYAAAAABhU4zm182dJLk/yb1X1riTfSfLLJH9wWkFr7d8mpjwAAAAAGAzjCdK+nl5oVklelVECtGG22JCiAAAAAGDQjCdIe1PWHZ4BAAAAwLTVOUhrrb1mMgsBAAAAgEE2nsMGAAAAAGCztc4grapeW1WHbqxiAAAAAGBQjfVo5+L+66tDDVX1siQva63tMXllAQAAAEwzi+cdNDWfe+f3puRzx+nOO++csd122z3uvPPOW3Laaaf9ZqrrGc36PNq5XZLdJ7oQAAAAABhkA79HWlU9qqour6p7qmppVb2+qrYYY84fVdWHquqG/ryfVNUZVTV7Y9UNAAAAMF2sWrUqK1eurKmuY6oNdJBWVfOTXJakJTkmyeuT/HWS140x9fgkeyY5K8kzkvxDklckuXDSigUAAACYJo477riF+++//yM/+tGPbrfXXnvtN3v27AOvuOKKbZ7znOcs3G233R49e/bsAxcuXLj/aaedtuvwgO0nP/nJrKo66P3vf//8E044Yfe5c+cesPPOOz/m5S9/+a6rV69+wGdccMEF2y1cuHD/2bNnH7ho0aJEFwePAAAgAElEQVRHXHPNNX+wAGrVqlV5xSteseuCBQsePWvWrAP32muv/d7znvc8eLRaL7roonl77rnnfnPmzHnck570pL1uu+22LX7wgx9sdcghh+wzZ86cx+2///6P/Na3vjVnQ/67jLVH2lR7SZI5SY5trS1P8qWq2jbJ4qo6u982mjNba7cPu76iqlYmeW9V7d5au3GS6wYAAADYpN1yyy2z/u7v/m63V73qVUt33XXX+5Nk/vz5q9785jf/54Mf/OBV11133eyzzjpr19tvv33Lj3/84w/IWs4444zdnvGMZyz7yEc+8vMvfelLc88999wF++2334qTTz55WZJ8/etf3/rkk0/e84gjjlh29tln3/T9739/zgknnLDnyBpe/vKXP+Td7373zq94xStuPeSQQ+7+9Kc/Pf/UU099eFXlxS9+8f8bGrd06dJZb3jDG3Z97Wtfe8vdd98949WvfvXDXvCCF+x+8803b/WCF7zg13/913/9y9e+9rW7nXDCCXtcf/31P5wxY/3WlnUJ0rarqocNv06SqnpoklGX9LXWblqvav7QUUm+OCIwuyi9lWaHJfnsWj7/9lGa/6P/vmsSQRoAAADAOtxxxx0zL7300p8+4QlPWDHU9vSnP/2uoT8/7WlPu2ubbbZZ87KXvWzhypUrb5o9e3Yb6jv44IN/+773ve/mJHn2s5+9/Mtf/vK8Sy65ZP5QkPamN71pl913333lpZde+vMZM2bkuc997vL77ruvzj777IcM3eO2227b4v3vf/9OL3vZy249++yzb02S4447bvnSpUu3fPOb37zr8CBt+fLlM7/2ta9dt99++92bJNdee+3W733ve3d+xzveseSlL33pb5KktXbLX/zFX+x19dVXzz7wwANXrs9/ky7x28uS/GLY67R++5IR7UOvn69PIWuxb5Lrhjf0Q7p7+n3j8fgka5L8bGJKAwAAAJi+dtppp/uHh2hr1qzJ61//+p323HPP/WbPnn3grFmzDjr11FMfft9999UNN9wwa/jcI4444gFPEe69994rbr311i2Hrq+55pptjjzyyDuGrww7/vjj7xg+56qrrpqzcuXKGSeccMKy4e1//ud/vuzGG2/caunSpb9bILbrrrveOxSiJclee+21MkmOOuqo39XxyEc+cmWS3HTTTVtmPY21Iu2m9PYnmyrzk9wxSvuyfl8nVbVLktck+Whr7VdrGXNKklOSZMGCBbn66qvHX+0AeO4eq8ceBJNgU/2dYQo99MSprmDT5/eO6cR3wobznQDABNthhx3uH379hje8Yac3vOENDz311FN/+eQnP/m322+//apvfvOb25x++ukPW7FixQOeWpw/f/4DAopZs2a1e++993ep2e23377lTjvttGr4mKHHR4fcfPPNWybJQx7ykAe0L1iw4P4k+fWvf73FrrvuuipJtt122z/4vP7P8Lv2rbbaqiXJihUr1vvMgHUGaa21het740FRVbOS/GOSu5K8fG3jWmvnJzk/SRYtWtQOOOCAjVPgBHvWRbdMdQlsps4+ZdP8nWEKXXLBVFew6TvpvKmuACaO74QN5zsBgAlW9cAdvS655JIHP/3pT1/2jne843fhw7XXXrtem/fvsMMO9//qV796QC61dOnSB6wU22233e4fat9ll11+F4gNrWzbcccdN/pqooE+tTO9lWfzRmmf3+9bp+r9jX8kyX5JntFaG3MOAAAAAH9o5cqVM2bNmrVmeNtFF1304LWNX5fHPOYxd3/xi1/cbs2a39/uk5/85HbDxxx44IErZs+evebjH//4A55K/MxnPjN/9913v3doNdrGNOindl6XEXuh9Q852Doj9k5bi3OTHJPkiNZal/EAAAAAjOKwww5b/qEPfWinM8888+6999773o997GMPvvHGG2evz71OP/30Xz75yU9+5NFHH73HSSeddPu1114758ILL9xx+Jidd9559cknn/yr8847b8HMmTPbwQcffM+nP/3p7a688sp5733veydyj/7OBj1I+3ySv6mqua213/bbjk+yIsmV65pYVacneWmS57bWvj65ZQIAAACMYfGd35vqEjbEWWedtfT222+f+eY3v/khSfL0pz992d///d/fdMIJJ+w13nsdeuih97zvfe/7+eLFix/yvOc9b6/999//7gsvvPBnT3rSkx45fNw555xzy8yZM9sFF1yw01vf+taZD3vYw+5917ve9YtTTjllSp46rNam8iyBdauq+Ul+lOQHSc5KskeStyU5t7X2mmHjbkhyZWvtpP71CUkuTHJBkveOuO3PWmu/XtfnLlq0qH33u9+dqB9jo1r46kunugQ2U0vOPHqqS2BTs3i0J/cZl8V3TnUFMHF8J2w43wkAk66qvtdaWzTWuGuuuWbJYx/72Ns3Rk1MvGuuuWaHxz72sQtH6xvoFWmttWVV9ZQk70zy2fRO8DwnyeIRQ2cm2WLY9dP67yf2X8O9ML2ADQAAAAA6G+ggLUlaaz9KcvgYYxaOuD4xfxigAQAAAMB6G/RTOwEAAABgIAjSAAAAAKCDcT/aWVU7JjkuySOTbNNaO3lY+8OTfL+1tmJCqwQAAADYdKxZs2ZNzZgxY3BPeGRUa9asqSRr1tY/rhVpVXVSkiVJ/iHJ/0xv4/4hOyf5ZpITxl0lAAAAwDRRVb9csWLF7Kmug/FbsWLF7Kr65dr6OwdpVXVEkvOT/DTJs5O8e3h/a+0HSX6Y5FnrVyoAAADApm/VqlWvW7Jkyay77757Tn+FEwNuzZo1dffdd89ZsmTJrFWrVr1ubePG82jn3ya5NclhrbXlVfW4UcZcm+Tx46wVAAAAYNo48MADv3jVVVe99Gc/+9kZrbVdYo/6TcGaqvrlqlWrXnfggQd+cW2DxhOkLUpyUWtt+TrG3Jxkl3HcEwAAAGDa6Ycxaw1k2DSNJ0ibleTuMcZsl2T1+pcDwERa+OpLp7qEtVpix4gNNtB/v2cePdUlAADAhBvP0sIlSQ4aY8whSX6y3tUAAAAAwIAaT5D2z0meWFXPGa2zql6Y5DFJPjMRhQEAAADAIBnPo51nJ/mLJJ+oqj9PMi9JquqlSZ6Y5Ngk1yd5x0QXCQAAAABTrXOQ1lpbVlWHJflIkuGr0t7ef/9akhNaa2PtowYAAAAAm5zxrEhLa+2mJE+qqsckeXyS7ZPcmeTfW2vfm4T6AAAAAGAgjCtIG9JauzbJtRNcCwAAAAAMrM5BWlWdneRDrbUfT2I9TGNLZp8w1SVs8hau/PhUlwAAAJNv8byprmDTt/jOqa4ApqXxnNr5yiQ/qKpvV9X/qKoHT1ZRAAAAADBoxhOk/WWSLyZ5XHoHDCytqk9X1Z9V1RaTUh0AAAAADIjOQVpr7ZOttWck2S3J3ya5PsmxSS5JL1R7W1UdMDllAgAAAMDUGs+KtCRJa+221tpbWmuPTnJQkncmqST/K8n3qurqCa4RAAAAAKbcuIO04Vpr/9Fae1mSXZP8TZJVSR49EYUBAAAAwCDpfGrnaKpqXpLjk7wgyR+ntzLN0SAAAAAATDvjDtKqakaSI9MLz/5Lkq2StCSXJ/lwkosnskAAAAAAGASdg7SqenSSv0ryvCQ7p7f67KdJPpLkI621myelQgAAAAAYAONZkXZN//3OJO9PckFr7ZsTXxIAAAAADJ7xBGn/muSCJP/UWrt3csoBAAAAgMHUOUhrrT19MgsBAAAAgEE2Y6oLAAAAAIBNwVpXpFXVB9M7jfP/a63d1r/uorXWTpqQ6gAAAABgQKzr0c4T0wvSzkpyW/+6i5ZEkAYAAADAtLKuIO3h/fdbRlwDAAAAwGZnrUFaa+3GdV0DAAAAwOak82EDVfXaqjp0jDFPrKrXbnhZAAAAADBYxnNq5+IkTxpjzKFJzljfYgAAAABgUI0nSOtiyyRrJvieAAAAADDlJjpIOzDJ7RN8TwAAAACYcus6tTNV9eURTSdW1ZNGGbpFkocm2T3JJyamNAAAAAAYHOsM0vLAPdFakoX910hrkvwmySeTvHwC6gIAAACAgbLOIK219rtHP6tqTZLFrbXXT3pVAAAAADBgxlqRNtwLk/zHZBUCAAAAAIOsc5DWWvvwZBYCAAAAAINsPCvSfqeqdkvykCRbjdbfWvvqhhQFAAAAAINmXEFaVT0tyTlJ9h1j6BbrXREAAAAADKAZYw/pqao/TvK5JNsleWeSSvLVJO9Lcl3/+rNJHEYAAAAAwLTTOUhLcnqSlUn+qLX2sn7bV1prL0myf5I3Jnlqkk9PbIkAAAAAMPXGE6Q9Psm/tNaWjpzfel6b5MdJXjeB9QEAAADAQBhPkDYvyU3Dru9Lss2IMd9IcuiGFjVcVT2qqi6vqnuqamlVvb6qxtyDrarmVdWHqmpZVd1ZVRdW1fYTWRsAAAAAm4/xHDbwqyTzR1zvOWLMlknmbGhRQ6pqfpLLkvwoyTH9z3tregHga8aY/o9J9klycpI1Sc5KckmSJ05UfQAAAABsPsYTpP00DwzO/j3JUVW1T2vtp1W1S5Ljklw/gfW9JL1g7tjW2vIkX6qqbZMsrqqz+21/oKoen+RpSQ5rrX2133ZLkm9V1VNba5dNYI0AAAAAbAbGE6R9Ickbq+rBrbX/l+S8JMcm+Y+q+lGSvZPMTfKqCazvqCRfHBGYXZTe6rLD0jsldG3zbhsK0ZKktfbtqvpFv0+QBgBsdha++tKpLmGtlsye6go2fQP993vm0VNdAgBMiPHskfbe9PY/uz9JWmvfSPKcJL9I79TOW5Oc2lr7yATWt2+S64Y3tNZuSnJPv6/zvL4fjzEPAAAAAEbVeUVaf1XYt0a0/VOSf5roooaZn+SOUdqX5YH7tY1n3h4TUBcAAMAmbaBXMVqlusEG+u/XKlU2YeN5tHNaq6pTkpzSv7yrqn4ylfVMRzXVBYxthyS3T3UR6/bMqS5greqsqa6ATY3vhIngO4Hpw3fCRPCdwPSxCXwnJAP/veA7YZLsPtUFMLUGPUhblmTeKO3z+33rmrfjeOa11s5Pcv54C2T6qKrvttYWTXUdwGDwnQAM5zsBGMn3Amye1hqkVdXP1/OerbW259jDOrkuI/Y0q6qHJtk6o++BNnzeE0dp3zfJJRNUGwAAAACbkXUdNjAjvRW1432N5wCDsXw+yZFVNXdY2/FJViS5cox5u1TVnw41VNWi9PZH+/wE1gcAAADAZmKtK9Jaaws3Yh1r854kpyW5uKrOSi8IW5zkbf3DD5IkVXVDkitbayclSWvtm1X1r0k+UlWvTLImyVlJvt5au2wj/wxsOjzaCwznOwEYzncCMJLvBdgMVWttqmtYp6p6VJJ3Jnl8eidxvj/J4tba6mFjliS5orV24rC27ZKck+TZ6a2S+1yS01prA7wZJAAAAACDar2DtKqan+RBrbX/nNiSAAAAAGDwjGs/s6p6UFW9tap+md4xv78Y1ndIVf3fqjpwoosEAAAAgKnWOUirqnlJvpnk5UmWJvlxeocLDPl+eidl/uVEFggAAAAAg2A8K9L+d5L9kpzYWjswyaeGd7bW7knvJM2nTFx5AAAAADAYxhOkHZvki621j6xjzI1JHrJhJQEAAADA4BlPkLZbkmvHGHNXknnrXw4AAAAADKbxBGm/TbLTGGMent4hBAAAAAAwrYwnSPtOkmdW1dzROqtqQZJnJPn6RBQGAAAAAINkPEHaeUm2T/J/q+qRwzv6159KMjvJ2yeuPAAAAAAYDNVa6z646owkZyRpSe5PsmWSZUnmJ6kkf9ta+/tJqBMAAAAAptS4grQkqaonJzktyR+nt0LtziT/nuSc1tqXJ7xCAAAAABgA4w7SAAAAAGBzNJ490jqpqh0n+p4AAAAAMNUmLEirqnlV9aYkP5uoewIAAADAoJjZZVBV7Z7koPQOGPh2a+22YX2zk7w8ySvTO3TgnkmoEwAAAACm1Jgr0qrq7emtMvtUkkuSLKmq/97ve1KSnyR5Y5Ktk5yXZI/JKhYAAAAApso6Dxuoqhck+VCSNUmu6zfv238/Kcl7k2yR5H1J3thaWzp5pQIAAADA1BlrRdqJSe5L8sTW2v6ttf2THJ5kdZIPJPllkgNba/9diAYAMLqqWlxVrb+aHwCATdRYQdpjkvxTa+2bQw2tta+m94hnJXlRa+37k1gfAMAGqaptq+rcqvpaVS2tqpVV9auq+nZV/a+q2maqa9yYqupJ/VBvba8zp7pGAIBBNdZhA/OS3DBK+/X992+O0gcAMEgenOSUJN9OcmmSX6f3b5zDk5yT5L9V1eNba8unrsQpcWWSK0Zp//pGrgMAYJMxVpA2I72TOke6P0laaysmvCIAgIn1n0nmtdb+4N80VfWxJM9L8pIkZ2/swqbYFa21xVNdBADApmTMUzuTrP00AgBgs1BVD6qq+6rqGyPa5/QflWxV9fwRfaf221+0cat9oNba6tFCtL5P9d/3nojPqqqDquoLVfXbqlpeVZdV1eMn4t4AAEy9sVakJcniqlo8WkdVrR6lubXWutwXANhEtNbuqqpvJzmkqua21n7b7/qTJFv1//yUJB8dNu0p/ffLN1KZ6+PP+u/XbuiNquoJSS5LMivJxeltj3FAeo9PfnlD7z8J9qqqlybZNr0DpL7WWrt+jDkAAJu1LoFXjfOe4x0PAGwavpxecHZoenuNJb2wbHV6+20NBWepqhlJnpzk5621G8e6cVVtl+R/jbOeS1prV3cdXFUzk7ymf/ngJE9ML+j6SpL3jfOzR967knwwyZwkz2qt/fOwvpclOXec9zsgybPGWca5rbU7xjH+ef3X8M/9TJL/1lpbNs7PBgDYLFRrntwEAMZWVYelt7rqnNbaK/pt305vG4iPJHlnkke01n5aVQcm+V6S97XWTulw74VJfjHOkl7YWrtgHPXPTjJyf9ePJvnvrbW7xvnZI+/9J+lt0v/V1tphI/q2SPKTJHsmeXJr7YoO9zsxyYfGWcbDW2tLOtx7vyTPTC8MXZJkdpJFSd6U5HFJvpHk0NbamnF+PgDAtNdljzQAgKR3WveK9FeeVdW8JAem9+jm0KOLQ6vSDu+/d3qksbW2pLVW43xdMJ7iW2srW2uV3r9/dktyYpKnJvluP8jbEAf2368c5XNXZ5wnYbbWLliP/x5LOt77h621s1prP2it3dVau7219oUkT0ovzPyT/P6RVwAAhhGkAQCdtNbuSy8QenRV7Zhe8LJFkstbaz9Ocmt+H6Q9Jb2VagO3N1jruaW19uEkxyZ5RHqr6TbEvP77bWvp/+UG3n/StdaWJ/l4//LQqawFAGBQORQAABiPLyc5Ir2g7AlJVqb3KOBQ31FVtVV6+4/9sLX2qy433Rh7pI2mtfbvVXVHeqHghriz/77zWvp3Gc/NNtIeaaP5df99mw28DwDAtCRIAwDGY+gEzqckeXySf2utrRzW97wkp6YXxIzntM7tkpwxzlqWJNmgIK2q5qZ3auVvxxo7hqv674eN7Ojvkfan47zfARn/f48LkmxokPbH/fefb+B9AACmJY92AgDjcVV6q6+OSbJfHhiWDT3GefqI6zFN5h5pVfXo/kEDI9tnpfdI54z8/hTS4f2tqrqeyvRv6R0ocGhVHTOi76XpHTTQ2WTukVZVi9bS/l+THJ/kviT/OJ56AQA2F1akAQCdtdZWV9UV6QVpybAgrbV2Y1X9LL3QaHVG2Xh/ipyU5IVV9Y0kN6a3amvXJE9L75HLnyR55fAJVTX0PxtXd/mA1lqrqpOSfCnJZ6rq4iQ3pLey7ClJvpDk6Rv+o0yIT1fVqiTfTXJzeqd2/lGSg5OsSvLirqEcAMDmRpAGAIzX5ekFacvTC2NG9u2Z5HuttTtHTpwin0ryoPQeRX18krnp1f6jJG9N8q7W2j0j5jy6/35R1w9prX2jqp6Y5P8kOarf/K309l87MoMTpL07vdNK/yTJDkkqyS3pPRp6bmvtmqkrDQBgsFVrXZ9Y2Piqaq8kf5PeP3r3S/K11tqTOsybl+Tc9DbpnZHkc0lOa639ZvKqBQCmi6o6Lb1/Szy6tfbDqa4HAIDBMOgr0vZL8owk/55ky3HM+8ck+yQ5OcmaJGcluSS9E8QAAMZyWJJ/EaIBADDcoK9Im9FaW9P/86eT7DDWirSqenx6G/4e1lr7ar/t4PQerTiitXbZ5FYNAAAAwHQ00Kd2DoVo43RUktuGQrT+fb6d5Bf5/X4lAAAAADAuAx2krad9k1w3SvuP+30AAAAAMG6Dvkfa+pif3rH2Iy1LssfaJlXVKUlOSZI5c+YctHDhwkkpDgAAANg0/fjHP769tbbjVNfB1JmOQdp6aa2dn+T8JFm0aFH77ne/O8UVAQAAAIOkqm6c6hqYWtPx0c5lSeaN0j6/3wcAAAAA4zYdg7TrMvpeaGvbOw0AAAAAxjQdg7TPJ9mlqv50qKGqFqW3P9rnp6wqAAAAADZpA71HWlVtneQZ/cuHJNm2qv68f/1/W2v3VNUNSa5srZ2UJK21b1bVvyb5SFW9MsmaJGcl+Xpr7bKN/CMAAAAAME0MdJCWZKcknxrRNnT98CRL0vsZthgx5vgk5yT5YHqr7j6X5LRJqxIAAACAaW+gg7TW2pIkNcaYhaO03ZHkhf0XAAAAAGyw6bhHGgAAAABMOEEaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdDBzqgtgYi189aVTXQKbqSVnHj3VJQAAAMCksiINAAAAADoQpAEAAABABwMfpFXVo6rq8qq6p6qWVtXrq2qLDvMWVdW/VtX/678uq6pDNkbNAAAAAEw/Ax2kVdX8JJclaUmOSfL6JH+d5HVjzHtof97MJM/vv2Ym+VJV7T6ZNQMAAAAwPQ36YQMvSTInybGtteXpBWHbJllcVWf320ZzdJK5SZ7dWrszSarq35LcnuQZSd49+aUDAAAAMJ0M9Iq0JEcl+eKIwOyi9MK1w9Yxb8skq5LcPaztrn5bTXSRAAAAAEx/gx6k7ZvkuuENrbWbktzT71ubz/THvLWqdqqqnZKck2RZkk9NUq0AAAAATGOD/mjn/CR3jNK+rN83qtba0qp6cpLPJTmt33xrkiNba78ebU5VnZLklCRZsGBBrr766g2pe8o8d4/VU10Cm6lN9XcGAAAAuhr0IG29VNWC9FaefS/Jyf3m/5Hk0qp6Qn9V2wO01s5Pcn6SLFq0qB1wwAEbq9wJ9ayLbpnqEthMnX3Kpvk7AwAAAF0NepC2LMm8Udrn9/vW5m/S2yftz1tr9ydJVX05yfVJXpnfr1IDAAAAgE4GfY+06zJiL7SqemiSrTNi77QR9k3yw6EQLUlaa/cl+WGSPSehTgAAAACmuUEP0j6f5Miqmjus7fgkK5JcuY55NybZv6pmDTVU1VZJ9k+yZBLqBAAAAGCaG/Qg7T1J7k1ycVU9tX8gwOIkb2utLR8aVFU3VNUHhs17f5Jdk/xTVR1dVc9MckmSBenvgwYAAAAA4zHQQVprbVmSpyTZIslnk7wuyTlJzhgxdGZ/zNC87yV5epK5ST6a5CPpPQ56RGvtmsmvHAAAAIDpZtAPG0hr7UdJDh9jzMJR2i5PcvkklQUAAADAZmagV6QBAAAAwKAQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoIOBD9Kq6lFVdXlV3VNVS6vq9VW1Rce5x1bVd6pqRVX9pqq+UFXbTHbNAAAAAEw/Ax2kVdX8JJclaUmOSfL6JH+d5HUd5p6c5ONJPp/kqCQnJ7k+yczJqhcAAACA6WvQQ6WXJJmT5NjW2vIkX6qqbZMsrqqz+21/oKp2SHJOkv/ZWnvfsK5/mvSKAQAAAJiWBnpFWnoryb44IjC7KL1w7bB1zHtu//3Dk1UYAAAAAJuXQQ/S9k1y3fCG1tpNSe7p963NIUl+kuSkqrq5qu6vqm9V1RMmr1QAAAAAprNBD9LmJ7ljlPZl/b612SXJI5K8JsnfJvmzJHcn+UJV7TzRRQIAAAAw/Q36Hmnrq5I8KMlzWmtfSJKq+rckNyZ5aZK/+4MJVackOSVJFixYkKuvvnrjVTuBnrvH6qkugc3Upvo7AwAAAF0NepC2LMm8Udrn9/vWNa8luWKoobW2vKq+l+RRo01orZ2f5PwkWbRoUTvggAPWs+Sp9ayLbpnqEnjB/J8AACAASURBVNhMnX3Kpvk7AwAAAF0N+qOd12XEXmhV9dAkW2fE3mkj/Di9VWk1or2SrJnIAgEAAADYPAx6kPb5JEdW1dxhbccnWZHkynXM+1z//clDDVU1L8lBSa6Z6CIBAAAAmP4GPUh7T5J7k1xcVU/t72O2OMnbWmvLhwZV1Q1V9YGh69bad5P8c5IPVNULquroJP+S5P4k/7AxfwAAAAAApoeBDtJaa8uSPCXJFkk+m+R1Sc5JcsaIoTP7Y4b7r0kuSfK2JJ9OL0Q7vH9PAAAAABiXQT9sIK21HyU5fIwxC0dpuyvJqf0XAAAAAGyQgV6RBgAAAACDQpAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0MHAB2lV9aiquryq7qmqpVX1+qraYhzzZ1TVd6uqVdUzJ7NWAAAAAKavmVNdwLpU1fwklyX5UZJjkuyZ5K3pBYCv6Xibk5PsNikFAgAAALDZGPQVaS9JMifJsa21L7XW3pPkdUleUVXbjjW5H8T9nyT/e3LLBAAAAGC6G/Qg7agkX2ytLR/WdlF64dphHea/Ick3klw+CbUBAAAAsBkZ9CBt3yTXDW9ord2U5J5+31pV1WOSvCjJKyetOgAAAAA2GwO9R1qS+UnuGKV9Wb9vXd6R5J2ttRuqauFYH1RVpyQ5JUkWLFiQq6++enyVDojn7rF6qktgM7Wp/s4AAABAV4MepK2XqvqLJI9I8mdd57TWzk9yfpIsWrSoHXDAAZNU3eR61kW3THUJbKbOPmXT/J0BAACArgb90c5lSeaN0j6/3/cHqmrLJH+f5KwkM6pquyRDBxNsU1VzJ6NQAAAAAKa3QQ/SrsuIvdCq6qFJts6IvdOG2SbJbknell7YtizJNf2+i5L8x6RUCgAAAMC0NuiPdn4+yd9U1dzW2m/7bccnWZHkyrXMuSvJk0e07ZLkE0n+vyRfnoxCAQAAAJjeBj1Ie0+S05JcXFVnJdkjyeIkb2utLR8aVFU3JLmytXZSa21VkiuG32TYYQPfb619a/LLBgAAAGC6GeggrbW2rKqekuSdST6b3gme56QXpg03M8kWG7c6AAAAADYnAx2kJUlr7UdJDh9jzMIx+pckqYmrCoANtni0s2QYl8V3TnUFMHF8J2w43wkAMOkG/bABAAAAABgIgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQw8EFaVT2qqi6vqnuqamlVvb6qthhjzh9V1Yeq6ob+vJ9U1RlVNXtj1Q0AAADA9DJzqgtYl6qan+SyJD9KckySPZO8Nb0A8DXrmHp8f+xZSa5P8pgkb+i/HzeJJQMAAAAwTQ10kJbkJUnmJDm2tbY8yZeqatski6vq7H7baM5srd0+7PqKqlqZ5L1VtXtr7cZJrhsAAACAaWbQH+08KskXRwRmF6UXrh22tkkjQrQh/9F/33XiygMAAABgczHoQdq+Sa4b3tBauynJPf2+8Xh8kjVJfjYxpQEAAACwORn0RzvnJ7ljlPZl/b5OqmqX9PZU+2hr7VdrGXNKklOSZMGCBbn66qvHX+0AeO4eq6e6BDZTm+rvDFPooSdOdQWbPr93TCe+Ezac7wQAmHSDHqRtsKqaleQfk9yV5OVrG9daOz/J+UmyaNGidsABB2ycAifYsy66ZapLYDN19imb5u8MU+iSC6a6gk3fSedNdQUwcXwnbDjfCQAw6QY9SFuWZN4o7fP7fetUVZXkI0n2S/InrbUx5wAAAADAaAY9SLsuI/ZCq6qHJtk6I/ZOW4tzkxyT5IjWWpfxAAAAADCqQT9s4PNJjqyqucPajk+yIsmV65pYVacneWmS/9pa+/rklQgAAADA5mDQg7T3JLk3ycVV9dT+gQCLk7yttbZ8aFBV3VBVHxh2fUKSN6X3WOctVfXHw147btwfAQAAAIDpYKAf7WytLauqpyR5Z5LPpneC5znphWnDzUyyxbDrp/XfT+y/hnthkgsmtlIAAAAApruBDtKSpLX2oySHjzFm4YjrE/OHARoAAAAArLdBf7QTAAAAAAaCIA0AAAAAOhCk8f+zd/dRntd13cdfb3ZVwGBdxQKTWCGNg1mc2m5QCQWVEDsgmZTVydSzR6+MbtQuuy46LnTVEbq4uU5cRZRKVl6kpZQakiyJKalBolfCqpgLV2AaurDigjfwuf74fSenYWbnM7sz8/vNzONxzpzfzvfmN+/ZOfNlffq9AQAAAKCDkAYAAAAAHSb+YQMA7L1Nr333uEeY0479xz3ByjfRP9/XnzruEQAAYNE5Iw0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6rB/3AKwdO/Z/0bhHWPE23f+WcY8AAABLb+uGcU+w8m29Z9wTwKrkjDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHSY+JBWVcdU1baq2l1Vd1bVuVW1rmO/DVX1pqraWVX3VNWfVdVjlmNmAAAAAFaf9eMeYE+qamOSa5LcnOS0JEcluSCjAHj2PLu/NcmTkrwsyYNJzktyZZLjl2peAAAAAFaviQ5pSV6e5IAkZ7TWdiV5b1UdnGRrVZ0/LHuIqjouyXOSnNBae/+w7I4kH66qZ7XWrlmm+QEAJsam17573CPMacf+455g5Zvon+/rTx33CACwKCb90s5Tklw9I5hdkVFcO2Ge/T4/FdGSpLX2kSSfHdYBAAAAwIJMekg7Osn26Qtaa7cn2T2s695vcMs8+wEAAADArCb90s6NSe6eZfnOYd3e7HfkbDtU1ZYkW4ZP762qTy5gTjrUuAeY3yFJ7hr3EHv2vHEPMKc6b9wTsNI4JiwGxwRWD8eExeCYAMtsso8L56yAI+vKdMS4B2C8Jj2kLZvW2mVJLhv3HIxPVd3QWts87jmAyeCYAEznmADM5LgAa9OkX9q5M8mGWZZvHNYt9n4AAAAAMKtJD2nbM+OeZlV1eJIDM/s90ObcbzDXvdMAAAAAYI8mPaRdleTkqjpo2rIzk9yX5Lp59ju0qp4+taCqNmd0f7SrlmJQVgWX9gLTOSYA0zkmADM5LsAaVK21cc8wp6ramOTmJP+c5LyMQtiFSS5urZ09bbtbk1zXWnvptGVXJ3liklcneXDY/wutteOX7zsAAAAAYLWY6DPSWms7k5yUZF2SdyY5J8lFSV43Y9P1wzbTnZnRWWtvTPLmJDcmef5SzgsAAADA6jXRZ6QBAAAAwKSY6DPSAAAAAGBSCGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AYBFU1daqalX1jHHPAgDA0hDSAIAVr6oOrqqLq+rvq+rOqrq/qr5QVR+pql+uqkeOe8blVFWPqqrXVNWfVdXNVfWNIfI9a5791lXVr1TVx6vqvqr6UlX9TVU9dblmBwCYZEIaALAaPDrJliQPJHl3kguTvC3JQUkuSvKRqjp4fOMtu01Jzk/yooz+Du6ab4eqqiRXZPR39/AklyR5R5IfSfL+qjptqYYFAFgp1o97AACARfD/kmxorX195oqq+tMkP53k5RnFpbXgtiTPSvLR1tqXquryJD83zz4/meQFSa5PclJr7f4kqapLk3wgyR9W1bWttS8v3dgAAJPNGWkAQJKkqr6lqr5WVR+csfyA4VLJVlU/O2PdK4blL1neaf+z1toDs0W0wduG1ycuxteqqu+vqvdU1ZeraldVXVNVxy3Gey+W1trO1tq21tqXFrDbK4bXs6ci2vBe/5jkz5M8NqPQBgCwZglpAECSpLV2b5KPJPnBqjpo2qqnJXnE8OeTZuw29fm2JR5vX/zY8PrxfX2j4V5hf5/R2V5XZXT549eSvC/JD+3r+49LVe2f5KlJdmf0/c101fB64rINBQAwgVzaCQBMd21G4exHMrrXWDKKZQ8kuS7TQlpV7ZfkmUn+pbV223xvXFWPSvLLC5znytbaTb0bV9X6JGcPnz46yfFJjk3yd0n+cIFfe+Z7V5I3Jjkgyemttb+atu6Xkly8wPc7NsnpCxzj4tba3Qvcp8dRSdZl9LP8xizrPz28PmkJvjYAwIohpAEA021L8hsZBbPpIe3GJG9PcklVPam19qmMAtWjk/xl53s/KsnrFjjPjiTdIS2jf9vM/Bp/kuS/TL9ccS89Ncl3JXn/9Ig2uCTJL2YUpHodm4X/fVyeZClC2obh9Z451k8tf9QSfG0AgBXDpZ0AwHT/kOS+DGeeVdWGJN+XUWC7dthm6qy0qcv8rk2H1tqO1lot8OPyhQzfWru/tVYZ/Rvn8UlenNFlmDdU1aaFvNcsvm94vW6Wr/tARjfk79Zau3wv/j527OP3AADAPhDSAID/0Fr7WkZB6ClV9dgkz8jokr9trbVbknwu3wxpJyVp6Qxpy6mN3NFa++MkZ2R0Jtkl+/i2U2dtfX6O9f+2j+8/TlNnnG2YY/3U8qU4Gw4AYMVwaScAMNO1SZ6dUSh7apL7k3xw2rpTquoRGd1/7BOttS/0vOly3CNtNq21D1XV3RlFwX0xFZu+bY71hy7kzSbsHmmfyeg+eEdW1fpZ7pM29cTTTy3B1wYAWDGENABgpqkncJ6U5Lgk10+7v9i2JD+d5BVJHpmFPa1zOe6R9hDDE0gPTvLlfXmfJP80vJ4wy9dYl+TpC3y/iblHWmvt/qq6PqM4enxGD2eY7pThdeLOPgQAWE4u7QQAZvqnjM6+Oi3Jk/OfY9lUSPn1GZ/PaynvkVZVT6mq/WdZ/vCMLuncL998eML09a2qWue3cH2STyb5kao6bca6V2ZhDxqYxHuk/f7w+j+m/11W1Q8kOTPJv6f/wRIAAKtStdb7b0cAYK2oqiszCmlJ8sOttQ9PW3drRtHogSSPaa3N9aTHZVNVFyf5+YwuQb0to7O2HpfkORldcvnJJM9srX1u2j77ZfQ9PNBa6zpLv6qeluS9SR6e0VNMb83ozLKTMoqKPzp8nfctyje2D6rqfyY5ZPj06Rn9zP42o/vcJaPLZq+ctn0leWuSFyTZnuSdSR6TUUTbP8mPz/K0UgCANcWlnQDAbLZlFNJ2JblhlnVHJblxEiLa4G1JviWjS1GPS3JQRrPfnOSCJL/XWts9Y5+nDK9X9H6R1toHq+r4JL+Vb17u+OGM7r92ckYhbVK8IMkRM5Y9Z9qfdyT5j5DWWmtV9VMZnXn3kiS/mNH98d6f5H+01q5f0mkBAFaAiT4jraq+M8lrMvoH8ZOT/H1r7Rkd+21IcnFGN/DdL8m7kpzVWvvi0k0LAKwkVXVWRv9eeEpr7RPjngcAgMk36WekPTnJc5N8KMnDFrDfW5M8KcnLkjyY5LyM/h/X4xd7QABgxTohyV+LaAAA9Jr0M9L2a609OPz5L5IcMt8ZaVV1XEaXJJzQWnv/sOwHM7rs4tmttWuWdmoAAAAAVqOJfmrnVERboFOSfH4qog3v85Ekn80372UCAAAAAAsy0SFtLx2d0ZOmZrplWAcAAAAACzbp90jbGxszeuT9TDuTHDnXTlW1JcmWJDnggAO+f9OmTUsyHAAAALAy3XLLLXe11h477jkYn9UY0vZKa+2yJJclyebNm9sNN9ww5okAAACASVJVt417BsZrNV7auTPJhlmWbxzWAQAAAMCCrcaQtj2z3wttrnunAQAAAMC8VmNIuyrJoVX19KkFVbU5o/ujXTW2qQAAAABY0Sb6HmlVdWCS5w6ffnuSg6vqBcPnf9Na211Vtya5rrX20iRprf1DVf1tkjdX1auTPJjkvCQfaK1ds8zfAgAAAACrxESHtCTfmuRtM5ZNff6EJDsy+h7WzdjmzCQXJXljRmfdvSvJWUs2JQAAAACr3kSHtNbajiQ1zzabZll2d5KfHz4AAAAAYJ+txnukAQAAAMCiE9IAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHdaPewAW16bXvnvcI7BG7Xj9qeMeAQAAAJaUM9IAAAAAoMPEh7SqOqaqtlXV7qq6s6rOrap1Hfttrqq/raovDR/XVNUPLcfMAAAAAKw+Ex3SqmpjkmuStCSnJTk3yauSnDPPfocP+61P8rPDx/ok762qI5ZyZgAAAABWp0m/R9rLkxyQ5IzW2q6MQtjBSbZW1fnDstmcmuSgJM9vrd2TJFV1fZK7kjw3ye8v/egAAAAArCYTfUZaklOSXD0jmF2RUVw7YQ/7PSzJN5J8Zdqye4dltdhDAgAAALD6TXpIOzrJ9ukLWmu3J9k9rJvLXw7bXFBV31pV35rkoiQ7k7xtiWYFAAAAYBWb9Es7Nya5e5blO4d1s2qt3VlVz0zyriRnDYs/l+Tk1tq/z7ZPVW1JsiVJDjvssNx00037MvfYvPDIB8Y9AmvUSv2dAQAAgF6THtL2SlUdltGZZzcmedmw+BeSvLuqnjqc1faftNYuS3JZkmzevLkde+yxyzXuojr9ijvGPQJr1PlbVubvDAAAAPSa9JC2M8mGWZZvHNbN5TUZ3SftBa21rydJVV2b5NNJXp1vnqUGAAAAAF0m/R5p2zPjXmhVdXiSAzPj3mkzHJ3kE1MRLUlaa19L8okkRy3BnAAAAACscpMe0q5KcnJVHTRt2ZlJ7kty3R72uy3Jd1fVw6cWVNUjknx3kh1LMCcAAAAAq9ykh7RLk3w1ydur6lnDAwG2JrmwtbZraqOqurWq3jBtvz9K8rgk76iqU6vqeUmuTHJYhvugAQAAAMBCTHRIa63tTHJSknVJ3pnknCQXJXndjE3XD9tM7Xdjkh9NclCSP0ny5owuB312a+1jSz85AAAAAKvNpD9sIK21m5OcOM82m2ZZti3JtiUaCwAAAIA1ZqLPSAMAAACASSGkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA4TH9Kq6piq2lZVu6vqzqo6t6rWde57RlX9Y1XdV1VfrKr3VNUjl3pmAAAAAFafiQ5pVbUxyTVJWpLTkpyb5FVJzunY92VJ3pLkqiSnJHlZkk8nWb9U8wIAAACwek16VHp5kgOSnNFa25XkvVV1cJKtVXX+sOwhquqQJBcl+cXW2h9OW/WOJZ8YAAAAgFVpos9Iy+hMsqtnBLMrMoprJ+xhvxcOr3+8VIMBAAAAsLZMekg7Osn26Qtaa7cn2T2sm8sPJflkkpdW1b9W1der6sNV9dSlGxUAAACA1WzSQ9rGJHfPsnznsG4uhyb5riRnJ/mvSX4syVeSvKeqvm2xhwQAAABg9Zv0e6TtrUryLUl+orX2niSpquuT3JbklUl+4yE7VG1JsiVJDjvssNx0003LN+0ieuGRD4x7BNaolfo7AwAAAL0mPaTtTLJhluUbh3V72q8led/Ugtbarqq6Mckxs+3QWrssyWVJsnnz5nbsscfu5cjjdfoVd4x7BNao87eszN8ZAAAA6DXpl3Zuz4x7oVXV4UkOzIx7p81wS0ZnpdWM5ZXkwcUcEAAAAIC1YdJD2lVJTq6qg6YtOzPJfUmu28N+7xpenzm1oKo2JPn+JB9b7CEBAAAAWP0mPaRdmuSrSd5eVc8a7mO2NcmFrbVdUxtV1a1V9Yapz1trNyT5qyRvqKqfq6pTk/x1kq8n+d/L+Q0AAAAAsDpMdEhrre1MclKSdUnemeScJBcled2MTdcP20z3M0muTHJhkr/IKKKdOLwnAAAAACzIpD9sIK21m5OcOM82m2ZZdm+SVwwfAAAAALBPJvqMNAAAAACYFEIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAECHiQ9pVXVMVW2rqt1VdWdVnVtV6xaw/35VdUNVtap63lLOCgAAAMDqtX7cA+xJVW1Mck2Sm5OcluSoJBdkFADP7nyblyV5/JIMCAAAAMCaMelnpL08yQFJzmitvbe1dmmSc5L8alUdPN/OQ4j7rST/fWnHBAAAAGC1m/SQdkqSq1tru6YtuyKjuHZCx/6/meSDSbYtwWwAAAAArCGTHtKOTrJ9+oLW2u1Jdg/r5lRV35PkJUlevWTTAQAAALBmTPQ90pJsTHL3LMt3Duv25HeTXNJau7WqNs33hapqS5ItSXLYYYflpptuWtikE+KFRz4w7hFYo1bq7wwAAAD0mvSQtleq6ieTfFeSH+vdp7V2WZLLkmTz5s3t2GOPXaLpltbpV9wx7hFYo87fsjJ/ZwAAAKDXpF/auTPJhlmWbxzWPURVPSzJ7yQ5L8l+VfWoJFMPJnhkVR20FIMCAAAAsLpNekjbnhn3Qquqw5McmBn3TpvmkUken+TCjGLbziQfG9ZdkeSjSzIpAAAAAKvapF/aeVWS11TVQa21Lw/LzkxyX5Lr5tjn3iTPnLHs0CT/J8l/S3LtUgwKAAAAwOo26SHt0iRnJXl7VZ2X5MgkW5Nc2FrbNbVRVd2a5LrW2ktba99I8r7pbzLtYQP/t7X24aUfGwAAAIDVZqJDWmttZ1WdlOSSJO/M6AmeF2UU06Zbn2Td8k4HAAAAwFoy0SEtSVprNyc5cZ5tNs2zfkeSWrypANhnW2d7lgwLsvWecU8Ai8cxYd85JgDAkpv0hw0AAAAAwEQQ0gAAAACg9AwWcgAAGlRJREFUg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAECHiQ9pVXVMVW2rqt1VdWdVnVtV6+bZ5weq6k1Vdeuw3yer6nVVtf9yzQ0AAADA6rJ+3APsSVVtTHJNkpuTnJbkqCQXZBQAz97DrmcO256X5NNJvifJbw6vP76EIwMAAACwSk10SEvy8iQHJDmjtbYryXur6uAkW6vq/GHZbF7fWrtr2ufvq6r7k/xBVR3RWrttiecGAAAAYJWZ9Es7T0ly9YxgdkVGce2EuXaaEdGmfHR4fdzijQcAAADAWjHpIe3oJNunL2it3Z5k97BuIY5L8mCSzyzOaAAAAACsJZN+aefGJHfPsnznsK5LVR2a0T3V/qS19oU5ttmSZEuSHHbYYbnpppsWPu0EeOGRD4x7BNaolfo7wxgd/uJxT7Dy+b1jNXFM2HeOCQCw5CY9pO2zqnp4krcmuTfJr8y1XWvtsiSXJcnmzZvbscceuzwDLrLTr7hj3COwRp2/ZWX+zjBGV14+7glWvpf+r3FPAIvHMWHfOSYAwJKb9JC2M8mGWZZvHNbtUVVVkjcneXKSp7XW5t0HAAAAAGYz6SFte2bcC62qDk9yYGbcO20OFyc5LcmzW2s92wMAAADArCb9YQNXJTm5qg6atuzMJPcluW5PO1bVryd5ZZKfaa19YOlGBAAAAGAtmPSQdmmSryZ5e1U9a3ggwNYkF7bWdk1tVFW3VtUbpn3+oiS/ndFlnXdU1Q9P+3js8n4LAAAAAKwGE31pZ2ttZ1WdlOSSJO/M6AmeF2UU06Zbn2TdtM+fM7y+ePiY7ueTXL64kwIAAACw2k10SEuS1trNSU6cZ5tNMz5/cR4a0AAAAABgr036pZ0AAAAAMBGENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAO68c9AABLZ9Nr3z3uEea0Y/9xT7DyTfTP9/WnjnsEAABYdM5IAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADqsH/cArB079n/RuEdY8Tbd/5ZxjwAAAEtv64ZxT7Dybb1n3BPAquSMNAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdFg/7gHmU1XHJPndJMcluTvJHyU5p7X2wDz7bUhycZLTMwqG70pyVmvti0s7MQDAZNr02nePe4Q57dh/3BOsfBP98339qeMeAQAWxUSHtKramOSaJDcnOS3JUUkuyCiMnT3P7m9N8qQkL0vyYJLzklyZ5PilmhcAAACA1WuiQ1qSlyc5IMkZrbVdSd5bVQcn2VpV5w/LHqKqjkvynCQntNbePyy7I8mHq+pZrbVrlml+AAAAAFaJSb9H2ilJrp4RzK7IKK6dMM9+n5+KaEnSWvtIks8O6wAAAABgQSY9pB2dZPv0Ba2125PsHtZ17ze4ZZ79AAAAAGBWk35p58aMHjAw085h3d7sd+RsO1TVliRbhk/vrapPLmBOOtS4B5jfIUnuGvcQe/a8cQ8wpzpv3BOw0jgmLAbHBFYPx4TF4JgAy2yyjwvnrIAj68p0xLgHYLwmPaQtm9baZUkuG/ccjE9V3dBa2zzuOYDJ4JgATOeYAMzkuABr06Rf2rkzyYZZlm8c1i32fgAAAAAwq0kPadsz455mVXV4kgMz+z3Q5txvMNe90wAAAABgjyY9pF2V5OSqOmjasjOT3Jfkunn2O7Sqnj61oKo2Z3R/tKuWYlBWBZf2AtM5JgDTOSYAMzkuwBpUrbVxzzCnqtqY5OYk/5zkvIxC2IVJLm6tnT1tu1uTXNdae+m0ZVcneWKSVyd5cNj/C62145fvOwAAAABgtZjoM9JaazuTnJRkXZJ3JjknyUVJXjdj0/XDNtOdmdFZa29M8uYkNyZ5/lLOCwAAAMDqNdFnpAEAAADApJjoM9JgOVTVMVW1rap2V9WdVXVuVc08wxFYA6rqO6vqD6rq41X1QFW9b9wzAeNTVT9RVX9dVXdU1b1VdWNV/dS45wLGo6peUFXXV9UXq+r+qvpkVZ1dVQ8f92zA8lk/7gFgnIb78F2T0b34TktyVJILMorMZ+9hV2B1enKS5yb5UJKHjXkWYPx+Nclnk/xKkrsyOj68paoOaa397lgnA8bhMUmuTfI7Se5O8oNJtiY5NMkrxzcWsJxc2smaVlW/nuTXkhzRWts1LPu1DP9BnFoGrA1VtV9r7cHhz3+R5JDW2jPGOxUwLkMwu2vGsrckOa619oQxjQVMkKr6rSS/kGRj8z+uYU1waSdr3SlJrp4RzK5IckCSE8YzEjAuUxENIElmRrTBR5M8brlnASbWF5O4tBPWECGNte7oJNunL2it3Z5k97AOAGC645J8atxDAONTVeuq6sCqenqSs5L8vrPRYO1wjzTWuo0Z3d9gpp3DOgCAJElVnZTk9CQvGfcswFh9Jckjhj+/OclrxjgLsMyckQYAAPOoqk1J3pLkr1prl491GGDcnprk+CSvyuiBZZeMdxxgOTkjjbVuZ5INsyzfOKwDANa4qnp0kquS3Jbkp8c8DjBmrbV/Gv74gaq6K8kfV9UFrbXPjHMuYHk4I421bntm3Autqg5PcmBm3DsNAFh7qurAJO/K6Gbiz2ut7R7zSMBkmYpqnuQLa4SQxlp3VZKTq+qgacvOTHJfkuvGMxIAMAmqan2StyV5YpIfba19YcwjAZPnacPrZ8c6BbBsXNrJWndpRk/aeXtVnZfkyCRbk1zYWts1zsGA5TecefLc4dNvT3JwVb1g+PxvnIkCa87vZXRM+KUkj6mqx0xb99HW2lfHMxYwDlX1niTXJPlEkgcyimivSvLnLuuEtaM8pZe1rqqOyegGocdl9ATPP0qytbX2wFgHA5bdcDPxuf4f5Se01nYs2zDA2FXVjiRHzLHaMQHWmKr6zSTPT7IpyTeS/EuSNyW5tLX29TGOBiwjIQ0AAAAAOrhHGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAN2q6sVV1arqxeOeZZJU1b9W1a2L8D5/Ovz9Pn4x5lpsVbWhqi6pqh1V9Y1h1u8e91wAAMtFSAOADkMwaPNss2PYbtPyTEVVHVJVD1bVv82x/ripn11VPXOObW4b1n/H0k67NBYr4nW6IMkvJPlYkt9Ock6SL+xph6r6wLSfwVwfZy/D7AAA+2z9uAcAAFaUdyT5UJLPjXuQJGmt3VVVH0/yvVX15NbaJ2ZsctLUpklOTPJ301dW1Xcm+Y4kn26t3b4Po5wwfI3V7nlJbm6tnbYX+74pyVx/x+/f+5EAAJaPkAYAdGut3ZPknnHPMcO1Sb43o1A2M6SdmOQzSXYNf/6NWdYnybZ9GaC19pl92X8lqKp1Sb4tyT/v5Vu8sbX2gUUcCQBg2bm0EwCWWFWdPtz76lNV9ZXh48aqOquqHvLf4qq6fLjc7QlV9cqqurmq7v//7d17sJVVGcfx708DLxEghoKm6BijdhvNVAyPysUxyyxtsMjMyzAaToqZVKLlpdHR1EIrb4k5pJY1JJqJGnIRNC8IM04ODmqhAiINhKioXHz6Y61XXjbvPmefw4GDnt9nhtlz1nrfdduHGXxc61n56OgYScrPDZP0ZG5vSc5dtV1FeyFpmqSdJd0q6bX8zmOSmvIzH5V0VT7m+K6kZyUNq2irMkdaHtv8Ujsv53ZekPTjYsw170jSqNL8FuY59Cjaa3CJiyDY4HKhpG2BQ0i70KYCB0rqVvNu3UCapKMlTZK0NM/lRUm/kNS94tnK45WSekq6Ls/tHUlzJZ0jqX9ex1vqzEmSzpT0r/zeYkk3lvuWNDQfN94V2KvmqGS9dms72UXSDaXvfYmkCZL2r3luJrAm/zik1M/kRvppjWJeki6UNEDS/ZKWqZQ7rljv/LsyNo9/tUpHRPPaXynp+byGyyQ9IGlwW/o0MzMzA+9IMzMz2xyuAN4DngAWAj1IAZxrgQOBk+q8dzVwBPA34CHgWOAyoKukZbndicAM4EhS7qqtgZEVbfUEHgXeAP4I9AK+BTwo6RDgplx2H9AFGA7cJemViHi8wXl2AR4EdgEmkQIvX8/j3JaUT6vst3msi4CbgVV5jgfltlY32O8jua8jJG0VEe/l8oG53yl53ucChwH3Q4pUAYNIRzJrj3xeStq9tpS0/v8l7XobDXxJ0hcj4s3mBiVp+9zufsBs4A/ADsBFpKOgzbmG9J3eR1rTIcAZwF65HODfpDU9N8//utL7s1toH0l7ATOBPsBk4E7SMddhwFckHRcRk/Ljt5LW8afAf4DxpTFsKocCPyN9v+OAnVj/d2JbYBrQHXiA9B3PB5DUi/T7vg/wJDAB6A2cAEyWdHpEVAUbW+rTzMzMOjlFdIZ0HmZmZhtH6y4aqA0GlZ1DCpLtGRHzS+/uVXv0T2kn2u+B7wIDIuKJUt1twMnAS8DAiFiYy3sCLwDbASuBwyJibq7bBphDCrTsFhFLSu0VY78JOLMINEk6iRQQ+R8p6DAsIt7JdU2kYMLEiDiu1NYpedynRsRtpfL5QD9SAO0bEfF2Lt8JmJcf6x0Rq2vanwccHBHLc3lXUlCnCXgpIvaov9zrredjpN1nB0bErFx2GTAG6JvXaxkwNiLOy/WfBZ4B5kTE50ttHUkKXM4EjsnHWYu6EcDvgKsjYnSpfAHwTkR8slR2CSkocwdwUuR/dEnqRwp09QLGRcSI0ju3AyeSAkJNEbEgl3cBpuc5HhARs0vvbNB3g2v2MCmg+5OIuLJU3kQKUC0D+kXEylz+EVJQ6eGIGNqKfmaSgprN5Ui7vvidlTQU+EcuHxER4yraXEDaifcgcHwxxlL9OOA04IaIOLNUvg/wFClQ2z8iXmm0TzMzMzPw0U4zM7PWuqiZPz2qXqjKn5WDWdfmH4+q09fPiyBafmc5cC+wPSlAMLdU9y5wF9AV2LeirZXA6NJuLUg7kNaQdkmNKoJoub0ZpGDOfnXGVs/ZRRAtt7MEuIe0NnuXnjs5f15WBNHy86uA81vZJ1Qf7xwMzI2IxRGxghS8qq0vv/v+HPLniHIQLY/vFlKOsBMbGNPJwFrg/CKIltt4ifV3j1W5pAii5XdWkwJRkHbsbRSlm2UHk3aXXVOuy9/9n4GPk3YUtpdTqf93Z6eK52c1END6YUUQbRvg26S8eGPKdRHxHPAbYBuqd4I20qeZmZl1Yg6kmZmZtUJEqN4f0g6yDUjaUdIVkp6R9GaRXwp4Oj+ya53uZlWULcqfT1fUFUG3qpxO8yLijZq5rAVeA5ZHRNURvYV12qrn9YjYIE8Y8Er+3KFUVuTgqko+/zjr8nE1akr+HAwg6WPAF1j/yOZU0u2evcrPsmEg7RDgXWC4pItr/5BSY/SVVBk4zf3vQNqh93Kx66lGS0n3q777qnVsq2L9H4mIqrWeUvNce2hq5u9P1QUGT7bQ3lsVt7QCfIp07HNOOUhb0tzcWurTzMzMOjnnSDMzM9uE8nHMp4A9Sf+RPp50ZG4NKW/ZKNLumCpVt2OuaaCuS4NtFe80V9eafytUBS3K49q6VFYEoV6rfTgi1kpa2op+AR4D3gaa8jHIw0ljn1J6ZhrwI2CQpIn5mVWkI6ZlvQCRdko1pxv1167u/FooL1StZdU6tlUxvlfr1BflPduhr7Za3EJ9vTXcmLm11KeZmZl1cg6kmZmZbVojSEG0SyLi4nJFTvI/qiMGtQVYkT93piZhvaStgR1Zt8OuRRHxbs6TNgQYQNptFqTgWWEGKRg1mLS7qwdpR9bK9VtjBbAqIqqOGzaqPL8q9co3lyIA2KdOfd+a5zpCS4l869VvzNycPNjMzMya5aOdZmZmm1aRAH5CRV1LNzd+mM3Jn4dW1A2gbf+zr5wnbTDwTES8v7Mt37I5q1RffqfscaC3pL0r6hoSEctIifV3l7RbxSNV826rtbR+l1qx/k05cFlrUP5s8fbPLdBc0tHc/SV1r6j/IM/NzMzMOpgDaWZmZpvW/Px5RLlQ0v60Lan+h8X4/HlBOddYvrXz8ja2WRzjHAZ8jvXzoxWmAvuw7rKAqkDaL/PnLZL61lZK6ibp4AbGM54U4Lpckkrv7866Cw3aw1Jgp5xkvyH5VtmppFtezyrXSRoIfDO3e0/7DXPzyJdm3EnacXhpuU5Sf+D7pCO9t2/+0ZmZmdkHnY92mpmZbVrjgdHAWEmDgOeB/sAxwF9JAYtOJyKmS7oZOB14VtIEYDXwVdKRu0XAe800UWVWfvfT+ecpFc9MJQUwPwO8SUVy+Yh4SNKFwM+B5yVNIt1u2Q3Yg7STcCrpO2zOFcDXgO8A+0qaTMrLdQIwnXQjZmvnWOVhUuL8ByTNIAWJ5kTE31t47wzSpQe/knQ06QKL3UmByDXAKRHxVjuMr3CapKF16mZHxL3t2Ndo0q6/UZIOIq13b9LadwNGRsTL7difmZmZdRIOpJmZmW1CEbFIUhMpqHIocBTwHHAmMJlOGkjLRpLW4gzge6QdUHcDY4AFwIutaSxfUjAdOJZ03LH2EgGAR0mBpq6k/Gir67R1WQ5KnQ0MJAXEXs/juhG4o4HxvCXpcFJA7njgB6R8cJcCT5ACaSvqt9CwS4DupMBeE2kX3Dig2UBaRDwv6QDgQuDLpCOPK/J7l0dE1c2hG+PUZurGAe0WSIuIpXnX4BjgOOBcYCXwT+CqiJjcXn2ZmZlZ56II51Q1MzOzLUc+fjcP+FNEDO/o8WwKkkYC1wMjImJcR4/HzMzMzBrjHGlmZmbWIST1kbRVTdn2wNj8492bf1TtS9IuFWX9gAtIR1lbOn5pZmZmZlsQH+00MzOzjnIOMFzSNOBVoA8wBPgEMAn4S8cNrd3ck+8ZmA0sB/YkHcHcDhgdEYs7cGxmZmZm1ko+2mlmZmYdQtIQ4DxgP6AXKcH9PNKNi2Pr5S/7IJF0FumG0P6kPGZvkoJqv46IiR05NjMzMzNrPQfSzMzMzMzMzMzMGuAcaWZmZmZmZmZmZg1wIM3MzMzMzMzMzKwBDqSZmZmZmZmZmZk1wIE0MzMzMzMzMzOzBjiQZmZmZmZmZmZm1gAH0szMzMzMzMzMzBrwf6H3ZWBtEFORAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], plot_rand_distr=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot all of the distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=None, depths=None, plot_rand_distr=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can study the sucess probablity, i.e. the zero hamming weight entry above as a function of depth. We first need to extract the data." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: 0.8824000000000002, 3: 0.8760999999999999, 4: 0.8922999999999999, 5: 0.8821, 10: 0.8854}, 3: {2: 0.8328999999999999, 3: 0.8171999999999999, 4: 0.8407999999999998, 5: 0.8301999999999998, 10: 0.8265}, 4: {2: 0.7817999999999998, 3: 0.7962, 4: 0.7868, 5: 0.7691, 10: 0.7376999999999999}, 5: {2: 0.7399, 3: 0.7308000000000001, 4: 0.7299, 5: 0.7182, 10: 0.7196999999999999}}\n", - "{2: {2: 0.9963000000000001, 3: 0.9948999999999998, 4: 0.9956999999999999, 5: 0.9967999999999998, 10: 0.9947000000000001}, 3: {2: 0.9894999999999999, 3: 0.9870000000000001, 4: 0.9895000000000002, 5: 0.9879, 10: 0.9789999999999999}, 4: {2: 0.9991, 3: 0.9987999999999999, 4: 0.9978, 5: 0.9978999999999998, 10: 0.9921}, 5: {2: 0.9965000000000002, 3: 0.9964000000000001, 4: 0.9930999999999998, 5: 0.9933, 10: 0.9864000000000003}}\n", - "{2: {2: 0.6324000000000001, 3: 0.6260999999999999, 4: 0.6423, 5: 0.6320999999999999, 10: 0.6354}, 3: {2: 0.7079, 3: 0.6922, 4: 0.7157999999999999, 5: 0.7051999999999999, 10: 0.7015}, 4: {2: 0.7192999999999999, 3: 0.7336999999999999, 4: 0.7243, 5: 0.7066, 10: 0.6751999999999999}, 5: {2: 0.7817, 3: 0.7767000000000002, 4: 0.7712000000000001, 5: 0.7657999999999999, 10: 0.7585999999999999}}\n" - ] - } - ], - "source": [ - "# extract data from avg_err_hamm_distrs\n", - "widths = list(avg_err_hamm_distrs.keys())\n", - "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", - "\n", - "avg_pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", - "# this is equivalently wrapped up in the following\n", - "assert avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", - " ideal_results))\n", - "\n", - "# count as success even if there are log many bits incorrect.\n", - "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", - " ideal_results, \n", - " allowed_errors = basement_log_function))\n", - "\n", - "ideal_distrs = {w: [1] + [0 for _ in range(w)] for w in widths}\n", - "rand_distrs = {w: get_random_hamming_wt_distr(w) for w in widths}\n", - "\n", - "pr_succ_rand = {w: 1/2**w for w in widths}\n", - "pr_succ_rand_allow_log_errors = {w: sum(rand_distrs[w][0:basement_log_function(w)+1]) for w in widths}\n", - "\n", - "# total variation distance\n", - "tvd_noisy_ideal = {w: {d: get_total_variation_dist(distr, ideal_distrs[w]) for d, distr in d_distrs.items()}\n", - " for w, d_distrs in avg_err_hamm_distrs.items()}\n", - "\n", - "# tvd_noisy_ideal is equivalent to 1 - success probability.\n", - "np.testing.assert_allclose([pr for d_vals in avg_pr_succ_arr.values() for pr in d_vals.values()], \n", - " [1 - val for d_vals in tvd_noisy_ideal.values() for val in d_vals.values()])\n", - "\n", - "tvd_noisy_rand = {w: {d: get_total_variation_dist(distr, rand_distrs[w]) for d, distr in d_distrs.items()}\n", - " for w, d_distrs in avg_err_hamm_distrs.items()}\n", - "\n", - "print(avg_pr_succ_arr)\n", - "print(avg_pr_succ_allow_log_errors)\n", - "print(tvd_noisy_rand)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Success probablity and success probablity including a small number of errors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we will plot the success probablity of a circuit with a certain width as a function of depth. " - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w=3\n", - "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Probability')\n", - "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", - "plt.ylim([-0.05,1.05])\n", - "plt.xlabel('Depth')\n", - "plt.xticks(depths)\n", - "plt.ylabel('Pr(success)')\n", - "plt.title('Pr(success) vs Depth for Width = {}'.format(w))\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Sucess if we allow for a small number of errors**\n", - "\n", - "Some near term algorithms have robustness to noise. In light of that we might want to consider as successes answers that are only a little wrong.\n", - "\n", - "To make this notion formal we allow a logarithmic number of bits to be flipped from the correct answer and call all such instances \"success\".\n", - "\n", - "The logarithmic number of bits that we allow to flip is defined by the \"basement\" ${\\mathcal B}$ of \n", - "\n", - "$\\log_2 ({\\rm number\\ of\\ bits})$\n", - "\n", - "where the basement of a number is ${\\mathcal B}(number) = 0$ if number$<=0$ and ${\\mathcal B}(number) = {\\rm floor (number)}$.\n", - "\n", - "\n", - "Supose we have a circuit of width 4 so that the correct string has four bits, e.g. 1010. Then a logarithmic number of flips is $\\log_2(4) = 2$.\n", - "\n", - "So any string with hamming weight zero, one, or two counts as a success.\n", - "\n", - "Such error metrics might be important in noisy near term algorithms where getting the exact answer is not vital." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w=4\n", - "plt.scatter(depths, [avg_pr_succ_arr[w][d] for d in depths], label='Sucess Prob')\n", - "plt.plot(depths, [pr_succ_rand[w] for _ in depths], label='random guess')\n", - "plt.scatter(depths, [avg_pr_succ_allow_log_errors[w][d] for d in depths], label='Sucess Prob w/ log errors')\n", - "plt.plot(depths, [pr_succ_rand_allow_log_errors[w] for _ in depths], label='random guess w/ log errors')\n", - "plt.ylim([-0.05, 1.05])\n", - "plt.xlabel('Depth')\n", - "plt.xticks(depths)\n", - "plt.ylabel('Pr(success)')\n", - "plt.title('Pr(success) (& w/ log errors) vs Depth for Width = {}'.format(w))\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Total variation distance from ideal answer and random distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RlZWVB30fNrRVdElJSXFmZua+hraKrq9fbR06dDhQbhg7dmyvRx555F8rVqwovuuuuzZUVf27IpOenn4gtmj3X6qqqkrKyMg4rG2iQUmCiIjEynl3rScl7eAvppS0Gs67S1tFN+DLL79MysnJ2VdVVWXPPvtsg3M4zjvvvF3PP/98p+rqasrKytosWLCgQ/j5NWvWpJ166qmHtU00KEkQEZFYOW30NobfW0b7LnvBoH2XvQy/t4zTRmur6AZMnDhxw9ChQ/sVFhbm9+nTp7Kh/jfccMOO3r17V5100kkDvvOd7+QOHjz4wITNtWvXpqSlpXlOTs5h3zahraJFRFoBbRVdv5a8VfSvf/3rzsccc0zNHXfcUed/N20VLSIiUoeWvFX0scceu/+WW245osROW0WLiEir15K3ig4NqxwJVRJEREQkIiUJIiIiEpGSBBEREYlISYKIiMTcul3rDnu1P4k/JQkiIhJTG3ZvaDNn9ZzjNuze0CiJwre//e3cTp06DerTp0//I3n/0KFD++bm5g7o27dvwamnnpq/ZMmSiNsoh/r9+c9/7ggwd+7cdieffHJ+fn5+Qe/evftPmDDhhEjvi5fp06cfl5OTM+CCCy44qbGuqSRBRERiqqi8qF1ldWVSUXlRu8a43k033bRl9uzZnx7NNZ566qnVpaWlxSNHjtxyxx139Kh9PrRd81NPPbU6dNfD6NGje02dOrWspKSkeMWKFcuvu+66mC0KdSRuvvnm7VOmTClrzGsqSRARkZjZsHtDm3W71qXmdMipWrdrXWpjVBMuuuii3dGsWhiNYcOG7S4rK0uDQ7drrt1327ZtKTk5OfsAUlJSGDJkSCXAhAkTTrj77ru7hPr16dOnf2lpaSrAI488kpmXl1fQt2/fgssvv7wXBFZA/NrXvnZi3759C/r27VvwxhtvtAOYMmVKp4EDB/bLz88vGDlyZM/q6mqqq6u56qqrcvv06dM/Ly+v4Ne//nVngP/8z//sfOKJJ/bPy8sruPjii3s3xp9FJFonQUREYqaovKhdRpuMGjMjo01GTVF5UbvLTrpsR7zjCnnhhRc65ufnH9jTILRdM8CMGTM6h/cdO3bspn79+g04/fTTd33961/f+cMf/nBrRkZGncsWL1y4MP2BBx7oOn/+/JKuXbtWb9q0KRlg/PjxOeecc86uu+++e1V1dTU7d+5MXrx4cfrzzz/faeHChSVpaWl+/fXX5zz22GOZgwYN2rNx48Y2n3766XKALVu2JANMnjz5+LKysqVt27b1UFssqJIgIiIxEaoidEztuB+gY2rH/Y1VTThao0aN6p2fn18wf/789g8//PDasPbtdb3ngQce2Dh//vxPLrzwws+fe+65zPPPPz+vvs947bXXjrnkkku2d+3atRqgS5cu+wHef//9Dj/5yU8qIFCRyMzM3P+Pf/yjw7JlyzIGDRrULz8/v+Ddd989ZvXq1Wn5+flVa9euTfvud7/b4/nnnz/muOOO2w/Qt2/fPVdccUWvKVOmdGrTpk3M9ldQJUFERGIivIoA0FTVhOrqagYMGFAAMGLEiB3/+7//u6F2n6eeemr1ueeee8hW0uHbNUfSv3//qv79+1dMmDChIjMz85Ty8vLklJQUD20OBVBVVWWHG7O727e//e2tjz766CE7ZC5btqz4xRdfPOaxxx7LnjlzZqe//vWvn82bN+/Tv//97x1eeumljg888EDX0tLS5W3aNH7uFdNKgpmNMLNSM1tpZhMjnM8xs3lm9pGZfWxm34hlPCIi0jRqVxFCmqKakJKSQklJSXFJSUlxpAThSD377LMdQ8nA0qVL05OTkz0rK2t/bm5u1T//+c92AO+++27G+vXr0wCGDx/++csvv3xceXl5MkBouOHss8/edf/992dDIKHZunVr8ogRIz6fM2fOcevXr08J9V2xYkXqxo0bU/bv38+NN9644957712/dOnSjP3797Nq1arUSy65ZNejjz66fvfu3ck7d+6MyZBDzCoJZpYMPAp8DVgHFJnZbHcvDuv2H8Bz7v57MysAXgVyYxWTiIg0jdpVhJDGqCZccsklvRYsWNBh+/btKV26dDl54sSJG+rb4bCxPP3005kTJ07skZ6eXpOSkuIzZsxYk5KSwqhRo7b/+c9/zjzppJP6Dx48+IuePXtWAhQWFlbeeeedG88555z8pKQkHzBgwJezZs367Pe///2/brzxxp55eXlZSUlJPPLII2UXXnjhF//xH/+xftiwYXk1NTW0adPGJ0+e/K+MjIya0aNH59bU1BjApEmT1lVXV9vIkSN77dq1K9ndbcyYMZuzsrL21x/9kYnlcMNQYKW7rwYws2eBy4DwJMGBY4KvOwKNlvGJiEh8lH9RnrJqx6r09JT0msrqykMq1o6zaseq9PIvylOOb3f8Yd+l8PLLL685mvg+/PDD0kjt69evX1rf++bMmbM6Unv79u39vffei3hL5q233rr11ltvPWiDpR49elTPnTt3Ve2+N9988/abb775kDkRoYmU4RYtWhTxZ2hssUwSugFrw47XAafX6nMP8LqZ3Qq0Ay6MdCEzGwuMBcjJyWn0QEVEpPG0a9Ou5hu9v9FglaBdm3b1jv/H27HHHls9evToXvfcc8+65rBD5PTp04+77777Thg4cOAhcy2OVLwnLn4H+KO7P2hmZwJ/MrMB7n7QXxx3nwZMAygsLIzZLE4REWlQTU1NjSUlJdX5/+IOqR1q8jvlVzZlULHw+uuvH/LbfiKrqxLRkOBQRsSELaqJi2bW3cwuCL5OM7NoVs1aD4SvYtU92BZuNPAcgLvPB9KBrGhiEhGRuFhWUVHRMTRGLs1bTU2NVVRUdASWRTrfYCXBzG4CbiEwZ+BEoCcwhTqGBsIUAX3MrBeB5OBaYGStPv8ChgF/NLN+BJKEioZiEhGR+Kiurh5TXl4+o7y8fABaa6clqAGWVVdXj4l0MprhhtsITEL8AMDdV5hZ5/rfAu5ebWa3AK8BycAT7r7czCYBC919NnAnMN3M7iAwifFGd9dwgohIghoyZMhm4NJ4xyFNI5okodLd94YthpEMRFVmcvdXCdzWGN52d9jrYuDsqKMVERGRJhNNqeg9M/spkB6clzATmBPbsERERCTeokkSfgrsAkqAHwFzgV/EMigRERGJv2iGG9oAU9399wBmlgSkAs3+9hYRERGpWzSVhHkEFjoKaQe8GZtwREREJFFEkyS0dfddoYPg64zYhdSKffwc/M8AuOfYwPPHz8U7IhERacWiSRK+NLNBoQMzOwUNNTS+j5+Dl2+DnWsBDzy/fJsSBRERiZto5iTcAbxoZmUEbn3sQWA5ZWlMcyfBvj0Ht+3bE2g/+er4xASBJGXuJNi5Djp2h2F3xzceERFpMg0mCe7+QXA1xH7BpmJ33xvbsJpAon357Vx3eO1NIVTdCCUvoeoGKFEQEWkFol1ScxCQBxQA3zKz2ssrNy+JWNrv2P3w2ptCfdUNERFp8RpMEszsj8AjBPZqOCf4+Epsw4qxRPzyG3Y3tGl7cFubtoH2eEnE6gZogqeISBOJZk7CGUBB7e2bm7VE/PILle8TaQikY/dgtSVCe7xoCEREpMlEkyQsB7KBTTGOpekk4pcfBL7kEumLbtjdB38hQ/yrG4k6wVNEpAWKZk5CR6DYzF4xsxdCj1gHFlOJWNpPRCdfDZdMho49AAs8XzJZEzxFRFqJaCoJ98Y8iqaWiKX9RJVo1Y1ErQKJiLRA0dwCObcpAmlyifblJ9FJxCEQEZEWKpq7G04zswVmttPMKs2sysw+b4rgRA6RiEMgIo1Jd+9IAolmuGEKcD3wLDAUuBHoGcOYROqnKpC0VLp7RxJMNBMXk9y9FEhx933uPh34ZozjEhFpfRJxDRdp1aKpJHxhZqnAEjP7L2AjkBzbsEREWiHdvSMJJppKwo3BfrcA+4E+wFUxjElEpHVKxOXZpVWLJkn4hrtXuvsOd/+lu98GDI91YCIirY7WcJEEE02ScFOEttGNHYiISKunu3ckwdQ5J8HMrgGuBXrVWmHxGGBHrAMTEWmVdPeOJJD6Ji5+CGwFugOPhrXvAj6KZVAiIiISf3UmCe6+BlhjZu8De9zdzexEoC/gTRWgiIiIxEc0cxLeBtqaWVfgTeBm4ImYRiUiIiJxF+1iSl8SuO3x9+5+BXBybMMSERGReIsqSTCz04DrgDnBNi2mJCIi0sJFkyRMAH4NzHH3ZWbWG3gntmGJiIhIvEWzVfSbBOYihI5XAz+IZVAiIiISf/Wtk/Cgu99pZi8S4W4Gd7+yoYub2QjgYQLDEzPc/b4Ifa4G7gl+xhJ3Hxl9+CIiIhIr9VUSZgafHzmSC5tZMoH1Fb4GrAOKzGy2uxeH9ekD/Aw42923m1nnI/ksERERaXz1rZPwYfB57hFeeyiwMjg8gZk9C1wGFIf1uRl41N23Bz9r8xF+loiIiDSy+oYbPqKeRZPc/dQGrt0NWBt2vA44vVafvOBnvUdgSOIed/9HhFjGAmMBcnJyGvhYERERaQz1DTd8K/g8nsAX+J+Cx9cR2DK6sT6/D3A+geWf3zazge5+0N4Q7j4NmAZQWFio1R5FRESaQH3DDasAzGxYrarBR2a2GLirgWuvB3qEHXcPtoVbB3zg7vsILAG9gkDSUBRl/CIiIhIj0ayTkGxmZ4QOzOx0oltMqQjoY2a9zCyVwI6Ss2v1+RuBKgJmlkVg+GF1FNcWERGRGGtwnQRgDPAHM0sPHu8BbmroTe5ebWa3AK8RSCqecPflZjYJWOjus4Pnvm5mxQSGMH7i7luP5AcRERGRxmXu0Q3xm1kmQLy/xAsLC33hwoXxDEFEpNkxs0XuXhjvOKR5iaaSAMQ/ORAREZGmFc2cBBEREWmFlCSIiIhIRFENN5jZUCA3vL+7/yVGMYmIiEgCaDBJMLM/AgXAP/n3IkoOKEkQERFpwaKpJJwBFLh7TayDERERkcQRzZyE5UB2rAMRERGRxBJNJaEjUGxmC4CqUKO7XxmzqERERCTuokkS7o15FCIiIpJwGkwS3H1ucF+F0EpdC919S2zDEhERkXhrcE6CmV0FLAZuAEYBC83silgHJiIiIvEVzXDD3cBp7r4JwMy6AK8DL8YyMBEREYmvaO5uSAolCEGbo3yfiIiINGPRVBJeN7NXgGeCx9cS2OJZREREWrBokoQfA1cDZwePnwSej1lEIiIikhCiubvBgZnBh4iIiLQSdSYJZvaWu59nZtsJ7NVw4BSB3KFTzKMTERGRuKmvknBB8DmrKQIRERGRxFLnXQphGzo97u77wx/A400TnoiIiMRLNLcynhx+YGbJwGmxCUdEREQSRZ1JgpndFZyPcLKZbQs+tgMVwKtNFqGIiIjERX2VhN8R2CL6f4LP2UCWu3dy9580RXAiIiISP3VOXAze+lgN/MTMOgInAulmFjr/fpNEKCIiInHR4DoJZnYTcCfQDVhKYD7CAuD8mEYmIiIicRXNxMU7CGwT/Zm7nwMMAbbGNCoRERGJu2iShEp33wNgZqnuvhzoG9uwREREJN6i2btho5kdC7wMvGZm24B1sQ1LRERE4i2avRsuDb78pZkNAzoCr8Q0KhEREYm7+vZuaOfuX5jZMWHNRcHnNKAqppGJiIhIXNVXSXgeuAhYTmCDJ6v1nBPz6ERERCRu6lsn4SILLIpwurtvaMKYREREJAHUe3dDcEGl14/04mY2wsxKzWylmU2sp99VZuZmVniknyUiIiKNK5pbIP9pZoMP98LBjaAeJTBkUQB8x8wKIvTrAPwI+OBwP0NERERiJ5okYTBQFKwILDazj8xscRTvGwqsdPfV7r4XeBa4LEK/3wD/DVRGHbWIiIjEXDTrJFzacJeIugFrw47XAaeHdzCzU4Ee7v6KmdW5aZSZjQXGAuTkaL6kiIhIU2iwkuDuq9x9FbAd2BP2OCpmlgQ8RGBfiIZimObuhe5emJ2dfbQfLSIiIlFoMEkws2+a2QoClYAPCFQH3ozi2uuBHmHH3YNtIR2AAcD/mdlnwBnAbE1eFBERSQzRzEn4LXA2UOruPYARwDtRvK8I6GNmvcwsFbgWmB066e473T3L3XPdPZfAznAtps0AAAzSSURBVJKXuvvCw/0hREREpPFFkyRUu3sFkGRm5u5vEJiUWC93rwZuAV4DPgGec/flZjbJzI50noOIiIg0kWgmLu40s/bAu8BTZraZKOckuPurwKu12u6uo+/50VxTREREmkY0lYTLCSQFtwP/R2BewSUxjElEREQSQDSVhO8RGCooBx6PcTwiIiKSIKKpJGQTuANhnpmNN7OsWAclIiIi8RfNOgm/dPd8AusZ9ALmm9k/Yh6ZiIiIxFU0lYSQtcBnwAa0TbSIiEiLF81iSmPN7P8RWBuhG3Crux+yUZOIiIi0LNFMXOwDTNQiRyIiIq1Lg0mCu9e58ZKIiIi0XIczJ0FERERaESUJIiIiEpGSBBEREYmozjkJZrYd8EinAHf3TjGLSkREROKuvomLWllRRESkFaszSXD3/eHHZtYJSA9r2hCroERERCT+ollM6ZtmtgJYB3wQfH4z1oGJiIhIfEUzcfG3wNlAqbv3AIYTWH1RREREWrBokoRqd68AkszM3P0NYGiM4xIRadU2fbEp3iGIRJUk7DSz9sC7wFNm9iCwJ7ZhiYi0XhVfVvB62etUfFkR71CklYtm74bLCSQFtwOjgI7AxbEMSkSktfrbR+v57f89x45965ncZgu/OP9qLh/cLd5hSSsVTSXhZ+6+3933ufvj7v4QMCHWgYmItDZ/+2g9P3vpPXbsLcf3ZbJjbzk/e+k9/vbR+niHJq1UNEnCiAht32zsQEREWrv7XytlX8oavCYNMLwmjX0pa7j/tdJ4hyatVH0rLo4DxgN5ZrY47FQHYFGsAxMRaW027t5EcodtePWxgYaatlibbWzcpUmMEh/1zUl4DpgL3AtMDGvf5e6bYxqVSBQ2fbGJLu26xDsMkUaTmVnO9spAFSEgUE3IzCyPZ1jSitU53ODu2919pbt/m8BKi18LPrKbKjiRumj2t7Q0FV9WcH5BMqnW7qD2VGvH+QXJ+rsucRHNios/BP4K5AQfz5nZD2IdmEhd/vbRer4x9WkmzVnCN6Y+rUld0iIsqVjC0NyujDy9J53apQLQqV0qI0/vydDcriypWBLnCKU1iuYWyHHAUHffDWBm/wW8D0yJZWBNRSXr5iU0+7u6bTlenckOD8z+hrN1m5g0W1v2bGH1jtWkp6TTs7Nx69fCC7b72FO9l+07trOl8xay2mrvPWk60SQJBuwNO97HvwfMmrVQyXpE7giyMzSKUp9ESaZCs785ZPZ3lpIEabYyUjK4MPfCqPqJNKX67m5Icfdq4E/AB2Y2K3jqCuDJpggulrRgSfQSKZnS7G9piTLaZNC7Y+94hyFyiPrmJHwI4O6/IzDk8GXwMd7dH2iC2GIm0RcsSaQ12xNt/D8zs/zAPeQBmv0tIhIr9SUJB4YU3P1Dd38o+ChqgrhiKpEXLEmkWfuJlkxp9reISNOqb05CtpnVufxycHnmepnZCOBhIBmY4e731To/ARgDVAMVwE3uXhZN4EcjUUvWiTYEkmjj/6HZ3x1T9zN7yQa2fbGXTu1SuXTQCfQ9IZklFUu4sGfD47oiIhKd+pKEZKA9RzhJ0cySgUcJrK2wDigys9nuXhzW7SOg0N2/NLPvA78DrjmSzzscibhgSSLO2k+kZEqzv0VEml59ScJGd590FNceCqx099UAZvYscBlwIElw93lh/RcA1x/F50UlVLJ+5aN27MUPtIeXrOMxOS/RfmuHxEqmNPtbRKTp1ZckHO1tjt2AtWHH64DT6+k/Gvh7xEDMxgJjAXJyco4qqEQtWSfSb+2QeMmUZn+LiDS9+pKEYU0VhJldDxQC50U67+7TgGkAhYWFHqlPNBK5ZJ1Iv7VD4iZTIiLSdOpMEtx921Feez3QI+y4e7DtIGZ2IfAL4Dx3rzrKz6xXopasE+239kROpkREpOlEs+LikSoC+phZLwLJwbXAyPAOZjYYmAqMaIqdJRO1ZJ1ov7UnajIlIiJNK2ZJgrtXm9ktwGsE7pR4wt2Xm9kkYKG7zwbuJ3AHxV/NDOBf7n5prGJKRIn4W3uiJlMiItK0YllJwN1fBV6t1XZ32OtWP6it39pFRCRRxTRJkIbpt3YREUlU9S3LLCIiIq2YkgQRERGJSEmCiIiIRKQkQURERCJSkiAiIiIRKUkQERGRiJQkiIiISERKEkRERCQiJQkiIiISkZIEERERiUhJgoiIiESkJEFEREQiUpIgIiIiESlJEBERkYiUJIiIiEhEShJEREQkIiUJIiIiEpGSBBEREYlISYKIiIhEpCRBREREIlKSICIiIhEpSRAREZGIlCSIiIhIREoSREREJCIlCSIiIhKRkgQRERGJSEmCiIiIRKQkQURERCJSkiAiIiIRKUkQERGRiGKaJJjZCDMrNbOVZjYxwvk0M5sZPP+BmeXGMh4RERGJXsySBDNLBh4FLgIKgO+YWUGtbqOB7e5+EvA/wH/HKh4RERE5PLGsJAwFVrr7anffCzwLXFarz2XAk8HXzwPDzMxiGJOIiIhEKZZJQjdgbdjxumBbxD7uXg3sBDJrX8jMxprZQjNbWFFREaNwRUREJFyzmLjo7tPcvdDdC7Ozs+MdjoiISKsQyyRhPdAj7Lh7sC1iHzNLAToCW2MYk4iIiEQplklCEdDHzHqZWSpwLTC7Vp/ZwHeDr78FvOnuHsOYREREJEopsbqwu1eb2S3Aa0Ay8IS7LzezScBCd58NPA78ycxWAtsIJBIiIiKSAGKWJAC4+6vAq7Xa7g57XQl8O5YxiIiIyJFpFhMXRUREpOkpSRAREZGIlCSIiIhIREoSREREJCJrbnccmlkFUNaIl8wCtjTi9RqDYopOIsYk0lga++93T3fXanRyWJpdktDYzGyhuxfGO45wiik6iRiTSGPR329JBBpuEBERkYiUJIiIiEhEShJgWrwDiEAxRScRYxJpLPr7LXHX6uckiIiISGSqJIiIiEhEShJEREQkolaZJJhZDzObZ2bFZrbczH6UADGlm9mHZrYkGNOv4x1TiJklm9lHZjYn3rEAmNlnZrbUzP5pZgvjHY/I0TKzJ8xss5ktC2vrZGZvmNmnwefj4hmjtE6tMkkAqoE73b0AOAP4oZkVxDmmKuCr7j4IOAUYYWZnxDmmkB8Bn8Q7iFoucPdTdB+5tBB/BEbUapsIzHX3PsDc4LFIk2qVSYK7b3T3xcHXuwh8AXaLc0zu7ruDh22Cj7jPKjWz7sA3gRnxjkWkpXL3t4FttZovA54Mvn4SuLxJgxKhlSYJ4cwsFxgMfBDfSA6U9f8JbAbecPe4xwT8L/BToCbegYRx4HUzW2RmY+MdjEiMdHH3jcHX5UCXeAYjrVOrThLMrD0wC7jd3T+Pdzzuvt/dTwG6A0PNbEA84zGzi4HN7r4onnFE8BV3PxW4iMBQ0bnxDkgkljxwr3rcK4vS+rTaJMHM2hBIEP7s7i/EO55w7r4DmMehY5RN7WzgUjP7DHgW+KqZPR3fkMDd1wefNwMvAkPjG5FITGwys64AwefNcY5HWqFWmSSYmQGPA5+4+0PxjgfAzLLN7Njg67bA14CSeMbk7j9z9+7ungtcC7zp7tfHMyYza2dmHUKvga8Dy+p/l0izNBv4bvD1d4GX4hiLtFIp8Q4gTs4GbgCWBucAAPzc3V+NY0xdgSfNLJlA8vacuyfELYcJpgvwYiDPIwX4i7v/I74hiRwdM3sGOB/IMrN1wK+A+4DnzGw0UAZcHb8IpbXSsswiIiISUascbhAREZGGKUkQERGRiJQkiIiISERKEkRERCQiJQkiIiISkZIEkVrMbH9wh8nlwV057zSzI/63YmY/D3udG77Tn4hIIlOSIHKoPcEdJvsTWNTqIgL3rR+pnzfcRUQk8ShJEKlHcOnnscAtFpBsZvebWZGZfWxm4wDM7Hwze9vMXjGzUjN7zMySzOw+oG2wMvHn4GWTzWx6sFLxenCFTRGRhKMkQaQB7r4aSAY6A6OBne5+GnAacLOZ9Qp2HQrcChQAJwJXuvtE/l2ZuC7Yrw/waLBSsQO4qul+GhGR6ClJEDk8XwdGBZfz/gDIJPClD/Chu6929/3AM8BX6rjGGncPLQe+CMiNYbwiIkeste7dIBI1M+sN7CewC58Bt7r7a7X6nM+hW/nWteZ5Vdjr/YCGG0QkIamSIFIPM8sGHgMe8cBGJ68B3w9uNY6Z5QV3owQYama9gndCXAO8G2zfF+ovItKcqJIgcqi2weGENkA18CcgtKX4DALDA4uDW45XAJcHzxUBjwAnAfOAF4Pt04CPzWwx8Ium+AFERBqDdoEUaQTB4YYfu/vF8Y5FRKSxaLhBREREIlIlQURERCJSJUFEREQiUpIgIiIiESlJEBERkYiUJIiIiEhEShJEREQkov8PydxmEFa8pnoAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.scatter(depths, [tvd_noisy_ideal[w][d] for d in depths], label='TVD(data, ideal)')\n", - "plt.scatter(depths, [tvd_noisy_rand[w][d] for d in depths], label='TVD(data, rand)')\n", - "plt.scatter(depths, 1-np.asarray([avg_pr_succ_arr[w][d] for d in depths]),\n", - " label='1 - Pr[Success]', alpha=0.33, marker='^', s=80)\n", - "plt.ylim([-0.05,1.05])\n", - "plt.xlabel('Depth')\n", - "plt.xticks(depths)\n", - "plt.ylabel('Total variation distance')\n", - "plt.title('Width = {}'.format(w))\n", - "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot success probablity landscape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just the success probablity as a function of depth and width." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "X, Y = np.meshgrid(widths, depths)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "Zdata = np.reshape([avg_pr_succ_arr[w][d] for d in depths for w in widths], X.shape)\n", - "Zrand = np.reshape([pr_succ_rand[w] for d in depths for w in widths], X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "extent = -0.5, len(widths) - 0.5, -0.5, len(depths) - 0.5\n", - "ax = plt.gca()\n", - "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0, vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Success Probability')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Zrand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Success Probability of Random Guess')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "success_threshold = .8\n", - "ckt_success_probs = get_single_target_success_probabilities(noisy_results, ideal_results)\n", - "successes = determine_successes(ckt_success_probs, num_shots)\n", - "plot_success(successes, f\"Volumetric Benchmark\\n Random Classical Circuits\\n Pr[Success] > {success_threshold}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(
,\n", - " )" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fake_successes = successes\n", - "plot_pareto_frontier(successes, 'Pareto Frontier', widths=[2,3,4,5,6], depths = [2,3,4,5,7,10,15])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot total variation distance landscape" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "Ztvd_ideal = np.reshape([tvd_noisy_ideal[w][d] for d in depths for w in widths], X.shape)\n", - "Ztvd_rand = np.reshape([tvd_noisy_rand[w][d] for d in depths for w in widths], X.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_ideal, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Total Variation Distance of Noisy to Ideal')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zyyJgaxcG8eqVfko6R0fEGkl7AtdIujMirituIGkxsBhg6uxZVcRoVgt1PryqbzpsEBFr8t91wKWke/I0brM0IuZHxPypM3csO0SzWhht02k3VaUvko6knSTNGH0MvBy4vdqozOorQm2nqvTL4dXTgEslQYr5woi4stqQzOrLDckTlG9rekjVcZj1g4h6t+n0RdIxs/EQwz57ZWZlqrLNph0nHbMB42uvzKxckdp16spJx2wA+eyVmZUm3JBsZmXz4ZWZlcpnr8ysNBFOOmZWMp8yN7NSuU3HzEoTiBGfvTKzMtW4otMf4+mY2ThE98bTkbRQ0l2SVkk6o8k2r5W0UtIKSRe2K9M1HbNB1IWqjqQhYAlwHLAauEnSsohYWdhmHnAmcFREPJyHE27JNR2zAdSlms4CYFVE3B0RTwAXAYsatnkLsCQiHk77jXXtCh3oms7QlJGqQ+jIdkPDVYcwLlPVP/FOqXXrRm8EMDLSUVLZQ9LywvzShrus7APcV5hfDRzRUMazAST9CBgCPtxuVM+BTjpmk1IAndVkHuzCnXKnAvOAFwNzgOskPS8iHmn2BB9emQ2giPZTB9YAcwvzc/KyotXAsojYEhG/Bv6HlISactIxG0TRwdTeTcA8SQdI2g44CVjWsM23SLUcJO1BOty6u1WhPrwyGzjducVMRGyVdDpwFam95ryIWCHpHGB5RCzL614uaSUwDLwvIja0KtdJx2wQdan9PCIuBy5vWHZ24XEA785TR5x0zAZNQHR29qoSTjpmA8lJx8zKVOPuSU46ZoNoMiYdSdsDrwb2L+4nIs7p1T7NjPF0DqxEL2s63wYeBW4GNvdwP2bWYLIO4jUnIhb2sHwza6bGZ6962SP5BknP62H5ZtaEov1Ula7XdCTdRjqqnAr8jaS7SYdXIvUlen6392lmBZ1f5lCJXhxendCDMs2sY5pcDckRcQ+ApAsi4pTiOkkXAKeM+UQz655JVtMZdXBxJg99+Kc93J+Zjarx+HVdb0iWdKakjcDzJT0maWOeX0c6jW5mvTTaT6fdVJGuJ52I+JeImAF8KiJmRsSMPO0eEWdOpGxJQ5JukXRZl8I1G0iT6uxVwVmS/go4mpR7/zsivjXBMt8B3AHMnGhwZgOtxm06veynswQ4DbgNuB04TdKSbS1M0hzglcC53QnPzKrQy5rOscCBeZAfJJ0PrJhAef8GvB+Y0WwDSYuBxQDTZs+awK7M+luVh0/t9LKmswrYtzA/Ny8bN0knAOsi4uZW20XE0oiYHxHzh2buuC27Mut/QboMot1UkV7WdGYAd0j6KeltWAAsl7QMICJOHEdZRwEnSjoemA7MlPSViHhDt4M2Gwg1run0Mumc3X6TzuSzXmcCSHox8F4nHLPm6nx41bOkExE/lLQfMC8ivitpB2BqRGzs1T7NLKtx0ulZm46ktwCXAF/Ii+aQ7pEzIRHxg4jw9V1mrXTnvlc90cuG5LeT2mIeA4iIXwJ79nB/ZkZnHQMHtXPg5oh4Qkqt5JKmUutKn9kAmaSDeP1Q0lnADpKOA74O/FcP92dmWZ1rOr1MOmcA60k9kt9KukvgB3u4PzMbVeM2nV6evRqR9C3gWxGxvlf7MbMGFddk2unF0BaS9GFJDwJ3AXdJWi+pa/12zKyNGtd0enF49S7SWavDI2K3iNgNOAI4StK7erA/M2ugkfZTVXqRdE4BTo6IX48uiIi7gTcAb+zB/sysj/SiTWdaRDzYuDAi1kua1oP9mVmjGrfp9CLpPLGN68ysG2rekNyLpHOIpMfGWC7SFeJm1muTKelExFC3yzSzcZpMScfMqiWqPTvVTi97JJtZFbp4waekhZLukrRK0hkttnu1pJA0v12ZTjpmg6gLnQPzDTKXAK8ADgJOlnTQGNvNIN2p5SedhOakYzaIutMjeQGwKiLujogngIuARWNs91HgE8DvOyl0oNt0tg73R0793y3bVR3CuOw+/X+rDqFjU+rcuNFDHR4+7SFpeWF+aUQsLczvA9xXmF9Nurrgyf1ILwDmRsR3JL2vk50OdNIxm7Q6SzoPRkTbNphmJE0BPgO8eTzPc9IxGzTRtbNXa0i3jho1Jy8bNQN4LvCDPFjf04Flkk6MiGIN6imcdMwGUXf66dwEzJN0ACnZnAS87g+7iHgU2GN0XtIPSHdqaZpwwA3JZgOpG6fMI2IrcDpwFXAHcHFErJB0jqTx3LfuKVzTMRtEXeqRHBGXk0b9LC4bc2ysiHhxJ2U66ZgNmooH6WrHScdswIjJd5W5mVXMScfMyuWkY2alctIxs9JMwpEDzaxqTjpmVqY6X+fqpGM2gHx4ZWblcedAMyudk46ZlcU9krtA0nTgOmB7UsyXRMSHqo3KrL40Ut+s0xdJB9gMHBsRm/Ktia+XdEVE3Fh1YGa14zadiYuIADbl2Wl5qvHbalatOh9e9c0gXpKGJP0cWAdcExEd3e7CbFLqzt0geqJvkk5EDEfEoaRxWhdIem7jNpIWS1ouafnWxx4vP0izmujWzfZ6oW+SzqiIeAS4Flg4xrqlETE/IuZPnblj+cGZ1YVrOhMjabakXfLjHYDjgDurjcqspvLdINpNVemLhmRgL+D8fJvTKaQBoi+rOCazWnI/nS6IiFuBw6qOw6xvRH2zTl8kHTMbH9d0zKw87hxoZmXzeDpmVionHTMrT+CGZDMrlxuSzaxcTjpmVhZ3DjSzckV4EC8zK1l9c46Tjtkg8uGVmZUnAB9emVmp6ptz+mM8HTMbn26NHChpoaS7JK2SdMYY698taaWkWyV9T9J+7cp00jEbQBqJtlPbMtL4VUuAVwAHASdLOqhhs1uA+RHxfOAS4JPtynXSMRs0nQxV2llNZwGwKiLujogngIuARU/ZVcS1ETE6IPmNpDHMWxroNh2p6gg6s/3Q1qpDGJcnhvvnazNNw1WHULrUObCjrLKHpOWF+aURsbQwvw9wX2F+NXBEi/JOBa5ot9P++faYWec6u8r8wYiY343dSXoDMB84pt22TjpmA6jDmk47a4C5hfk5edlT9yW9DPgAcExEbG5XqNt0zAZN99p0bgLmSTpA0nbAScCy4gaSDgO+AJwYEes6KdQ1HbOB051rryJiq6TTgauAIeC8iFgh6RxgeUQsAz4F7Ax8XakR9d6IOLFVuU46ZoOoS4N4RcTlwOUNy84uPH7ZeMt00jEbNOHhSs2sbB6u1MxKVd+c46RjNog0Ut/jKycds0ETdNo5sBJOOmYDRkS3Ogf2hJOO2SBy0jGzUjnpmFlp3KZjZmXz2SszK1H48MrMShQ46ZhZyep7dNUf4+lImivp2jzq/ApJ76g6JrM6U0TbqSr9UtPZCrwnIn4maQZws6RrImJl1YGZ1ZIPryYmIu4H7s+PN0q6gzRotJOOWaMIGK7v8VVfJJ0iSfsDhwE/GWPdYmAxwNTZs0qNy6xWalzT6Ys2nVGSdga+AbwzIh5rXB8RSyNifkTMnzpzx/IDNKuLiPZTRfqmpiNpGinhfDUivll1PGa1FUAXxkjulb5IOkojPn8JuCMiPlN1PGb1FhD1bdPpl8Oro4BTgGMl/TxPx1cdlFktBakhud1Ukb6o6UTE9aS7pZpZJ2rckNwXScfMxslJx8zK4ws+zaxMAXhoCzMrlWs6ZlYeXwZhZmUKiBr303HSMRtE7pFsZqVym46ZlSbCZ6/MrGSu6ZhZeYIYHq46iKacdMwGjYe2MLPS1fiUeb8MbWFmHQogRqLt1AlJCyXdJWmVpDPGWL+9pP/M63+ShxNuyUnHbNBEHsSr3dSGpCFgCfAK4CDgZEkHNWx2KvBwRDwL+CzwiXblOumYDaAYHm47dWABsCoi7o6IJ4CLgEUN2ywCzs+PLwFemkf6bEpR41NrEyFpPXBPD4reA3iwB+X2Qj/FCv0Vb69i3S8iZk+kAElXkuJrZzrw+8L80ohYWijnNcDCiPi7PH8KcEREnF7Y5va8zeo8/6u8TdP3ZmAbkif6wTUjaXlEzO9F2d3WT7FCf8Vb51gjYmHVMbTiwysza2YNMLcwPycvG3MbSVOBWcCGVoU66ZhZMzcB8yQdIGk74CRgWcM2y4A35cevAb4fbdpsBvbwqoeWtt+kNvopVuivePsp1m0SEVslnQ5cBQwB50XECknnAMsjYhnp1lAXSFoFPERKTC0NbEOymdWTD6/MrFROOmZWKiedDkiaK+laSSslrZD0jqpjakXSdEk/lfSLHO9Hqo6pHUlDkm6RdFnVsbQj6TeSbst3ml1edTz9xg3JndkKvCcifiZpBnCzpGsiYmXVgTWxGTg2IjZJmgZcL+mKiLix6sBaeAdwBzCz6kA69JJWHeCsOdd0OhAR90fEz/LjjaQfxz7VRtVcJJvy7LQ81faMgaQ5wCuBc6uOxXrPSWec8lW0hwE/qTaS1vLhys+BdcA1EVHneP8NeD9Q3/EYniqAqyXdLGlx1cH0GyedcZC0M/AN4J0R8VjV8bQSEcMRcSipF+kCSc+tOqaxSDoBWBcRN1cdyzgcHREvIF19/XZJL6o6oH7ipNOh3DbyDeCrEfHNquPpVEQ8AlwL1PV6nKOAEyX9hnQV87GSvlJtSK1FxJr8dx1wKelqbOuQk04H8qX6XwLuiIjPVB1PO5JmS9olP94BOA64s9qoxhYRZ0bEnIjYn9Sb9fsR8YaKw2pK0k75ZAKSdgJeDtxebVT9xWevOnMUcApwW24nATgrIi6vMKZW9gLOz4MwTQEujojan4ruE08DLs1DxkwFLoyIK6sNqb/4MggzK5UPr8ysVE46ZlYqJx0zK5WTjpmVyknHzErlpDOJSfqspHcW5q+SdG5h/tOSzpJ0SZPn/0DS/Pz4rMLy/fNdAsz+iJPO5PYj4EgASVNIty05uLD+SFJnvdd0UNZZ7Tcxc9KZ7G4AXpgfH0zqWbtR0q6StgcOBB4arbVI2kHSRZLukHQpsENe/nFghzy+zFdzeUOSvpjH87k694w2c9KZzCLit8BWSfuSajU/Jl09/0JgPnAb8EThKW8DHo+IA4EPAX+ayzkD+F1EHBoRr8/bzgOWRMTBwCPAq0t4SdYHnHTsBlLCGU06Py7M/6hh2xcBXwGIiFuBW1uU++uIGL1k5GZg/+6FbP3MScdG23WeRzq8upFU0zmSlJC21ebC42F8nZ9lTjp2A3AC8FAeg+chYBdS4mlMOtcBrwPI4/M8v7BuSx7+w6wlJx27jXTW6saGZY+OMQbw54CdJd0BnEM6bBq1FLi10JBsNiZfZW5mpXJNx8xK5aRjZqVy0jGzUjnpmFmpnHTMrFROOmZWKicdMyvV/wfdOO7eAWXELgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Ztvd_rand, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Total Variation Distance of Noisy to Random')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data exploration" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.optimize import curve_fit" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 20)\n", - "(1, 20)\n" - ] - } - ], - "source": [ - "shape = Zdata.shape\n", - "size = Zdata.size\n", - "width_1d = X.reshape((1,size))\n", - "depth_1d = Y.reshape((1,size))\n", - "data_1d = Zdata.reshape((1,size))\n", - "print(data_1d.shape)\n", - "print(width_1d.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0],\n", - " [ 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5, 2, 3, 4, 5,\n", - " 2, 3, 4, 5],\n", - " [ 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5,\n", - " 10, 10, 10, 10]])" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dims = np.zeros_like(width_1d)\n", - "dims[0,0] = shape[0]\n", - "dims[0,1] = shape[1]\n", - "\n", - "xdata = np.vstack((dims, width_1d, depth_1d))\n", - "xdata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting models" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Two parameter model \n", - "\n", - "\n", - "$f(W,D,p_W,p_D) = (1-p_W)^W * (1-p_D)^D $\n", - "\n", - "The fidelity is proporional to $1 - p$" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "def two_param(x, pw, pd):\n", - " num_depths, num_widths = x[0][:2]\n", - " widths = x[1].reshape(num_depths, num_widths)\n", - " depths = x[2].reshape(num_depths, num_widths)\n", - " pcheck = (1-pw)**(widths) * (1-pd)**depths\n", - " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One parameter model\n", - "\n", - "$f(W,D,p) = (1-p)^{W * D} $" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "def one_param(x,p):\n", - " num_depths, num_widths = x[0][:2]\n", - " widths = x[1].reshape(num_depths, num_widths)\n", - " depths = x[2].reshape(num_depths, num_widths)\n", - " pcheck = (1-p)**(widths * depths)\n", - " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Josh: \"From my prior work a better model to fit to is \"\n", - "\n", - "Pcheck$(W,D,p,a,b,c) = \\exp[ -(a p^2 + b p + c)* W*D] $\n" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "def two_param_exp(x,p,a,b):\n", - " num_depths, num_widths = x[0][:2]\n", - " widths = x[1].reshape(num_depths, num_widths)\n", - " depths = x[2].reshape(num_depths, num_widths)\n", - " pcheck = np.exp(-(a*p + b) * widths * depths)\n", - " rpcheck = pcheck.reshape((1, num_depths * num_widths))\n", - " return rpcheck.ravel()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Start with one paramter model**" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "pguess = 0.1\n", - "popt, pcov = curve_fit(one_param, xdata, data_1d.ravel(), p0=pguess, bounds=(0, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The estimated error is p = 0.0111\n", - "The estimated product of the one and two qubit fidelity is F = 0.9889\n" - ] - } - ], - "source": [ - "print('The estimated error is p = ', str(np.round(popt[0],4)))\n", - "print('The estimated product of the one and two qubit fidelity is F = ', str(1-np.round(popt[0],4)))\n", - "#print('The one standard deviation on the estimate is ', str(np.round(np.sqrt(np.diag(pcov)[0]),5)))" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "zfit = one_param(xdata, popt)\n", - "Z_fit = zfit.reshape(shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Z_fit, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('One parameter fit to success prob')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Zdata, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0.0,vmax=1.0)\n", - "\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Success prob')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Two parameter model**" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [], - "source": [ - "# pguess2d_exp = [0.0276, 0.01, 0.4]\n", - "# popt2d, pcov2d = curve_fit(two_param_exp, xdata, data_1d.ravel(), p0=pguess2d, bounds=(0., 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "popt2d, pcov2d = curve_fit(two_param, xdata, data_1d.ravel(), bounds=(0., 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.05929668, 0.00165794])" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "popt2d" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.88199086 0.82969173 0.78049376 0.73421307]\n", - " [0.88052857 0.82831615 0.77919975 0.73299579]\n", - " [0.87906871 0.82694285 0.77790788 0.73178053]\n", - " [0.87761126 0.82557182 0.77661815 0.73056728]\n", - " [0.8703602 0.81875073 0.77020153 0.72453113]]\n" - ] - } - ], - "source": [ - "zfit2d = two_param(xdata, popt2d[0], popt2d[1])\n", - "Z_fit2d = zfit2d.reshape(shape)\n", - "print(Z_fit2d)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = plt.gca()\n", - "img = ax.imshow(Z_fit2d, interpolation='none', extent=extent,\n", - " cmap='viridis', origin='lowerleft', vmin=0, vmax=1.0)\n", - "\n", - "ax.set_xticks(range(len(widths)))\n", - "ax.set_xticklabels(widths)\n", - "\n", - "ax.set_yticks(range(len(depths)))\n", - "ax.set_yticklabels(depths)\n", - "\n", - "ax.set_aspect('equal')\n", - "plt.colorbar(img, ax=ax)\n", - "plt.xlabel('Width')\n", - "plt.ylabel('Depth')\n", - "plt.title('Two parameter fit success prob')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the distribution of sublattice widths" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "G = perfect_qc.qubit_topology()\n", - "len(perfect_qc.qubit_topology())\n", - "# distribution of graph lengths\n", - "distr = []\n", - "for num_nodes in range(1, len(G.nodes) + 1):\n", - " listg = generate_connected_subgraphs(G, num_nodes)\n", - " distr.append(len(listg))\n", - "\n", - "cir_wid = list(range(1, len(G.nodes) + 1))\n", - "plt.bar(cir_wid, distr, width=0.61, align='center')\n", - "plt.xticks(cir_wid)\n", - "plt.xlabel('sublattice / circuit width')\n", - "plt.ylabel('Frequency of Occurence')\n", - "plt.grid(axis='y', alpha=0.75)\n", - "plt.title('Distribution of sublattice widths')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 255ce08d4d6d62a5241c60a53168277d1a9b8439 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 16 Sep 2019 17:09:52 -0400 Subject: [PATCH 37/49] Remove pattern, clean up some todos. --- docs/examples/volumetrics.ipynb | 656 +++++++++++------------------ forest/benchmarking/volumetrics.py | 121 ++---- 2 files changed, 279 insertions(+), 498 deletions(-) diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb index d664b126..636db13c 100644 --- a/docs/examples/volumetrics.ipynb +++ b/docs/examples/volumetrics.ipynb @@ -31,7 +31,10 @@ "from pyquil.gates import CNOT, CCNOT, Z, X, I, H, CZ, MEASURE, RESET\n", "from pyquil.quilbase import Pragma\n", "\n", - "from forest.benchmarking.volumetrics import *" + "from forest.benchmarking.volumetrics import *\n", + "\n", + "\n", + "bm = get_benchmarker()" ] }, { @@ -76,7 +79,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -161,39 +164,20 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [ - "from forest.benchmarking.randomized_benchmarking import get_rb_gateset" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# my config has gone all cattywampus so i need to do this\n", - "bm = get_benchmarker()#endpoint='tcp://localhost:6000')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'tcp://127.0.0.1:5555'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-pi/2) 0\n", + "RX(pi/2) 0\n", + "\n" + ] } ], "source": [ - "bm.client.endpoint" + "from forest.benchmarking.randomized_benchmarking import get_rb_gateset\n", + "print(bm.generate_rb_sequence(depth=2, gateset=get_rb_gateset([0]))[0])" ] }, { @@ -205,38 +189,38 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", - "I 1\n", - "I 2\n", + "Z 0\n", + "Z 1\n", + "Z 2\n", "I 3\n", "I 4\n", - "X 5\n", - "I 6\n", + "I 5\n", + "Z 6\n", "I 7\n", "Z 8\n", "CZ 0 3\n", - "I 0\n", + "CZ 0 1\n", "I 1\n", - "CZ 1 4\n", + "I 4\n", "I 1\n", "I 2\n", - "I 2\n", - "I 5\n", + "CZ 2 5\n", "I 3\n", "I 6\n", "CZ 3 4\n", - "CZ 4 7\n", + "I 4\n", + "I 7\n", "CZ 4 5\n", - "I 5\n", - "I 8\n", - "CZ 6 7\n", + "CZ 5 8\n", + "I 6\n", + "I 7\n", "I 7\n", "I 8\n", "\n" @@ -251,38 +235,34 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 0\n", - "RX(-pi) 0\n", + "RX(-pi/2) 0\n", "RX(pi/2) 1\n", "RZ(-pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(-pi) 3\n", - "RZ(pi/2) 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 5\n", + "RZ(-pi) 2\n", + "RZ(pi/2) 3\n", + "RZ(-pi/2) 4\n", + "RZ(-pi) 5\n", "RZ(-pi) 5\n", - "RX(-pi/2) 6\n", + "RX(pi/2) 6\n", "RZ(-pi) 6\n", - "RX(pi/2) 7\n", "RZ(-pi) 7\n", - "RX(-pi/2) 8\n", - "RZ(pi/2) 8\n", - "RX(-pi/2) 8\n", + "RZ(-pi) 7\n", + "RZ(-pi) 8\n", + "RZ(-pi) 8\n", "\n" ] } ], "source": [ - "progy = random_single_qubit_cliffords(bm, G)\n", - "print(progy)" + "rand1qcliff = random_single_qubit_cliffords(bm, G)\n", + "print(rand1qcliff)" ] }, { @@ -294,15 +274,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 4\n", - "I 5\n", "I 4\n", "X 5\n", "\n" @@ -316,17 +294,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 2\n", - "I 5\n", - "I 2\n", - "I 5\n", + "CNOT 0 1\n", "\n" ] } @@ -338,44 +313,21 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H 0\n", - "H 1\n", - "H 2\n", - "H 4\n", - "\n" - ] - } - ], - "source": [ - "switch_basis_layer = get_switch_basis_x_z_template()\n", - "print(switch_basis_layer.sample_program(G, repetitions=1, width=4))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", + "RX(-pi/2) 3\n", "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi) 0\n", + "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 0\n", "CZ 0 3\n", - "RX(-pi/2) 3\n", + "RZ(-pi/2) 3\n", "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", "\n" ] } @@ -388,16 +340,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE Perm102 AS PERMUTATION:\n", - " 0, 2, 1, 3, 4, 6, 5, 7\n", - "Perm102 1 2 4\n", + "DEFGATE Perm120 AS PERMUTATION:\n", + " 0, 4, 1, 5, 2, 6, 3, 7\n", + "Perm120 0 3 4\n", "\n" ] } @@ -410,20 +362,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE LYR0_RSU4_2_5:\n", - " -0.09969160814430622+0.0902156122395286i, -0.33709519811871885-0.048136235456428166i, 0.7703918235977348+0.48727367053674936i, -0.02107343025834974-0.18598165363223637i\n", - " -0.4675035064037158+0.20523079122648438i, 0.038445207841979606-0.25078075097360014i, -0.05647270554583689+0.019169708299199825i, -0.7802899318958636+0.25008549090639387i\n", - " -0.34089008290794076-0.7272853767121489i, 0.0027518127263039885-0.2069765795381728i, 0.2215611250339229-0.12193916055391807i, 0.20591837586243+0.4534778789801883i\n", - " -0.0036986206424105654+0.27582562028872515i, 0.7923348800993039-0.38605604774942803i, 0.3168040759021317-0.034363504424924196i, 0.21335791201204357-0.002332725678737959i\n", + "DEFGATE LYR0_RSU4_4_7:\n", + " -0.12199079971763926-0.8649198443726955i, 0.274615772788525+0.3041112377388371i, -0.12039142645980519+0.04447186269594815i, 0.22946587597712897+0.0028301301474696478i\n", + " 0.2876933119119516-0.0658590695663348i, 0.44382440864829387-0.40618240445500203i, 0.5448213602522904+0.4062939105280664i, 0.11557814784292778+0.2750773077845369i\n", + " 0.2961881426835967+0.04007749099266393i, 0.5623040426685924+0.010247843130115501i, 0.06926633628932952-0.43595017123785745i, -0.25000849797732155-0.5805350101010037i\n", + " -0.16027345597301254-0.1868310653809832i, -0.22190059815206353+0.3234309058598983i, 0.5624157410478605+0.10228029438901522i, -0.6769786666331948-0.022052358565452367i\n", "\n", - "LYR0_RSU4_2_5 2 5\n", + "LYR0_RSU4_4_7 4 7\n", "\n" ] } @@ -442,30 +394,21 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "I 0\n", + "I 1\n", "X 3\n", - "X 4\n", - "I 6\n", - "I 0\n", - "I 3\n", - "I 3\n", - "I 6\n", - "I 3\n", "I 4\n", - "I 0\n", + "X 5\n", + "CNOT 1 4\n", "I 3\n", "I 4\n", - "X 6\n", - "CNOT 0 3\n", - "CNOT 3 6\n", - "CNOT 3 4\n", + "CNOT 4 5\n", "\n" ] } @@ -479,131 +422,76 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Classical Logic in X basis" + "Classical Logic in X basis\n", + "\n", + "Add a `sequence_transform` for the template to insert the basis change H gates at beginning and end." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H 7\n", - "H 8\n", - "Z 7\n", - "Z 8\n", - "I 7\n", - "I 8\n", - "Z 7\n", - "I 8\n", - "I 7\n", - "I 8\n", - "I 7\n", - "Z 8\n", - "H 7\n", - "CZ 7 8\n", - "H 7\n", - "H 7\n", - "H 8\n", + "H 0\n", + "H 3\n", + "Z 0\n", + "I 3\n", + "H 0\n", + "CZ 0 3\n", + "H 0\n", + "H 0\n", + "H 3\n", "\n" ] } ], "source": [ - "logic_layers = get_rand_1q_template(one_x_c_gates) + get_rand_2q_template(two_x_c_gates)\n", - "classical_x_1q_2q = switch_basis_layer + logic_layers + switch_basis_layer\n", - "# here we demonstrate a simple use of a pattern. We want to do the basis switch at beginning and end \n", - "# while doing the repetitions in between some variable number of times.\n", - "# The pattern says to do the 0 idx generator, do [1,2] idx generators n times, then finish with 3 idx generator\n", - "classical_x_1q_2q.pattern = [0, ([1, 2], 'n'), 3]\n", + "classical_x_1q_2q = get_rand_1q_template(one_x_c_gates) + get_rand_2q_template(two_x_c_gates)\n", + "# here we demonstrate a simple use of a sequence_transform. We want to switch to the x basis \n", + "# at thebeginning of our circuite and switch back to the Z basis before measurement. \n", + "# To accomplish this we set a 'sequnce_transform' to be applied after the circuit sequence is generated.\n", + "classical_x_1q_2q.sequence_transforms.append(hadamard_sandwich)\n", "print(classical_x_1q_2q.sample_program(G, repetitions=3, width=2))\n", "# note that the x basis CNOT(0, 1) is H(0) CZ(0, 1) H(0)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Daggering the circuit to get a self-inverting sandwich.\n", + "Here we again add a `sequence_transform` to transform the sampled sequence by appending its dagger (aka Hermitian conjugate, adjoint, etc.)." + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RZ(-pi/2) 2\n", - "RZ(-pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi) 2\n", - "RX(-pi) 2\n", - "CZ 1 2\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RX(-pi/2) 2\n", - "RX(-pi/2) 1\n", - "CZ 1 2\n", + "RX(pi/2) 0\n", "RX(-pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(pi/2) 1\n", "RX(-pi/2) 1\n", - "RX(-pi/2) 2\n", - "RZ(pi/2) 2\n", - "RX(-pi/2) 2\n", - "RX(pi/2) 2\n", - "RX(pi/2) 1\n", - "CZ 1 2\n", - "RX(pi/2) 2\n", + "CZ 0 1\n", + "RX(pi/2) 0\n", + "CZ 0 1\n", "RX(-pi/2) 1\n", - "CZ 1 2\n", - "RZ(-pi/2) 2\n", "RZ(-pi/2) 1\n", "DAGGER RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER RZ(pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(pi/2) 1\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 1 2\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER CZ 1 2\n", + "DAGGER CZ 0 1\n", + "DAGGER RX(pi/2) 0\n", + "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(pi/2) 2\n", - "DAGGER RZ(pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi) 2\n", - "DAGGER RZ(-pi) 2\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER RX(-pi/2) 2\n", - "DAGGER CZ 1 2\n", - "DAGGER RX(-pi) 2\n", - "DAGGER RZ(-pi/2) 2\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(pi/2) 0\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -631,120 +519,81 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-0.6633765634144329) 0\n", - "RX(pi/2) 0\n", - "RZ(2.1992567304350827) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.213273479007638) 0\n", - "RZ(-2.1790140703661987) 1\n", - "RX(pi/2) 1\n", - "RZ(1.3833680725337012) 1\n", - "RX(-pi/2) 1\n", - "RZ(-1.5430363103535998) 1\n", - "CZ 1 0\n", - "RZ(pi/2) 0\n", - "RX(pi/2) 0\n", - "RZ(2.1382446014645566) 0\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(pi/2) 0\n", - "RZ(-1.6745691134157568) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.8121261481912123) 1\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(1.6380912332362045) 3\n", + "RZ(2.4246593511866825) 3\n", "RX(pi/2) 3\n", - "RZ(1.2911009982026904) 3\n", + "RZ(1.1994625091249727) 3\n", "RX(-pi/2) 3\n", - "RZ(2.905707049360048) 3\n", - "RZ(-0.3198967078677877) 0\n", - "RX(pi/2) 0\n", - "RZ(1.9993474339045234) 0\n", - "RX(-pi/2) 0\n", - "RZ(-2.045982310794382) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 3 0\n", - "RZ(pi) 0\n", - "RX(pi/2) 0\n", + "RZ(1.6596967975055819) 3\n", + "RZ(-1.12840384641332) 4\n", + "RX(pi/2) 4\n", + "RZ(1.39668867273419) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.7543726433217182) 4\n", + "CZ 3 4\n", + "RZ(-pi/2) 3\n", "RX(-pi/2) 3\n", - "CZ 0 3\n", - "RZ(-1.7211797008449619) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.1291752835794742) 1\n", - "RX(-pi/2) 1\n", - "RZ(-0.9663730999073499) 2\n", - "RX(pi/2) 2\n", - "RZ(1.8104685056998722) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.1562967037020901) 2\n", - "CZ 1 2\n", - "RZ(-2.2067063329930843) 1\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 2\n", - "RX(pi/2) 2\n", - "CZ 2 1\n", - "RZ(pi) 1\n", - "RX(pi/2) 1\n", - "RX(-pi/2) 2\n", - "CZ 1 2\n", - "RZ(-1.7823332810518906) 0\n", - "RX(pi/2) 0\n", - "RZ(0.5707386474274007) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.0405476330691377) 0\n", - "RZ(-1.6504916090017687) 1\n", - "RX(pi/2) 1\n", - "RZ(2.578029427303778) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.6934673043744666) 1\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 1\n", - "RZ(2.640187735366899) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(1.3955050168956022) 0\n", - "RX(pi/2) 0\n", - "RX(pi/2) 1\n", - "RZ(-2.0662135365992644) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(-1.7203952944369068) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.644187513360958) 0\n", - "RX(-pi/2) 0\n", - "RZ(1.4122248717631236) 0\n", - "RZ(0.37149567048520904) 1\n", - "RX(pi/2) 1\n", - "RZ(2.29520538060332) 1\n", - "RX(-pi/2) 1\n", - "RZ(2.599836886240988) 1\n", - "RZ(-1.2615159694384492) 2\n", - "RX(pi/2) 2\n", - "RZ(1.043867674689562) 2\n", - "RX(-pi/2) 2\n", - "RZ(-1.7558737696969433) 2\n", - "RZ(2.681912008883467) 3\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.289442254207697) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(1.7406983874554172) 3\n", "RX(pi/2) 3\n", - "RZ(0.5879267224374873) 3\n", + "RX(pi/2) 4\n", + "RZ(-1.631702489085865) 4\n", + "RX(-pi/2) 4\n", + "CZ 3 4\n", + "RZ(1.3545441126274091) 5\n", + "RX(pi/2) 5\n", + "RZ(2.497677361431593) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.5792111549892525) 5\n", + "RZ(-0.3101456732893413) 8\n", + "RX(pi/2) 8\n", + "RZ(2.1740792824123987) 8\n", + "RX(-pi/2) 8\n", + "RZ(-2.4474460793927824) 8\n", + "CZ 5 8\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "RZ(2.30751710516067) 8\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RZ(2.2067087792137023) 5\n", + "RX(pi/2) 5\n", + "RX(pi/2) 8\n", + "RZ(-1.7460274856187237) 8\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RZ(2.0970755291047958) 3\n", "RX(-pi/2) 3\n", - "RZ(-1.3152784290894761) 3\n", + "RZ(2.681106403947665) 3\n", + "RX(-pi/2) 3\n", + "RZ(0.6278160958652292) 3\n", + "RZ(-0.00415633983752528) 4\n", + "RX(pi/2) 4\n", + "RZ(1.4957403844866333) 4\n", + "RX(-pi/2) 4\n", + "RZ(-0.9104498155522833) 4\n", + "RZ(2.95063772413107) 5\n", + "RX(-pi/2) 5\n", + "RZ(0.856298302301197) 5\n", + "RX(-pi/2) 5\n", + "RZ(0.35269534528741975) 5\n", + "RZ(0.1098505168838746) 8\n", + "RX(pi/2) 8\n", + "RZ(1.0699948541344888) 8\n", + "RX(-pi/2) 8\n", + "RZ(-0.6807290975657327) 8\n", "\n" ] } @@ -770,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -786,15 +635,15 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [0.6720000000000005, 0.8240000000000006, 0.9260000000000007, 0.8420000000000006, 0.8780000000000007, 0.6560000000000005, 0.5840000000000004, 0.8820000000000007, 0.8220000000000006, 0.8620000000000007, 0.8140000000000006, 0.7160000000000005, 0.8860000000000007, 0.7420000000000005, 0.6640000000000005, 0.8240000000000006, 0.6520000000000005, 0.5800000000000004, 0.7420000000000005, 0.6680000000000005, 0.9320000000000007, 0.8100000000000006, 0.6600000000000005, 0.9280000000000007, 0.6720000000000005, 0.6020000000000004, 0.7580000000000006, 0.7900000000000006, 0.8220000000000006, 0.9260000000000007, 0.6420000000000005, 0.7320000000000005, 0.7480000000000006, 0.9740000000000008, 0.8020000000000006, 0.7740000000000006, 0.7800000000000006, 0.9220000000000007, 0.7720000000000006, 0.8020000000000006, 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[, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" ] } ], "source": [ "widths = [2, 3, 4, 5]\n", "depths = [2, 3, 4, 5, 10]\n", - "ckt = classical_1q_2q\n", - "prog_array = generate_volumetric_program_array(noisy_qc, ckt, widths, depths, num_circuit_samples=20)\n", + "ckt_family = classical_1q_2q\n", + "prog_array = generate_volumetric_program_array(noisy_qc, ckt_family, widths, depths, num_circuit_samples=20)\n", "print(prog_array)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -867,14 +716,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [array([[1, 0]]), array([[1, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 0]])], 3: [array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 1]]), array([[0, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[0, 0]]), array([[1, 1]]), array([[0, 1]]), array([[0, 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1.000e-04]), 10: array([0.7197, 0.2264, 0.0403, 0.0098, 0.0027, 0.0011])}}\n" + "{2: {2: array([0.8836, 0.1128, 0.0036]), 3: array([0.9052, 0.0919, 0.0029]), 4: array([0.8935, 0.1031, 0.0034]), 5: array([0.8762, 0.1199, 0.0039]), 10: array([0.8747, 0.1211, 0.0042])}, 3: {2: array([0.8466, 0.1445, 0.0089, 0. ]), 3: array([8.352e-01, 1.563e-01, 8.400e-03, 1.000e-04]), 4: array([8.333e-01, 1.566e-01, 1.000e-02, 1.000e-04]), 5: array([8.116e-01, 1.769e-01, 1.130e-02, 2.000e-04]), 10: array([8.486e-01, 1.424e-01, 8.600e-03, 4.000e-04])}, 4: {2: array([8.061e-01, 1.805e-01, 1.280e-02, 6.000e-04, 0.000e+00]), 3: array([7.840e-01, 1.968e-01, 1.810e-02, 1.000e-03, 1.000e-04]), 4: array([0.7558, 0.2174, 0.0255, 0.0013, 0. ]), 5: array([0.7909, 0.1928, 0.0155, 0.0008, 0. ]), 10: array([7.816e-01, 1.987e-01, 1.920e-02, 5.000e-04, 0.000e+00])}, 5: {2: array([0.7399, 0.2326, 0.0261, 0.0014, 0. , 0. ]), 3: array([0.7649, 0.2113, 0.0223, 0.0015, 0. , 0. ]), 4: array([7.314e-01, 2.351e-01, 3.150e-02, 1.800e-03, 2.000e-04, 0.000e+00]), 5: array([0.7569, 0.218 , 0.0238, 0.0013, 0. , 0. ]), 10: array([7.663e-01, 2.115e-01, 2.090e-02, 1.200e-03, 1.000e-04, 0.000e+00])}}\n" ] } ], @@ -928,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -943,12 +792,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -980,12 +829,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1007,12 +856,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1034,12 +883,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1061,16 +910,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: 0.8824000000000002, 3: 0.8760999999999999, 4: 0.8922999999999999, 5: 0.8821, 10: 0.8854}, 3: {2: 0.8328999999999999, 3: 0.8171999999999999, 4: 0.8407999999999998, 5: 0.8301999999999998, 10: 0.8265}, 4: {2: 0.7817999999999998, 3: 0.7962, 4: 0.7868, 5: 0.7691, 10: 0.7376999999999999}, 5: {2: 0.7399, 3: 0.7308000000000001, 4: 0.7299, 5: 0.7182, 10: 0.7196999999999999}}\n", - "{2: {2: 0.9963000000000001, 3: 0.9948999999999998, 4: 0.9956999999999999, 5: 0.9967999999999998, 10: 0.9947000000000001}, 3: {2: 0.9894999999999999, 3: 0.9870000000000001, 4: 0.9895000000000002, 5: 0.9879, 10: 0.9789999999999999}, 4: {2: 0.9991, 3: 0.9987999999999999, 4: 0.9978, 5: 0.9978999999999998, 10: 0.9921}, 5: {2: 0.9965000000000002, 3: 0.9964000000000001, 4: 0.9930999999999998, 5: 0.9933, 10: 0.9864000000000003}}\n", - "{2: {2: 0.6324000000000001, 3: 0.6260999999999999, 4: 0.6423, 5: 0.6320999999999999, 10: 0.6354}, 3: {2: 0.7079, 3: 0.6922, 4: 0.7157999999999999, 5: 0.7051999999999999, 10: 0.7015}, 4: {2: 0.7192999999999999, 3: 0.7336999999999999, 4: 0.7243, 5: 0.7066, 10: 0.6751999999999999}, 5: {2: 0.7817, 3: 0.7767000000000002, 4: 0.7712000000000001, 5: 0.7657999999999999, 10: 0.7585999999999999}}\n" + "{2: {2: 0.8836, 3: 0.9052, 4: 0.8935000000000001, 5: 0.8761999999999999, 10: 0.8747000000000001}, 3: {2: 0.8466000000000001, 3: 0.8352, 4: 0.8333, 5: 0.8116, 10: 0.8486}, 4: {2: 0.8061, 3: 0.784, 4: 0.7558, 5: 0.7908999999999999, 10: 0.7816000000000001}, 5: {2: 0.7399, 3: 0.7649, 4: 0.7314, 5: 0.7568999999999999, 10: 0.7663}}\n", + "{2: {2: 0.9963999999999998, 3: 0.9971, 4: 0.9966000000000002, 5: 0.9960999999999999, 10: 0.9958}, 3: {2: 0.9911000000000001, 3: 0.9914999999999997, 4: 0.9898999999999999, 5: 0.9884999999999999, 10: 0.991}, 4: {2: 0.9994, 3: 0.9989000000000001, 4: 0.9987, 5: 0.9992000000000001, 10: 0.9995}, 5: {2: 0.9986, 3: 0.9985000000000002, 4: 0.9979999999999999, 5: 0.9987, 10: 0.9987}}\n", + "{2: {2: 0.6335999999999999, 3: 0.6552, 4: 0.6435, 5: 0.6262, 10: 0.6247}, 3: {2: 0.7216, 3: 0.7102, 4: 0.7083, 5: 0.6866000000000001, 10: 0.7236}, 4: {2: 0.7436, 3: 0.7214999999999999, 4: 0.6933, 5: 0.7283999999999999, 10: 0.7191000000000001}, 5: {2: 0.785, 3: 0.7887, 4: 0.779, 5: 0.7873999999999999, 10: 0.7903000000000001}}\n" ] } ], @@ -1079,10 +928,13 @@ "widths = list(avg_err_hamm_distrs.keys())\n", "depths = list(avg_err_hamm_distrs[widths[0]].keys())\n", "\n", + "# get the probability of success for each circuit sampled\n", + "succ_probs = get_single_target_success_probabilities(noisy_results, ideal_results)\n", + "\n", + "# get the average probability of success over all samples\n", "avg_pr_succ_arr = {w: {d: distr[0] for d, distr in d_distrs.items()} for w, d_distrs in avg_err_hamm_distrs.items()}\n", "# this is equivalently wrapped up in the following\n", - "assert avg_pr_succ_arr == average_distributions(get_single_target_success_probabilities(noisy_results, \n", - " ideal_results))\n", + "assert avg_pr_succ_arr == average_distributions(succ_probs)\n", "\n", "# count as success even if there are log many bits incorrect.\n", "avg_pr_succ_allow_log_errors = average_distributions(get_single_target_success_probabilities(noisy_results, \n", @@ -1127,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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PZ+bMmfWKs5A8xISZNYo+ffrQr18/dtttN7bffnsqKio2us5ll13G8OHD6dmzJ/vssw877LADAHvssQcjRoxgwIABAJx++un069evxqafjTn77LM55ZRTKC0tZbfddqNnz560b98egGuuuYYjjjiCLl26UF5ezsqVKwH47W9/yznnnEPv3r1Zs2YNgwcP5sYbb+SGG25g6tSptGjRgp49e3LooYdyxx13cO2119K6dWvatm3Ln//857xjLLRm98zi8vLy8INpzDZu7ty57L777sUOo8lbu3Ytq1evpqSkhNdee40DDzyQ+fPns8UWWxQ7tHqr6XcvaUZElNdU3jUCM8u0VatWsd9++7F69WoigjFjxjTrJFAfTgRmlmnt2rXL/ONv3VlsZpZxTgRmZhnnRGBmlnFOBGZmGedEYGYFc9VVV9GzZ0969+5N3759eeaZZ4odEgsXLqRNmzb07duX0tJSzjzzzLxGRl24cCFlZWV57XPIkCE1dkhPnDiRa665BoDLL7+c6667DoBLL72UKVOmAHDDDTcUfHwiXzVkZgXx1FNP8cADDzBz5ky23HJL3n//fT777LNihwXATjvtxKxZs1izZg37778/9913X9VYRwBr1qyhVavCfz0OGzaMYcOGfWH+lVdeWfX+hhtu4KSTTuJLX/pSweJwjcDMALjv+SVUXPMoPUZNouKaR7nv+U0bFf6dd96hc+fOVWP9dO7cma5duwLrP2Bm+vTpDBkyBICVK1fyne98h169etG7d2/+9re/AfDwww+z9957s8cee3DsscdW3eE7atQoSktL6d27NxdeeCEAd999N2VlZfTp04fBgwfXGmOrVq3YZ599WLBgAf/4xz/Yd999GTZsGKWlpUDNQ11DkihOPPFEdt99d771rW9VnbFfeeWV9O/fn7KyMkaOHLnesNm33XZb1VDUlcNh33rrrZx77rlfiGvEiBFMmDCB3/72t7z99tvst99+7Lffftxyyy2cf/75VeVuuukmLrjggrr8OmrlRGBm3Pf8En5yz0ssWfEJASxZ8Qk/ueelTUoGBx98MIsWLWKXXXbh7LPP5rHHHtvoOj//+c9p3749L730Ei+++CL7778/77//Pr/4xS+YMmUKM2fOpLy8nOuvv55ly5Zx7733Mnv2bF588UUuueQSIPkynjx5Mi+88AITJ06sdX+rVq3ikUceqRqtdObMmfzmN7/hlVdeWW+o66effpqbbrqJ559/HoD58+dz9tlnM3fuXLbeemvGjBkDwLnnnstzzz3Hyy+/zCeffMIDDzyw3r5mzZrFmDFjOPXUU+v0GX7/+9+na9euTJ06lalTp3Lcccdx//33s3r1agD+9Kc/1XlbtXEiMDOunTyfT1avXW/eJ6vXcu3k+fXeZtu2bZkxYwZjx46lS5cuHH/88dx66621rjNlyhTOOefzQYk7duzI008/zZw5c6ioqKBv376MGzeON998k/bt21NSUsJpp53GPffcU9V0UlFRwYgRI7jppptYu3Ztjft57bXX6Nu3LxUVFRx++OEceuihAAwYMIAePXoA6w913bZt26qhroH1xko66aSTmDZtGgBTp05l4MCB9OrVi0cffZTZs2dX7XP48GRA5sGDB/Phhx+yYsWKfD9S2rZty/77788DDzzAvHnzWL16dVUS2xTuIzAz3l7xSV7z66ply5YMGTKEIUOG0KtXL8aNG8eIESNo1apVVQdt7vDONYkIDjroIG6//fYvLHv22Wd55JFHmDBhAr///e959NFHufHGG3nmmWeYNGkSe+65JzNmzKBTp07rrVfZR1Bd7rDZtckdnrpy+tNPP+Xss89m+vTpbL/99lx++eXrHVtN69TH6aefztVXX81uu+3Gd77TMI9xcY3AzOjaoU1e8+ti/vz5vPrqq1XTs2bNYscddwSSPoIZM2YAVPUDABx00EGMHj26anr58uXstddePPnkkyxYsACAjz/+mFdeeYWVK1fywQcfcNhhh/HrX/+aF154AUjO9gcOHMiVV15Jly5dWLRoUb3ir22o67feeounnnoKgPHjxzNo0KCqL/3OnTuzcuVKJkyYsN727rzzTiCpabRv375qhNONadeuHR999FHV9MCBA1m0aBHjx4+vqmVsKicCM+OiobvSpnXL9ea1ad2Si4buWu9trly5smp45969ezNnzhwuv/xyIBle+rzzzqO8vJyWLT/f7yWXXMLy5curOnunTp1Kly5duPXWWxk+fDi9e/dm7733Zt68eXz00UccccQR9O7dm0GDBnH99dcnx3LRRfTq1YuysjL22Wcf+vTpU6/4c4e6HjhwYNVQ1wC77roro0ePZvfdd2f58uWcddZZdOjQgTPOOIOysjKGDh1K//7919teSUkJ/fr148wzz6x65GZdjBw5kkMOOYT99tuvat5xxx1HRUVF1RPbNpWHoTbbTOU7DPV9zy/h2snzeXvFJ3Tt0IaLhu7KUf22K2CEVl9HHHEEF1xwAQcccECNyz0MtZnVy1H9tvMXfxO3YsUKBgwYQJ8+fTaYBOrDicDMrJno0KEDr7zySoNv130EZpux5tb0a5uuPr9z1wjMNlMlJSUsW7aMTp061ftSRSu+5as+490PPuWztevYomULtm1fQscv1fwEtYhg2bJllJSU5LWPzCQCd4RZ1nTr1o3FixezdOnSYodi9bTqszWsWLWadTkn+UsEHb7Umi9tUfPXd0lJCd26dctrP5lIBJW3z1feOVl5+zzgZGCbrdatW1fdJWvNU8U1j7Kkhpv6tuvQhidH7d9g+8lEIqjt9vliJ4KmWlNpqnGZZUmh7viuLhOJoLE+zHw11ZpKU43LLGu6dmhTY41gU+74rkkmrhoqxO3zDaEQA301hKYal1nWFOKO75pkIhE01oeZr6ZaU2mqcZllzVH9tuO/junFdh3aIJK+gf86pleD18wz0TRU+aE1tTbvxqr25aupxgXuu7DsaYw7vjORCKBp3j5/0dBd12uLh6ZRU2mqcbnvwqwwMtE01FQ1VrVvc4nLfRdmhVHQGoGkQ4DfAC2BP0bENdWW7wCMAzqkZUZFxIOFjKmpaYo1FWiacbnvwqwwClYjkNQSGA0cCpQCwyWVVit2CXBXRPQDTgDGFCoea/6a6tVfZs1dIZuGBgALIuL1iPgMuAM4slqZALZO37cH3i5gPNbMNdWrv8yau0I2DW0H5D4jbjEwsFqZy4GHJX0P2Ao4sKYNSRoJjATYYYcdGjxQax6a6tVfZs1dsa8aGg7cGhG/krQ3cJuksohYl1soIsYCYyF5QlkR4rQmoin2XZg1d4VsGloCbJ8z3S2dl+s04C6AiHgKKAE6FzAmMzOrppCJ4DlgZ0k9JG1B0hk8sVqZt4ADACTtTpIIPGaumVkjKlgiiIg1wLnAZGAuydVBsyVdKWlYWuyHwBmSXgBuB0aEH6lkZtaoCtpHkN4T8GC1eZfmvJ8DVBQyBjMzq53vLDYzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLuFZ1LSipBdAH6Ap8ArwcEe8VKjAzM2scG00EknYCfgwcCLwKLAVKgF0krQL+AIyLiHWFDNTMzAqjLjWCXwD/DXw3IiJ3gaQvA98G/hMY1/DhmZlZoW20jyAihkfE49WTQLrsvYi4ISJqTAKSDpE0X9ICSaM2UOY4SXMkzZY0Pv9DMDOzTVHnzmJJx0pql77/maR7JO1RS/mWwGjgUKAUGC6ptFqZnYGfABUR0RM4vx7HYGZmmyCfq4Z+FhEfSRoEHADcTNJktCEDgAUR8XpEfAbcARxZrcwZwOiIWA5JDSOPeMzMrAHkkwjWpj8PB8ZGxCRgi1rKbwcsyplenM7LtQtJp/OTkp6WdEhNG5I0UtJ0SdOXLl2aR8hmZrYx+SSCJZL+ABwPPChpyzzXr0krYGdgCDAcuElSh+qFImJsRJRHRHmXLl02cZdmZpYrny/y44DJwNCIWAFsA1xUS/klwPY5093SebkWAxMjYnVEvAG8QpIYzMyskeSTCL4CTIqIVyUNAY4Fnq2l/HPAzpJ6SNoCOAGYWK3MfSS1ASR1Jmkqej2PmMzMbBPlkwj+BqyV9DVgLMnZ/gYv94yINcC5JLWIucBdETFb0pWShqXFJgPLJM0BpgIXRcSyehyHmZnVk2q4PaDmgtLMiNhD0o+ATyLid5Kej4h+hQ1xfeXl5TF9+vTG3KWZWbMnaUZElNe0LJ8awWpJw4GTgQfSea03NTgzMyuufBLBd4C9gasi4g1JPYDbChOWmZk1ljqPPhoRcyT9GNghnX4D+GWhAjMzs8aRzxAT3wBmAf+bTveVVP0qIDMza2byaRq6nGTYiBUAETEL+GoBYjIzs0aUV2dxRHxQbZ6fQWBm1szVuY8AmC3p20DLdNTQ7wP/LExYZmbWWPKpEXwP6An8m+RGsg/wsNFmZs1ePlcNrQIuTl9mZraZyOeqob/njgwqqaOkyYUJy8zMGks+TUOd01FHAUgfJvPlhg/JzMwaUz6JYJ2kHSonJO0I1G2gIjMza7LyuWroYmCapMcAAfsCIwsSlZmZNZp8Oov/N31Y/V7prPMj4v3ChGVmZo0ln87io0luKnsgIh4A1kg6qnChmZlZY8inj+Cy3DuL047jyxo+JDMza0z5JIKayubTx2BmZk1QPolguqTrJe2Uvq4HZhQqMDMzaxz5DjHxGXBn+vo3cE4hgjIzs8aTz1VDHwOjChiLmZkVQZ0TgaSp1HADWUTs36ARmZlZo8qns/fCnPclwDeBNQ0bjpmZNbZ8moaqdww/KenZBo7HzMwaWT5NQ9vkTLYA9gTaN3hEZmbWqPJpGppB0kcgkiahN4DTChGUmZk1nnyahnoUMhAzMyuOfMYaOlZSu/T9JZLuSQehMzOzZiyfG8p+FhEfSRoEHAjcDPx3YcIyM7PGkk8iWJv+PBwYGxGTgC0aPiQzM2tM+SSCJZL+ABwPPChpyzzXNzOzJiifL/LjgMnA0HQI6m2AiwoSlZmZNZqNJgJJbQEiYlVE3BMRr6bT70TEw7llalj3EEnzJS2QtMFxiiR9U1JIKq/fYZiZWX3VpUbwP5J+JWmwpK0qZ0r6qqTTJE0GDqm+kqSWwGjgUKAUGC6ptIZy7YDzgGfqexBmZlZ/G00EEXEA8AjwXWC2pA8kLQP+AvwHcEpETKhh1QHAgoh4PSI+A+4Ajqyh3M+BXwKf1vMYzMxsE9T1hrKHgJciYlEe294OyC2/GBiYWyC9D2H7iJgkaYP9DZJGAiMBdthhhzxCMDOzjalTZ3FEBPBgQ+5YUgvgeuCHddj/2Igoj4jyLl26NGQYZmaZl89VQzMl9c+j/BJg+5zpbum8Su2AMuAfkhYCewET3WFsZta48hl0biBwUvql/THJ4HMREb03UP45YGdJPUgSwAnAtysXRsQHQOfKaUn/AC6MiOn5HICZmW2afBLB0Hw2HBFrJJ1Lcu9BS+CWiJgt6UpgekRMzGd7ZmZWGBtNBJJKgDOBrwEvATdHRJ2eTBYRD1KtbyEiLt1A2SF12aaZmTWsuvQRjAPKSZLAocCvChqRmZk1qro0DZVGRC8ASTcDfjylmdlmpC41gtWVb+raJGRmZs1HXWoEfSR9mL4X0CadrrxqaOuCRWdmZgW30UQQES0bIxAzMysOP0/AzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjnAjMzDLOicDMLOOcCMzMMs6JwMws45wIzMwyzonAzCzjCpoIJB0iab6kBZJG1bD8B5LmSHpR0iOSdixkPGZm9kUFSwSSWgKjgUOBUmC4pNJqxZ4HyiOiNzAB+H+FisfMzGpWyBrBAGBBRLweEZ8Bd6aLoWcAAAcsSURBVABH5haIiKkRsSqdfBroVsB4zMysBoVMBNsBi3KmF6fzNuQ04KGaFkgaKWm6pOlLly5twBDNzKxJdBZLOgkoB66taXlEjI2I8ogo79KlS+MGZ2a2mWtVwG0vAbbPme6WzluPpAOBi4GvR8S/CxiPmZnVoJA1gueAnSX1kLQFcAIwMbeApH7AH4BhEfFeAWMxM7MNKFgiiIg1wLnAZGAucFdEzJZ0paRhabFrgbbA3ZJmSZq4gc2ZmVmBFLJpiIh4EHiw2rxLc94fWMj9m5nZxjWJzmIzMyseJwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4wr6A1lTc0V989mztsfFjsMM7O8lXbdmsu+0bMg23aNwMws4zJVIyhUNjUza85cIzAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4xzIjAzyzgnAjOzjCtoIpB0iKT5khZIGlXD8i0l3Zkuf0ZS90LGY2ZmX1SwRCCpJTAaOBQoBYZLKq1W7DRgeUR8Dfg18MtCxWNmZjUrZI1gALAgIl6PiM+AO4Ajq5U5EhiXvp8AHCBJBYzJzMyqKWQi2A5YlDO9OJ1XY5mIWAN8AHSqviFJIyVNlzR96dKlBQrXzCybmkVncUSMjYjyiCjv0qVLscMxM9usFDIRLAG2z5nuls6rsYykVkB7YFkBYzIzs2oKmQieA3aW1EPSFsAJwMRqZSYCp6TvvwU8GhFRwJjMzKyaVoXacESskXQuMBloCdwSEbMlXQlMj4iJwM3AbZIWAP9HkizMzKwRFSwRAETEg8CD1eZdmvP+U+DYQsZgZma1axadxWZmVjhOBGZmGedEYGaWcU4EZmYZp+Z2taakpcCbm7CJzsD7DRROQ3Jc+WmqcZkVyqb+ze8YETXekdvsEsGmkjQ9IsqLHUd1jis/TTUus0Ip5N+8m4bMzDLOicDMLOOymAjGFjuADXBc+WmqcZkVSsH+5jPXR2BmZuvLYo3AzMxyOBGYmWVcJhKBpO0lTZU0R9JsSecVOyYASSWSnpX0QhrXFcWOKZeklpKel/RAsWOpJGmhpJckzZI0vdjxmBWCpFskvSfp5Zx520j6u6RX058dG2p/mUgEwBrghxFRCuwFnCOptMgxAfwb2D8i+gB9gUMk7VXkmHKdB8wtdhA12C8i+vo+AtuM3QocUm3eKOCRiNgZeCSdbhCZSAQR8U5EzEzff0Ty5Vb9+cmNLhIr08nW6atJ9N5L6gYcDvyx2LGYZU1EPE7yjJZcRwLj0vfjgKMaan+ZSAS5JHUH+gHPFDeSRNr8Mgt4D/h7RDSJuIAbgB8B64odSDUBPCxphqSRxQ7GrBFtGxHvpO//BWzbUBvOVCKQ1Bb4G3B+RHxY7HgAImJtRPQleabzAEllxY5J0hHAexExo9ix1GBQROwBHErSxDe42AGZNbb0kb4N1nqQmUQgqTVJEvhrRNxT7Hiqi4gVwFS+2C5YDBXAMEkLgTuA/SX9pbghJSJiSfrzPeBeYEBxIzJrNO9K+gpA+vO9htpwJhKBJJE8H3luRFxf7HgqSeoiqUP6vg1wEDCvuFFBRPwkIrpFRHeS50g/GhEnFTksJG0lqV3le+Bg4OXa1zLbbEwETknfnwL8T0NtuKDPLG5CKoD/BF5K2+MBfpo+U7mYvgKMk9SSJCnfFRFN5lLNJmhb4N4kr9MKGB8R/1vckMwanqTbgSFAZ0mLgcuAa4C7JJ1GMhT/cQ22Pw8xYWaWbZloGjIzsw1zIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwq0bS2nR009npyLA/lFTv/xVJP8153z13REmzpsCJwOyLPklHN+1JcpPfoSTXcdfXTzdexKx4nAjMapEOZTESOFeJlpKulfScpBclfRdA0hBJj0uaJGm+pBsltZB0DdAmrWH8Nd1sS0k3pTWOh9O7ys2KxonAbCMi4nWgJfBl4DTgg4joD/QHzpDUIy06APgeUArsBBwTEaP4vIZxYlpuZ2B0WuNYAXyz8Y7G7IucCMzyczBwcjpUyTNAJ5IvdoBnI+L1iFgL3A4M2sA23oiIyqFOZgDdCxiv2UZlZawhs3qT9FVgLclojwK+FxGTq5UZwheHBd7Q+C3/znm/FnDTkBWVawRmtZDUBbgR+H06Bvxk4Kx0WHMk7ZKOhArJ8yR6pFcYHQ9MS+evrixv1hS5RmD2RW3Spp/WJM+7vg2oHL78jyRNOTPT4c2X8vkjA58Dfg98jeTZEvem88cCL0qaCVzcGAdglg+PPmrWANKmoQsj4ohix2KWLzcNmZllnGsEZmYZ5xqBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxv1/u0oUJr42HQYAAAAASUVORK5CYII=\n", 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" ] @@ -1180,12 +1032,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1218,12 +1070,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1263,7 +1115,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1272,7 +1124,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1282,12 +1134,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1318,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1353,23 +1205,23 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 43, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1387,23 +1239,23 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 44, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1426,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1436,12 +1288,12 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1472,12 +1324,12 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1514,7 +1366,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1523,7 +1375,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1547,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1561,7 +1413,7 @@ " 10, 10, 10, 10]])" ] }, - "execution_count": 50, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1596,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1620,7 +1472,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1644,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1666,7 +1518,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1676,15 +1528,15 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0111\n", - "The estimated product of the one and two qubit fidelity is F = 0.9889\n" + "The estimated error is p = 0.0101\n", + "The estimated product of the one and two qubit fidelity is F = 0.9899\n" ] } ], @@ -1696,7 +1548,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -1706,12 +1558,12 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1742,12 +1594,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1785,7 +1637,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1795,7 +1647,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1804,16 +1656,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.05929668, 0.00165794])" + "array([0.05658055, 0.00064265])" ] }, - "execution_count": 61, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1824,18 +1676,18 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.88199086 0.82969173 0.78049376 0.73421307]\n", - " [0.88052857 0.82831615 0.77919975 0.73299579]\n", - " [0.87906871 0.82694285 0.77790788 0.73178053]\n", - " [0.87761126 0.82557182 0.77661815 0.73056728]\n", - " [0.8703602 0.81875073 0.77020153 0.72453113]]\n" + "[[0.88889666 0.83860239 0.79115381 0.74638989]\n", + " [0.88832541 0.83806347 0.79064538 0.74591022]\n", + " [0.88775453 0.83752489 0.79013727 0.74543087]\n", + " [0.88718402 0.83698666 0.78962949 0.74495182]\n", + " [0.88433695 0.83430068 0.78709548 0.74256119]]\n" ] } ], @@ -1847,12 +1699,12 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 60, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1889,7 +1741,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 61, "metadata": {}, "outputs": [ { diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index f6cfdd13..39ab0986 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -26,20 +26,6 @@ from forest.benchmarking.utils import bit_array_to_int -def make_default_pattern(num_generators): - """ - By default sweep over each generator in sequence n many times - - :param num_generators: - :return: - """ - return [(list(range(num_generators)), 'n')] - -# TODO: perhaps best for pattern to be sample-time specified, given ambiguity in append; however, -# it is convenient to keep a persistent state. Appending sequence_transforms is also not well -# motivated, so instead maybe it is better to remove support for appending CircuitTemplates -# altogether? - @dataclass class CircuitTemplate: """ @@ -58,8 +44,8 @@ class CircuitTemplate: The primary purpose of this class is to sample circuits, which we represent by a list of pyquil Programs, or a 'sequence'; this core functionality is found in :func:`sample_sequence`. - In this function `generators` are applied in series according to the order specified by - `pattern`. Each call to a generator will contribute an element to the output sequence, + In this function `generators` are applied in series in a loop `repetitions` number of times. + Each call to a generator will contribute an element to the output sequence, and some combination of the generators will constitute a unit of depth. After a sequence is generated from the output of the various `generators`, each `sequence_transform` is then applied in series on the sequence to create a final output sequence. See @@ -77,15 +63,11 @@ class CircuitTemplate: """ generators: List[Callable] = field(default_factory=lambda : []) sequence_transforms: List[Callable] = field(default_factory=lambda : []) - pattern: List[Union[int, Tuple[List, int], Tuple[List, str]]] = field(init=False, repr=False) - - def __post_init__(self): - self.pattern = make_default_pattern(len(self.generators)) def append(self, other): """ - Mutates the CircuitTemplate object by appending new generators. It is ambiguous how to - append patterns, so we reset the pattern to the default. + Mutates the CircuitTemplate object by appending new generators. + TODO: The behavior of sequence_transforms may not conform with expectations. :param other: :return: @@ -95,8 +77,6 @@ def append(self, other): elif isinstance(other, CircuitTemplate): self.generators += other.generators self.sequence_transforms += other.sequence_transforms - # make default pattern since it is unclear how to compose general patterns. - self.pattern = make_default_pattern(len(self.generators)) else: raise ValueError(f'Cannot append type {type(other)}.') @@ -120,97 +100,41 @@ def __iadd__(self, other): self.append(other) return self - def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None, pattern=None): + def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None): """ - The introduction of `pattern` is an attempt to enable some flexibility in specifying what - exactly constitutes a single unit of 'depth'. The default behavior is to sample from each - generator in series and consider these combined samples as a single unit. Thus, - the default pattern is - - [(list(range(num_generators)), 'n')] - - indicating that we combine samples from the generators at sequential indices and repeat - this depth many, or 'n' times. - - Another common family this will enable is 'do depth many layers of gates, then invert - them at the end'. If the last generator is the inversion generator this is specified by the - pattern - - [(list(range(num_generators - 1)), 'n'), -1] - - In general, a `pattern` is a list whose elements are either - - 1) an index of a generator - 2) a tuple of a `pattern` and a number of repetitions - 3) a tuple of a `pattern` and 'n', indicating depth many repetitions - The sequence_transforms are distinct from generators in that they take in a sequence and output a new sequence. These are applied in series after the entire sequence has been - generated. A family of interest that is not easily generated by generators + patterns - alone is given by + generated. A motivating family of interest is C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t - where C_j is a clifford, P_j is a random local Pauli. We could accomplish this with a - bespoke 'alternate conjugate and random local pauli layer' that is applied as the last step - after P_N is added to the sequence and steps through the entire sequence in reverse. - Instead, we introduce sequence_transforms that Conjugation of a sequence and Pauli frame - randomization are sequence level operations that can be conceptually distinguished. + where C_j is a clifford, P_j is a random local Pauli. We can specify this family by a + generator of random Cliffords, a conjugation sequence transform, and a Pauli frame + randomization transform. :param graph: :param repetitions: :param qc: :param width: :param sequence: - :param pattern: :return: """ if width is not None: graph = random.choice(generate_connected_subgraphs(graph, width)) - if pattern is None: - pattern = self.pattern - if sequence is None: sequence = [] - def _do_pattern(patt): - for elem in patt: - if isinstance(elem, int): - # the elem is an index; we use the generator at this index to generate the - # next program in the sequence - sequence.append(self.generators[elem](graph=graph, qc=qc, width=width, - sequence=sequence)) - elif len(elem) == 2: - - # elem[0] is a pattern that we will execute elem[1] many times - if elem[1] == 'n': - # n indicates `repetitions` number of times - reps = repetitions - elif isinstance(elem[1], int) and elem[1]>=0: - reps = elem[1] - else: - raise ValueError('Repetitions must be specified by int or `n`.') - - for _ in range(reps): - _do_pattern(elem[0]) - else: - raise ValueError('Pattern is malformed. A pattern is a list where each element ' - 'can either be a generator index or a (pattern_i, num) tuple, ' - 'where num is an integer indicating how many times to ' - 'repeat the associated pattern_i.') - - _do_pattern(pattern) + for generator in self.generators: + sequence.append(generator(graph=graph, qc=qc, width=width, sequence=sequence)) for sequence_transform in self.sequence_transforms: sequence = sequence_transform(graph=graph, qc=qc, width=width, sequence=sequence) return sequence - def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None, - pattern = None): - # TODO: replace with merge_programs after permutation issue fixed. - return sum(self.sample_sequence(graph, repetitions, qc, width, sequence, pattern), Program()) + def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None): + return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) # ================================================================================================== @@ -387,6 +311,13 @@ def graph_restricted_compilation(qc, graph, program): ### # Sequence Transforms ### +def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs): + prog = Program() + for node in graph.nodes: + prog.inst(H(node)) + return [prog] + sequence + [prog.copy()] + + def dagger_sequence(sequence: List[Program], **kwargs): return sequence + [prog.dagger() for prog in reversed(sequence)] @@ -411,8 +342,7 @@ def compile_individual_sequence_elements(qc, sequence: List[Program], graph: nx. def compile_merged_sequence(qc, sequence: List[Program], graph: nx.Graph, **kwargs): # compile all of the sequence at once. - # TODO: replace sum with merge_programs after permutation issue fixed. - native_quil = graph_restricted_compilation(qc, graph, sum(sequence, Program())) + native_quil = graph_restricted_compilation(qc, graph, merge_programs(sequence)) return [Program([instr for instr in native_quil.instructions][:-1])] ### # Templates @@ -525,7 +455,7 @@ def sample_random_connected_graphs(graph: nx.Graph, widths: List[int], num_ckts_ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, - graphs: Dict[int, List[nx.Graph]] = None, pattern = None): + graphs: Dict[int, List[nx.Graph]] = None): if graphs is None: graphs = sample_random_connected_graphs(qc.qubit_topology(), widths, len(depths)*num_circuit_samples) @@ -538,8 +468,7 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, for _ in range(num_circuit_samples): graph = graphs[width][circuit_number] circuit_number += 1 - prog = ckt.sample_program(graph, repetitions=depth, width=width, - qc=qc, pattern=pattern) + prog = ckt.sample_program(graph, repetitions=depth, width=width, qc=qc) prog_list.append(prog) return programs @@ -632,14 +561,14 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, # TODO: # def do_volumetric_measurements(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], # depths: List[int], -# num_circuit_samples: int, graph: nx.Graph = None, pattern = None, +# num_circuit_samples: int, graph: nx.Graph = None, # num_shots: int = 500, # use_active_reset: bool = False, # compile_circuits: bool = False): # # # prog_array = generate_volumetric_program_array(qc, ckt, widths, depths, num_circuit_samples, -# graph, pattern) +# graph) # # return [] From a16fbccdabdb2015b9462615276db389dacb7a85 Mon Sep 17 00:00:00 2001 From: Kyle Date: Mon, 16 Sep 2019 17:17:02 -0400 Subject: [PATCH 38/49] Fix sampling repetitions. --- docs/examples/volumetrics.ipynb | 402 ++++++++++++++++++----------- forest/benchmarking/volumetrics.py | 7 +- 2 files changed, 257 insertions(+), 152 deletions(-) diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb index 636db13c..074571eb 100644 --- a/docs/examples/volumetrics.ipynb +++ b/docs/examples/volumetrics.ipynb @@ -79,7 +79,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -169,8 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 0\n", - "RX(pi/2) 0\n", + "RZ(-pi) 0\n", "\n" ] } @@ -196,31 +195,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Z 0\n", - "Z 1\n", + "X 0\n", + "I 1\n", "Z 2\n", "I 3\n", - "I 4\n", - "I 5\n", - "Z 6\n", + "X 4\n", + "Z 5\n", + "I 6\n", "I 7\n", "Z 8\n", "CZ 0 3\n", "CZ 0 1\n", - "I 1\n", - "I 4\n", + "CZ 1 4\n", "I 1\n", "I 2\n", - "CZ 2 5\n", + "I 2\n", + "I 5\n", "I 3\n", "I 6\n", - "CZ 3 4\n", + "I 3\n", "I 4\n", - "I 7\n", - "CZ 4 5\n", + "CZ 4 7\n", + "I 4\n", + "I 5\n", "CZ 5 8\n", - "I 6\n", - "I 7\n", + "CZ 6 7\n", "I 7\n", "I 8\n", "\n" @@ -242,20 +241,25 @@ "name": "stdout", "output_type": "stream", "text": [ + "RX(-pi/2) 0\n", + "RZ(pi/2) 0\n", "RX(-pi/2) 0\n", "RX(pi/2) 1\n", "RZ(-pi/2) 1\n", "RZ(-pi) 2\n", - "RZ(pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 3\n", "RZ(-pi/2) 4\n", - "RZ(-pi) 5\n", - "RZ(-pi) 5\n", - "RX(pi/2) 6\n", - "RZ(-pi) 6\n", - "RZ(-pi) 7\n", - "RZ(-pi) 7\n", - "RZ(-pi) 8\n", - "RZ(-pi) 8\n", + "RX(-pi/2) 4\n", + "RX(-pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "RZ(pi/2) 6\n", + "RX(-pi/2) 6\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "RX(pi/2) 8\n", + "RZ(pi/2) 8\n", "\n" ] } @@ -281,8 +285,10 @@ "name": "stdout", "output_type": "stream", "text": [ + "I 3\n", + "I 4\n", + "X 3\n", "I 4\n", - "X 5\n", "\n" ] } @@ -301,7 +307,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "CNOT 0 1\n", + "CNOT 1 4\n", + "I 1\n", + "I 4\n", "\n" ] } @@ -321,13 +329,19 @@ "output_type": "stream", "text": [ "RX(-pi/2) 3\n", - "CZ 0 3\n", + "CZ 3 6\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "CZ 3 6\n", + "RX(-pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(-pi/2) 3\n", "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", - "RX(pi/2) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 0\n", + "CZ 3 6\n", + "RX(-pi/2) 6\n", + "RX(-pi/2) 3\n", + "CZ 3 6\n", "\n" ] } @@ -347,9 +361,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE Perm120 AS PERMUTATION:\n", - " 0, 4, 1, 5, 2, 6, 3, 7\n", - "Perm120 0 3 4\n", + "DEFGATE Perm012 AS PERMUTATION:\n", + " 0, 1, 2, 3, 4, 5, 6, 7\n", + "Perm012 5 7 8\n", "\n" ] } @@ -369,13 +383,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE LYR0_RSU4_4_7:\n", - " -0.12199079971763926-0.8649198443726955i, 0.274615772788525+0.3041112377388371i, -0.12039142645980519+0.04447186269594815i, 0.22946587597712897+0.0028301301474696478i\n", - " 0.2876933119119516-0.0658590695663348i, 0.44382440864829387-0.40618240445500203i, 0.5448213602522904+0.4062939105280664i, 0.11557814784292778+0.2750773077845369i\n", - " 0.2961881426835967+0.04007749099266393i, 0.5623040426685924+0.010247843130115501i, 0.06926633628932952-0.43595017123785745i, -0.25000849797732155-0.5805350101010037i\n", - " -0.16027345597301254-0.1868310653809832i, -0.22190059815206353+0.3234309058598983i, 0.5624157410478605+0.10228029438901522i, -0.6769786666331948-0.022052358565452367i\n", + "DEFGATE LYR0_RSU4_4_1:\n", + " 0.26858053105152213+0.10864973894203411i, 0.4587166902639551-0.020767812561925177i, 0.027376027977825214+0.5929760856558103i, -0.06436729517958666+0.590503340249926i\n", + " 0.09066940819787443+0.158726543334475i, 0.017470105331415284-0.5429604064332522i, 0.36401273746231755-0.04704070321985694i, -0.7169278738708781-0.15089750891395345i\n", + " 0.17881148520035228+0.356810637344199i, 0.5065785412641826-0.2797787222124536i, -0.3005402292453411-0.6076110685630518i, 0.1931623195201898+0.09480162577031309i\n", + " -0.797672269362934+0.29508684815474884i, -0.10807959528043765-0.3840017418086259i, -0.15315108213887452+0.17303444785000938i, 0.11617211173412989+0.22497118139799177i\n", "\n", - "LYR0_RSU4_4_7 4 7\n", + "LYR0_RSU4_4_1 4 1\n", "\n" ] } @@ -401,14 +415,22 @@ "name": "stdout", "output_type": "stream", "text": [ + "X 1\n", + "X 2\n", + "I 3\n", + "I 4\n", "I 1\n", - "X 3\n", "I 4\n", - "X 5\n", + "CNOT 1 2\n", + "CNOT 3 4\n", + "I 1\n", + "X 2\n", + "I 3\n", + "I 4\n", "CNOT 1 4\n", + "CNOT 1 2\n", "I 3\n", "I 4\n", - "CNOT 4 5\n", "\n" ] } @@ -436,15 +458,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 0\n", - "H 3\n", - "Z 0\n", - "I 3\n", - "H 0\n", - "CZ 0 3\n", - "H 0\n", - "H 0\n", - "H 3\n", + "H 1\n", + "H 2\n", + "I 1\n", + "Z 2\n", + "I 1\n", + "I 2\n", + "I 1\n", + "I 2\n", + "I 1\n", + "I 2\n", + "I 1\n", + "I 2\n", + "H 1\n", + "CZ 1 2\n", + "H 1\n", + "H 1\n", + "H 2\n", "\n" ] } @@ -476,22 +506,70 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 0\n", + "RZ(pi/2) 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", "RX(-pi/2) 1\n", + "RX(-pi) 0\n", + "RZ(pi/2) 0\n", + "RX(-pi) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", "RX(-pi/2) 1\n", "CZ 0 1\n", - "RX(pi/2) 0\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 0\n", + "RX(-pi/2) 0\n", "CZ 0 1\n", "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", + "RX(-pi/2) 0\n", + "CZ 0 1\n", + "RX(-pi/2) 0\n", + "RZ(-pi) 0\n", + "RZ(pi/2) 1\n", + "RX(-pi) 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RX(pi/2) 1\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 0\n", + "DAGGER RX(pi/2) 0\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RZ(-pi) 0\n", + "DAGGER RX(-pi/2) 0\n", "DAGGER CZ 0 1\n", - "DAGGER RX(pi/2) 0\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 0 1\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RZ(pi/2) 0\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(-pi) 0\n", + "DAGGER RZ(pi/2) 0\n", + "DAGGER RX(-pi) 0\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(pi/2) 0\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RZ(pi/2) 0\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -526,74 +604,98 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(2.4246593511866825) 3\n", + "RZ(-2.659459316672841) 1\n", + "RX(pi/2) 1\n", + "RZ(0.461798249424243) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.63880900969572) 1\n", + "RZ(2.609090341343644) 3\n", "RX(pi/2) 3\n", - "RZ(1.1994625091249727) 3\n", + "RZ(2.7304530401071103) 3\n", "RX(-pi/2) 3\n", - "RZ(1.6596967975055819) 3\n", - "RZ(-1.12840384641332) 4\n", + "RZ(-2.3724233319953845) 3\n", + "RZ(-2.6259067850591453) 4\n", "RX(pi/2) 4\n", - "RZ(1.39668867273419) 4\n", + "RZ(1.792266675205438) 4\n", "RX(-pi/2) 4\n", - "RZ(0.7543726433217182) 4\n", - "CZ 3 4\n", + "RZ(0.35479411869176225) 4\n", + "CZ 4 3\n", "RZ(-pi/2) 3\n", + "RX(pi/2) 3\n", + "RZ(2.134251918388017) 3\n", "RX(-pi/2) 3\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(2.289442254207697) 4\n", + "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RZ(1.7406983874554172) 3\n", + "CZ 4 3\n", "RX(pi/2) 3\n", + "RZ(-1.7178107510675495) 3\n", + "RX(-pi/2) 3\n", + "RZ(1.4165522865636788) 4\n", + "RX(pi/2) 4\n", + "CZ 4 3\n", + "RZ(-1.785708772007812) 4\n", "RX(pi/2) 4\n", - "RZ(-1.631702489085865) 4\n", + "RZ(0.49800681616645737) 4\n", "RX(-pi/2) 4\n", - "CZ 3 4\n", - "RZ(1.3545441126274091) 5\n", - "RX(pi/2) 5\n", - "RZ(2.497677361431593) 5\n", - "RX(-pi/2) 5\n", - "RZ(-2.5792111549892525) 5\n", - "RZ(-0.3101456732893413) 8\n", - "RX(pi/2) 8\n", - "RZ(2.1740792824123987) 8\n", - "RX(-pi/2) 8\n", - "RZ(-2.4474460793927824) 8\n", - "CZ 5 8\n", - "RZ(-pi/2) 5\n", - "RX(-pi/2) 5\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "RZ(2.30751710516067) 8\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RZ(2.2067087792137023) 5\n", - "RX(pi/2) 5\n", - "RX(pi/2) 8\n", - "RZ(-1.7460274856187237) 8\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RZ(2.0970755291047958) 3\n", - "RX(-pi/2) 3\n", - "RZ(2.681106403947665) 3\n", + "RZ(-0.49097992983251526) 7\n", + "RX(pi/2) 7\n", + "RZ(2.5825051567284976) 7\n", + "RX(-pi/2) 7\n", + "RZ(3.0107917077638024) 7\n", + "CZ 4 7\n", + "RZ(0.39042473708143177) 4\n", + "RX(-pi/2) 4\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(pi) 4\n", + "RX(pi/2) 4\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RZ(-2.668242265098992) 1\n", + "RX(pi/2) 1\n", + "RZ(2.286627324701731) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.145846853797221) 1\n", + "RZ(-1.4951524783114967) 4\n", + "RX(pi/2) 4\n", + "RZ(0.49048086074218056) 4\n", + "RX(-pi/2) 4\n", + "RZ(-1.272117698325029) 4\n", + "CZ 4 1\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(2.6109851757459044) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 1\n", + "RX(pi/2) 1\n", + "RZ(-1.5880815347394472) 1\n", + "RX(-pi/2) 1\n", + "RZ(1.245957303906056) 4\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RZ(-2.031121110990684) 1\n", + "RX(pi/2) 1\n", + "RZ(1.2784529064338348) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.135913750567923) 1\n", + "RZ(-1.0373111688471863) 3\n", + "RX(pi/2) 3\n", + "RZ(1.4897961313128554) 3\n", "RX(-pi/2) 3\n", - "RZ(0.6278160958652292) 3\n", - "RZ(-0.00415633983752528) 4\n", + "RZ(-0.5617250744521731) 3\n", + "RZ(0.5222384500519812) 4\n", "RX(pi/2) 4\n", - "RZ(1.4957403844866333) 4\n", + "RZ(1.2779579232074691) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.9104498155522833) 4\n", - "RZ(2.95063772413107) 5\n", - "RX(-pi/2) 5\n", - "RZ(0.856298302301197) 5\n", - "RX(-pi/2) 5\n", - "RZ(0.35269534528741975) 5\n", - "RZ(0.1098505168838746) 8\n", - "RX(pi/2) 8\n", - "RZ(1.0699948541344888) 8\n", - "RX(-pi/2) 8\n", - "RZ(-0.6807290975657327) 8\n", + "RZ(-0.1771720131335055) 4\n", + "RZ(0.2994224201693585) 7\n", + "RX(pi/2) 7\n", + "RZ(1.6131475136490458) 7\n", + "RX(-pi/2) 7\n", + "RZ(0.34720986671375864) 7\n", "\n" ] } @@ -642,8 +744,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [0.9100000000000007, 0.8940000000000007, 0.7880000000000006, 0.8960000000000007, 0.7860000000000006, 0.9060000000000007, 0.8360000000000006, 0.8340000000000006, 0.8640000000000007, 0.8360000000000006, 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\n", 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\n", 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" ] @@ -834,7 +936,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -861,7 +963,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -888,7 +990,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1037,7 +1139,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1075,7 +1177,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -1212,7 +1314,7 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 40, @@ -1221,7 +1323,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1246,7 +1348,7 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 41, @@ -1255,7 +1357,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -1293,7 +1395,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1329,7 +1431,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1535,8 +1637,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0101\n", - "The estimated product of the one and two qubit fidelity is F = 0.9899\n" + "The estimated error is p = 0.0108\n", + "The estimated product of the one and two qubit fidelity is F = 0.9892\n" ] } ], @@ -1563,7 +1665,7 @@ "outputs": [ { "data": { - "image/png": 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zK6vKNo52HEDMasz3wphZeZHaQerKAcSs5nwVxsxKCTeimtlo+BTGzErzVRgzKyXCAcTMRsGXcc2sNLeBmFkpgRjyVRgzK6vGFZBqBhQysw5F98YDkbRI0u2SVko6rUmaV0m6RdIKSV9pl6drIGZ114UqiKQBYAlwNLAGuEHSsoi4pZBmAXA6cFhEbMxDjrbkGohZzXWpBnIIsDIiVkXENuBC4LiGNG8ElkTExrTfWNcu076ogQwwxExtrboYHdlz8paqi7BTHoq+OAQmrACGhjoKEHMkLS/ML2142sFcYHVhfg1waEMeTwCQ9GNgADir3WiBPnrM6iyAzmoY67vwhMfJwALgSGAecLWkJ0fE/c028CmMWc1FtJ86sBaYX5ifl5cVrQGWRcRDEXEH8D+kgNKUA4hZ3UUHU3s3AAskPU7SLsDxwLKGNJeSah9ImkM6pVnVKlOfwpjVWnce2xAR2yW9Bbic1L7x+YhYIelsYHlELMvrXizpFtKD798VEb9tla8DiFnddaknWURcBlzWsOzMwusA3pGnjjiAmNVZQHR2FaYSDiBmtecAYmZl1fhmGAcQs7rr9wAiaVfglcD+xW0i4uzeFMvMgJ3pSFaJTmsg3wA2ATcC/dGn3GycGA8DCs2LiEU9LYmZjazGV2E67Yl6jaQn97QkZjYiRfupKi1rIJJuJp2FTQZeL2kV6RRGpH4nT+l9Ec0msM67qlei3SnMy8ekFGbWhPq3ETUi7gKQdH5EnFRcJ+l84KQRNzSz7unjGsiwg4ozeXi0Z3a/OGb2CENVF6C5lo2okk6XtBl4iqQHJG3O8+tIl3bNrJeG+4G0myrSMoBExIciYgbw0YiYGREz8rRnRJw+mh1LGpD0M0nfHE0+ZuNd316FKThD0p8Dh5Ni4n9FxKWj3PfbgFuBmaPMx2x8q3EbSKf9QJYAJwM3A78ATpa0pOxOJc0DXgZ8tmweZla9TmsgRwEH5AFHkHQesGIU+/0k8G5gRrMEkhYDiwEeO3dgFLsy629VnqK002kNZCWwb2F+fl620yS9HFgXETe2ShcRSyNiYUQsnDXbQ7faBBWkruztpop0WgOZAdwq6XrSv3QIsFzSMoCIOHYn9nkYcKykY4CpwExJF0TEiTuRh9nEUeMaSKcB5Mz2STqTr96cDiDpSOCdDh5mzdX5FKajABIRP5S0H7AgIr4naRowOSI297Z4ZlbnGkhHjQuS3ghcDJyTF80jPUNiVCLiqojw/TZmrXTnuTA90Wnr5JtJbRcPAETEL4G2T+42s9HppBNZP3Qk2xoR26TU2itpMrWuWJmNI+NgQKEfSjoDmCbpaODfgf/sXbHMbFidayCdBpDTgPtIPVHfRHq61Xt7VSgzK6hxG0inV2GGJF0KXBoR9/W4TGY2rOIaRjvtbueXpLMkrQduB26XdJ+krvULMbM2alwDaXcK83bS1ZdnRcTsiJgNHAocJuntPS+dmaGh9lNV2gWQk4ATIuKO4QURsQo4EXhNLwtmZvXXrg1kSkSsb1wYEfdJmtKjMplZUY3bQNoFkG0l15lZN9S8EbVdAHmqpAdGWC7SnbRm1mv9GkAiwiP5mFWtXwOImVVLVHuVpR0P9WVWZ128mU7SIkm3S1op6bQW6V4pKSQtbJenA4hZ3XWhI1l+GNwS4KXAgcAJkg4cId0M0hMTruukaA4gZnXXnZ6ohwArI2JVRGwDLgSOGyHdB4CPAA92kmlftIFMAh41aXvVxejInvG7qouwc/qoN88U9ccx0G0dnqLMkbS8ML80IpYW5ucCqwvza0i9yh/ej/QMYH5EfEvSuzrZaV8EELMJrbMAsj4i2rZZNCNpEvBx4HU7s50DiFmdRdeuwqwlPY5l2Ly8bNgM4GDgqjxw2GOBZZKOjYhizWYHDiBmddedfiA3AAskPY4UOI4HXv3HXURsAuYMz0u6ivTEhKbBA9yIalZ73biMGxHbgbcAl5OeSX1RRKyQdLaknXmu0w5cAzGruy71RI2Iy0ijCRaXjTi2T0Qc2UmeDiBmdVbxgEHtOICY1Zjo77txzaxiDiBmVp4DiJmV5gBiZqX0+YhkZlY1BxAzK6vOAwo5gJjVnE9hzKwcdyQzs1FxADGzMtwTtYGkqcDVwK55/xdHxPvHuhxm/UJD9Y0gVdRAtgJHRcSW/HjMH0n6dkRcW0FZzOrNbSA7iogAtuTZKXmq8VtkVq06n8JUMqCQpAFJPwfWAVdEREdDyJtNSN0Zlb0nKgkgETEYEU8jjct4iKSDG9NIWixpuaTlGzfUuCeNWY9168FSvVDpkIYRcT9wJbBohHVLI2JhRCycNdsjL9oE5hrIwyQ9WtIe+fU04GjgtrEuh1lfyKOyt5uqUsVVmL2B8/Kj9iaRBnf9ZgXlMKs99wNpEBE3AU8f6/2a9a2obwRxT1SzmnMNxMzKcUcyMxsNjwdiZqU5gJhZOYEbUc2sPDeimll5DiBmVoY7kplZeREeUMjMRqG+8cMBxKzufApjZuUE4FMYMyutvvGj2gGFzKy9bo1IJmmRpNslrZR02gjr3yHpFkk3Sfq+pP3a5ekAYlZzGoq2U9s80vg7S4CXAgcCJ0g6sCHZz4CFEfEU4GLgn9vl6wBiVmedDGfYWQ3kEGBlRKyKiG3AhcBxO+wq4sqI+H2evZY0ZnFLfdEGMkAwo85N0UUDD1Zdgp0yVdurLkLHZkzqr/e2G1JHso6O/TmSlhfml0bE0sL8XGB1YX4NcGiL/N4AfLvdTvsigJhNaJ3djbs+IhZ2Y3eSTgQWAke0S+sAYlZzHdZA2lkLzC/Mz8vLdtyX9CLgH4AjImJru0zdBmJWZ91rA7kBWCDpcZJ2AY4HlhUTSHo6cA5wbESs6yRT10DMaq0798JExHZJbwEuBwaAz0fECklnA8sjYhnwUWA34N8lAfw6Io5tla8DiFnddWlAoYi4DLisYdmZhdcv2tk8HUDM6iw8pKGZjYaHNDSz0uobPxxAzOpOQ/U9h3EAMauzoNOOZJVwADGrMRHd6kjWEw4gZnXnAGJmpTmAmFkpbgMxs9HwVRgzKyl8CmNmJfnh2mY2KvU9gxn78UAkzZd0ZR79eYWkt411Gcz6iSLaTlWpogayHTg1In4qaQZwo6QrIuKWCspiVn8+hXlYRNwD3JNfb5Z0K2nAVwcQs0YRMFjfc5hK20Ak7Q88HbhuhHWLgcUAc+d65EWbwGpcA6nsmylpN+DrwCkR8UDj+ohYGhELI2LhnrMdQGwCi2g/VaSSGoikKaTg8eWI+I8qymDWF/xw7R0pjdb6OeDWiPj4WO/frL8ERH3bQKo4NzgMOAk4StLP83RMBeUwq78gNaK2mypSxVWYH5Ge2GdmnahxI6p7oprVnQOImZXjm+nMrKwAfDu/mZXmGoiZleOu7GZWVkDUuB+IA4hZ3bknqpmV5jYQMyslwldhzGwUXAMxs3KCGBysuhBNOYCY1Zlv5zezUanxZVwP9WVWYwHEULSdOiFpkaTbJa2UdNoI63eV9LW8/ro85GhLDiBmdRZ5QKF2UxuSBoAlwEuBA4ETJB3YkOwNwMaIeDzwCeAj7fJ1ADGruRgcbDt14BBgZUSsiohtwIXAcQ1pjgPOy68vBl6YRxBsqi/aQG66efv6efPvvasHWc8B1vcg317op7JCf5W3V2Xdb7QZbGbj5d+Li+d0kHSqpOWF+aURsbQwPxdYXZhfAxzakMcf00TEdkmbgD1p8d70RQCJiEf3Il9JyyNiYS/y7rZ+Kiv0V3nrXNaIWFR1GVrxKYzZxLAWmF+Yn5eXjZhG0mRgd+C3rTJ1ADGbGG4AFkh6nKRdgOOBZQ1plgGvza//AvhBROtusH1xCtNDS9snqY1+Kiv0V3n7qayl5DaNtwCXAwPA5yNihaSzgeURsYz0uJXzJa0ENpCCTEtqE2DMzJryKYyZleYAYmalTbgAImm+pCsl3SJphaS3VV2mViRNlXS9pP/O5f3HqsvUjqQBST+T9M2qy9KOpDsl3ZyfkLi8/RZWNBEbUbcDp0bETyXNAG6UdEVE3FJ1wZrYChwVEVvyQ8l/JOnbEXFt1QVr4W3ArcDMqgvSoRdERL90equVCVcDiYh7IuKn+fVm0oE+t9pSNRfJljw7JU+1bfmWNA94GfDZqstivTfhAkhRvtvw6cB11ZaktXxK8HNgHXBFRNS5vJ8E3g3U9x70HQXwXUk3SlpcdWH6zYQNIJJ2A74OnBIRD1RdnlYiYjAinkbqPXiIpIOrLtNIJL0cWBcRN1Zdlp1weEQ8g3SX6pslPb/qAvWTCRlAclvC14EvR8R/VF2eTkXE/cCVQF3vjzgMOFbSnaS7PY+SdEG1RWotItbmv+uAS0h3rVqHJlwAybcnfw64NSI+XnV52pH0aEl75NfTgKOB26ot1cgi4vSImBcR+5N6Mf4gIk6suFhNSZqeG9KRNB14MfCLakvVXybiVZjDgJOAm3O7AsAZEXFZhWVqZW/gvDwgzCTgooio/eXRPrEXcEke8mIy8JWI+E61Reov7spuZqVNuFMYM+seBxAzK80BxMxKcwAxs9IcQMysNAeQcUDSJySdUpi/XNJnC/Mfk3SGpIubbH+VpIX59RmF5ftLcr8Ia8oBZHz4MfBcAEmTSI8pOKiw/rmkTl1/0UFeZ7RPYpY4gIwP1wDPya8PIvWm3CxplqRdgQOADcO1CUnTJF0o6VZJlwDT8vIPA9Py2BhfzvkNSDo3j0Xy3dwb1gxwABkXIuJuYLukfUm1jZ+Q7jB+DrAQuBnYVtjkb4HfR8QBwPuBZ+Z8TgP+EBFPi4i/ymkXAEsi4iDgfuCVY/AvWZ9wABk/riEFj+EA8pPC/I8b0j4fuAAgIm4CbmqR7x0RMdzl/0Zg/+4V2fqdA8j4MdwO8mTSKcy1pBrIc0nBpaythdeDTMz7p6wJB5Dx4xrg5cCGPH7IBmAPUhBpDCBXA68GyGOLPKWw7qE83IFZWw4g48fNpKsv1zYs2zTCeJ+fBna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\n", 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" ] @@ -1599,7 +1701,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1662,7 +1764,7 @@ { "data": { "text/plain": [ - "array([0.05658055, 0.00064265])" + "array([0.05700418, 0.00220137])" ] }, "execution_count": 58, @@ -1683,11 +1785,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.88889666 0.83860239 0.79115381 0.74638989]\n", - " [0.88832541 0.83806347 0.79064538 0.74591022]\n", - " [0.88775453 0.83752489 0.79013727 0.74543087]\n", - " [0.88718402 0.83698666 0.78962949 0.74495182]\n", - " [0.88433695 0.83430068 0.78709548 0.74256119]]\n" + "[[0.88533033 0.8348628 0.78727213 0.74239433]\n", + " [0.88338139 0.83302496 0.78553905 0.74076004]\n", + " [0.88143674 0.83119116 0.78380979 0.73912936]\n", + " [0.87949637 0.8293614 0.78208433 0.73750226]\n", + " [0.86985841 0.82027285 0.77351386 0.72942034]]\n" ] } ], @@ -1704,7 +1806,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 39ab0986..193c1e68 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -125,8 +125,11 @@ def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None if sequence is None: sequence = [] - for generator in self.generators: - sequence.append(generator(graph=graph, qc=qc, width=width, sequence=sequence)) + # run through the generators 'repetitions' many times; append each generated program to + # the sequence. + for _ in range(repetitions): + for generator in self.generators: + sequence.append(generator(graph=graph, qc=qc, width=width, sequence=sequence)) for sequence_transform in self.sequence_transforms: sequence = sequence_transform(graph=graph, qc=qc, width=width, sequence=sequence) From 94ccd555f2ce7689368f1bd1c4fd7186c9cf7ce6 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 7 Oct 2019 17:32:50 -0400 Subject: [PATCH 39/49] Clean up and comment, remove some unnecessary methods. --- docs/examples/volumetrics.ipynb | 683 ++++++++++++++++------------- forest/benchmarking/volumetrics.py | 523 +++++++++++++++------- 2 files changed, 727 insertions(+), 479 deletions(-) diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb index 074571eb..c5cd10fb 100644 --- a/docs/examples/volumetrics.ipynb +++ b/docs/examples/volumetrics.ipynb @@ -18,7 +18,16 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kylegulshen/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", + " return f(*args, **kwds)\n" + ] + } + ], "source": [ "import random\n", "import itertools\n", @@ -79,7 +88,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -169,7 +178,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi) 0\n", + "RX(-pi) 0\n", "\n" ] } @@ -195,31 +204,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 0\n", - "I 1\n", - "Z 2\n", + "Z 0\n", + "X 1\n", + "I 2\n", "I 3\n", - "X 4\n", - "Z 5\n", + "I 4\n", + "I 5\n", "I 6\n", "I 7\n", "Z 8\n", "CZ 0 3\n", - "CZ 0 1\n", - "CZ 1 4\n", + "I 0\n", "I 1\n", - "I 2\n", + "I 1\n", + "I 4\n", + "CZ 1 2\n", "I 2\n", "I 5\n", - "I 3\n", - "I 6\n", - "I 3\n", - "I 4\n", + "CZ 3 6\n", + "CZ 3 4\n", "CZ 4 7\n", - "I 4\n", - "I 5\n", + "CZ 4 5\n", "CZ 5 8\n", - "CZ 6 7\n", + "I 6\n", + "I 7\n", "I 7\n", "I 8\n", "\n" @@ -242,24 +250,20 @@ "output_type": "stream", "text": [ "RX(-pi/2) 0\n", - "RZ(pi/2) 0\n", - "RX(-pi/2) 0\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RZ(-pi) 2\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 3\n", + "RZ(pi/2) 1\n", + "RX(-pi/2) 2\n", + "RZ(-pi/2) 2\n", + "RX(pi/2) 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 4\n", "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "RX(-pi/2) 5\n", "RZ(-pi/2) 5\n", - "RX(-pi/2) 5\n", - "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RZ(pi/2) 7\n", + "RX(pi/2) 5\n", + "RZ(-pi) 6\n", + "RX(-pi) 6\n", "RX(pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(pi/2) 8\n", + "RZ(-pi) 7\n", + "RZ(-pi) 8\n", "\n" ] } @@ -285,10 +289,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 3\n", - "I 4\n", - "X 3\n", - "I 4\n", + "I 2\n", + "I 5\n", + "X 2\n", + "X 5\n", "\n" ] } @@ -307,9 +311,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "CNOT 1 4\n", - "I 1\n", - "I 4\n", + "I 5\n", + "I 8\n", + "CNOT 5 8\n", "\n" ] } @@ -328,20 +332,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 3\n", - "CZ 3 6\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "CZ 3 6\n", - "RX(-pi/2) 3\n", - "RZ(pi/2) 3\n", - "RX(-pi/2) 3\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 3 6\n", - "RX(-pi/2) 6\n", - "RX(-pi/2) 3\n", - "CZ 3 6\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "RX(pi/2) 5\n", + "CZ 5 8\n", + "RX(-pi/2) 8\n", + "RX(-pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "RX(-pi/2) 8\n", + "CZ 5 8\n", + "RZ(pi/2) 8\n", + "RX(pi/2) 8\n", + "RX(-pi/2) 5\n", + "CZ 5 8\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 5\n", "\n" ] } @@ -354,49 +360,27 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DEFGATE Perm012 AS PERMUTATION:\n", - " 0, 1, 2, 3, 4, 5, 6, 7\n", - "Perm012 5 7 8\n", - "\n" - ] - } - ], - "source": [ - "rand_perm_layer = get_rand_qubit_perm_template()\n", - "# sometimes this returns an empty program, i.e. no permutation\n", - "print(rand_perm_layer.sample_program(G, 1, qc=noisy_qc, width=3))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE LYR0_RSU4_4_1:\n", - " 0.26858053105152213+0.10864973894203411i, 0.4587166902639551-0.020767812561925177i, 0.027376027977825214+0.5929760856558103i, -0.06436729517958666+0.590503340249926i\n", - " 0.09066940819787443+0.158726543334475i, 0.017470105331415284-0.5429604064332522i, 0.36401273746231755-0.04704070321985694i, -0.7169278738708781-0.15089750891395345i\n", - " 0.17881148520035228+0.356810637344199i, 0.5065785412641826-0.2797787222124536i, -0.3005402292453411-0.6076110685630518i, 0.1931623195201898+0.09480162577031309i\n", - " -0.797672269362934+0.29508684815474884i, -0.10807959528043765-0.3840017418086259i, -0.15315108213887452+0.17303444785000938i, 0.11617211173412989+0.22497118139799177i\n", + "DEFGATE LYR0_RSU4_7_8:\n", + " -0.022877395095540765-0.14714221203233244i, 0.0722782327401761-0.5230331737668792i, -0.5468187982472037-0.47520128026247194i, -0.313443145826728-0.2756162003544303i\n", + " 0.392699429249816+0.2055594278331732i, -0.055160984125693674-0.20954418637277003i, 0.02393672258156379+0.4840588024816986i, 0.0684176071325038-0.7190369390856951i\n", + " -0.0021993657190741353+0.14768568142801533i, -0.742594131004298+0.3360770236723023i, -0.032070743614688424-0.4462636686845488i, 0.18121322556374395-0.2842046187979819i\n", + " 0.8713454847296828-0.017303833691206028i, -0.07232091739381188-0.06809208224027807i, -0.14673506533933722-0.13629409447403606i, 0.14085261365942156+0.41309085981289256i\n", "\n", - "LYR0_RSU4_4_1 4 1\n", + "LYR0_RSU4_7_8 7 8\n", "\n" ] } ], "source": [ "rand_su4_layer = get_rand_su4_template()\n", - "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))" + "print(rand_su4_layer.sample_program(G, 1, qc=noisy_qc, width=2))\n" ] }, { @@ -408,29 +392,32 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "X 1\n", - "X 2\n", - "I 3\n", - "I 4\n", - "I 1\n", - "I 4\n", - "CNOT 1 2\n", - "CNOT 3 4\n", - "I 1\n", - "X 2\n", - "I 3\n", - "I 4\n", - "CNOT 1 4\n", - "CNOT 1 2\n", - "I 3\n", + "X 4\n", + "X 5\n", + "X 7\n", + "I 8\n", "I 4\n", + "I 7\n", + "CNOT 4 5\n", + "I 5\n", + "I 8\n", + "CNOT 7 8\n", + "X 4\n", + "X 5\n", + "I 7\n", + "X 8\n", + "CNOT 4 7\n", + "CNOT 4 5\n", + "I 5\n", + "I 8\n", + "CNOT 7 8\n", "\n" ] } @@ -451,30 +438,31 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "H 1\n", - "H 2\n", - "I 1\n", - "Z 2\n", - "I 1\n", - "I 2\n", - "I 1\n", - "I 2\n", - "I 1\n", - "I 2\n", - "I 1\n", - "I 2\n", - "H 1\n", - "CZ 1 2\n", - "H 1\n", - "H 1\n", - "H 2\n", + "H 4\n", + "H 5\n", + "Z 4\n", + "Z 5\n", + "I 4\n", + "I 5\n", + "Z 4\n", + "Z 5\n", + "H 4\n", + "CZ 4 5\n", + "H 4\n", + "I 4\n", + "Z 5\n", + "H 4\n", + "CZ 4 5\n", + "H 4\n", + "H 4\n", + "H 5\n", "\n" ] } @@ -499,77 +487,83 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "RX(pi/2) 0\n", "RZ(pi/2) 0\n", + "RX(-pi) 3\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RX(pi/2) 3\n", + "RX(pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RX(-pi/2) 3\n", + "RZ(-pi) 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", + "RX(-pi/2) 0\n", + "CZ 0 3\n", + "RZ(-pi/2) 3\n", + "RX(-pi/2) 3\n", + "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RX(-pi) 0\n", - "RZ(pi/2) 0\n", - "RX(-pi) 0\n", - "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(-pi/2) 1\n", - "RX(pi/2) 1\n", "RZ(pi/2) 0\n", "RX(-pi/2) 0\n", - "CZ 0 1\n", - "RX(-pi/2) 1\n", + "RX(-pi) 3\n", + "RZ(pi/2) 3\n", + "RX(pi/2) 3\n", + "CZ 0 3\n", + "RX(pi/2) 3\n", "RX(-pi/2) 0\n", - "CZ 0 1\n", + "CZ 0 3\n", + "RX(-pi/2) 3\n", "RX(-pi/2) 0\n", - "RZ(-pi) 0\n", - "RZ(pi/2) 1\n", - "RX(-pi) 1\n", - "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RX(pi/2) 1\n", - "RZ(pi/2) 1\n", - "RX(pi/2) 0\n", - "DAGGER RX(pi/2) 0\n", - "DAGGER RZ(pi/2) 1\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER CZ 0 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(pi/2) 1\n", - "DAGGER RZ(-pi) 0\n", "DAGGER RX(-pi/2) 0\n", - "DAGGER CZ 0 1\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER CZ 0 3\n", "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 0 1\n", + "DAGGER RX(pi/2) 3\n", + "DAGGER CZ 0 3\n", + "DAGGER RX(pi/2) 3\n", + "DAGGER RZ(pi/2) 3\n", + "DAGGER RX(-pi) 3\n", "DAGGER RX(-pi/2) 0\n", "DAGGER RZ(pi/2) 0\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER CZ 0 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RX(pi/2) 1\n", - "DAGGER RX(-pi) 0\n", - "DAGGER RZ(pi/2) 0\n", - "DAGGER RX(-pi) 0\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER RX(pi/2) 1\n", "DAGGER RX(-pi/2) 0\n", + "DAGGER RZ(-pi/2) 0\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER RZ(-pi/2) 3\n", + "DAGGER CZ 0 3\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER CZ 0 3\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER RZ(-pi) 3\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RZ(-pi/2) 0\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RX(pi/2) 0\n", + "DAGGER RX(pi/2) 3\n", + "DAGGER CZ 0 3\n", + "DAGGER RX(-pi/2) 0\n", + "DAGGER RX(-pi/2) 3\n", + "DAGGER CZ 0 3\n", + "DAGGER RX(-pi) 3\n", "DAGGER RZ(pi/2) 0\n", + "DAGGER RX(pi/2) 0\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -597,105 +591,164 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RZ(-2.659459316672841) 1\n", + "RZ(0.6023647634493379) 1\n", "RX(pi/2) 1\n", - "RZ(0.461798249424243) 1\n", + "RZ(1.6859512736760711) 1\n", "RX(-pi/2) 1\n", - "RZ(0.63880900969572) 1\n", - "RZ(2.609090341343644) 3\n", - "RX(pi/2) 3\n", - "RZ(2.7304530401071103) 3\n", - "RX(-pi/2) 3\n", - "RZ(-2.3724233319953845) 3\n", - "RZ(-2.6259067850591453) 4\n", + "RZ(3.056524885694591) 1\n", + "RZ(2.2179491881590767) 4\n", "RX(pi/2) 4\n", - "RZ(1.792266675205438) 4\n", + "RZ(1.3277711306691324) 4\n", "RX(-pi/2) 4\n", - "RZ(0.35479411869176225) 4\n", - "CZ 4 3\n", - "RZ(-pi/2) 3\n", - "RX(pi/2) 3\n", - "RZ(2.134251918388017) 3\n", - "RX(-pi/2) 3\n", + "RZ(-2.2659729961186983) 4\n", + "RZ(0.9691266498934911) 7\n", + "RX(pi/2) 7\n", + "RZ(2.465960148492124) 7\n", + "RX(-pi/2) 7\n", + "RZ(1.1085351901192695) 7\n", + "CZ 4 7\n", "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", - "CZ 4 3\n", - "RX(pi/2) 3\n", - "RZ(-1.7178107510675495) 3\n", - "RX(-pi/2) 3\n", - "RZ(1.4165522865636788) 4\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 7\n", + "RZ(2.3101407699370347) 7\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RZ(1.5986380822234807) 4\n", "RX(pi/2) 4\n", - "CZ 4 3\n", - "RZ(-1.785708772007812) 4\n", + "RX(pi/2) 7\n", + "RZ(-1.5980656434963532) 7\n", + "RX(-pi/2) 7\n", + "CZ 4 7\n", + "RZ(0.35220587162615624) 4\n", "RX(pi/2) 4\n", - "RZ(0.49800681616645737) 4\n", + "RZ(1.8272873235224791) 4\n", + "RX(-pi/2) 4\n", + "CZ 4 1\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(-1.335693650229591) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.49097992983251526) 7\n", + "CZ 4 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 4\n", + "CZ 4 1\n", + "RZ(-1.2127863732485493) 6\n", + "RX(pi/2) 6\n", + "RZ(2.813574980498982) 6\n", + "RX(-pi/2) 6\n", + "RZ(2.6511818466376313) 6\n", + "RZ(-1.7588018883352659) 7\n", "RX(pi/2) 7\n", - "RZ(2.5825051567284976) 7\n", + "RZ(2.447676653604526) 7\n", "RX(-pi/2) 7\n", - "RZ(3.0107917077638024) 7\n", - "CZ 4 7\n", - "RZ(0.39042473708143177) 4\n", + "CZ 7 6\n", + "RZ(1.4850227517340677) 6\n", + "RX(pi/2) 6\n", + "RZ(-1.3478885055934553) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 6\n", + "RX(-pi/2) 6\n", + "RX(pi/2) 7\n", + "CZ 7 6\n", + "RZ(-0.8750443882926295) 1\n", + "RX(pi/2) 1\n", + "RZ(1.8132470278556063) 1\n", + "RX(-pi/2) 1\n", + "RZ(-0.012768271568142753) 1\n", + "RZ(-0.8445460555738014) 4\n", + "RX(pi/2) 4\n", + "RZ(0.3461777273306826) 4\n", "RX(-pi/2) 4\n", - "RZ(pi/2) 7\n", + "RZ(2.7370929367653405) 4\n", + "RZ(1.7387427456999776) 7\n", "RX(pi/2) 7\n", + "RZ(0.47509385340919574) 7\n", + "RX(-pi/2) 7\n", + "RZ(-1.0914455197496056) 7\n", "CZ 7 4\n", - "RZ(pi) 4\n", + "RZ(-pi/2) 4\n", "RX(pi/2) 4\n", + "RZ(2.5624071888083417) 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 7\n", "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RZ(-2.668242265098992) 1\n", - "RX(pi/2) 1\n", - "RZ(2.286627324701731) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.145846853797221) 1\n", - "RZ(-1.4951524783114967) 4\n", + "CZ 7 4\n", "RX(pi/2) 4\n", - "RZ(0.49048086074218056) 4\n", + "RZ(-2.3510254685615237) 4\n", "RX(-pi/2) 4\n", - "RZ(-1.272117698325029) 4\n", + "RZ(1.3731189583322312) 7\n", + "RX(pi/2) 7\n", + "CZ 7 4\n", + "RZ(-1.5136911753196136) 4\n", + "RX(pi/2) 4\n", + "RZ(1.8575956476481248) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.19940209614281246) 4\n", "CZ 4 1\n", - "RZ(-pi/2) 1\n", "RX(pi/2) 1\n", - "RZ(2.6109851757459044) 1\n", + "RZ(1.946333840120635) 1\n", "RX(-pi/2) 1\n", - "RZ(-pi/2) 4\n", + "RZ(1.9594783139215677) 4\n", "RX(-pi/2) 4\n", "CZ 4 1\n", "RX(pi/2) 1\n", - "RZ(-1.5880815347394472) 1\n", + "RZ(-1.6371508636416152) 1\n", "RX(-pi/2) 1\n", - "RZ(1.245957303906056) 4\n", + "RZ(1.1378421770433667) 4\n", "RX(pi/2) 4\n", "CZ 4 1\n", - "RZ(-2.031121110990684) 1\n", + "RZ(-1.393034832970288) 6\n", + "RX(pi/2) 6\n", + "RZ(1.2381481428840744) 6\n", + "RX(-pi/2) 6\n", + "RZ(2.4314509433880076) 6\n", + "RZ(1.0937764024777272) 7\n", + "RX(pi/2) 7\n", + "RZ(1.7174278721482343) 7\n", + "RX(-pi/2) 7\n", + "RZ(-2.214136247801944) 7\n", + "CZ 7 6\n", + "RZ(pi/2) 6\n", + "RX(pi/2) 6\n", + "RZ(2.2246709800845528) 6\n", + "RX(-pi/2) 6\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "CZ 7 6\n", + "RX(pi/2) 6\n", + "RZ(-1.6577485406407506) 6\n", + "RX(-pi/2) 6\n", + "RZ(1.1856374823534352) 7\n", + "RX(pi/2) 7\n", + "CZ 7 6\n", + "RZ(3.043883448401183) 1\n", "RX(pi/2) 1\n", - "RZ(1.2784529064338348) 1\n", + "RZ(1.6000604308810171) 1\n", "RX(-pi/2) 1\n", - "RZ(2.135913750567923) 1\n", - "RZ(-1.0373111688471863) 3\n", - "RX(pi/2) 3\n", - "RZ(1.4897961313128554) 3\n", - "RX(-pi/2) 3\n", - "RZ(-0.5617250744521731) 3\n", - "RZ(0.5222384500519812) 4\n", + "RZ(1.1195803228105756) 1\n", + "RZ(1.943186188160554) 4\n", "RX(pi/2) 4\n", - "RZ(1.2779579232074691) 4\n", + "RZ(1.3826477167179847) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.1771720131335055) 4\n", - "RZ(0.2994224201693585) 7\n", - "RX(pi/2) 7\n", - "RZ(1.6131475136490458) 7\n", + "RZ(-0.0835077926150376) 4\n", + "RZ(-2.04526392259349) 6\n", + "RX(pi/2) 6\n", + "RZ(1.0894626786873618) 6\n", + "RX(-pi/2) 6\n", + "RZ(-2.8946394683160683) 6\n", + "RZ(2.3815486509409176) 7\n", + "RX(-pi/2) 7\n", + "RZ(2.0204295038357807) 7\n", "RX(-pi/2) 7\n", - "RZ(0.34720986671375864) 7\n", + "RZ(0.42771697574953826) 7\n", "\n" ] } @@ -721,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -737,15 +790,15 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [0.8880000000000007, 0.6740000000000005, 0.8220000000000006, 0.6400000000000005, 0.8480000000000006, 0.7180000000000005, 0.7160000000000005, 0.6220000000000004, 0.7920000000000006, 0.6600000000000005, 0.9040000000000007, 0.8400000000000006, 0.8900000000000007, 0.9420000000000007, 0.6900000000000005, 0.7240000000000005, 0.8780000000000007, 0.7000000000000005, 0.7920000000000006, 0.6600000000000005, 0.9240000000000007, 0.8860000000000007, 0.8120000000000006, 0.8320000000000006, 0.8560000000000006, 0.6400000000000005, 0.5920000000000004, 0.8640000000000007, 0.9120000000000007, 0.9480000000000007, 0.8220000000000006, 0.8740000000000007, 0.7600000000000006, 0.8320000000000006, 0.7420000000000005, 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2.294e-01, 3.420e-02, 5.400e-03, 6.000e-04, 3.000e-04]), 5: array([0.7241, 0.2343, 0.036 , 0.0042, 0.0014, 0. ]), 10: array([6.985e-01, 2.447e-01, 4.340e-02, 1.130e-02, 1.900e-03, 2.000e-04])}}\n" ] } ], @@ -879,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -894,12 +947,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -931,12 +984,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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YA1xVxXySJEmSJEnqJIrs2vlCC8OPRMRNwKPALUBLc4sYCMxt4vic8liTMnNmRHwGuAH4VvnwLGCHzHyjStkkSZIkSZLUiRS5tbNFmflSRFwP/DdwUbWu2xYRMYzSyrNH+Nctqd8AboyIrcqr2hqfMwmYBDBs2DCmTp26ouJW1d6jlnZ0BHVSK+vvjCRJkiRJlapakVY2m+re2jkH6N/E8YHlseYcRek5aXtm5nsAEXEH8BxwJP9apfa+zDwXOBdg/PjxOWbMmOVL3kG+eMWrHR1BndQpk1bO3xlJkiRJkipV5BlpLYqILsBngOZ20myLp2n0LLSIWBPoTaNnpzWyAfBEfYkGkJmLgSeAdaqYT5IkSZIkSZ1ExSvSImKrFq6xJnAgsDlwQRVy1bsJOCoi+mbm2+Vj+wALgLtbOO8lYOeI6FEu0IiInsAmNL/TpyRJkiRJktSsIrd23gtkC+MB3A98Z7kSfdCvKd2GeW1EnAyMAo4FTsvM91e+RcTzwN2ZeVD50PmUno32vxFxdjnbN4BhlG/flCRJkiRJkoooUqSdQNNF2jJKzyt7MDPvr0qqssycExHbAWdRWkk2FzidUpnWUDega4PzHomIHYFjgEvKhx8Dts/MadXMKEmSJEmSpM6h4iItM49uzyAtfO+TwLatzBnZxLHbgdvbKZYkSZIkSZI6maptNiBJkiRJkiStyiou0iJi84j4fkQMaWZ8SHl80+rFkyRJkiRJkmpDkRVpRwKHAq83M/4GcAhwxPKGkiRJkiRJkmpNkSJtK+DOzGxy587MXAbcAXyqGsEkSZIkSZKkWlKkSBsKvNzKnFeBYW2PI0mSJEmSJNWmIkXafGD1VuasDixuexxJkiRJkiSpNhUp0qYBX4iIPk0NRkRf4AvleZIkSZIkSdIqpUiRdh4wGLglIjZuOBARmwA3U1qRdn714kmSJEmSJEm1oVulEzPz8ojYBZgITIuImZSeibYGMJxSKXdZZl7aLkklSZIkSZKkDlRxkQaQmV+OiPuBbwLrAyPKQ08DkzPz11XOJ0mSJEmSJNWEQkUaQGaeDZwdEf2AAcDczJxX9WSSJEmSJElSDSlcpNUrl2cWaJIkSZIkSeoUKt5sICLGRMT3I2JIM+NDyuObVi+eJEmSJEmSVBuK7Np5FHAo8Hoz428AhwBHLG8oSZIkSZIkqdYUKdK2Au7MzGxqMDOXAXcAn6pGMEmSJEmSJKmWFCnShgIvtzLnVWBY2+NIkiRJkiRJtalIkTYfWL2VOasDi9seR5IkSZIkSapNRYq0acAXIqJPU4MR0Rf4QnmeJEmSJEmStEopUqSdBwwGbomIjRsORMQmwM2UVqSdX714kiRJkiRJUm3oVunEzLw8InYBJgLTImImpWeirQEMp1TKXZaZl7ZLUkmSJEmSJKkDVVykAWTmlyPifuCbwPrAiPLQ08DkzPx1lfNJkiRJkiRJNaFQkQaQmWcDZ0dEP2AAMDcz51U9mSRJkiRJklRDChdp9crlmQWaJEmSJEmSOoVCRVpEfBL4JKVnogHMBO7LzPuqHUySJEmSJEmqJRUVaRHxKeBXwEb1h8rvWR5/AjjUQk2SJEmSJEmrqlaLtIjYDbgC6A7MBu4GXi4PrwlMADYB7oiIvTPz+nbKKkmSJEmSJHWYFou0iBgGXAwso7RT5zmZuaTRnG7AfwKnApdExPqZOaud8kqSJEmSJEkdoksr4/8N9AH2z8xfNi7RADJzSWb+Ctgf+BBwePVjSpIkSZIkSR2rtSJtR+ChzLy6tQtl5jXAg8BO1QgmSZIkSZIk1ZLWirSRwL0Frndf+RxJkiRJkiRpldJakdYdWFzgeovL50iSJEmSJEmrlNaKtFmUduSs1MbAa22PI0mSJEmSJNWm1oq0e4DtI2K91i4UEesDOwB/rkYwSZIkSZIkqZa0VqT9EugB3FAuyppULtr+CHQDzq5ePEmSJEmSJKk2dGtpMDMfiojTgCOAqRFxFXA78HJ5yprAfwB7Aj2BMzLzwXbMK0mSJEmSJHWIFou0sqOA+cD3gC8D+zUaD2AZcCJwdFXTSZIkSZIkSTWi1SItMxP4UURcBBwEfBIYVh5+DbgXuDAzn2+vkJIkSZIkSVJHq2RFGgCZ+SLwg3bMIkmSJEmSJNWs1jYbkCRJkiRJkoRFmiRJkiRJklQRizRJkiRJkiSpAhZpkiRJkiRJUgUs0iRJkiRJkqQKWKRJkiRJkiRJFWi2SIuI1yPiyAafvx8Rn1oxsSRJkiRJkqTa0tKKtNWA3g0+/wTYtn3jSJIkSZIkSbWppSJtNrDGigoiSZIkSZIk1bJuLYw9COwfEYuBWeVjW0fE91u5ZmbmiVVJJ0mSJEmSJNWIloq0o4DrgW80OLYtrd/emYBFmiRJkiRJklYpzRZpmflsRGwCjKZ0i+dtwMXAJSsomyRJkiRJklQzWlqRRmYuBZ4BnokIgBcz8/YVEUySJEmSJEmqJS0WaY10B5a1VxBJkiRJkiSpllVcpJVXpwEQEcOAMcAA4C3gb5k5q7lzJUmSJEmSpJVdlyKTI2JERNwAvALcAFwK/BF4JSJuiIiPVDtgRGwUEbdHxPyImBkRx0dE1wrP3T0iHoqIBRHxj4i4OSL6VDujJEmSJEmSVn0Vr0iLiCHAfcCawMvAPcAsYBjwSWBn4N6I+Fhmzq5GuIgYSGmTgyeBXYF1gFMpFYBHt3LuwcBZwCmUdiAdSGnH0SK3s0qSJEmSJElAsVLpaEol2g+An2XmkvqBiOgGHAmcUJ73zSrlOwSoA3bPzHnArRHRDzg2Ik4pH/s3EbEacDrwzcw8r8HQ/1YplyRJkiRJkjqZIrd2fg64LTNPbFiiAWTmksw8Cbi1PK9adgJuaVSYXUGpXJvQwnl7l99/W8UskiRJkiRJ6sSKFGnDgIdamfNweV61bAA83fBAZs4A5pfHmrMl8AxwUES8EhHvRcQDEbFVFbNJkiRJkiSpEylya+c8oLXNBNYsz6uWgcDcJo7PKY81ZyiwPqXbTL8D/KP8fnNErNvUM9wiYhIwCWDYsGFMnTp1OaN3jL1HLW19ktQOVtbfGUmSJEmSKlWkSLsP2DMizsrMBxoPRsR4YC/gpmqFWw4BfAjYKzNvBoiI+4GXgMOAHzY+ITPPBc4FGD9+fI4ZM2bFpa2iL17xakdHUCd1yqSV83dGkiRJkqRKFSnSfkppZ857IuIy4E5Ku3YOBbYBvlyed2IV880B+jdxfGB5rKXzErir/kBmzouIR4CNqphPkiRJkiRJnUTFRVpmPhwR+wAXAl8FvtJgOCjdgnlQZrb2HLUinqbRs9AiYk2gN42endbIU+VM0eh4AMuqmE+SJEmSJEmdRJHNBsjM6yg9J+0A4Ezg4vL714C1MvN/q5zvJmCHiOjb4Ng+wALg7hbOu6H8/pn6AxHRHxgHTKtyRkmSJEmSJHUCRW7tBCAz36ZUoF1c/Tj/5tfAt4BrI+JkYBRwLHBaZr6/qUFEPA/cnZkHlTM+HBHXAxdExHeBNyltNvAe8MsVkFuSJEmSJEmrmEIr0la0zJwDbAd0Bf4IHAecDhzTaGq38pyGvgxcB5wGXE2pRNu2fE1JkiRJkiSpkMIr0la0zHwS2LaVOSObOPYOcGj5JUmSJEmSJC2Xml6RJkmSJEmSJNUKizRJkiRJkiSpAhZpkiRJkiRJUgUs0iRJkiRJkqQKVFykRcRq7RlEkiRJkiRJqmVFVqS9HBGXRcTW7ZZGkiRJkiRJqlFFirS/A18C7oyIJyPi8IgY2E65JEmSJEmSpJpScZGWmRsB2wCXA2sDpwOvRsRvI2Kr9oknSZIkSZIk1YZCmw1k5p8z88vAcOB/gOnA/sA9EfFYRHwjIvpVP6YkSZIkSZLUsdq0a2dmzsnM0xusUvsdMBqYDMyMiPMjYvPqxZQkSZIkSZI6VpuKtEZeBWYB7wAB1AEHAg9HxNURMaAK3yFJkiRJkiR1qG5tOSkiugK7AV8HPkOpkHsROBm4ENgcOArYHVgMTKxGWEmSJEmSpJXBlClTdujWrdsxmTmU6ixkUvtaFhGvLVmy5LixY8fe0tykQkVaRKwN/CfwNWAwkMCNwNmZ2fBLbgNui4hrgR0LR5ckSZIkSVpJTZkyZYeePXueNXLkyMV1dXVzunTpkh2dSS1btmxZLFiwoP/06dPPmjJlymHNlWkVN6IRcQvwHPDd8qETgbUzc9dGJVpDDwH9iwSXJEmSJElamXXr1u2YkSNHLu7Tp88CS7SVQ5cuXbJPnz4LRo4cubhbt27HNDevyIq07YF7gLOBazPzvQrOuQF4vcB3SJIkSZIkrdQyc2hdXd2cjs6h4urq6haWb8dtUpEi7aOZ+USRL8/Mx4DHipwjSZIkSZK0kuviSrSVU/n/bs3ewVnxrZ1FSzRJkiRJkiRpVVLkGWl7RMSfImKNZsaHl8d3rV48SZIkSZIk1YKHHnqoV0SMu+GGG/pWes7Pf/7z1S655JIB7ZlrRSpya+d/Aqtn5qtNDWbmzIgYBEwCrq9GOEmSJEmSpFXFyO/eOK4jvnf6Sbs80hHfC3DRRRetvv766y/Yf//953ZUhmqqeEUa8FFKu3C25CFgs7bHkSRJkiRJkmpTkSJtNVrfgfMf5XmSJEmSJElaiZ100kmrDx06dNO6urrNt91229GvvPJKj4bjxxxzzJBNNtlkw759+44ZNGjQZttuu+3oxx9/vGf9+BZbbLH+E0880fvaa68dFBHjImLc5MmTBwGcddZZg8aNG7d+//79x/Tr12/Mlltuud6f//zn3iv6z1hUkVs73wRGtzJnHWCVWKonSZIkSZLUWV166aUDvve9731k4sSJb+y+++5z77zzzr6HHnroyIZzXnnllR5f//rXX1977bUXv/XWW13OPffc1bfeeusNnnvuuccHDRq09Fe/+tVLe+211zof+chHFv3whz+cBbDhhhsuApg+fXqPL33pS/9Yd911Fy1atCguv/zyD3/2s5/dYMqUKY9vtNFGizvgj1yRIkXafcAXImK9zHy28WBErA/sCvxftcJJkiRJkiRpxTv55JOHffrTn5532WWXzQDYY4895r355pvdrrzyyvfvRLzgggterv95yZIl7LrrrvOGDBky5vLLLx9w2GGH/WPcuHELe/fuvWzQoEFLtttuu3cbXv/nP//5rPqfly5dym677TZvvfXW6/Ob3/xmUMOxWlPk1s7TgB7AvRHxXxExKiJ6lt+/AdxLqZj7eXsElSRJkiRJUvt77733eOqpp3p/7nOf+8Bdh7vvvvuchp9vv/32PltttdW6AwYMGNO9e/dxffv2HTt//vwuzz77bE9aMWXKlF7bb7/9OoMGDdqsW7du43r06DFu+vTpvZ577rle1f7zVFPFK9Iy868RcRhwZvnV2DLgm5n5l2qFkyRJkiRJ0oo1a9asbkuXLmXIkCHvNTw+bNiwJfU/P/fccz123XXX9TbddNN3Tz/99JdGjBixuGfPnrnbbrutu3DhwhYXbs2ZM6fLzjvvvN5qq6323k9+8pOXR40atbiurm7ZpEmTRi5atCja689VDUVu7SQzfx0R9wH/BWwJDKD0TLS/Amdn5uPVjyhJkiRJkqQVZdiwYUu6du3K7Nmzuzc8PmvWrPd7pOuvv77fwoULu9x8883P9+vXbxmUVrK99dZbXVu7/p133vmh2bNnd7/pppue3XzzzRfWH3/77bdbPbejFbm1E4DMfCwzD83MsZk5qvz+X5ZokiRJkiRJK7/u3buzwQYbzL/hhhsGNDx+7bXXDqz/ecGCBV0iIrt37571xy644IIPL126NBpdKxctWvSB/mn+/PldAOrq6pbVH7v11lv7zJw58wO7gtaiQivSJEmSJEmStOr7zne+M+urX/3qOvvtt99H9thjj7l33nln37vuuqt//fgOO+zw9rHHHht77733yIMPPvjNxx57rO6Xv/zlkL59+y5teJ3Ro0cvvPvuu/tdc801/VZfffUl66233qIJEya807t372UHHnjgyCOPPPK1GTNmdD/55JOHDx48+L1/T1JbCq9Ii5L1ImLLiNiqqVd7BJUkSZIkSdKK8ZWvfGXuT3/60xm33XbbgP3222+dR33ZQI0AACAASURBVB99tO7ss8+eXj++xRZbLJg8efLfp06d2mefffZZ96qrrvrwZZdd9mLjIu24446bOXr06IUHHHDAqAkTJmz4+9//fsCaa6655Le//e0Lb7zxRveJEyeOPvvss4ecccYZM9Zaa61FK/wPWlBkZuuz6idHfA/4H2BgS/Mys+bvaW3J+PHj8+GHH+7oGG0y8rs3dnQEdVLTT9qloyNIkiRJUruKiEcyc3xr86ZNmzZ9s802e3NFZFL1TZs2bbXNNttsZFNjFd/aGRH/A/wUeBu4HHgZWNLiSZIkSZIkSdIqosgz0r4OzATGZebsdsojSZIkSZIk1aQiz0j7CPC/lmiSJEmSJEnqjIoUabOBlfrZZ5IkSZIkSVJbFSnSrga2j4ie7RVGkiRJkiRJqlVFirQfAm8AV0bEmu2UR5IkSZIkSapJRTYbmAr0ALYEPh8R/wDmNjEvM3P9aoSTJEmSJEmSakWRIq03kJR27qxXV904kiRJkiRJUm2quEjLzBHtGUSSJEmSJEmqZUWekSZJkiRJkiS1i7feeqtLRIybPHnyoI7O0pw2F2kR0TcihlUzjCRJkiRJklSrijwjjYjoDRwD7AcMo/TMtG7lsS2Ao4EfZebUKueUJEmSJElauR3bf1zHfO9bjyzvJZYsWcKSJUuiV69eWY1IK6uKV6RFRF/gfuAo4J/AM0A0mPIEsC0wsZoBJUmSJEmStGLtscceIzfZZJMNL7nkkgGjR4/euFevXmPvuuuuPnvttdfIESNGfLRXr15jR44cucm3vvWt4QsXLny/H3rmmWd6RMS4888/f+DEiRPX6tu375ghQ4Zs+u1vf3v40qVLP/AdF1100YCRI0du0qtXr7Hjx49ff9q0ab0a51iyZAlHHHHE8GHDhn20R48eY0ePHr3xr3/96w83lfWKK67ov84662xcV1e3+TbbbDN69uzZXR9//PGeW2655Xp1dXWbb7LJJhs+8MADy7VxZpFbO48GNgUOzsxNgd83HMzMd4G7ge2WJ5AkSZIkSZI63quvvtrjhz/84Ygjjjhi1tVXX/0cwMCBA5eceOKJL19zzTXPfvOb33ztiiuuWO3AAw/8SONzjznmmBF9+vRZevHFF7+4xx57/OOMM84YduGFFw6sH7/33nt7H3zwwetsuOGG8y+++OLnd9ppp7kTJ05cp/F1vv3tb68xefLkofvvv/+bl19++fMf+9jH3jn00EPXPueccz5Qps2cObPHj3/84+E/+tGPXj311FNfmjJlyoe++tWvrrXvvvuO2nPPPf/529/+9oUlS5bExIkTRy1btqzNfydFbu3cA/hTZv6m/LmppXzTgfFtTiNJkiRJkqSaMHfu3G433njjs1tttdWC+mM77rjjO/U/f/azn32nT58+yw4//PCRCxcunNHwts8tttji7fPOO+8VgN12223eHXfc0f+6664bePDBB88BOOGEE4autdZaC2+88cYXu3Tpwt577z1v8eLFccopp6xRf43Zs2d3Pf/88wcffvjhs0455ZRZAHvssce8mTNndj/xxBOHf/3rX/9n/dx58+Z1u+eee57eeOONFwE8+uijvc8555whZ5555vTDDjvsHwCZ+eq+++47eurUqb3Gjh27sC1/J0VWpI0AprUy5x2gf1uCSJIkSZIkqXYMHjz4vYYl2rJlyzj++OMHr7POOhv36tVrbI8ePcYdeuihay9evDief/75Hg3P3X777ec1/LzuuusumDVrVvf6z9OmTeuzww47zO3S5V/V1D777DO34TlTpkypW7hwYZeJEyfOaXh8zz33nPPSSy/1nDlz5vsLxIYPH76ovkQDGD169EKAnXba6f0cG2644UKAGTNmdKeNihRp7wCrtzJnbeDNtoaRJEmSJElSbVhttdXea/j5xz/+8eDjjz9+zZ133nnu7373u+fvuuuup0488cQZAAsWLGj4HH0GDhz4gQei9ejRIxctWvR+D/Xmm292Hzx48JKGc4YPH/6B73vllVe6A6yxxhofOD5s2LD3AN54442u9cf69ev3b99X/jO8f7xnz55ZzlqkD/uAIrd2PgR8LiI+lJnvNB6MiKHATsBNbQ0jSZIkSZKk2hDxgW6M66677sM77rjjnDPPPPPV+mOPPvpomx7ev9pqq733+uuvf6CXmjlz5gdWio0YMeK9+uNDhw59vxCrX9m2+uqrf3D3ghWgSAM3GVgNuCEi1m04UP58JVBXnidJkiRJkqRVyMKFC7v06NHjA0/qv+KKKz7c3PyWbLrppu/ecsstAxo++P/KK68c0HDO2LFjF/Tq1WvZ7373u4ENj19zzTUD11prrUXDhw//wIq2FaHiFWmZeVNE/ITS7p1PA4sAIuI1Srd8BvCDzLy3PYJKkiRJkiSp40yYMGHehRdeOPikk056d91111106aWXfvill17q1ZZrfe9733vtM5/5zIa77LLLqIMOOujNRx99tO6yyy77wCPFhgwZsvTggw9+/Re/+MWwbt265RZbbDH/6quvHnD33Xf3P+ecc16szp+qmEL3hGbmj4AdgP8D3i0f7gn8CdghM0+sbjxJkiRJkiTVgpNPPnnm5z//+X+eeOKJaxx44IGjevTokT/72c9mtOVaW2+99fzzzjvvxSeeeKL3fvvtN/rGG28ccNlll73QeN7pp5/+6mGHHfbaRRddNHifffYZ/cADD/Q9++yz/z5p0qQ5TV23vUVmtj6rkxk/fnw+/PDDHR2jTUZ+98aOjqBOavpJu3R0BEmSJElqVxHxSGaOb23etGnTpm+22WZuxriSmjZt2mqbbbbZyKbG2rxLwYoSERtFxO0RMT8iZkbE8RHRtfUz3z+/S0Q8HBEZEZ9rz6ySJEmSJEladRXZtXOFi4iBwG3Ak8CuwDrAqZQKwKMrvMzBwIh2CShJkiRJkqROo+IiLSLeAyq5DzQzs2fbI33AIZR2At09M+cBt0ZEP+DYiDilfKxZ5SLup8B3gfOrlEmSJEmSJEmdUJEVaQ/QdJE2ABhNadOBx4AWy62CdgJuaVSYXQGcDEwA/tjK+T8G7gNur2ImSZIkSZIkdUIVF2mZ+anmxsqrxCYD44HPVyFXvQ2AOxrlmBER88tjzRZpEbEpcCCwaRXzSJIkSZIkqZOqyjPSMnNeRBwETKV0K+U3qnFdYCAwt4njc8pjLTkTOCszn4+Ika19UURMAiYBDBs2jKlTpxZLWiP2HrW0oyOok1pZf2ckSZIkqR0sW7ZsWXTp0qWSR2SphixbtiyAZc2NV22zgcxcGhF3AntSvSKtTSJiX2B9CqyOy8xzgXMBxo8fn2PGjGmndO3ri1e82tER1EmdMmnl/J2RJEmSpGqLiNcWLFjQv0+fPgs6OouKWbBgQa+IeK258S5V/r4etL5SrIg5QP8mjg8sj/2biOgO/IzSc9S6RMQAoF95uE9E9K1iPkmSJEmSpA9YsmTJcdOnT+/x7rvv1pVXOKnGLVu2LN5999266dOn91iyZMlxzc2r2oq0iFgX2At4oVrXBJ6m9Cy0ht+zJtC7PNaUPsAI4LTyq6EryvlGVzGjJEmSJEnS+8aOHXvLlClTDnvhhReOycyhVH8hk6pvWUS8tmTJkuPGjh17S3OTKi7SIuLcFq6xJrB1+ef/Vyhmy24CjoqIvpn5dvnYPsAC4O5mznkH+EyjY0OBy4Hv02jzAkmSJEmSpGorlzHNFjJaORVZkXZwK+PPAz/LzPOXI09jvwa+BVwbEScDo4BjgdMyc179pIh4Hrg7Mw/KzCXAXQ0v0mCzgccy84Eq5pMkSZIkSVInUaRIW7eZ48uAOZnZ1O6ayyUz50TEdsBZwB8p7eB5OqUyraFuQNdqf78kSZIkSZJUr+IiLTOr+eyzimXmk8C2rcwZ2cr4dMCH+0mSJEmSJKnNfNidJEmSJEmSVIEimw1s1dYvycz723quJGkVdWz/jk6w8jv2rY5OIFWP/yYsP/9NkCSp3RV5Rtq9QLbxe3x+mSRJkiRJklZqRYq0E4BxwA7AdOA+4DVgKPBJYCRwM/BIVRNKkiRJkiRJNaBIkfYH4H/Kr8mZubR+ICK6Av8N/Bg4JjMfqmpKSZIkSZIkqYMV2WzgJ8AdmXl6wxINIDOXZuapwF2UyjRJkiRJkiRplVKkSNsC+Fsrc/4GfLztcSRJkiRJkqTaVKRI6wKMamXOqILXlCRJkiRJklYKRUqvvwB7RsSOTQ1GxM7AnsD91QgmSZIkSZIk1ZIimw0cDdwN3BgRtwN/BmYDQ4AJwLbAIuAH1Q4pSZIkSZIkdbSKi7TMfCgidgB+A/xH+ZVAlKe8AByYmY9UPaUkSZIkSZLUwYqsSCMz74mI9YBPA2OB/sBbwBTgnszM6keUJEmSJEmSOl6hIg2gXJb9ufySJEmSJEmSOoU27bAZEXUR8dGI+ES1A0mSJEmSJEm1qFCRFhHDIuJKYC4wFbinwdgnI+LRiNi6yhklSZIkSZKkDldxkRYRQ4EHgT2AW4AH+NdGA5TH1gD2rmZASZIkSZIkqRYUWZF2DDAM2DEzv0CpTHtfZr5HaYWaK9IkSZIkSZK0yilSpO0C/CEzb2thzgxg+PJFkiRJkiRJkmpPkSJtCPBsK3MWAX3aHkeSJEmSJEmqTUWKtDnAiFbmrAu81vY4kiRJkiRJUm0qUqTdB3whIgY3NRgR6wA7AXdVIZckSZIkSZJUU4oUaT8HegN3RcT2QC+AiOhZ/vxHIIHTqp5SkiRJkiRJ6mDdKp2YmX+JiEOBs4CbGwzNL78vBQ7KzMeqmE+SJEmSJEmqCRUXaQCZeV5E3AN8A/g4MAh4C/grcGZmPln9iJIkSZIkSVLHK1SkAWTm08A32yGLJEmSJEmSVLMqfkZaRDwbEZPbM4wkSZIkSZJUq4psNjAMeKe9gkiSJEmSJEm1rEiR9iQwqr2CSJIkSZIkSbWsSJF2FvD5iNikvcJIkiRJkiRJtarIZgMvALcD90fE2cBDwGtANp6YmfdXJ54kSZIkSZJUG4oUafdSKs0C+A5NFGgNdF2eUJIkSZIkSVKtKVKknUDL5ZkkSZIkSZK0yqq4SMvMo9sziCRJkiRJklTLimw2IEmSJEmSJHVaLRZpEfGjiNh6RYWRJEmSJEmSalVrK9KOBbZpeCAiDo+IF9srkCRJkiRJklSL2nJr5wBgrWoHkSRJkiRJkmqZz0iTJEmSJEmSKmCRJkmSJEmSJFXAIk2SJEmSJEmqQLcK5gyIiI80/AwQEWsC0dQJmTmjCtkkSZIkSZKkmlFJkXZ4+dXY9GbmZ4XXlSRJkiRJklYarRVeMygVY5IkSZIkSVKn1mKRlpkjV1AOSZIkSZIkqaa52YAkSZIkSZJUAYs0SZIkSZIkqQIWaZIkSZIkSVIFLNIkSZIkSZKkClikSZIkSZIkSRWwSJMkSZIkSZIqYJEmSZIkSZIkVcAiTZIkSZIkSapA4SItIlaPiEMi4hcRcX6j41tERF01A0bERhFxe0TMj4iZEXF8RHRt5ZyPRcSFEfF8+bxnIuKYiOhVzWySJEmSJEnqPLoVmRwRBwGTgV5AAAkcXB4eAvwFmARcUI1wETEQuA14EtgVWAc4lVIBeHQLp+5Tnnsy8BywKfDj8vse1cgmSZIkSZKkzqXiIi0itgfOBR4FjgF2AA6pH8/MxyPiCeCLVKlIK1+/Dtg9M+cBt0ZEP+DYiDilfKwpJ2Xmmw0+3xURC4FzImKtzHypSvkkSZIkSZLUSRS5tfP/AbOACZn5B+D1JuY8CmxUjWBlOwG3NCrMrqBUrk1o7qRGJVq9v5Xfh1cvniRJkiRJkjqLIkXaeOCGFlaBAbwCDF2+SB+wAfB0wwOZOQOYXx4r4hPAMuCF6kSTJEmSJElSZ1KkSOsBvNvKnAHA0rbH+TcDgblNHJ9THqtIRAyl9Ey1SzKzqZV0kiRJkiRJUouKbDYwHRjXypwtgWfanKYdREQP4PfAO8C3W5g3idJGCQwbNoypU6eumIBVtveoavaYUuVW1t8ZdaA1D+joBCs/f++0KvHfhOXnvwmSJLW7IkXa9cB3ImKvzLyq8WBEfI3Srpg/qFY4SivP+jdxfGB5rEUREcDFwMbAJzOz2XMy81xKmykwfvz4HDNmTJsCd7QvXvFqR0dQJ3XKpJXzd0Yd6LqLOjrByu+gX3R0Aql6/Ddh+flvgiRJ7a5IkXYKsC9weUTsSbngiojDgE8DuwPPAWdWMd/TNHoWWkSsCfSm0bPTmnEGsCuwfWZWMl+SJEmSJElqUsVFWmbOiYgJlFZ47dVgaHL5/R5gYma29hy1Im4CjoqIvpn5dvnYPsAC4O6WToyI7wGHAXtn5r1VzCRJkiRJkqROqMiKtPodM7eJiE0p7YI5CHgL+GtmPtIO+X4NfAu4NiJOBkYBxwKnNdw9NCKeB+7OzIPKnycCJwAXAa9GxMcbXPOFzHyjHbJKkiRJkiRpFVaoSKuXmY8Cj1Y5S1PfMycitgPOAv5IaQfP0ymVaQ11A7o2+PzZ8vsB5VdDX6NUsEmSJEmSJEkVq7hIi4hTgAsz86l2zPNvMvNJYNtW5oxs9PkA/r1AkyRJkiRJktqsS4G5RwKPR8SDEfGNiPhwe4WSJEmSJEmSak2RIu1LwC3A5pQ2GJgZEVdHxOcjomvLp0qSJEmSJEkrt4qLtMy8MjN3BkYA/w94DtgduI5SqXZaRIxpn5iSJEmSJElSxyqyIg2AzJydmf+/vTuPlqwq7z7+/THIEKRtEMUBgSAJTomzEG1m44RjQgzxNaIvyylGHIJRJAo4LImiaIxTRLGjRGMUnEC0AUFUVIY3RAVBpEFAIMxCCzTwvH+cU1pdVN1b1V33VnXf72etu+qevffZ5zmnmgM8vYf3VdWjgMfRbAQQ4HXA2Un+35hjlCRJkiRJkiZu5ERat6o6t6oOBB4IHATcCTxqHIFJkiRJkiRJ02ToXTv7SbIIeCHwEmBnmpFpN40hLkmSJEmSJGmqjJxIS7Ie8DSa5NlzgI2AAk4GPgN8eZwBSpIkSZIkSdNg6ERakkcBfwu8CLg/zeizC4GlwNKqunxOIpQkSZIkSZKmwCgj0v67/bwJ+CRwTFX9YPwhSZIkSZIkSdNnlETat4BjgOOq6va5CUeSNE7bvfkbkw5hoOUbTzqCtd9Uf7/vedakQ5AkSZLGbuhEWlU9fS4DkSRJkiRJkqbZepMOQJIkSZIkSVobDByRluRTNLtxHlxVV7fHw6iq+r9jiU6SJEmSJEmaEjNN7dyfJpF2BHB1ezyMAkykSZIkSZIkaZ0yUyJt+/bzip5jSZIkSZIkacEZmEirqktnOpYkSZIkSZIWkqE3G0jytiS7ztJmSZK3rXlYkiRJkiRJ0nSZaWpnr0Pbn9NnaLMr8Hbg8NUPSeuq5Rv/zaRDWOttd9uxkw5BkiRJmnuHLpp0BGu/Q2+adATSOmnoEWlD2hC4e8x9SpIkSZIkSRM37kTaY4Frx9ynJEmSJEmSNHEzTu1MckpP0f5Jdu/TdH1gG2Bb4D/GE5okSZIkSZI0PWZbI233rt8L2K796XU3cB3wBeD1Y4hLkiRJkiRJmiozJtKq6ndTP5PcDRxaVW4kIEmSJEmSpAVnlF07XwqcO1eBSJIkSZIkSdNs6ERaVX1mLgORJEmSJEmSptkoI9J+J8mDgQcBG/Wrr6rT1yQoSZIkSZIkadqMlEhL8ufAB4CdZmm6/mpHJEmSJEmSJE2h9WZv0kiyM/B14D7Ah4EApwP/BlzQHn8NcDMCSZIkSZIkrXOGTqQBbwFuA55QVQe2ZadW1SuBRwLvBPYG/mu8IUqSJEmSJEmTN0oibRfgq1V1Ze/51XgbcD5w2BjjkyRJkiRJkqbCKIm0RcBlXcd3AH/Q0+Z7wK5rGpQkSZIkSZI0bUZJpF0DLO453qGnzYbAJmsalCRJkiRJkjRtRkmkXciqibMzgacm+SOAJFsDfwFcNL7wJEmSJEmSpOkwSiLtm8BuSbZojz9IM/rs3CQ/ptm5cyvgqPGGKEmSJEmSJE3eKIm0j9Osf7YSoKq+B+wLXEKza+evgVdV1dJxBylJkiRJkiRN2gbDNqyqm4Ef9pQdBxw37qAkSZIkSZKkaTPKiDRJkiRJkiRpwTKRJkmSJEmSJA1h4NTOJL9czT6rqnaYvZkkSZIkSZK09phpjbT1gFqNPrOasUiSJEmSJElTa2Airaq2m8c4JEmSJEmSpKnmGmmSJEmSJEnSEFY7kZZkcZJtxhmMJEmSJEmSNK1GSqQl2SzJkUmuAq4FLumqe1KSE5I8dtxBSpIkSZIkSZM2dCItySLgB8DrgSuB81l1Y4H/AZYA+40zQEmSJEmSJGkajDIi7a3AI4D9q+qxwBe7K6tqBXAasNf4wpMkSZIkSZKmwyiJtBcAJ1XV0hnaXAo8aM1CkiRJkiRJkqbPKIm0BwPnzdLmFmDR6ocjSZIkSZIkTadREmm/Ae43S5vtaTYhkCRJkiRJktYpoyTSfgzsk+Te/SqTPAB4JnDGOAKTJEmSJEmSpskoibQPAlsCJyR5WHdFe/xFYGPgQ+MLT5IkSZIkSZoOGwzbsKpOSnIY8HbgJ8BKgCTXAouBAP9YVd+fi0AlSZIkSZKkSRplRBpVdRiwF/BV4AbgLqCAE4C9q+q94w4wycOTnJxkRZIrkxyeZP0hzluU5NNJbkhyU5LPJdly3PFJkiRJkiRpYRh6RFpHVZ0KnDoHsdxDksXAMuBnwHOBHYAjaRKAh8xy+n8CfwQcANwNHAEcDyyZq3glSZIkSZK07ho5kTabJFtV1f+OqbtXApsAL6iqm4FvJ9kcODTJP7dl/WLYBfhzYLeqOr0tuwL4YZK9q2rZmOKTJElaa2z35m9MOoSBlm886QjWflP9/b7nWZMOQZKksRhpaudM2qmU7wYuHlefwDOAk3oSZp+nSa7tNst5V3eSaABV9SPgkrZOkiRJkiRJGslQibQk2yZ5QZJnJ7l/T93GSd4C/BJ487B9Dmkn4ILugqq6DFjR1g19Xuv8Wc6TJEmSJEmS+po16ZXkQzSjzL5Is8bY8iSvbut2B34OvBPYFPgg8IdjjG8xcGOf8hvaunGfJ0mSJEmSJPU14xppSV4CvIZmsf7z2+KdgA8luRX4OLB++/nOqrpyDmOdU0leDry8Pbwlyc8nGc+6KJMOYHb3Ba6ddBAz22fSAQyUIyYdgdY2vhPGwXeC1h2+E8bBd4I0z6b7vXDYWvBmXTttO+kANFmzbTawP3AHsEdV/QAgya7At4GjgcuBZ1fV/8xRfDcAi/qUL27rZjpvq1HOq6pPAJ8YNUCtO5KcVVWPn3QckqaD7wRJ3XwnSOrle0FamGab2vknwHGdJBpAu4D/8TR/cfiyOUyiQbPO2SprmiXZhmYaab810Aae1xq0dpokSZIkSZI0o9kSaYuAX/Qpv6j9/EGfunE6EXhaknt3lb0Q+C1w2iznbZ3kKZ2CJI+nWb/txLkIVJIkSZIkSeu22RJp6wEr+5SvBKiq3449olV9DLgd+HKSvdt1zA4F3l9VN3caJflFkqM7x+0Ium8BS9vdRp8HfA44o6qWzXHMWns5tVdSN98Jkrr5TpDUy/eCtADNumsnUHMexaALV90A7EWzocHXgMOADwBv72m6Qdum2wtpRq19ClgKnA08fy7j1dqtXSdPkgDfCZJW5TtBUi/fC9LClKrBebIkdzN6Iq2qarZNDCRJkiRJkqS1yjAj0jLizzB9SlMjycOTnJxkRZIrkxyepHeEo6QFIMlDk3w8yXlJ7krynUnHJGlykuyb5KtJrkhyS5Kzk+w36bgkTUaSv0zy/STXJbktyc+THJLkXpOOTdL8mXHkWFWZFNM6LcliYBnwM+C5wA7AkTQJ4UMmGJqkyXgE8EzgTGDDCcciafLeAFwCvB64lub9cGyS+1bVv0w0MkmTsCVwCvBe4EbgiTRreG8NvGZyYUmaTzNO7ZTWdUneArwJ2LazgUWSN9H+C7F7UwtJ674k61XV3e3v/wXct6p2n2xUkialTZhd21N2LLBLVW0/obAkTZEk7wL+Dlhc/s+1tCA44kwL3TOAk3oSZp8HNgF2m0xIkialk0STJIDeJFrrXOCB8x2LpKl1HeDUTmkBMZGmhW4n4ILugqq6DFjR1kmSJHXbBbhw0kFImpwk6yfZNMlTgNcCH3U0mrRwuLumFrrFNOsb9LqhrZMkSQIgyV7A84CXTToWSRN1K7BR+/tS4KAJxiJpnjkiTZIkSZpFku2AY4GvVNUxEw1G0qT9GbAEeCPNhmUfnmw4kuaTI9K00N0ALOpTvritkyRJC1ySLYATgUuBF004HEkTVlXntL+ekeRa4DNJjqyqiycZl6T54Yg0LXQX0LMWWpJtgE3pWTtNkiQtPEk2Bb5Os5j4PlW1YsIhSZounaSaO/lKC4SJNC10JwJPS3LvrrIXAr8FTptMSJIkaRok2QD4IrAj8PSqumbCIUmaPk9uPy+ZaBSS5o1TO7XQfYxmp50vJzkC+EPgUOD9VXXzJAOTNP/akSfPbA8fBGye5C/b4xMciSItOB+heSccCGyZZMuuunOr6vbJhCVpEpJ8E1gG/BS4iyaJ9kbgC07rlBaOuEuvFrokD6dZIHQXmh08PwkcWlV3TTQwSfOuXUx80N8ob19Vy+ctGEkTl2Q5sO2Aat8J0gKT5B3A84HtgDuBXwKfBj5WVSsnGJqkeWQiTZIkSZIkSRqCa6RJkiRJkiRJQzCRJkmSJEmSJA3BRJokSZIkSZI0BBNpkiRpaEn2T1JJ9p90LNMkyeVJfjGGfj7bPt8HjyOucUuyKMmHkyxPcmcb6yMnHZckSdJ8MZEmSdIQ2oTBjDv0tMmFanf/1DxIct8kdye5akD9Lp3vLskeA9pc2tY/ZG6jnRvjSuIN6Ujg74D/Bt4NHAZcM9MJSc7o+g4G/RwyD7FLjX4WNQAACy1JREFUkiStsQ0mHYAkSVqrHAecCfx60oEAVNW1Sc4D/jTJI6rqpz1N9uo0BfYETu2uTPJQ4CHARVV12RqEslt7jXXdPsDPquq5q3Hup4FBz/j01Q9JkiRp/phIkyRJQ6uqm4CbJh1Hj1OAP6VJlPUm0vYELgZubn//pz71ACevSQBVdfGanL82SLI+cH/gJ6vZxaeq6owxhiRJkjTvnNopSdIcS/K8du2rC5Pc2v6cneS1Se7x7+Ikx7TT3bZP8pokP0tyWzt19OAkadvtm+RHbX/XtGtXbdKnv0rynST3T/KpJFe353w/yZK2zR8keW87zfH2JD9Nsm+fvvqukdbGtryrn8vafn6R5B87MfeckyQHdt3fFe09LOr0N+Qj7iTB9uwuTLIxsAvNKLRTgSck2azn3IGJtCTPSHJikuvae7k4yT8n2bxP277TK5PcJ8mH2nu7Lcn5SV6XZMf2OX5ywD0lyauT/KQ976okH+u+dpK92+nGDwJ26JkqOajf3os8MMlHu773a5J8KcljetqdAdzZHu7VdZ1lw1xnFJ37SnJIkp2TnJDk+nStHdd53u2flaPa+Fema4po++yPSHJR+wyvT/LNJHuuzjUlSZLAEWmSJM2H9wB3Az8ErgAW0SRwPgg8AXjxgPPeB+wOfA34FvAc4F3AvZJc3/Z7PPBd4Kk0a1etD7yqT1/3Ab4H/Ab4D2AL4K+Bk5LsAny8Lfs6sCGwH/CFJL+qqjOHvM8NgZOABwIn0iRentfGuTHNelrd/rWN9UrgE8Ad7T0+se1r5ZDXPb291u5J1ququ9vyJ7fXPaW97zcAuwInQJOpAvagmZLZO+XzcJrRa9fRPP//pRn1dhDw9CR/VlW3zBRUkk3bfh8NnAP8O7AYeDvNVNCZHEnznX6d5pnuBbwC2KEtB/glzTN9Q3v/H+o6/5xZ+ifJDsAZwNbAMuBYmmmu+wLPSvL8qjqxbf4pmuf4T8AlwNKuGObKU4C30Xy/RwP3Y9U/ExsD3wE2B75J8x0vB0iyBc2f952AHwFfArYC/gpYluTlVdUv2TjbNSVJ0gKXqoWwnIckSWsmv99ooDcZ1O11NEmy7atqede5O/RO/UszEu3TwN8CO1fVD7vqjgFeAlwKPLmqrmjL7wP8AtgEWAHsWlXnt3UbAefSJFq2qapruvrrxP5x4NWdRFOSF9MkRG6gSTrsW1W3tXVLaJIJx1fV87v62r+N+6VVdUxX+XJgW5oE2l9U1W/b8vsBF7bNtqqqlT39Xwg8qapubMvvRZPUWQJcWlXbDX7cqzzP79OMPntCVZ3Vlr0LOBh4QPu8rgeOqqp/aOsfBZwHnFtVj+3q66k0icszgH3a6aydugOAfwPeV1UHdZVfDtxWVQ/tKjuMJinzOeDF1f5HV5JtaRJdWwBHV9UBXed8FngRTUJoSVVd3pZvCJzW3uPjquqcrnPuce0hn9nJNAndN1fVEV3lS2gSVNcD21bVirZ8A5qk0slVtfcI1zmDJqk50xppH+n8mU2yN/DttvyAqjq6T5+X04zEOwl4QSfGrvqjgZcBH62qV3eV7wT8mCZRu2NV/WrYa0qSJIFTOyVJGtXbZ/hZ1O+EfutntcmsD7aHTxtwrXd0kmjtOTcCXwU2pUkQnN9VdzvwBeBewMP69LUCOKhrtBY0I5DupBkldWAnidb2912aZM6jB8Q2yGs7SbS2n2uAr9A8mz/uaveS9vNdnSRa2/4O4C0jXhP6T+/cEzi/qq6qqptpkle99d3n/u4e2s8DupNobXyfpFkj7EVDxPQS4C7gLZ0kWtvHpaw6eqyfwzpJtPaclTSJKGhG7K2RNDvL7kkzuuzI7rr2u/9P4L40IwrH5aUM/mfnfn3anzVEQuuNfZJoGwF/Q7Mu3sHddVV1AfBhYCP6jwQd5pqSJGkBM5EmSdIIqiqDfmhGkN1Dki2TvCfJeUlu6awvBZzdNnnQgMud1afsyvbz7D51naRbvzWdLqyq3/Tcy13A1cCNVdVvit4VA/oa5Kaqusc6YcCv2s/FXWWdNbj6LT5/Jr9fj2tYp7SfewIkuTfweFadsnkqze6eW3S35Z6JtF2A24H9khza+0OzNMYDkvRNnLbXX0wzQu+yzqinHrMtut/vu+/3HFdX5/mfXlX9nvUpPe3GYckM//z028DgR7P0d2ufXVoBHk4z7fPc7iRtl5nubbZrSpKkBc410iRJmkPtdMwfA9vT/E/6Upopc3fSrFt2IM3omH767Y555xB1Gw7ZV+ecmepG+W+FfkmL7rjW7yrrJKGu7m1cVXcluW6E6wJ8H/gtsKSdBrkbTeyndLX5DvAmYI8kx7dt7qCZYtptCyA0I6VmshmDn93A+5ulvKPfs+z3HFdXJ75fD6jvlN9nDNdaXVfNUj/oGa7Jvc12TUmStMCZSJMkaW4dQJNEO6yqDu2uaBf5P3ASQU2Bm9vP+9OzYH2S9YEt+f0Iu1lV1e3tOml7ATvTjDYrmuRZx3dpklF70ozuWkQzImvFqr1xM3BHVfWbbjis7vvrZ1D5fOkkALceUP+AnnaTMNtCvoPq1+TeXDxYkiTNyKmdkiTNrc4C8F/qUzfbzo3rsnPbz6f0qduZ1fvLvu510vYEzquq341sa3fZPKurvvucbmcCWyX54z51Q6mq62kW1n9Ikm36NOl336vrLkYfpdZ5/kvaxGWvPdrPWXf/nELn00zNfUySzfvUr833JkmSJsxEmiRJc2t5+7l7d2GSx7B6i+qvK5a2n2/tXmus3bXz3avZZ2ca577An7Dq+mgdpwI78fvNAvol0t7ffn4yyQN6K5NsluRJQ8SzlCbB9e4k6Tr/Ifx+Q4NxuA64X7vI/lDaXWVPpdnl9e+765I8GXhh2+9Xxhfm/Gg3zTiWZsTh4d11SXYEXkMzpfez8x+dJEla2zm1U5KkubUUOAg4KskewEXAjsA+wJdpEhYLTlWdluQTwMuBnyb5ErASeDbNlLsrgbtn6KKfs9pzH9Een9Knzak0CcxHArfQZ3H5qvpWkkOAdwAXJTmRZnfLzYDtaEYSnkrzHc7kPcBzgf8DPCzJMpp1uf4KOI1mR8xR77Gfk2kWzv9mku/SJInOrapvzHLeK2g2PfhAkmfQbGDxEJpE5J3A/lV16xji63hZkr0H1J1TVV8d47UOohn1d2CSJ9I8761onv1mwKuq6rIxXk+SJC0QJtIkSZpDVXVlkiU0SZWnAE8DLgBeDSxjgSbSWq+ieRavAF5JMwLqOOBg4HLg4lE6azcpOA14Ds10x95NBAC+R5NouhfN+mgrB/T1rjYp9VrgyTQJsZvauD4GfG6IeG5NshtNQu4FwOtp1oM7HPghTSLt5sE9DO0wYHOaxN4SmlFwRwMzJtKq6qIkjwMOAZ5JM+Xx5va8d1dVv51D18RLZ6g7GhhbIq2qrmtHDR4MPB94A7AC+AHw3qpaNq5rSZKkhSVVrqkqSZKmRzv97kLg81W136TjmQtJXgV8BDigqo6edDySJEkajmukSZKkiUiydZL1eso2BY5qD4+b/6jGK8kD+5RtC7yVZirrbNMvJUmSNEWc2ilJkibldcB+Sb4D/BrYGtgLeDBwIvDFyYU2Nl9p9xk4B7gR2J5mCuYmwEFVddUEY5MkSdKInNopSZImIslewD8Ajwa2oFng/kKaHRePGrR+2dokyd/T7BC6I806ZrfQJNX+paqOn2RskiRJGp2JNEmSJEmSJGkIrpEmSZIkSZIkDcFEmiRJkiRJkjQEE2mSJEmSJEnSEEykSZIkSZIkSUMwkSZJkiRJkiQNwUSaJEmSJEmSNIT/D16I2ZH1SSRbAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -958,12 +1011,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -985,12 +1038,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1012,16 +1065,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: 0.8962000000000001, 3: 0.9098, 4: 0.8749, 5: 0.8736, 10: 0.8807999999999998}, 3: {2: 0.8631, 3: 0.8403, 4: 0.8380000000000001, 5: 0.826, 10: 0.8180000000000002}, 4: {2: 0.7794000000000001, 3: 0.7955999999999999, 4: 0.7867999999999999, 5: 0.7590999999999999, 10: 0.7672}, 5: {2: 0.7362, 3: 0.7309999999999999, 4: 0.7289, 5: 0.75, 10: 0.7214999999999999}}\n", - "{2: {2: 0.9969999999999997, 3: 0.9975999999999999, 4: 0.9943, 5: 0.9942, 10: 0.9930999999999999}, 3: {2: 0.9928999999999999, 3: 0.9884000000000001, 4: 0.9870000000000001, 5: 0.9869999999999999, 10: 0.9842000000000001}, 4: {2: 0.9987, 3: 0.9995, 4: 0.9984, 5: 0.9967, 10: 0.9946999999999999}, 5: {2: 0.9961000000000004, 3: 0.9965000000000002, 4: 0.9946999999999999, 5: 0.9931000000000001, 10: 0.9842000000000001}}\n", - "{2: {2: 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\n", + "image/png": 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\n", 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" ] @@ -1134,12 +1187,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1172,12 +1225,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1217,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1226,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1236,12 +1289,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1272,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1307,23 +1360,23 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 40, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1341,23 +1394,23 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 41, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1380,7 +1433,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1390,12 +1443,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1426,12 +1479,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1468,7 +1521,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1477,7 +1530,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1501,7 +1554,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1515,7 +1568,7 @@ " 10, 10, 10, 10]])" ] }, - "execution_count": 47, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1550,7 +1603,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1574,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1598,7 +1651,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1620,7 +1673,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1630,15 +1683,15 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0108\n", - "The estimated product of the one and two qubit fidelity is F = 0.9892\n" + "The estimated error is p = 0.011\n", + "The estimated product of the one and two qubit fidelity is F = 0.989\n" ] } ], @@ -1650,7 +1703,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1660,12 +1713,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1696,12 +1749,12 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1739,7 +1792,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1749,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1758,16 +1811,16 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.05700418, 0.00220137])" + "array([0.05881116, 0.00176676])" ] }, - "execution_count": 58, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1778,18 +1831,18 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.88533033 0.8348628 0.78727213 0.74239433]\n", - " [0.88338139 0.83302496 0.78553905 0.74076004]\n", - " [0.88143674 0.83119116 0.78380979 0.73912936]\n", - " [0.87949637 0.8293614 0.78208433 0.73750226]\n", - " [0.86985841 0.82027285 0.77351386 0.72942034]]\n" + "[[0.88270907 0.83079593 0.78193585 0.73594929]\n", + " [0.88114954 0.82932811 0.78055436 0.73464905]\n", + " [0.87959276 0.82786289 0.77917531 0.7333511 ]\n", + " [0.87803874 0.82640026 0.7777987 0.73205545]\n", + " [0.87030969 0.81912576 0.77095203 0.72561144]]\n" ] } ], @@ -1801,12 +1854,12 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1843,7 +1896,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 60, "metadata": {}, "outputs": [ { diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 193c1e68..f6a50b2b 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -24,17 +24,21 @@ from forest.benchmarking.distance_measures import total_variation_distance as tvd from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary from forest.benchmarking.utils import bit_array_to_int +from forest.benchmarking.compilation import basic_compile @dataclass class CircuitTemplate: """ - We want to be able to specify various families of circuits and, once specified, randomly - sample from the family circuits of various width and depth. 'Width' is simply the number of - qubits. 'Depth' is not simply circuit depth, but rather the number of some repeated group of - gates that constitute some distinct unit. A depth d circuit could consist of d consecutive - rounds of random single qubit, then two qubit gates. It could also mean d consecutive - random Cliffords followed by the d conjugated Cliffords that invert the first d gates. + This dataclass enables us to specify various families of circuits and sample from a specified + family random circuits of various width and depth acting on different groups of qubits. + + 'Width' is simply the number of qubits measured at then end of the circuit. 'Depth' is not + simply circuit depth, but rather the number of repeated structured groups of gates, + each of which constitutes some distinct unit. A depth d circuit could consist of d + consecutive rounds of random single qubit, then two qubit gates. It could also mean d + consecutive random Cliffords followed by the d conjugated Cliffords that invert the first d + gates. Because these families of circuits are quite diverse, specifying the family and drawing samples can potentially require a wide variety of parameters. The compiler may be required to @@ -42,14 +46,20 @@ class CircuitTemplate: may be desired; the sequence of 'layers' generated so far may be necessary to compute an inverse. - The primary purpose of this class is to sample circuits, which we represent by a list of - pyquil Programs, or a 'sequence'; this core functionality is found in :func:`sample_sequence`. - In this function `generators` are applied in series in a loop `repetitions` number of times. - Each call to a generator will contribute an element to the output sequence, - and some combination of the generators will constitute a unit of depth. After a sequence is - generated from the output of the various `generators`, each `sequence_transform` is then - applied in series on the sequence to create a final output sequence. See - :func:`sample_sequence` for more information. + We represent each sampled circuit as a list of PyQuil Programs, which we call a 'sequence' + since each element of the list holds a distinctly structured group of gates that, + when applied altogether in series, constitute the circuit. This core functionality is found in + :func:`sample_sequence`. In this function `generators` are applied in series in a loop + `repetitions` number of times. Each call to a generator will contribute an element to the + output sequence (some combination of which will constitute a unit of depth). After a + sequence is generated from the output of the various `generators`, each `sequence_transform` + is then applied in series on the generated sequence to create a final output sequence. The + sequence transforms account for any features of the circuit that do increase with depth, + cannot neatly be fit into repeated units, or otherwise require performing a global + transformation on the sequence. See :func:`sample_sequence` for more information. + + This functionality is intended to enable creation and use of any of a wide variety of + 'volumetric benchmarks' described in the sources below. .. [Vol] A volumetric framework for quantum computer benchmarks. Blume-Kohout and Young. @@ -68,9 +78,6 @@ def append(self, other): """ Mutates the CircuitTemplate object by appending new generators. TODO: The behavior of sequence_transforms may not conform with expectations. - - :param other: - :return: """ if isinstance(other, list): self.generators += other @@ -83,10 +90,6 @@ def append(self, other): def __add__(self, other): """ Concatenate two circuits together, returning a new one. - - :param Circuit other: Another circuit to add to this one. - :return: A newly concatenated circuit. - :rtype: Program """ ckt = CircuitTemplate() ckt.append(self) @@ -100,11 +103,12 @@ def __iadd__(self, other): self.append(other) return self - def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None): + def sample_sequence(self, graph: nx.Graph, repetitions: int, qc: QuantumComputer = None, + width: int = None, sequence: List[Program] = None): """ The sequence_transforms are distinct from generators in that they take in a sequence and output a new sequence. These are applied in series after the entire sequence has been - generated. A motivating family of interest is + generated. A family of interest that motivates this distinction is C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t @@ -112,12 +116,17 @@ def sample_sequence(self, graph, repetitions, qc=None, width=None, sequence=None generator of random Cliffords, a conjugation sequence transform, and a Pauli frame randomization transform. - :param graph: - :param repetitions: - :param qc: - :param width: - :param sequence: - :return: + :param graph: the qubit topology on which the circuit should act. Unless width is + specified, the number of qubits in the graph should be considered circuit width. + :param repetitions: the number of times the loop of generators should be applied. + :param qc: a quantum computer, likely the one on which the circuit will be run, providing + access to the full chip topology and associated compiler. + :param width: the number of qubits that will be measured at the end of the circuit. If + the supplied graph contains more qubits, an induced subgraph of width-many qubits + will be selected uniformly at random from the graph. + :param sequence: an optional initialization of a sequence to build off of/append to. + :return: the list of programs whose sum constitutes a circuit sample from the family of + circuits specified by the generators and sequence_transforms. """ if width is not None: graph = random.choice(generate_connected_subgraphs(graph, width)) @@ -140,13 +149,58 @@ def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None) return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) +def graph_restricted_compilation(qc: QuantumComputer, graph: nx.Graph, + program: Program) -> Program: + """ + A useful helper that temporarily modifies the supplied qc's qubit topology to match the + supplied graph so that the given program may be compiled onto the graph topology. + + :param qc: a qc object with a compiler where the given graph is a subraph of the qc's qubit + topology. + :param graph: The desired subraph of the qc's full topology on which we wish to run a program. + :param program: a program we wish to run on a particular graph on the qc. + :return: the program compiled into native quil gates respecting the graph topology. + """ + qubits = list(graph.nodes) + + # restrict compilation to chosen qubits + isa_dict = qc.device.get_isa().to_dict() + single_qs = isa_dict['1Q'] + two_qs = isa_dict['2Q'] + + new_1q = {} + for key, val in single_qs.items(): + if int(key) in qubits: + new_1q[key] = val + new_2q = {} + for key, val in two_qs.items(): + q1, q2 = key.split('-') + if (int(q1), int(q2)) in graph.edges: + new_2q[key] = val + + new_isa = {'1Q': new_1q, '2Q': new_2q} + + new_compiler = copy(qc.compiler) + new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) + # try to compile with the restricted qubit topology + try: + native_quil = new_compiler.quil_to_native_quil(program) + except RPCErrorError as e: + if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ + in str(e): + raise ValueError("The program could not be compiled onto the given subgraph.") + raise + + return native_quil + + # ================================================================================================== # Generators # ================================================================================================== -def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): +def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: """ - Create a program comprised of single qubit gates randomly placed on the nodes of the - specified graph. The gates are chosen uniformly from the list provided. + Create a program comprised of random single qubit gates acting on the qubits of the + specified graph; each gate is chosen uniformly at random from the list provided. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :param gates: A list of gates e.g. [I, X, Z] or [I, X]. @@ -159,9 +213,9 @@ def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): return program -def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): +def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: """ - Write a program to randomly place two qubit gates on edges of the specified graph. + Create a program to randomly place two qubit gates on edges of the specified graph. :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] @@ -175,9 +229,9 @@ def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]): return program -def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): +def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: """ - Create a program comprised of single qubit Cliffords gates randomly placed on the nodes of + Create a program comprised of single qubit Clifford gates randomly placed on the nodes of the specified graph. Each uniformly random choice of Clifford is implemented in the native gateset. @@ -201,9 +255,9 @@ def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog -def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): +def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: """ - Write a program to place random two qubit Cliffords gates on edges of the graph. + Write a program to place random two qubit Clifford gates on edges of the graph. :param bm: A benchmark connection that will do the grunt work of generating the Cliffords :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. @@ -218,8 +272,8 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): rand_cliffords = clif_n_inv[0:num_2q_gates] prog = Program() - # do the two coloring with pragmas? - # no point until fencing is over + # TODO: two coloring with PRAGMAS? + # TODO: longer term, fence to be 'simultaneous'? for edges, clif in zip(graph.edges, rand_cliffords): gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], q_placeholders[1]: edges[1]}) @@ -227,38 +281,41 @@ def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph): return prog -def dagger_previous(sequence: List[Program], n: int = 1): - return merge_programs(sequence[-n:]).dagger() +def dagger_previous(sequence: List[Program], n: int = 1) -> Program: + """ + Create a program which is the inverse (conjugate transpose; adjoint; dagger) of the last n + layers of the provided sequence. + :param sequence: a sequence of PyQuil programs whose elements are layers in a circuit + :param n: the number of layers at the end of the sequence that will be inverted + :return: a program that inverts the last n layers of the provided sequence. + """ + return merge_programs(sequence[-n:]).dagger() -def _qubit_perm_to_bitstring_perm(qubit_permutation: List[int]): - bitstring_permutation = [] - for bitstring in range(2**len(qubit_permutation)): - permuted_bitstring = 0 - for idx, q in enumerate(qubit_permutation): - permuted_bitstring |= ((bitstring >> q) & 1) << idx - bitstring_permutation.append(permuted_bitstring) - return bitstring_permutation +def random_su4_pairs(graph: nx.Graph, idx_label: int) -> Program: + """ + Create a program that enacts a Haar random 2 qubit gate on random pairs of qubits in the + graph, irrespective of graph topology. -def random_qubit_permutation(graph: nx.Graph): - qubits = list(graph.nodes) - permutation = list(np.random.permutation(range(len(qubits)))) + If the graph contains an odd number of nodes, then one random qubit will not be acted upon by + any gate. - gate_definition = DefPermutationGate("Perm" + "".join([str(q) for q in permutation]), - _qubit_perm_to_bitstring_perm(permutation)) - PERMUTE = gate_definition.get_constructor() - p = Program() - p += gate_definition - p += PERMUTE(*qubits) - return p + The output program will need to be compiled into native gates. + This generator is the repeated unit of the quantum volume circuits described in [QVol]_. Note + that the qubit permutation is done implicitly--the compiler will have to figure out how to + move potentially distant qubits onto a shared edge in order to enact the random two qubit gate. -def random_su4_pairs(graph: nx.Graph, idx_label, randomly_permute_qubits: bool = True): + :param graph: a graph containing qubits that will be randomly paired together. Note that + the graph topology (the edges) are ignored. + :param idx_label: a label that uniquely identifies the set of gate definitions used in the + output program. This prevents subsequent calls to this method from producing a program + with definitions that overwrite definitions in previously generated programs. + :return: a program with random two qubit gates between random pairs of qubits. + """ qubits = list(graph.nodes) - if randomly_permute_qubits: - permutation = list(np.random.permutation(range(len(qubits)))) - qubits = [qubits[idx] for idx in permutation] + qubits = [qubits[idx] for idx in np.random.permutation(range(len(qubits)))] prog = Program() # ignore the edges in the graph for q1, q2 in zip(qubits[::2], qubits[1::2]): @@ -270,51 +327,39 @@ def random_su4_pairs(graph: nx.Graph, idx_label, randomly_permute_qubits: bool = return prog -def maxcut_cost_unitary(graph: nx.Graph, layer_number): +def maxcut_cost_unitary(graph: nx.Graph, idx_label: int) -> Program: + """ + Creates a parameterized program used in QAOA that enacts commuting parameterized 2 qubit + gates on every edge of the graph. + + :param graph: + :param idx_label: a label that uniquely identifies the set of gate definitions used in the + output program. This prevents subsequent calls to this method from producing a program + with definitions that overwrite definitions in previously generated programs. + :return: + """ prog = Program() - theta = prog.declare('theta_' + str(layer_number), memory_type='REAL') + theta = prog.declare('theta_' + str(idx_label), memory_type='REAL') for edge in graph.edges: - exponential_map(sZ(edge[0] * sZ(edge[1])))(theta) + exponential_map(sZ(edge[0]) * sZ(edge[1]))(theta) return prog -def graph_restricted_compilation(qc, graph, program): - qubits = list(graph.nodes) - - # restrict compilation to chosen qubits - isa_dict = qc.device.get_isa().to_dict() - single_qs = isa_dict['1Q'] - two_qs = isa_dict['2Q'] - - new_1q = {} - for key, val in single_qs.items(): - if int(key) in qubits: - new_1q[key] = val - new_2q = {} - for key, val in two_qs.items(): - q1, q2 = key.split('-') - if (int(q1), int(q2)) in graph.edges: - new_2q[key] = val - - new_isa = {'1Q': new_1q, '2Q': new_2q} - - new_compiler = copy(qc.compiler) - new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) - # try to compile with the restricted qubit topology - try: - native_quil = new_compiler.quil_to_native_quil(program) - except RPCErrorError as e: - if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ - in str(e): - raise ValueError("The program could not be compiled onto the given subgraph.") - raise - - return native_quil - ### # Sequence Transforms ### -def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs): +def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs) -> List[Program]: + """ + Insert a Hadamard gate on each qubit at the beginning and end of the sequence. + + This can be viewed as switching from the computational Z basis to the X basis. + + :param sequence: the sequence to be sandwiched by Hadamards + :param graph: the graph containing the qubits to be acted on by Hadamards + :param kwargs: extraneous arguments + :return: a new sequence which is the input sequence with new starting and ending layers of + Hadamards. + """ prog = Program() for node in graph.nodes: prog.inst(H(node)) @@ -322,10 +367,38 @@ def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs): def dagger_sequence(sequence: List[Program], **kwargs): + """ + Returns the original sequence with its layer-by-layer inverse appended on the end. + + The net result of the output sequence is the Identity. + + .. CAUTION:: + Merging this sequence and compiling the resulting program will result in a trivial + empty program. To avoid this, consider using a sequence transform to compile each + element of the sequence first, then combine the result. For example, see + :func:`compile_individual_sequence_elements`. Using :func:`compile_merged_sequence` + with `use_basic_compile` set to True will also avoid this issue, but will not compile + gate definitions and will not compile gates onto the chip topology. + + :param sequence: the sequence of programs comprising a circuit that will be inverted and + appended to the sequence. + :param kwargs: extraneous arguments + :return: a new sequence the input sequence and its inverse + """ return sequence + [prog.dagger() for prog in reversed(sequence)] -def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **kwargs): +def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **kwargs) \ + -> List[Program]: + """ + Inserts random single qubit Pauli gates on each qubit in between elements of the input sequence. + + :param sequence: + :param graph: a graph containing qubits that will be randomly paired together. Note that + the graph topology (the edges) are ignored. + :param kwargs: extraneous arguments + :return: + """ paulis = [I, X, Y, Z] random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] new_sequence = [None for _ in range(2*len(sequence) + 1)] @@ -334,7 +407,18 @@ def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **k return new_sequence -def compile_individual_sequence_elements(qc, sequence: List[Program], graph: nx.Graph, **kwargs): +def compile_individual_sequence_elements(qc: QuantumComputer, sequence: List[Program], + graph: nx.Graph, **kwargs) -> List[Program]: + """ + Returns the sequence where each element is individually compiled into native quil in a way + that respects the given graph topology. + + :param qc: + :param sequence: + :param graph: + :param kwargs: extraneous arguments + :return: + """ compiled_sequence = [] for prog in sequence: native_quil = graph_restricted_compilation(qc, graph, prog) @@ -343,72 +427,109 @@ def compile_individual_sequence_elements(qc, sequence: List[Program], graph: nx. return compiled_sequence -def compile_merged_sequence(qc, sequence: List[Program], graph: nx.Graph, **kwargs): - # compile all of the sequence at once. - native_quil = graph_restricted_compilation(qc, graph, merge_programs(sequence)) - return [Program([instr for instr in native_quil.instructions][:-1])] +def compile_merged_sequence(qc: QuantumComputer, sequence: List[Program], graph: nx.Graph, + use_basic_compile: bool = False, **kwargs) -> List[Program]: + """ + Merges the sequence into a Program and returns a 'sequence' comprised of the corresponding + compiled native quil program that respects the given graph topology. + + .. CAUTION:: + The option to only use basic_compile will only result in native quil if the merged + sequence contains no gate definitions and if all multi-qubit gates already respect + the graph topology. If this is not the case, the output program may not be able to be + converted properly to an executable that can be run on the qc. + + :param qc: + :param sequence: + :param graph: + :param use_basic_compile: + :param kwargs: extraneous arguments + :return: + """ + merged = merge_programs(sequence) + if use_basic_compile: + return [basic_compile(merged)] + else: + native_quil = graph_restricted_compilation(qc, graph, merged) + # remove gate definitions and terminous HALT + return [Program([instr for instr in native_quil.instructions][:-1])] ### # Templates ### def get_rand_1q_template(gates: Sequence[Gate]): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random single qubit gates pulled from the input set of gates. + + :param gates: + :return: + """ def func(graph, **kwargs): return random_single_qubit_gates(graph, gates=gates) return CircuitTemplate([func]) def get_rand_2q_template(gates: Sequence[Gate]): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random two qubit gates pulled from the input set of gates. + + :param gates: + :return: + """ def func(graph, **kwargs): return random_two_qubit_gates(graph, gates=gates) return CircuitTemplate([func]) def get_rand_1q_cliff_template(bm: BenchmarkConnection): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random single qubit Clifford gates. + """ def func(graph, **kwargs): return random_single_qubit_cliffords(bm, graph) return CircuitTemplate([func]) def get_rand_2q_cliff_template(bm: BenchmarkConnection): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random two qubit Clifford gates. + """ def func(graph, **kwargs): return random_two_qubit_cliffords(bm, graph) return CircuitTemplate([func]) -def get_dagger_all_template(): - def func(sequence, **kwargs): - return dagger_previous(sequence, len(sequence)) - return CircuitTemplate([func]) - - def get_dagger_previous(n: int = 1): + """ + Creates a CircuitTemplate that can be appended to another template to generate families of + circuits with repeated (layer, inverse-layer) units. + """ def func(sequence, **kwargs): return dagger_previous(sequence, n) return CircuitTemplate([func]) -def get_rand_qubit_perm_template(): - def func(graph, **kwargs): - return random_qubit_permutation(graph) - return CircuitTemplate([func]) - - -def get_rand_su4_template(randomly_permute_qubits: bool = True): +def get_rand_su4_template(): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + Haar-random two qubit gates acting on random pairs of qubits. This is the generator used in + quantum volume [QVol]_ . + """ def func(graph, sequence, **kwargs): - return random_su4_pairs(graph, len(sequence), randomly_permute_qubits) - return CircuitTemplate([func]) - - -def get_switch_basis_x_z_template(): - def func(graph, **kwargs): - prog = Program() - for node in graph.nodes: - prog.inst(H(node)) - return prog + return random_su4_pairs(graph, len(sequence)) return CircuitTemplate([func]) -def get_all_H_template(): - return get_switch_basis_x_z_template() +def get_quantum_volume_template(): + """ + Creates a quantum volume CircuitTemplate. See [QVol]_ . + """ + template = get_rand_su4_template() + template.sequence_transforms.append(compile_merged_sequence) + return template def get_param_local_RX_template(): @@ -448,6 +569,15 @@ def func(graph, qc, sequence, **kwargs): # Data acquisition # ================================================================================================== def sample_random_connected_graphs(graph: nx.Graph, widths: List[int], num_ckts_per_width): + """ + Helper to uniformly randomly sample `num_ckts_per_width` many connected induced subgraphs of + `graph` for each width in `widths` + + :param graph: + :param widths: + :param num_ckts_per_width: + :return: + """ samples = {w: [] for w in widths} for w in widths: connected_subgraphs = generate_connected_subgraphs(graph, w) @@ -459,6 +589,18 @@ def sample_random_connected_graphs(graph: nx.Graph, widths: List[int], num_ckts_ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], depths: List[int], num_circuit_samples: int, graphs: Dict[int, List[nx.Graph]] = None): + """ + Creates a dictionary containing random circuits sampled from the input `ckt` family for each + width and depth. + + :param qc: + :param ckt: + :param widths: + :param depths: + :param num_circuit_samples: + :param graphs: + :return: + """ if graphs is None: graphs = sample_random_connected_graphs(qc.qubit_topology(), widths, len(depths)*num_circuit_samples) @@ -477,10 +619,23 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, return programs -def acquire_volumetric_data(qc: QuantumComputer, program_array, num_shots: int = 500, +def acquire_volumetric_data(qc: QuantumComputer, program_array:Dict[int, Dict[int, List[Program]]], + num_shots: int = 500, measure_qubits: Dict[int, Dict[int, List[int]]] = None, - use_active_reset: bool = False, - use_compiler: bool = False): + use_active_reset: bool = False, use_compiler: bool = False)\ + -> Dict[int, Dict[int, List[np.ndarray]]]: + """ + Runs each program in `program_array` on the qc and stores the results, organized again by + width and depth. + + :param qc: + :param program_array: + :param num_shots: + :param measure_qubits: + :param use_active_reset: + :param use_compiler: + :return: + """ reset_prog = Program() if use_active_reset: reset_prog += RESET() @@ -561,30 +716,23 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, return heavy_output_array -# TODO: -# def do_volumetric_measurements(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], -# depths: List[int], -# num_circuit_samples: int, graph: nx.Graph = None, -# num_shots: int = 500, -# use_active_reset: bool = False, -# compile_circuits: bool = False): -# -# -# prog_array = generate_volumetric_program_array(qc, ckt, widths, depths, num_circuit_samples, -# graph) -# -# return [] - # ================================================================================================== # Analysis # ================================================================================================== -def get_error_hamming_weight_distributions(noisy_results, ideal_results): +def get_error_hamming_weight_distributions(noisy_results: Dict[int, Dict[int, List[np.ndarray]]], + ideal_results: Dict[int, Dict[int, List[np.ndarray]]]): + """ + Calculate the hamming distance to the ideal for each noisy shot of each circuit sampled for + each width and depth. - # allow for ideal result to depend only on width (pass in a dict {w: result}) - # if not isinstance(ideal_results.values()[0], dict): - # ideal_results = {width: {depth: ideal_results[width] for depth in depth_array.keys()} - # for width, depth_array in noisy_results.items()} + Note that this method is only appropriate when the ideal result for each circuit is a single + deterministic (circuit-dependent) output; therefore, ideal_results should only contain one + shot per circuit. + :param noisy_results: + :param ideal_results: + :return: + """ distrs = {width: {depth: [] for depth in depth_array.keys()} for width, depth_array in noisy_results.items()} @@ -594,6 +742,7 @@ def get_error_hamming_weight_distributions(noisy_results, ideal_results): noisy_ckt_sample_results = noisy_results[width][depth] ideal_ckt_sample_results = ideal_results[width][depth] + # iterate over circuits for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): if len(ideal_result) > 1: @@ -613,6 +762,20 @@ def get_error_hamming_weight_distributions(noisy_results, ideal_results): def get_single_target_success_probabilities(noisy_results, ideal_results, allowed_errors: Union[int, Callable[[int], int]] = 0): + """ + For circuit results of various width and depth, calculate the fraction of noisy results + that match the single ideal result for each circuit. + + Note that this method is only appropriate when the ideal result for each circuit is a single + deterministic (circuit-dependent) output. + + :param noisy_results: noisy shots from each circuit sampled for each width and depth + :param ideal_results: a single ideal result for each circuit + :param allowed_errors: either a number indicating the maximum hamming distance from the ideal + result is still considered a success, or a function which returns the max hamming + distance allowed for a given width. + :return: + """ if isinstance(allowed_errors, int): error_func = lambda num_bits: allowed_errors else: @@ -621,10 +784,24 @@ def get_single_target_success_probabilities(noisy_results, ideal_results, hamming_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results) return {w: {d: [sum(distr[0:error_func(w)+1]) for distr in distrs] - for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} + for d, distrs in d_distrs.items()} + for w, d_distrs in hamming_distrs.items()} def get_success_probabilities(noisy_results, ideal_results): + """ + For circuit results of various width and depth, calculate the fraction of noisy results + that are also found in the collection of ideal results for each circuit. + + Quantum volume employs this method to calculate success_probabilities where the ideal_results + are the heavy hitters of each circuit. + + :param noisy_results: noisy shots from each circuit sampled for each width and depth + :param ideal_results: a collection of ideal results for each circuit; membership of a noisy + shot from a particular circuit in the corresponding set of ideal_results constitutes a + success. + :return: the estimated success probability for each circuit. + """ prob_success = {width: {depth: [] for depth in depth_array.keys()} for width, depth_array in noisy_results.items()} @@ -634,6 +811,7 @@ def get_success_probabilities(noisy_results, ideal_results): noisy_ckt_sample_results = noisy_results[width][depth] ideal_ckt_sample_results = ideal_results[width][depth] + # iterate over circuits for noisy_shots, ideal_results in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): targets = ideal_results @@ -678,19 +856,34 @@ def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_ def determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt): - return {w: - {d: - calculate_success_prob_est_and_err( + """ + Wrapper around `calculate_success_prob_est_and_err` to determine success lower bounds for a + collection of circuits at various depths and widths. + + :param ckt_success_probs: + :param num_shots_per_ckt: + :return: + """ + return {w: {d: calculate_success_prob_est_and_err( sum(np.asarray(succ_probs) * num_shots_per_ckt), len(succ_probs), - num_shots_per_ckt - )[1] for d, succ_probs in d_ckt_succ_probs.items() - } for w, d_ckt_succ_probs in ckt_success_probs.items() - } + num_shots_per_ckt)[1] + for d, succ_probs in d_ckt_succ_probs.items()} + for w, d_ckt_succ_probs in ckt_success_probs.items()} -def determine_successes(ckt_success_probs, num_shots_per_ckt, +def determine_successes(ckt_success_probs: Dict[int, Dict[int, List[float]]], num_shots_per_ckt, success_threshold: float = 2 / 3): + """ + Indicate whether the collection of circuit success probabilities for given width and depth + recorded in `ckt_success_probs` is considered a success with respect to the specified + `success_threshold` and given the number of shots used to estimate each success probability. + + :param ckt_success_probs: + :param num_shots_per_ckt: + :param success_threshold: + :return: + """ lower_bounds = determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt) return {w: {d: lb > success_threshold for d, lb in d_lower_bounds.items()} for w, d_lower_bounds in lower_bounds.items()} @@ -700,11 +893,13 @@ def average_distributions(distrs): """ E.g. take in output of :func:`get_error_hamming_weight_distributions` or :func:`get_single_target_success_probabilities` + :param distrs: :return: """ return {w: {d: sum([np.asarray(distr) for distr in distr_list]) / len(distr_list) - for d, distr_list in d_arr.items()} for w, d_arr in distrs.items()} + for d, distr_list in d_arr.items()} + for w, d_arr in distrs.items()} def get_total_variation_dist(distr1, distr2): From cf59cdbd373f917d7daed39ab8c418e2f92f088c Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 4 Nov 2019 18:05:39 -0500 Subject: [PATCH 40/49] Allow more flexible qv api and add qv test. --- docs/examples/volumetrics.ipynb | 568 +++++++++--------- forest/benchmarking/tests/test_volumetrics.py | 31 + forest/benchmarking/volumetrics.py | 46 +- 3 files changed, 330 insertions(+), 315 deletions(-) create mode 100644 forest/benchmarking/tests/test_volumetrics.py diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb index c5cd10fb..0c676750 100644 --- a/docs/examples/volumetrics.ipynb +++ b/docs/examples/volumetrics.ipynb @@ -88,7 +88,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -178,7 +178,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi) 0\n", + "RX(-pi/2) 0\n", "\n" ] } @@ -208,28 +208,28 @@ "X 1\n", "I 2\n", "I 3\n", - "I 4\n", + "X 4\n", "I 5\n", - "I 6\n", - "I 7\n", - "Z 8\n", + "X 6\n", + "X 7\n", + "I 8\n", "CZ 0 3\n", "I 0\n", "I 1\n", + "CZ 1 4\n", "I 1\n", - "I 4\n", - "CZ 1 2\n", "I 2\n", - "I 5\n", + "CZ 2 5\n", "CZ 3 6\n", - "CZ 3 4\n", + "I 3\n", + "I 4\n", "CZ 4 7\n", - "CZ 4 5\n", + "I 4\n", + "I 5\n", "CZ 5 8\n", "I 6\n", "I 7\n", - "I 7\n", - "I 8\n", + "CZ 7 8\n", "\n" ] } @@ -249,21 +249,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", + "RZ(-pi) 0\n", "RZ(pi/2) 1\n", - "RX(-pi/2) 2\n", + "RX(-pi) 1\n", "RZ(-pi/2) 2\n", + "RX(-pi) 2\n", "RX(pi/2) 3\n", "RZ(pi/2) 3\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(-pi/2) 4\n", - "RZ(-pi/2) 5\n", - "RX(pi/2) 5\n", - "RZ(-pi) 6\n", - "RX(-pi) 6\n", - "RX(pi/2) 7\n", - "RZ(-pi) 7\n", - "RZ(-pi) 8\n", + "RZ(pi/2) 5\n", + "RX(-pi) 5\n", + "RZ(pi/2) 6\n", + "RX(-pi/2) 6\n", + "RX(-pi/2) 7\n", + "RZ(-pi/2) 7\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 8\n", + "RZ(-pi/2) 8\n", "\n" ] } @@ -289,10 +292,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 2\n", - "I 5\n", - "X 2\n", - "X 5\n", + "X 0\n", + "X 3\n", + "X 0\n", + "I 3\n", "\n" ] } @@ -313,7 +316,8 @@ "text": [ "I 5\n", "I 8\n", - "CNOT 5 8\n", + "I 5\n", + "I 8\n", "\n" ] } @@ -332,22 +336,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi/2) 8\n", - "RX(-pi/2) 8\n", - "RX(pi/2) 5\n", - "CZ 5 8\n", - "RX(-pi/2) 8\n", - "RX(-pi/2) 5\n", - "RZ(-pi/2) 5\n", - "RX(-pi/2) 5\n", - "RX(-pi/2) 8\n", - "CZ 5 8\n", - "RZ(pi/2) 8\n", - "RX(pi/2) 8\n", - "RX(-pi/2) 5\n", - "CZ 5 8\n", - "RZ(-pi/2) 8\n", - "RX(-pi/2) 5\n", + "RX(-pi/2) 7\n", + "RX(pi/2) 6\n", + "CZ 6 7\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(pi/2) 6\n", + "CZ 6 7\n", + "RX(pi/2) 7\n", + "RZ(pi/2) 7\n", + "RX(-pi/2) 7\n", + "RX(-pi/2) 6\n", "\n" ] } @@ -360,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -368,10 +367,10 @@ "output_type": "stream", "text": [ "DEFGATE LYR0_RSU4_7_8:\n", - " -0.022877395095540765-0.14714221203233244i, 0.0722782327401761-0.5230331737668792i, -0.5468187982472037-0.47520128026247194i, -0.313443145826728-0.2756162003544303i\n", - " 0.392699429249816+0.2055594278331732i, -0.055160984125693674-0.20954418637277003i, 0.02393672258156379+0.4840588024816986i, 0.0684176071325038-0.7190369390856951i\n", - " -0.0021993657190741353+0.14768568142801533i, -0.742594131004298+0.3360770236723023i, -0.032070743614688424-0.4462636686845488i, 0.18121322556374395-0.2842046187979819i\n", - " 0.8713454847296828-0.017303833691206028i, -0.07232091739381188-0.06809208224027807i, -0.14673506533933722-0.13629409447403606i, 0.14085261365942156+0.41309085981289256i\n", + " -0.17133680286283015+0.5771855029770466i, -0.02466156348536916+0.22179234545831975i, 0.12228377642992853+0.14496205601880133i, -0.2297884080173649+0.7063501439074774i\n", + " -0.11960102370866607-0.3304433900630011i, -0.11032673672938412-0.42880536505067257i, 0.7357619987654005+0.3068282033583267i, 0.06383311224299526+0.2022196818708112i\n", + " -0.26202392947509057+0.06665775229723633i, -0.3849027928296902-0.5194528926129671i, -0.5291269370112102+0.144305312790629i, 0.3979354224512658+0.22308473201253587i\n", + " -0.042541944416586486-0.6626414895218656i, -0.19559242672520571+0.545816881274874i, -0.1609407499642989+0.04292029081848708i, 0.14224663636027113+0.418265217456327i\n", "\n", "LYR0_RSU4_7_8 7 8\n", "\n" @@ -400,24 +399,22 @@ "output_type": "stream", "text": [ "X 4\n", - "X 5\n", + "I 5\n", + "I 6\n", "X 7\n", - "I 8\n", + "CNOT 4 7\n", "I 4\n", + "I 5\n", + "I 6\n", "I 7\n", - "CNOT 4 5\n", + "I 4\n", "I 5\n", - "I 8\n", - "CNOT 7 8\n", - "X 4\n", - "X 5\n", + "X 6\n", "I 7\n", - "X 8\n", "CNOT 4 7\n", - "CNOT 4 5\n", + "I 4\n", "I 5\n", - "I 8\n", - "CNOT 7 8\n", + "CNOT 6 7\n", "\n" ] } @@ -445,23 +442,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 4\n", + "H 2\n", "H 5\n", - "Z 4\n", + "Z 2\n", "Z 5\n", - "I 4\n", + "I 2\n", "I 5\n", - "Z 4\n", - "Z 5\n", - "H 4\n", - "CZ 4 5\n", - "H 4\n", - "I 4\n", - "Z 5\n", - "H 4\n", - "CZ 4 5\n", - "H 4\n", - "H 4\n", + "Z 2\n", + "I 5\n", + "H 2\n", + "CZ 2 5\n", + "H 2\n", + "I 2\n", + "I 5\n", + "I 2\n", + "I 5\n", + "H 2\n", "H 5\n", "\n" ] @@ -495,74 +491,62 @@ "output_type": "stream", "text": [ "RX(pi/2) 0\n", - "RZ(pi/2) 0\n", - "RX(-pi) 3\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(pi/2) 3\n", - "RX(pi/2) 0\n", - "RX(-pi/2) 0\n", + "RZ(-pi) 0\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", + "CZ 0 1\n", + "RX(-pi/2) 1\n", "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", - "RX(-pi/2) 3\n", - "RZ(-pi) 3\n", - "RX(-pi/2) 3\n", + "CZ 0 1\n", + "RX(-pi/2) 1\n", "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", + "CZ 0 1\n", "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RZ(-pi/2) 3\n", - "RX(-pi/2) 3\n", "RZ(-pi/2) 0\n", "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 1\n", + "CZ 0 1\n", + "RZ(-pi/2) 1\n", + "RX(pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RZ(pi/2) 1\n", + "RX(-pi) 1\n", + "CZ 0 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", "RZ(pi/2) 0\n", - "RX(-pi/2) 0\n", - "RX(-pi) 3\n", - "RZ(pi/2) 3\n", - "RX(pi/2) 3\n", - "CZ 0 3\n", - "RX(pi/2) 3\n", - "RX(-pi/2) 0\n", - "CZ 0 3\n", - "RX(-pi/2) 3\n", - "RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 3\n", - "DAGGER CZ 0 3\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(pi/2) 3\n", - "DAGGER CZ 0 3\n", - "DAGGER RX(pi/2) 3\n", - "DAGGER RZ(pi/2) 3\n", - "DAGGER RX(-pi) 3\n", - "DAGGER RX(-pi/2) 0\n", "DAGGER RZ(pi/2) 0\n", + "DAGGER CZ 0 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 0 1\n", + "DAGGER RX(-pi) 1\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RZ(-pi/2) 0\n", + "DAGGER RX(pi/2) 0\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER CZ 0 1\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(pi/2) 1\n", "DAGGER RX(-pi/2) 0\n", "DAGGER RZ(-pi/2) 0\n", - "DAGGER RX(-pi/2) 3\n", - "DAGGER RZ(-pi/2) 3\n", - "DAGGER CZ 0 3\n", "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 3\n", - "DAGGER CZ 0 3\n", + "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 3\n", - "DAGGER RZ(-pi) 3\n", - "DAGGER RX(-pi/2) 3\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 0\n", "DAGGER RZ(-pi/2) 0\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(pi/2) 0\n", - "DAGGER RX(pi/2) 3\n", - "DAGGER CZ 0 3\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 3\n", - "DAGGER CZ 0 3\n", - "DAGGER RX(-pi) 3\n", - "DAGGER RZ(pi/2) 0\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER CZ 0 1\n", + "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(-pi/2) 1\n", + "DAGGER RZ(-pi) 0\n", "DAGGER RX(pi/2) 0\n", "\n", "This program compiles away to nothing: \n", @@ -598,157 +582,157 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(0.6023647634493379) 1\n", + "RZ(-2.231268474427302) 1\n", "RX(pi/2) 1\n", - "RZ(1.6859512736760711) 1\n", + "RZ(2.0191195178095818) 1\n", "RX(-pi/2) 1\n", - "RZ(3.056524885694591) 1\n", - "RZ(2.2179491881590767) 4\n", + "RZ(-1.1356120065188826) 1\n", + "RZ(1.888479542178537) 4\n", "RX(pi/2) 4\n", - "RZ(1.3277711306691324) 4\n", + "RZ(1.5084431913342584) 4\n", "RX(-pi/2) 4\n", - "RZ(-2.2659729961186983) 4\n", - "RZ(0.9691266498934911) 7\n", - "RX(pi/2) 7\n", - "RZ(2.465960148492124) 7\n", - "RX(-pi/2) 7\n", - "RZ(1.1085351901192695) 7\n", - "CZ 4 7\n", - "RZ(-pi/2) 4\n", - "RX(-pi/2) 4\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 7\n", - "RZ(2.3101407699370347) 7\n", - "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RZ(1.5986380822234807) 4\n", - "RX(pi/2) 4\n", - "RX(pi/2) 7\n", - "RZ(-1.5980656434963532) 7\n", - "RX(-pi/2) 7\n", - "CZ 4 7\n", - "RZ(0.35220587162615624) 4\n", + "RZ(-2.483042302320226) 4\n", + "CZ 1 4\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(1.8272873235224791) 4\n", + "RZ(2.2967439629061275) 4\n", "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RZ(-pi/2) 1\n", + "CZ 1 4\n", + "RZ(1.911765822813055) 1\n", "RX(pi/2) 1\n", - "RZ(-1.335693650229591) 4\n", - "RX(-pi/2) 4\n", - "CZ 4 1\n", - "RX(-pi/2) 1\n", "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(-1.2127863732485493) 6\n", - "RX(pi/2) 6\n", - "RZ(2.813574980498982) 6\n", - "RX(-pi/2) 6\n", - "RZ(2.6511818466376313) 6\n", - "RZ(-1.7588018883352659) 7\n", - "RX(pi/2) 7\n", - "RZ(2.447676653604526) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RZ(1.4850227517340677) 6\n", - "RX(pi/2) 6\n", - "RZ(-1.3478885055934553) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RX(-pi/2) 6\n", - "RX(pi/2) 7\n", - "CZ 7 6\n", - "RZ(-0.8750443882926295) 1\n", + "RZ(-2.0754631531787293) 4\n", + "RX(-pi/2) 4\n", + "CZ 1 4\n", + "RZ(1.3367732406777537) 5\n", + "RX(pi/2) 5\n", + "RZ(1.2981763099836934) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.7352147829475575) 5\n", + "RZ(0.22008114899935705) 0\n", + "RX(pi/2) 0\n", + "RZ(1.7971328131102209) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.9352242822990644) 0\n", + "RZ(-2.8993914186145995) 1\n", "RX(pi/2) 1\n", - "RZ(1.8132470278556063) 1\n", + "RZ(1.447488760525238) 1\n", + "RX(-pi/2) 1\n", + "CZ 1 0\n", + "RZ(-0.8424737294501448) 0\n", + "RX(pi/2) 0\n", + "RZ(2.2991189241396484) 1\n", "RX(-pi/2) 1\n", - "RZ(-0.012768271568142753) 1\n", - "RZ(-0.8445460555738014) 4\n", + "CZ 1 0\n", + "RX(-pi/2) 0\n", + "RX(pi/2) 1\n", + "CZ 1 0\n", + "RZ(-1.8937028386273451) 4\n", "RX(pi/2) 4\n", - "RZ(0.3461777273306826) 4\n", + "RZ(1.8351213808969726) 4\n", "RX(-pi/2) 4\n", - "RZ(2.7370929367653405) 4\n", - "RZ(1.7387427456999776) 7\n", - "RX(pi/2) 7\n", - "RZ(0.47509385340919574) 7\n", - "RX(-pi/2) 7\n", - "RZ(-1.0914455197496056) 7\n", - "CZ 7 4\n", - "RZ(-pi/2) 4\n", + "CZ 5 4\n", + "RZ(-1.2810926274779872) 4\n", "RX(pi/2) 4\n", - "RZ(2.5624071888083417) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", "RX(-pi/2) 4\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 4\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(0.9186884109193686) 0\n", + "RX(pi/2) 0\n", + "RZ(0.9723685883655276) 0\n", + "RX(-pi/2) 0\n", + "RZ(-1.6544074654845327) 0\n", + "RZ(2.9575035555583264) 1\n", + "RX(pi/2) 1\n", + "RZ(2.4047972055723332) 1\n", + "RX(-pi/2) 1\n", + "RZ(-3.03642949604237) 1\n", + "RZ(-2.3831689665638613) 4\n", "RX(pi/2) 4\n", - "RZ(-2.3510254685615237) 4\n", + "RZ(0.3896425336237815) 4\n", "RX(-pi/2) 4\n", - "RZ(1.3731189583322312) 7\n", - "RX(pi/2) 7\n", - "CZ 7 4\n", - "RZ(-1.5136911753196136) 4\n", + "RZ(2.948778430174902) 4\n", + "CZ 1 4\n", + "RZ(-pi/2) 1\n", + "RX(-pi/2) 1\n", + "RZ(pi/2) 4\n", "RX(pi/2) 4\n", - "RZ(1.8575956476481248) 4\n", + "RZ(2.5883512304295575) 4\n", "RX(-pi/2) 4\n", - "RZ(0.19940209614281246) 4\n", - "CZ 4 1\n", + "CZ 1 4\n", + "RZ(1.271621627309611) 1\n", "RX(pi/2) 1\n", - "RZ(1.946333840120635) 1\n", - "RX(-pi/2) 1\n", - "RZ(1.9594783139215677) 4\n", + "RX(pi/2) 4\n", + "RZ(-1.9445778666179079) 4\n", "RX(-pi/2) 4\n", - "CZ 4 1\n", + "CZ 1 4\n", + "RZ(-2.6117730760463704) 1\n", + "RX(-pi/2) 1\n", + "RZ(0.47543492642538066) 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.8821307716867253) 1\n", + "RX(pi/2) 1\n", + "RZ(1.9093624007809584) 1\n", + "RX(-pi/2) 1\n", + "CZ 0 1\n", + "RZ(2.2280745687358383) 0\n", + "RX(pi/2) 0\n", "RX(pi/2) 1\n", - "RZ(-1.6371508636416152) 1\n", + "RZ(-1.7133806045912134) 1\n", "RX(-pi/2) 1\n", - "RZ(1.1378421770433667) 4\n", + "CZ 0 1\n", + "RZ(0.2103781586053466) 4\n", "RX(pi/2) 4\n", - "CZ 4 1\n", - "RZ(-1.393034832970288) 6\n", - "RX(pi/2) 6\n", - "RZ(1.2381481428840744) 6\n", - "RX(-pi/2) 6\n", - "RZ(2.4314509433880076) 6\n", - "RZ(1.0937764024777272) 7\n", - "RX(pi/2) 7\n", - "RZ(1.7174278721482343) 7\n", - "RX(-pi/2) 7\n", - "RZ(-2.214136247801944) 7\n", - "CZ 7 6\n", - "RZ(pi/2) 6\n", - "RX(pi/2) 6\n", - "RZ(2.2246709800845528) 6\n", - "RX(-pi/2) 6\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "CZ 7 6\n", - "RX(pi/2) 6\n", - "RZ(-1.6577485406407506) 6\n", - "RX(-pi/2) 6\n", - "RZ(1.1856374823534352) 7\n", - "RX(pi/2) 7\n", - "CZ 7 6\n", - "RZ(3.043883448401183) 1\n", + "RZ(2.5754503762693504) 4\n", + "RX(-pi/2) 4\n", + "RZ(0.32100749829558906) 4\n", + "RZ(2.4743248049333153) 5\n", + "RX(pi/2) 5\n", + "RZ(1.277712615858308) 5\n", + "RX(-pi/2) 5\n", + "RZ(-0.6741096473055608) 5\n", + "CZ 5 4\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "RZ(2.520547806744591) 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 5\n", + "CZ 5 4\n", + "RX(pi/2) 4\n", + "RZ(-1.6057091437181117) 4\n", + "RX(-pi/2) 4\n", + "RZ(1.748787745637994) 5\n", + "RX(pi/2) 5\n", + "CZ 5 4\n", + "RZ(-2.914320351960506) 0\n", + "RX(-pi/2) 0\n", + "RZ(0.8327635388561472) 0\n", + "RX(-pi/2) 0\n", + "RZ(2.956528289916145) 0\n", + "RZ(0.5125842665655067) 1\n", "RX(pi/2) 1\n", - "RZ(1.6000604308810171) 1\n", + "RZ(2.254684286471854) 1\n", "RX(-pi/2) 1\n", - "RZ(1.1195803228105756) 1\n", - "RZ(1.943186188160554) 4\n", + "RZ(-2.2874737604929907) 1\n", + "RZ(2.119938303427798) 4\n", "RX(pi/2) 4\n", - "RZ(1.3826477167179847) 4\n", + "RZ(1.597075906524711) 4\n", "RX(-pi/2) 4\n", - "RZ(-0.0835077926150376) 4\n", - "RZ(-2.04526392259349) 6\n", - "RX(pi/2) 6\n", - "RZ(1.0894626786873618) 6\n", - "RX(-pi/2) 6\n", - "RZ(-2.8946394683160683) 6\n", - "RZ(2.3815486509409176) 7\n", - "RX(-pi/2) 7\n", - "RZ(2.0204295038357807) 7\n", - "RX(-pi/2) 7\n", - "RZ(0.42771697574953826) 7\n", + "RZ(1.6057829712702203) 4\n", + "RZ(-0.07947205210704489) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.5330844791589164) 5\n", + "RX(-pi/2) 5\n", + "RZ(-0.309112271404403) 5\n", "\n" ] } @@ -782,8 +766,9 @@ "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", "wfn_sim = NumpyWavefunctionSimulator(9)\n", "d = 2\n", + "dimensions = {d: [d]}\n", "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, \n", - " widths=[d], depths=[d], num_circuit_samples=200)\n", + " dimensions, num_circuit_samples=200)\n", "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" ] @@ -797,8 +782,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [0.7080000000000005, 0.7700000000000006, 0.8540000000000006, 0.8700000000000007, 0.7280000000000005, 0.6800000000000005, 0.6880000000000005, 0.8620000000000007, 0.9580000000000007, 0.7080000000000005, 0.7440000000000005, 0.9660000000000007, 0.7020000000000005, 0.7780000000000006, 0.8240000000000006, 0.9260000000000007, 0.7500000000000006, 0.7360000000000005, 0.8440000000000006, 0.7340000000000005, 0.9180000000000007, 0.5640000000000004, 0.8420000000000006, 0.8500000000000006, 0.6480000000000005, 0.7760000000000006, 0.7800000000000006, 0.8760000000000007, 0.8900000000000007, 0.7560000000000006, 0.6060000000000004, 0.9500000000000007, 0.8700000000000007, 0.9700000000000008, 0.7120000000000005, 0.6480000000000005, 0.6880000000000005, 0.7640000000000006, 0.8940000000000007, 0.7620000000000006, 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, , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" ] } ], "source": [ "widths = [2, 3, 4, 5]\n", "depths = [2, 3, 4, 5, 10]\n", + "dimensions = {w: depths for w in widths}\n", "ckt_family = classical_1q_2q\n", - "prog_array = generate_volumetric_program_array(noisy_qc, ckt_family, widths, depths, num_circuit_samples=20)\n", + "prog_array = generate_volumetric_program_array(noisy_qc, ckt_family, dimensions, num_circuit_samples=20)\n", "print(prog_array)" ] }, @@ -878,7 +864,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [array([[1, 1]]), array([[1, 0]]), array([[0, 1]]), array([[0, 1]]), 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\n", 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Z0bKMEfSLiG8UFI+ZmRUsyxiBr+oxM+vGsnQNNaXjA9fz/jGC3+YWlZmZFSZLIugFrAD+taQsACcCM7NuIMvso0cXEYiZmdVHltlHf0krcwtFxJdyicjMzAqVpWvo9yXLvYBJ+AEyZmbdRpauod+Urkv6FTAnt4jMzKxQWWYfLTeCbM8jMDOzLiDLGMFK3j9G8CzwrdwiMjOzQmXpGvK0EmZm3Vi7XUOSJknqX7K+saSD8g3LzMyKkmWMYGpEvNKyEhEvA1PzC8nMzIqUJRG0VifLZadmZtYFZEkE8yVdKGmr9HUh8GDegZmZWTGyJIKTgLeAXwPXAquBr+YZlJmZFSfLVUOvkTzA3szMuqEsVw3dLmnjkvUBkm7LNywzMytKlq6hgemVQgBExEv4zmIzs24jSyJ4R9IWLSuShtLKbKRmZtY1ZbkM9DvAHEmzAAF7AFNyjcrMzAqTZbD4VkkTgJ3TolMj4sV8wzIzs6JUTASSNgCOBMakRQuBlXkHZWZmxWlzjEDSaGARsBfwVPraC1iYvmdmZt1ApRbBT4HjI+L20kJJ+wAXAx/PMzAzMytGpauGNi9PAgARcQfwz/mFZGZmRaqUCHpI2rC8UFIvPOmcmVm3USkRXAn8Jr1vAABJw4DrgKvyDcvMzIrS5i/7iPi+pBOB2ZI2SotfA86PiJ8WEp2ZmeWuYhdPRFwEXCSpX7ruS0fNzLqZLFNMEBErq0kCkvaTtETSUkmtzmAq6VBJiyQtlHTNuh7DzMw6JrdBX0k9SS4z3RdoBuZJmhkRi0rqjABOB3aLiJckeTI7M7OCVbqh7LPpn8Or3PeOwNKIWBYRb5E81ObAsjrHARenM5oSEc9XeSwzM6tSpRbB6cD1wG+ACVXse3PgbyXrzcBOZXVGAki6B+gJTIuIW8t3JGkK6UR3gwYNoqmpqYpw4NAt11a1XWdV7XkwMytVKRGskPQHYLikmeVvRsQBNTr+CJKpKwYDd0vatvT5B+mxpgPTASZOnBjjx4+v6mAHXft0h4LtbM6bUt15MDMrVSkR7E/SErgKuKCKfT8NDClZH5yWlWoG7o+INcCTkv5CkhjmVXE8MzOrQqX7CN4C5kraNSJekNQ3LV+Vcd/zgBHpGMPTwOHA58rqzACOAH4paSBJV9GydfwMZmbWAVkuH/0nSQ+TTEG9SNKDksa2t1FEvA2cCNwGLAaui4iFks6S1NKtdBtJF9Qi4E7gmxGxoqpPYmZmVcly+eh04GsRcSeApL3Ssl3b2zAibgZuLis7s2Q5gK+lLzMzq4MsLYI+LUkAICLuAvrkFpGZmRUqS4tgmaT/x3sTzR2F+/HNzLqNLC2CLwGbAr8luadgYFpmZmbdQJaH178EnFxALGZmVgeZJp0zM7Puy4nAzKzBtZsIJG1SRCBmZlYfWVoEcyVdL+nTkpR7RGZmVqgsiWAkyQ1knwcel3S2pJH5hmVmZkVpNxFE4vaIOILk+QFfBB6QNEvSLrlHaGZmuWr38tF0jOAokhbBc8BJwExgPMnzCqp9cI2ZmXUCWe4svo/kruKDIqK5pHy+pEvyCcvMzIqSJRGMSieH+4CIOLfG8ZiZWcGyDBb/QdLGLSuSBki6LceYzMysQFkSwaalj45Mp5zYLL+QzMysSFkSwVpJW7SsSBoKtNpVZGZmXU+WMYLvAHMkzQIE7AFMyTUqMzMrTJbZR2+VNAHYOS06NSJezDcsMzMrSpYWAcCGwD/S+qMlERF35xeWmZkVJcsNZecCh5E8vP6dtDgAJwIzs24gS4vgIJJ7Cd7MOxgzMytelquGlgHr5x2ImZnVR5YWwetAk6Q/Au+2CiLCj680M+sGsiSCmenLzMy6oSyXj14hqTewRUQsKSAmMzMrUJZHVf4b0ATcmq6Pl+QWgplZN5FlsHgasCPwMkBENAFb5hiTmZkVKEsiWBMRr5SVvdNqTTMz63KyDBYvlPQ5oKekEcDJwL35hmVmZkXJ0iI4CRhDcunor4BXgVPzDMrMzIqT5aqh10lmIP1O/uGYmVnRssw1dCetPH8gIv41l4jMzKxQWcYIvlGy3As4BHg7n3DMzKxoWbqGHiwrukfSAznFY2ZmBcvSNfThktUewPZA/9wiMjOzQmW5auhBYH76533A14Fjsuxc0n6SlkhaKum0CvUOkRSSJmbZr5mZ1U6WrqHh1exYUk/gYmBfoBmYJ2lmRCwqq9cPOAW4v5rjmJlZx2TpGjq40vsR8ds23toRWBoRy9L9XAscCCwqq/c94Fzgm+1Ga2ZmNZflqqFjgF2BP6XrHye5s/gFkstK20oEmwN/K1lvBnYqrSBpAjAkIm6S1GYikDQFmAIwaNAgmpqaMoT9QYduubaq7Tqras+DmVmpLIlgfWB0RPwdQNIg4PKIOLojB5bUA7gQmNxe3YiYDkwHmDhxYowfP76qYx507dNVbddZnTeluvNgZlYqy2DxkJYkkHoO2CLDdk8DQ0rWB6dlLfoBY4G7JC0HdgZmesDYzKxYWVoEf5R0G8k8QwCHAXdk2G4eMELScJIEcDjwuZY30xlNB7asS7oL+EZEzM8WupmZ1UKWq4ZOlDQJ2DMtmh4RN2bY7m1JJwK3AT2ByyJioaSzgPkR4YfbmJl1AllaBAAPASsj4g5JG0nqFxEr29soIm4Gbi4rO7ONuntljMW6smkF3Ys4rfwRGt2Yz6l1UJZHVR4H3AD8LC3aHJiRZ1BmZlacLIPFXwV2I3kOARHxOLBZnkGZmVlxsiSCNyPirZYVSevRyrTUZmbWNWVJBLMkfRvoLWlf4Hrgd/mGZWZmRcmSCE4juYv4EeDLJIO/Z+QZlJmZFafiVUPpxHFXRsSRwKXFhGRmZkWq2CKIiLXAUEkbFBSPmZkVLMt9BMtInko2E3itpTAiLswtKjMzK0yWRPBE+upBMj+QmZl1I20mAknrRcTbEfHdIgMyM7NiVRojePcB9ZJ+WkAsZmZWB5USgUqWd8s7EDMzq49KicB3D5uZNYBKg8VbS1pA0jLYKl0mXY+I+JfcozMzs9xVSgTbFBaFmZnVTZuJICL+WmQgZmZWH1nmGjIzs27MicDMrMFlSgSSeksalXcwZmZWvCyPqvw3oAm4NV0fn847ZGZm3UCWFsE0YEfgZYCIaAKG5xiTmZkVKEsiWBMRr5SV+WYzM7NuIsvsowslfQ7oKWkEcDJwb75hmZlZUbK0CE4CxgBvAtcArwCn5hmUmZkVJ0uLYOuI+A7wnbyDMTOz4mVpEVwgabGk70kam3tEZmZWqHYTQUR8HPg48ALwM0mPSDoj98jMzKwQmW4oi4hnI+InwFdI7ik4M9eozMysMFluKNtG0jRJjwA/JbliaHDukZmZWSGyDBZfBvwa+GREPJNzPGZmVrB2E0FE7FJEIGZmVh9tJgJJ10XEoWmXUOmdxH5CmZlZN1KpRXBK+uf/KSIQMzOrjzYHiyPi7+niCRHx19IXcEIx4ZmZWd6yXD66bytln8qyc0n7SVoiaamk01p5/2uSFklaIOmPkoZm2a+ZmdVOm4lA0vHp+MCo9Iu65fUksKC9HUvqCVxMkjRGA0dIGl1W7WFgYjrecANwXrUfxMzMqlNpjOAa4Bbgh0Dpr/mVEfGPDPveEVgaEcsAJF0LHAgsaqkQEXeW1J8LHJUxbjMzq5E2E0H6DIJXgCMAJG0G9AL6SuobEU+1s+/Ngb+VrDcDO1WofwxJ4vkASVOAKQCDBg2iqampnUO37tAt11a1XWdV7XmoqyGTizlOVzw31fI5tQ5q9z6C9FGVFwIfAZ4HhgKLSaamrglJRwETgY+19n5ETAemA0ycODHGjx9f1XEOuvbpakPslM6bUt15qKsZlxdznGP+s5jjdAY+p9ZBWQaLvw/sDPwlIoYDe5N047TnaWBIyfrgtOx9JO1DMsX1ARHxZob9mplZDWV9VOUKoIekHmm//sQM280DRkgaLmkD4HDgfQ+9l7Qd8DOSJPD8OsZuZmY1kGWuoZcl9QXuBq6W9DzwWnsbRcTbkk4EbgN6ApdFxEJJZwHzI2Im8COgL3C9JICnIuKAKj+LmZlVIUsiOBBYDfxf4EigP3BWlp1HxM3AzWVlZ5Ys75M5UjMzy0WWSedKf/1fkWMsZmZWB5UmnVtJK5PN8d6kcx/KOTYzMytApfsI+hUZiHVNw067aZ3qL++VUyBl1jUugOXn7J9DJGadX6ZHVUraXdLR6fJAScPzDcvMzIqS5VGVU4FvAaenRRsA/5NnUGZmVpwsLYJJwAGkl4ymj6t0t5GZWTeRJRG8FRFBOnAsqU++IZmZWZGyJILrJP0M2FjSccAdwM/zDcvMzIqS5T6C8yXtC7wKjALOjIjbc4/MzMwKkeXOYtIv/tsBJPWQdGREXJ1rZGZmVohKTyj7kKTTJV0k6RNKnAgsAw4tLkQzM8tTpRbBVcBLwH3AscC3Se4qPigiGuYJFct7fa6Q4wxbfU0hxzGzdkzrX9BxXinmOBlUSgRbRsS2AJJ+Dvwd2CIiVhcSmZmZFaLSVUNrWhYiYi3Q7CRgZtb9VGoRjJP0arosoHe67knnzMy6kUqTzvUsMhAzM6uPTJPOmZlZ9+VEYGbW4JwIzMwanBOBmVmDcyIwM2twTgRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4JwIzMwanBOBmVmDcyIwM2twTgRmZg3OicDMrME5EZiZNTgnAjOzBudEYGbW4JwIzMwaXKWH13eYpP2A/wR6Aj+PiHPK3t8QuBLYHlgBHBYRy/OMyawzG3baTeu8zfJeOQTSinWNbfk5++cUidVabi0CST2Bi4FPAaOBIySNLqt2DPBSRHwU+A/g3LziMTOz1uXZNbQjsDQilkXEW8C1wIFldQ4ErkiXbwD2lqQcYzIzszKKiHx2LH0G2C8ijk3XPw/sFBEnltR5NK3TnK4/kdZ5sWxfU4Ap6eooYEkuQdfOQODFdmtZVj6ftedzWltd4XwOjYhNW3sj1zGCWomI6cD0eseRlaT5ETGx3nF0Fz6ftedzWltd/Xzm2TX0NDCkZH1wWtZqHUnrAf1JBo3NzKwgeSaCecAIScMlbQAcDswsqzMT+GK6/BngT5FXX5WZmbUqt66hiHhb0onAbSSXj14WEQslnQXMj4iZwC+AqyQtBf5Bkiy6gy7TjdVF+HzWns9pbXXp85nbYLGZmXUNvrPYzKzBORGYmTU4J4Iak7SfpCWSlko6rd7xdGWSLpP0fHq/iXWQpCGS7pS0SNJCSafUO6auTlIvSQ9I+nN6Tr9b75iq4TGCGkqn1fgLsC/QTHLl1BERsaiugXVRkvYEVgFXRsTYesfT1UkaBAyKiIck9QMeBA7yv8/qpTMh9ImIVZLWB+YAp0TE3DqHtk7cIqitLNNqWEYRcTfJ1WRWAxHx94h4KF1eCSwGNq9vVF1bJFalq+unry7369qJoLY2B/5Wst6M/6NZJyRpGLAdcH99I+n6JPWU1AQ8D9weEV3unDoRmDUYSX2B3wCnRsSr9Y6nq4uItRExnmT2hB0ldbluTCeC2soyrYZZ3aT92L8Bro6I39Y7nu4kIl4G7gT2q3cs68qJoLayTKthVhfpwOYvgMURcWG94+kOJG0qaeN0uTfJhSKP1TeqdedEUEMR8TbQMq3GYuC6iFhY36i6Lkm/Au4DRklqlnRMvWPq4nYDPg/8q6Sm9PXpegfVxQ0C7pS0gOSH4O0R8fs6x7TOfPmomVmDc4vAzKzBORGYmTU4JwIzswbnRGBm1uCcCMzMGpwTgdWcpFVl65MlXVTg8T8i6YYa7EeSXpQ0IF0fJCkk7V5S5wVJm1TYxwHtzUIraS9JrV5yKOlUSRutY9x7pDNhNqXXtpe+t7bk0tEmz5Br4ERg3VBEPBMRn6nBfgKYC+ySFu0KPJz+iaRRwIqIWFFhHzMj4pwOhHEqsE6JADgS+GFEjI+IN8reeyMtb3l9ILZ0Ft3S9UyPtM1azzofJwIrlKR/k3S/pIcl3SHpn9LyaZKukDRb0l8lHSzpPEmPSLo1nRoBScsl/TD9NTtf0gRJt0l6QtJX0jrDWp5hkLZGfpvu43FJ55XEcoykv6TzyV/aRqvlXtIv/vTP/+D9ieGedF+bSvqNpPOD1rsAAAN5SURBVHnpa7eS41+ULm8laW76mb5f1nLqK+kGSY9JujptjZwMfITkhqU7WzmXe6fn8RElz27YUNKxwKHA9yRdvQ5/L8slnSvpIeCzku6S9GNJ84FT0nP6J0kLJP1R0hbpdpdLukTS/cB5FQ9inVdE+OVXTV/AWqCp5PUUcFH63gDeu5HxWOCCdHkayVzu6wPjgNeBT6Xv3Ugybz7AcuD4dPk/gAVAP2BT4Lm0fBjwaLo8GVgG9Ad6AX8lmQ/qI+m+Ppwec3ZLjGWf5WPAn9Ll2UBfYH66filwTLp8DbB7urwFyTQOLcdv+ey/J3k+BcBXgFXp8l7AKyRzU/UguZt695LPO7CVuHqRzHQ7Ml2/kmQSOYDLgc9k/Ls5rOQ4/15S7y7gv0rWfwd8MV3+EjCj5Fi/B3rW+9+dX9W/3JSzPLwRyWyMQPKrGJiYrg4Gfq3kISkbAE+WbHdLRKyR9AjQE7g1LX+E5Mu9xcyS8r6RzK2/UtKbLfO+lPljRLySxrIIGAoMBGZFxD/S8uuBka1sOw/YTlIfYP1IHkCyTNJHSVoEF6T19gFGS2rZ7kNKZvkstQtwULp8DXB+yXsPRERzGktT+nnntBJPi1HAkxHxl3T9CuCrwI8rbANlfzdlfl1hfRfg4HT5Kt7/6//6iFjbznGtE3MisKL9FLgwImZK2oukJdDiTYCIeEfSmkh/cgLv8P5/q2+WlL9ZUl5er7w+JL+IM/+7j4jXJT1O8iv4obR4LvBpYDNgSVrWA9g5IlaXbl+SGNpTdYw19Fo761m3sy7GYwRWtP68NzX3F+sYxzzgY5IGpIOch1Soey/JoO196fp9wCnA3JJk9QfgpJYNJLX2q3tuyXEOzxjnSpKur3JLgGFpywSSyeRmZdxnNe7lvZiPJOkms27CicCKNg24XtKDwIv1CiIingbOBh4gGfBdTtJP35p7gC15LxE8RNLFdW9JnZOBielg6iKSMYBypwJfUzJT5UcrHK/UdODW8sHitOVxNMm5fISkNXRJhv31Lrt8NOsVTScBR6exf54kEVo34dlHrWFJ6pv2+a9HMiB9WUTcmOPxNiLpow9Jh5MMHPuZ1lZ3HiOwRjZN0j4kV+D8AZiR8/G2By5SMnDwMsm4g1nduUVgZtbgPEZgZtbgnAjMzBqcE4GZWYNzIjAza3BOBGZmDe5/ASvpkPcByW/tAAAAAElFTkSuQmCC\n", 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" ] @@ -989,7 +975,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1016,7 +1002,7 @@ "outputs": [ { "data": { - "image/png": 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6TXtI21uV5NlJ/m5r7YYkqaovJrkvybuT/Ob4Dq21LUm2JMnGjRvbhg0bVm7aJXTWVfdPegQOUBdv2j9/ZgAAAKDXtF/auSPJmgXW1w7bdrdfS3LL3MJwn7U7kxy3hPMBAAAAcICY9pB2T8buhVZVRyU5NGP3Thtzd0ZnpdXYeiV5aikHBAAAAODAMO0h7fokp1XV4fPWzknyWJJbd7Pfp4bH180tVNWaJC9P8pWlHhIAAACA2TftIe2KJN9Pck1VnTp8IMDmJJcMl2omSarq3qr60Nzz1todST6e5ENV9Q+q6owkn0jyZJJ/u5LfAAAAAACzYapDWmttR5JTkqxK8skkFyS5NMn7x166enjNfG9Ncm2SS5L8UUYR7eThmAAAAACwKFP/qZ2ttbuSnLyH16xfYO17Sd41fAEAAADAPpnqM9IAAAAAYFoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKDD1Ie0qjquqrZW1aNV9UBVXVhVqxax/0FVdUdVtap643LOCgAAAMDsWj3pAXanqtYmuSnJXUnOTHJMkg9mFADf13mYc5P82LIMCAAAAMABY9rPSDsvySFJzm6t3dhauyLJBUn+r6o6Yk87DyHut5L88+UdEwAAAIBZN+0h7fQkn26tPTJv7aqM4tpJHfv/iyRfSLJ1GWYDAAAA4AAy7SHt2CT3zF9orX0jyaPDtl2qqr+V5JeTvGfZpgMAAADggDHV90hLsjbJQwus7xi27c6/SXJ5a+3eqlq/pzeqqk1JNiXJunXrsm3btsVNOiXefPTOSY/AAWp//ZkBAACAXtMe0vZKVf29JC9J8gu9+7TWtiTZkiQbN25sGzZsWKbpltdZV90/6RE4QF28af/8mQEAAIBe035p544kaxZYXztse5qqekaS/zfJRUkOqqrnJJn7YILDqurw5RgUAAAAgNk27SHtnozdC62qjkpyaMbunTbPYUl+LMklGcW2HUm+Mmy7KsmfL8ukAAAAAMy0ab+08/ok762qw1tr3x3WzknyWJJbd7HP95K8bmztyCR/mOSfJbl5OQYFAAAAYLZNe0i7Isn5Sa6pqouSHJ1kc5JLWmuPzL2oqu5Ncmtr7Z2ttR8kuWX+QeZ92MB/ba3dtvxjAwAAADBrpjqktdZ2VNUpSS5P8smMPsHz0oxi2nyrk6xa2ekAAAAAOJBMdUhLktbaXUlO3sNr1u9h+/YktXRTAQAAAHCgmfqQBsCM2rzQhzKzKJsfnvQEsHT8Tth3ficAwLKb9k/tBAAAAICpIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBh6kNaVR1XVVur6tGqeqCqLqyqVXvY5+9U1X+sqnuH/b5WVe+vqmet1NwAAAAAzJbVkx5gd6pqbZKbktyV5MwkxyT5YEYB8H272fWc4bUXJfl6kr+V5F8Mj7+0jCMDAAAAMKOmOqQlOS/JIUnObq09kuTGqjoiyeaqunhYW8gHWmvfnvf8lqp6PMl/qKofb63dt8xzAwAAADBjpv3SztOTfHosmF2VUVw7aVc7jUW0OX8+PL5o6cYDAAAA4EAx7SHt2CT3zF9orX0jyaPDtsU4IclTSf5iaUYDAAAA4EAy7Zd2rk3y0ALrO4ZtXarqyIzuqfbR1tq3dvGaTUk2Jcm6deuybdu2xU87Bd589M5Jj8ABan/9mWGCjnr7pCfY//m5Y5b4nbDv/E4AgGU37SFtn1XVwUk+luR7Sf7Jrl7XWtuSZEuSbNy4sW3YsGFlBlxiZ111/6RH4AB18ab982eGCbr2yklPsP975/836Qlg6fidsO/8TgCAZTftIW1HkjULrK8dtu1WVVWSjyR5aZJXtdb2uA8AAAAALGTaQ9o9GbsXWlUdleTQjN07bRcuS3Jmkte31npeDwAAAAALmvYPG7g+yWlVdfi8tXOSPJbk1t3tWFW/keTdSd7aWvv88o0IAAAAwIFg2kPaFUm+n+Saqjp1+ECAzUkuaa09Mveiqrq3qj407/nfT/IvM7qs8/6qeuW8rxes7LcAAAAAwCyY6ks7W2s7quqUJJcn+WRGn+B5aUYxbb7VSVbNe/5zw+Pbh6/53pHkyqWdFAAAAIBZN9UhLUlaa3clOXkPr1k/9vzteXpAAwAAAIC9Nu2XdgIAAADAVBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAh6n/1E4A9t76X79u0iPs0vZnTXqC/d9U//f7gTMmPQIAACw5Z6QBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOiwetIDcODY/qy/P+kR9nvrH/+DSY8AAADLb/OaSU+w/9v88KQngJnkjDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHSY+pBWVcdV1daqerSqHqiqC6tqVcd+a6rqP1bVjqp6uKp+v6qetxIzAwAAADB7Vk96gN2pqrVJbkpyV5IzkxyT5IMZBcD37WH3jyV5cZJzkzyV5KIk1yZ5zXLNCwAAAMDsmuqQluS8JIckObu19kiSG6vqiCSbq+riYe1pquqEJD+X5KTW2meHtfuT3FZVp7bWblqh+QEAAACYEdMe0k5P8umxYHZVRmeXnZTkk7vZ78G5iJYkrbXbq+ovh21CGgBwwFn/69dNeoRd2v6sSU+w/5vq/34/cMakRwCAJTHt90g7Nsk98xdaa99I8uiwrXu/wd172A8AAAAAFjTtIW1tkocWWN8xbFvq/QAAAABgQdN+aeeKqapNSTYNT79XVV+b5DyzqCY9wJ49P8m3Jz3E7r1x0gPsUl006QnY3/idsBT8TmB2+J2wFPxOgBU23b8XLtgPfrPun3580gMwWdMe0nYkWbPA+tph2+72e8Fi9mutbUmyZbEDMjuq6o7W2sZJzwFMB78TgPn8TgDG+b0AB6Zpv7Tznozd06yqjkpyaBa+B9ou9xvs6t5pAAAAALBb0x7Srk9yWlUdPm/tnCSPJbl1D/sdWVWvnluoqo1Jjh62AQAAAMCiTHtIuyLJ95NcU1WnDvcx25zkktbaI3Mvqqp7q+pDc89ba19K8pkkH6mqs6vqrCS/n+TzrbWbVvQ7YH/i0l5gPr8TgPn8TgDG+b0AB6BqrU16ht2qquOSXJ7khIw+ifN3kmxure2c95rtSW5prb193tpzklya5BczCoafSnJ+a216bwYJAAAAwNSa+pAGAAAAANNg2i/tBAAAAICpIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAgGVWVZurqlXVayc9CwAAe09IAwBmWlUdUVWXVdXnquqBqnq8qr5VVbdX1a9W1WGTnnElVdXxVfU7VfXnVfXXVfX9qvqrqrqpqs6uqpr0jAAA00pIAwBm3XOTbEqyM8l1SS5JcnWSw5NcmuT2qjpicuOtuJcnOSvJ/Uk+luSDSW5M8reT/HGSD09uNACA6bZ60gMAACyzv0qyprX25PiGqvq9JG9Jcl6Si1d6sAn5w9baleOLQ0z8cpK3VdXlrbXbV3wyAIAp54w0AGCPqurZVfVEVX1hbP2Q4VLJVlVvG9v2rmH9l1d22r+ptbZzoYg2uHp4/KmleK+qenlV3VBV362qR4bLJU9YimMvldba93ex/kiSTw9Pl+TfAwBg1ghpAMAetda+l+T2JD9TVYfP2/SqJM8c/vMpY7vNPd+6zOPti18YHv/Lvh6oqk5M8rkkpya5PsnlSZ5IckuSV+zr8ZdbVR2a5OTh6X+d5CwAANPKpZ0AQK+bMwpnP5vRvcaSUSzbmeTWzAtpVXVQktcl+R+ttfv2dOCqek6SX13kPNe21rb1vriqVid53/D0uUlek2RDkj9N8tuLfO/xY1eS301ySJKzWmsfn7ftHye5bJHH25DRfcwW47LW2kOLeI+fTPLWJKuS/EiSM5K8KMm/aq3tc1gEAJhFQhoA0Gtrkt/MKJjND2l3JrkmyeVV9eLW2n/PKFA9N6Ob1/d4TpL3L3Ke7Um6Q1pGf/eMv8dHk/zD1trji3zvcScmeUmSz86PaIPLk/xKkmMWcbwNWfy/x5VJukNakp8ce48nkrw3ow8fAABgAS7tBAB6fSnJYxnOPKuqNUlellFgu3l4zdxZaXOXCN6cDq217a21WuTXlYsZvrX2eGutMvr758eSvD2jyzDvqKr1iznWAl42PN66wPvuTPL5xRystXblXvx7bF/ke9ww/HscnFFU+60k/zLJJ6rq4MUcCwDgQCGkAQBdWmtPZBSEjq+qFyR5bUaXBW5trd2d5Jv5YUg7JUlLZ0hbSW3k/tbah5OcndGZZJfv42HXDI8P7mL7/9rH4y+b1tqTrbW/aK1dmOT/SfLGJOdPeCwAgKnk0k4AYDFuTvL6jELZiUkeT/KFedtOr6pnZnT/sa+21r7Vc9CVuEfaQlprX66qhzKKgvvi4eHxR3ax/cjFHGwl7pG2C9cn+VcZ/Xv86308FgDAzBHSAIDFmPsEzlOSnJDki/PuL7Y1yVuSvCvJYVncp3WuxD3Snmb4BNIjknx3X46T5M+Gx5MWeI9VSV69yOOtxD3SFvKjw+MP9vE4AAAzyaWdAMBi/FlGZ1+dmeSl+ZuxbO4yzt8Ye75Hy3mPtKo6vqqetcD6wRld0nlQfvjhCfO3t6pqnd/CF5N8LcnPVtWZY9vencV90MCy3iOtqjbuYv0FST4wPH3avwcAAEm11vv3IQBAUlXXZhTSkuSVrbXb5m27N6NotDPJ81prDy9wiBVVVZcleUdGl6Del9FZWy9K8nMZXXL5tSSva619c94+B2X0PexsrXWdwV9Vr0pyY0Y3778myb0ZnVl2SkZR8eeH97llSb6xvVRV25I8L8ntSb6R0fe5PskbkhyS5Nokbxo+JAEAgHlc2gkALNbWjELaI0nuWGDbMUnunIaINrg6ybMzuhT1hCSHZzT7XUk+mOTftVFS8mkAACAASURBVNYeHdvn+OHxqt43aa19oapek9GnX54+LN+W0f3GTssopE2Df53R/ddeltFcByf5dkax76NJPtb8P60AAAua6jPSquonk7w3oz96X5rkc62113bstybJZRn9kXhQkk8lOb+19p3lmxYAmBVVdX5Gf0sc31r76qTnAQBgOkz7GWkvzegygy8necYi9vtYkhcnOTfJU0kuyugyhdcs9YAAwEw6KcknRDQAAOab9jPSDmqtPTX85z9K8vw9nZFWVSdkdMPfk1prnx3WfiajSyte31q7aXmnBgAAAGAWTfWnds5FtEU6PcmDcxFtOM7tSf4yP7xfCQAAAAAsylSHtL10bJJ7Fli/e9gGAAAAAIs27fdI2xtrM/pY+3E7khy9q52qalOSTUlyyCGHvHz9+vXLMhwAAACwf7r77ru/3Vp7waTnYHJmMaTtldbaliRbkmTjxo3tjjvumPBEAAAAwDSpqvsmPQOTNYuXdu5IsmaB9bXDNgAAAABYtFkMafdk4Xuh7ereaQAAAACwR7MY0q5PcmRVvXpuoao2ZnR/tOsnNhUAAAAA+7WpvkdaVR2a5A3D0x9NckRVvWl4/p9aa49W1b1Jbm2tvTNJWmtfqqrPJPlIVb0nyVNJLkry+dbaTSv8LQAAAAAwI6Y6pCV5YZKrx9bmnv9Eku0ZfQ+rxl5zTpJLk/xuRmfdfSrJ+cs2JQAAAAAzb6pDWmtte5Law2vWL7D2UJJ3DF8AAAAAsM9m8R5pAAAAALDkhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAh9WTHoCltf7Xr5v0CBygtn/gjEmPAAAAAMvKGWkAAAAA0GHqQ1pVHVdVW6vq0ap6oKourKpVHfttrKrPVNX/Hr5uqqpXrMTMAAAAAMyeqQ5pVbU2yU1JWpIzk1yY5NeSXLCH/Y4a9lud5G3D1+okN1bVjy/nzAAAAADMpmm/R9p5SQ5JcnZr7ZGMQtgRSTZX1cXD2kLOSHJ4kl9srT2cJFX1xSTfTvKGJP9++UcHAAAAYJZM9RlpSU5P8umxYHZVRnHtpN3s94wkP0jyf+atfW9Yq6UeEgAAAIDZN+0h7dgk98xfaK19I8mjw7Zd+ePhNR+sqhdW1QuTXJpkR5Krl2lWAAAAAGbYtIe0tUkeWmB9x7BtQa21B5K8LskvJXlw+Do7yWmttb9ehjkBAAAAmHHTfo+0vVJV6zI68+zOJOcOy/8oyXVVdeJwVtv4PpuSbEqSdevWZdu2bSs17pJ689E7Jz0CB6j99WcGAAAAek17SNuRZM0C62uHbbvy3ozuk/am1tqTSVJVNyf5epL3JDl/fIfW2pYkW5Jk48aNbcOGDfs2+YScddX9kx6BA9TFm/bPnxkAAADoNe2Xdt6TsXuhVdVRSQ7N2L3Txhyb5KtzES1JWmtPJPlqkmOWYU4AAAAAZty0h7Trk5xWVYfPWzsnyWNJbt3Nfvcl+emqOnhuoaqemeSnk2xfhjkBAAAAmHHTHtKuSPL9JNdU1anDfcw2J7mktfbI3Iuq6t6q+tC8/X4nyYuS/ElVnVFVb0xybZJ1GS7fBAAAAIDFmOqQ1lrbkeSUJKuSfDLJBUkuTfL+sZeuHl4zt9+dSX4+yeFJPprkIxldDvr61tpXln9yAAAAAGbNtH/YQFprdyU5eQ+vWb/A2tYkW5dpLAAAAAAOMFN9RhoAAAAATAshDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgw9SHtKo6rqq2VtWjVfVAVV1YVas69z27qv5zVT1WVd+pqhuq6rDlnhkAAACA2TPVIa2q1ia5KUlLcmaSC5P8WpILOvY9N8kfJLk+yelJzk3y9SSrl2teAAAAAGbXtEel85IckuTs1tojSW6sqiOSbK6qi4e1p6mq5ye5NMmvtNZ+e96mP1n2iQEAAACYSVN9RlpGZ5J9eiyYXZVRXDtpN/u9eXj88HINBgAAAMCBZdpD2rFJ7pm/0Fr7RpJHh2278ookX0vyzqr6n1X1ZFXdVlUnLt+oAAAAAMyyab+0c22ShxZY3zFs25Ujk7wkyfuS/NMk3xkeb6iqn2qtPTi+Q1VtSrIpSdatW5dt27bt4+iT8eajd056BA5Q++vPDAAAAPSa9pC2tyrJs5P83dbaDUlSVV9Mcl+Sdyf5zfEdWmtbkmxJko0bN7YNGzas3LRL6Kyr7p/0CBygLt60f/7MAAAAQK9pv7RzR5I1C6yvHbbtbr+W5Ja5heE+a3cmOW4J5wMAAADgADHtIe2ejN0LraqOSnJoxu6dNubujM5Kq7H1SvLUUg4IAAAAwIFh2kPa9UlOq6rD562dk+SxJLfuZr9PDY+vm1uoqjVJXp7kK0s9JAAAAACzb9pD2hVJvp/kmqo6dfhAgM1JLhku1UySVNW9VfWhueettTuSfDzJh6rqH1TVGUk+keTJJP92Jb8BAAAAAGbDVIe01tqOJKckWZXkk0kuSHJpkvePvXT18Jr53prk2iSXJPmjjCLaycMxAQAAAGBR/n/27j3e0rquF/jnC6OGyGVSk1HJCfNyvJ1RJ++FgoaoiZJJx7QsjTDLOqZdlBLNG5TgMTIiLdNK0jTzchC5JAl4ARUzEYt0FMH7GUQDReB7/lhrdLfds/ezZtaevWbv9/v12q896/n9nmd9Bl57vfbrM8/z+838rp3dfUmSQ5aYs3GBY99M8ozxFwAAAADslJm+Iw0AAAAAZoUiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAA8x8kVZVd6uqs6vqmqq6sqpeVFV7TnD+HlV1UVV1VT1mObMCAAAAsHqtW+kAi6mq9UnOSnJJkiOS3DHJKzIqAI8deJmnJ7n9sgQEAAAAYM2Y9TvSjkmyV5Iju/vM7j4lyQuTPLuq9l3q5HER95Ikz1/emAAAAACsdrNepB2e5IzuvnrOsdMyKtcOHnD+HyY5P8nZy5ANAAAAgDVk1ou0uya5dO6B7v5ckmvGY9tVVfdK8ktJnrNs6QAAAABYM2Z6jbQk65NctcDxreOxxfxJkpO7+7Kq2rjUG1XV0UmOTpINGzbk4osvnizpjHjiQTesdATWqN31ZwYAAACGmvUibYdU1c8muUuSnxp6TnefmuTUJNm8eXNv2rRpmdItr8eddsVKR2CNOuHo3fNnBgAAAIaa9Uc7tybZb4Hj68dj36eqbpLkj5Icn2SPqto/ybaNCfauqn2WIygAAAAAq9usF2mXZt5aaFV1YJKbZ97aaXPsneT2SU7MqGzbmuRj47HTknx0WZICAAAAsKrN+qOdpyd5blXt093fGB87Ksm1Sc7dzjnfTPKweccOSPLGJM9Lcs5yBAUAAABgdZv1Iu2UJM9K8taqOj7JQUmOS3Jid1+9bVJVXZbk3O5+Wndfn+S9cy8yZ7OBj3f3B5c/NgAAAACrzUwXad29taoOTXJykndktIPnSRmVaXOtS7Lnrk0HAAAAwFoy00VaknT3JUkOWWLOxiXGtySp6aUCYKcdt9BeMkzkuK+vdAKYHp8JO89nAgAsu1nfbAAAAAAAZoIiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAA8x8kVZVd6uqs6vqmqq6sqpeVFV7LnHOj1XVX1XVZePzPlVVL6iqH9hVuQEAAABYXdatdIDFVNX6JGcluSTJEUnumOQVGRWAxy5y6lHjuccn+Y8k90ryh+PvP72MkQEAAABYpWa6SEtyTJK9khzZ3VcnObOq9k1yXFWdMD62kJd391fnvH5vVX0ryZ9X1R26+7PLnBsAAACAVWbWH+08PMkZ8wqz0zIq1w7e3knzSrRtPjr+ftvpxQMAAABgrZj1Iu2uSS6de6C7P5fkmvHYJB6Y5MYk/zmdaAAAAACsJbP+aOf6JFctcHzreGyQqjogozXV3tDdX97OnKOTHJ0kGzZsyMUXXzx52hnwxINuWOkIrFG7688MK+jAp650gt2fnztWE58JO89nAgAsu1kv0nZaVd00yZuSfDPJ/97evO4+NcmpSbJ58+betGnTrgk4ZY877YqVjsAadcLRu+fPDCvoba9b6QS7v6f9n5VOANPjM2Hn+UwAgGU360Xa1iT7LXB8/XhsUVVVSV6f5O5JHtzdS54DAAAAAAuZ9SLt0sxbC62qDkxy88xbO207XpnkiCSP6O4h8wEAAABgQbO+2cDpSQ6rqn3mHDsqybVJzl3sxKr6vSS/luTJ3X3e8kUEAAAAYC2Y9SLtlCTfTvLWqnr4eEOA45Kc2N1Xb5tUVZdV1WvnvH5Skpdm9FjnFVX1gDlft961fwUAAAAAVoOZfrSzu7dW1aFJTk7yjox28DwpozJtrnVJ9pzz+ifH3586/prrF5O8brpJAQAAAFjtZrpIS5LuviTJIUvM2Tjv9VPz/QUaAAAAAOywWX+0EwAAAABmgiINAAAAAAZQpAEAAADAAIo0AAAAABhg5jcbAGDHbfzdd610hO3a8gMrnWD3N9P/f1/+6JWOAAAAU+eONAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGGDdSgdg7djyA09a6Qi7vY3f+ruVjgAAAMvvuP1WOsHu77ivr3QCWJXckQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBAAAAwACKNAAAAAAYQJEGAAAAAAMo0gAAAABgAEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGmPkiraruVlVnV9U1VXVlVb2oqvYccN5+VfVXVbW1qr5eVX9bVbfcFZkBAAAAWH3WrXSAxVTV+iRnJbkkyRFJ7pjkFRkVgMcucfqbktw5ydOT3Jjk+CRvS/Ljy5UXAAAAgNVrpou0JMck2SvJkd19dZIzq2rfJMdV1QnjY9+nqh6Y5CeTHNzd/zI+dkWSD1bVw7v7rF2UHwBgZmz83XetdITt2vIDK51g9zfT/39f/uiVjgAAUzHrj3YenuSMeYXZaRmVawcvcd6XtpVoSdLdH0rymfEYAAAAAExk1ou0uya5dO6B7v5ckmvGY4PPG/vkEucBAAAAwIJm/dHO9UmuWuD41vHYjpx30BRyAQAA7NZm+nFgj3vvtJn+/+txb3Zjs16k7TJVdXSSo8cvv1lVn1rJPKtRrXSApd0qyVdXOsTiHrPSAbarjl/pBOxufCZMg88EVg+fCdPgM4HVYzf4TEhm/nPBZ8IyucNKB2BlzXqRtjXJfgscXz8eW+y8W09yXnefmuTUSQOyelTVRd29eaVzALPBZwIwl88EYD6fC7A2zfoaaZdm3ppmVXVgkptn4TXQtnve2PbWTgMAAACARc16kXZ6ksOqap85x45Kcm2Sc5c474Cqesi2A1W1OaP10U5fjqAAAAAArG6zXqSdkuTbSd5aVQ8fr2N2XJITu/vqbZOq6rKqeu221939/iTvSfL6qjqyqh6X5G+TnNfdZ+3SvwG7E4/2AnP5TADm8pkAzOdzAdag6u6VzrCoqrpbkpOTPDCjnThfk+S47r5hzpwtSd7b3U+dc2z/JCcleXxGheE7kzyru2d4MUgAAAAAZtXMF2kAAAAAMAtm/dFOAAAAAJgJijQAAAAAGECRBgAAAAADKNIAAAAAYABFGgAAAAAMoEgDAAAAgAEUaQAAAAAwgCINAAAAAAZQpAEAAADAAIo0AAAAABhAkQYAAAAAAyjSAAAAAGAARRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAGUKQBACyzqjquqrqqHrrSWQAA2HGKNABgVauqfavqlVX1vqq6sqq+VVVfrqoPVdVvVtXeK51xJdXImeOir6tq3UpnAgCYVYo0AGC1+8EkRye5Icm7kpyY5M1J9klyUpIPVdW+Kxdvxf1akocl+dZKBwEAmHUT/4tjVd0yyeOT/I8ke3f3MXOO3yHJJd3tFzEAYFZcnmS/7v7O/IGq+pskP5fkmCQn7OpgK62q7pLk+CR/nORnM/pdDgCA7ZjojrSq+oUkW5L8eZL/neSX5wzfLsmFSZ40rXAAwGyoqltU1XVVdf6843uNH5XsqnrKvLFnjI//0q5N+9919w0LlWhjbx5/v9M03quq7ltV766qb1TV1VV1VlU9cBrXnrbxI5xvSPLpJC9Y4TgAALuFwUVaVR2a5C+TfCbJz2RUpn1Xd/9rkk8medw0AwIAK6+7v5nkQ0nuV1X7zBl6cJKbjf986LzTtr0+e5nj7YyfGn//1529UFU9KMn7kjw8yelJTk5yXZL3Jrn/zl5/GRyb5N5Jntrd317pMAAAu4NJHu38nSRfTPLj3f31qrrnAnMuTvKAqSQDAGbNORkVZz+R0VpjyagsuyHJuZlTpFXVHhmtu/Xp7v7sUheuqv2T/OaEed7W3RcPnTy+A+vY8csfTPLjSTYl+eckfzHhe8+/dmX0D457JXlcd//TnLHfSPLKCa+3KZP/4+Qru/uqgdf/sSTPT/Ly7r5owvcBAFizJinSfizJm7r764vM+XySA3YuEgAwo85O8vsZFWZzi7QPJ3lrkpOr6s7d/e8ZFVQ/mOQtA6+9fyZ/vHBLRv+IN9S6Bd7jDUl+dQrruz4oyV2S/MvcEm3s5CS/nuSOE1xvUyb/7/G6JEsWaVW1V0Z/708kedGE7wEAsKZNskbaDyT5xhJz9k9y447HAQBm2PuTXJvxnWdVtV+S+2RUsJ0znrPtrrRDxt/PyQDdvaW7a8Kv100Svru/1d2V0e8/t0/y1Iwew7yoqjZOcq0F3Gf8/dwF3veGJOdNcrHuft0O/PfYMvDyJyQ5KMkvLLJ2HAAAC5ikSNuS5L5LzLlfkn/f4TQAwMzq7usyKoTuWVW3TvLQJHsmObu7P5nkC/lekXZoks7AIm1X6pEruvuvkxyZ0Z1kJ+/kZfcbf//Sdsa/uJPXn4qqOjjJM5O8uLs/ttJ5AAB2N5M82vn2JM+pqiO7+63zB6vq55P8z4we+QAAVqdzkjwio6LsQUm+leT8OWOHV9XNMlp/7BPd/eUhF90Va6QtpLs/UFVXZVQK7oxtS1/cZjvjEy19sYxrpN07SSV5YVW9cDtzvjNa8i333tn/vgAAq80kRdrxSY5K8qaq+vsk65Okqo7J6JflJya5LMmrph0SAJgZ23bgPDTJA5NcMGd9sbOT/FySZyTZO5Pt1rkr1kj7PuMdSPfN0stXLOUj4+8HL/AeeyZ5yITXW6410v4tyWu3M3ZUkltktGlCJ/nahO8PALDqVXcPnzxaP+RvMvoX6Pnen+Rnu/vyqSQDAGbOuBT6WpLrktw6yfO7+6XjsTtkVG59OckPJTmiu9++QlG/a7zT+H/M31Cgqm6a0W6dP5/k77r75+aNd5KM11Vb6j0qySczekx0sV07H9bd793xv83yqaotSe6Q5Cbdff0KxwEAmEkTFWnfPanqPhn9K/QtM3qU4QPd/cEpZwMAZlBVvS3JEeOXD5j7O0BVXZbR7pQ3JLnlErt97xJV9cokv5jRI6ifzeiurdsm+cmMHrn8VEYF1xfmnLNHRn+HG7p70B38VfXgJGcmuWlGu5heltGdZYdm9NjrI6NIAwDYrU3yaOd3dfdH8r1HGACAteXsjIq0q5NctMDYHZN8eBZKtLE3Z/TI4gPHX/tklP2SJK9I8uruvmbeOfccfz9t6Jt09/lV9eNJXpLk8PHhD2a0/tphGRVpAADsxgbfkTZeOPiWSb6y0Fbp48cjbpXka9397amEq/rRJM/N6Jfeuyd5X3c/dMB5+2X0CMXjMtqZ9J1JntXd1voAAJZUVc/K6HeJe3b3J1Y6DwAAs2GPCeb+QZL/zGhB3oXsMx5/3s6GmuPuSR6V0SMX/z7BeW/K6F9/n57kqUl+LMnbppgLAFjdDk7ydiUaAABzTXJH2keTXNHdj1lkztuT3K677zuVcFV7dPeN4z//Q5JbLXVHWlU9MMkFSQ7u7n8ZH7tfRo9WPKK7z5pGNgAAAADWlknuSPuRjO4MW8y/J9m4w2nm2VaiTejwJF/aVqKNr/OhJJ/J99YrAQAAAICJTFKk3SSj3asWc2OSvXY8zlTcNcmlCxz/5HgMAAAAACY2ya6dn8lovZDFHJzkczseZyrWZ7St/Xxbkxy0vZOq6ugkRyfJXnvtdd+NGzcuSzgAAABg9/TJT37yq91965XOwcqZpEh7e5Lfqapnd/eJ8wer6jlJNif542mF25W6+9QkpybJ5s2b+6KLLlrhRAAAAMAsqarPrnQGVtYkRdofJ3lykj+qqicmeU+SK5LcLslhGZVon09ywrRDTmhrkoXa4fXjMQAAAACY2OAirbv/X1U9NMkbk9xv/NVJajzlQ0me1N1fm3bICV2a5McXOH7XJG/bxVkAAAAAWCUmuSMt3f3pJPevqvsleUCS/TNaj+wD450xZ8HpSX6/qh7S3eclSVVtzmh9tNNXNBkAAAAAu62JirRtxqXZshdnVXXzJI8av7xdkn2r6gnj1/+3u6+pqsuSnNvdTxtne39VvSfJ68frtt2Y5Pgk53X3WcudGQAAAIDVaYeKtF3oh5K8ed6xba9/JMmWjP4Oe86bc1SSk5L8ZZI9krwzybOWLSUAAAAAq95ERVpVrUvymIzWR1uf7y+wkqS7+1emkC3dvSXfW4Nte3M2LnDsqiS/OP4CAAAAgJ02uEirqgOSnJnkblm83OokUynSAAAAAGBWTHJH2iuS3D2jRyv/IsnlSa5fjlAAAAAAMGsmKdIOy2jB/qOWKwwAAAAAzKo9Jpi7V5L3L1cQAAAAAJhlkxRpn0jyw8sVBAAAAABm2SRF2iuSPLaq7rpcYQAAAABgVk2yRtrlSd6Z5P1VdWKSDye5aqGJ3X3BFLIBAAAAwMyYpEg7L0knqSTHLTF3zx0NBAAAAACzaJIi7aUZFWkAAAAAsOYMLtK6+9jlDAIAAAAAs2ySzQYAAAAAYM2a5NHOJElVrUvy0CT/I8ktuvtl4+M3TXKLJFu72yOgAAAAAKwqE92RVlUPT/LpJGck+T9JXjxn+L5JvpLkqKmlAwAAAIAZMbhIq6r7JHlnRnexPTfJaXPHu/v9SbYkefwU8wEAAADATJjkjrQ/SHJtks3dfWKSTy0w58Ikm6YRDAAAAABmSKdO8AAAIABJREFUySRF2kOS/GN3X7nInM8l2bBzkQAAAABg9kxSpN0iozXQFrPXhNcEAAAAgN3CJKXXFUnuvsScTUk+s+NxAAAAAGA2TVKknZHkkVX1wIUGq+onkzw4ow0JAAAAAGBVmaRIe2mSryc5q6pekuSuSVJVh41fvyXJl5KcOPWUAAAAALDC1g2d2N2fr6rDkrwpye8l6SSV5P+Ov29JcmR3L7WOGgAAAADsdgYXaUnS3RdV1Z2THJHkAUlumdFdah/IaEfP66YfEQAAAABW3uAirapum+Q74zvO3jL+AgAAAIA1YZI10i5PcsJyBQEAAACAWTZJkXZVki8vVxAAAAAAmGWTFGkfTHLv5QoCAAAAALNskiLthUkOrqqnLlMWAAAAAJhZk+zaeWiSc5K8tqqOSXJhki8m6XnzurtfNqV8AAAAADATJinSXjznz/cbfy2kkyjSAAAAAFhVJinSHrFsKQAAAABgxg0u0rr77OUMAgAAAACzbPBmA1X1nqo6bhmzAAAAAMDMmmTXzockuelyBQEAAACAWTZJkXZZkgOXKwgAAAAAzLJJirTXJnlUVd1+ucIAAAAAwKyaZNfOtyQ5NMn5VfWyJBcm+WKSnj+xu6+cTjwAAAAAmA2TFGmfy6g0qyR/usi8nvC6AAAAADDzJim8/i4L3H3GbNn4u+9a6QisUVte/uiVjgAAAADLanCR1t1PXs4gAAAAADDLJtlsAAAAAADWLEUaAAAAAAww+NHOqjp14NTu7l/ZwTwAAAAAMJMm2Wzg6UuMb9vRs5Mo0gAAAABYVSYp0u60neP7J/mxJMcmed/4OwAAAACsKpPs2vmfiwx/uKpOT/KvSc5IsthcAAAAANjtTG2zge7+bJJ/SvKb07pmklTV3arq7Kq6pqqurKoXVdWeA87bXFXvqar/N/46q6ruP81sAAAAAKwd096180tJ7jyti1XV+iRnZbTu2hFJXpTkt5K8cInzDhyfty7JU8Zf65KcWVV3mFY+AAAAANaOSdZIW1RV7ZHkYUmuntY1kxyTZK8kR3b31RkVYfsmOa6qThgfW8ijk+yT5PHd/fVxvguSfDXJo5L82RQzAgAAALAGDC7SqupBi1zjwCS/lOTeSV47hVzbHJ7kjHmF2WlJjk9ycJJ3bOe8myS5Psl/zTn2zfGxmmI+AAAAANaISe5IOy+jRyy3p5JckOS3dyrRf3fXJOfMPdDdn6uqa8Zj2yvS3pLRY6CvqKqXjI/9QZKtSd48xXwAAAAArBGTFGkvzcJF2o0ZFVQf6u4LppLqe9YnuWqB41vHYwvq7iur6mFJ3pnkWePDX0hyWHd/ZcoZAQAAAFgDBhdp3X3scgaZpqrakNGdZx9O8vTx4WcmeVdVPai7P7fAOUcnOTpJNmzYkIsvvnhXxZ2qJx50w0pHYI3aXX9mAAAAYKipbTawTLYm2W+B4+vHY9vz3IzWSXtCd38nSarqnCT/keQ5+d5dat/V3acmOTVJNm/e3Js2bdq55CvkcaddsdIRWKNOOHr3/JkBAACAofYYOrGq7l1Vz6uq22xn/Dbj8XtNL14uzWgttLnvc2CSm4/HtueuST6xrURLku6+LsknktxxivkAAAAAWCMGF2kZ3cn1jCRf3s74V5Ick+TZOxtqjtOTHFZV+8w5dlSSa5Ocu8h5n01yj6q66bYDVXWzJPdIsmWK+QAAAABYIyYp0h6U5J+7e8GdO7v7xox22HzINIKNnZLk20neWlUPH69jdlySE7v76m2TquqyqnrtnPNek+S2Sf6xqh5dVY9J8rYkGzJ+fBMAAAAAJjFJkXZAksuXmHNFRmXVVHT31iSHJtkzyTuSvDDJSUleMG/quvGcbed9OMkjk+yT5A1JXp/R46CP6O6PTSsfAAAAAGvHJJsNXJPk1kvMuXWS63Y8zvfr7kuSHLLEnI0LHDs7ydnTzAIAAADA2jXJHWkfS/LYqtp7ocHxOmaPHc8DAAAAgFVlkiLtL5L8UJIzqurucweq6h5J3p3RHWmvmV48AAAAAJgNgx/t7O43VtWjkzwpyceq6sqM1kS7XUYL+++R5G+7+2+WJSkAAAAArKBJ1khLdz+5qi5I8utJ7pLk9uOhS5O8qrtPmXI+AAAAAJgJExVpSdLdr07y6qraN8n+Sa7q7qunngwAAAAAZsjERdo24/JMgQYAAADAmjB4s4Gq2lRVz6uq22xn/Dbj8XtNLx4AAAAAzIZJdu18bpJnJPnydsa/kuSYJM/e2VAAAAAAMGsmKdIelOSfu7sXGuzuG5Ock+Qh0wgGAAAAALNkkiLtgCSXLzHniiQbdjwOAAAAAMymSYq0a5Lceok5t05y3Y7HAQAAAIDZNEmR9rEkj62qvRcarKp9kjx2PA8AAAAAVpVJirS/SPJDSc6oqrvPHaiqeyR5d0Z3pL1mevEAAAAAYDasGzqxu99YVY9O8qQkH6uqKzNaE+12SW6bUSn3t939N8uSFAAAAABW0OAiLUm6+8lVdUGSX09ylyS3Hw9dmuRV3X3KlPMBAAAAwEyYqEhLku5+dZJXV9W+SfZPclV3Xz31ZAAAAAAwQyYu0rYZl2cKNAAAAADWhImKtKp6cJIHZ7QmWpJcmeT87j5/2sEAAAAAYJYMKtKq6iFJ/izJ3bYdGn/v8fgnkjxDoQYAAADAarVkkVZVj09yWpKbJPlSknOTXD4ePjDJwUnukeScqnpid//TMmUFAAAAgBWzaJFWVRuSvD7JjRnt1Pnn3X39vDnrkvxyklckeUNV3aW7v7BMeQEAAABgReyxxPhvJtk7yVO6+0/nl2hJ0t3Xd/efJXlKklsk+Y3pxwQAAACAlbVUkfbIJBd29z8sdaHufkuSDyU5fBrBAAAAAGCWLFWkbUxy3gTXO398DgAAAACsKksVaTdJct0E17tufA4AAAAArCpLFWlfyGhHzqHunuSLOx4HAAAAAGbTUkXa+5I8oqruvNSFquouSQ5L8i/TCAYAAAAAs2SpIu1Pk9w0yTvHRdmCxkXbO5KsS/Lq6cUDAAAAgNmwbrHB7r6wqk5M8uwkF1fVm5OcneTy8ZQDkzw8yROS3CzJK7v7Q8uYFwAAAABWxKJF2thzk1yT5PeSPDnJz80bryQ3JnlZkmOnmg4AAAAAZsSSRVp3d5I/qKrXJXlakgcn2TAe/mKS85L8VXdftlwhAQAAAGClDbkjLUnS3Z9O8vxlzAIAAAAAM2upzQYAAAAAgCjSAAAAAGAQRRoAAAAADKBIAwAAAIABFGkAAAAAMIAiDQAAAAAG2G6RVlVfrqrnzHn9vKp6yK6JBQAAAACzZbE70m6V5OZzXr84ySHLGwcAAAAAZtNiRdqXktxuVwUBAAAAgFm2bpGxDyV5SlVdl+QL42M/UVXPW+Ka3d0vm0o6AAAAAJgRixVpz03yT0meOefYIVn68c5OokgDAAAAYFXZbpHW3f9eVfdI8qMZPeJ5VpLXJ3nDLsoGAAAAADNjsTvS0t03JPlUkk9VVZJ8urvP3hXBAAAAAGCWLLbZwHw3SfKHyxVke6rqblV1dlVdU1VXVtWLqmrPgeceWVUXVtW1VfW1qnp3Ve293JkBAAAAWH0WvSNtrvHdaUmSqtqQZFOS/ZN8PclHu/sL2zt3R1XV+oweKb0kyRFJ7pjkFRkVgMcuce7Tk5yc5ISM1ntbn9H6boP/zgAAAACwzUSlUlXdPskpSQ5fYOz0JL/a3Z+bUrYkOSbJXkmO7O6rk5xZVfsmOa6qThgfWyjnrZKclOTXu/sv5gz94xSzAQAAALCGDH60s6puk+T8JI9K8vkkb0xy4vj758bHzxvPm5bDk5wxrzA7LaNy7eBFznvi+PtfTzELAAAAAGvYJGukHZvkwCTPT3LH7n5ydz+3u5+c5E5Jnpfk9lnikcsJ3TXJpXMPjO94u2Y8tj33z2iThKdV1eer6jtV9cGqetAUswEAAACwhkxSpD0myVnd/bLuvn7uQHdf390vT3LmeN60rE9y1QLHt47HtueAJHfJqNT7nSQ/leS/krx7ynfMAQAAALBGTLJG2oYkf7fEnIuy+COXu0oluUWSn+nudydJVV2Q5LNJfi3J73/fCVVHJzk6STZs2JCLL75416WdoicedMPSk2AZ7K4/MwAAADDUJEXa1Ul+eIk5B47nTcvWJPstcHz9eGyx8zrJe7cd6O6rq+rDSe620AndfWqSU5Nk8+bNvWnTph2MvLIed9oVKx2BNeqEo3fPnxkAAAAYapJHO89P8oSquv9Cg1W1OcnPJDlvGsHGLs28tdCq6sAkN8+8tdPm+WRGd6XV/JhJbpxiPgAAAADWiEmKtJeM57+vqv6qqn6+qh5RVU+pqtdmVLTtkeRlU8x3epLDqmqfOceOSnJtknMXOe+d4+8P23agqvZLct8kH5tiPgAAAADWiMGPdnb3RVV1VJK/SvILSX5+znBltCnA07r7winmOyXJs5K8taqOT3JQkuOSnNjd332EtKouS3Judz9tTtZ/SvLaqvrdJF9N8ttJvpPkT6eYDwAAAIA1YpI10tLdb6uqs5M8Psl9Mlq/7OtJPprkrd39jWmG6+6tVXVokpOTvCOjsu6kjMq0udYl2XPesScn+aMkJ2b0KOj5SQ7p7sXWVgMAAACABU1UpCXJuCx7/fhr2XX3JUkOWWLOxgWOfTPJM8ZfAAAAALBTJlkjDQAAAADWLEUaAAAAAAygSAMAAACAARRpAAAAADCAIg0AAAAABlCkAQAAAMAAg4u0qrrVcgYBAAAAgFk2yR1pl1fV31bVTyxbGgAAAACYUZMUaZ9J8r+S/HNVXVJVv1FV65cpFwAAAADMlMFFWnffLclDk7wxyY8kOSnJFVX111X1oOWJBwAAAACzYaLNBrr7X7r7yUlum+S3kmxJ8pQk76uqj1fVM6tq3+nHBAAAAICVtUO7dnb31u4+ac5dan+X5EeTvCrJlVX1mqq69/RiAgAAAMDK2qEibZ4rknwhyTeTVJK9kvxSkouq6h+qav8pvAcAAAAArKgdKtKqas+qekJVnZnkU0mek+TrSX47yQ8l+ckkZyU5Msmrp5QVAAAAAFbMukkmV9WPJPnlJL+YUWHWSd6V5NXdfcacqWclOauq3prkkVPKCgAAAAArZnCRVlVnJDk0o7vYvpTkZUn+vLsvX+S0C5McsVMJAQAAAGAGTHJH2iOSvC+jRzXf2t3fGXDOO5N8eUeCAQAAAMAsmaRIu2d3f2KSi3f3x5N8fLJIAAAAADB7Bm82MGmJBgAAAACryeAirap+uqreU1W32874bcfj1kQDAAAAYNUZXKRltFvnrbv7ioUGu/vKJLdMcvQ0ggEAAADALJmkSLtnRrtwLubCJP9zx+MAAAAAwGyapEi7VZbegfNr43kAAAAAsKpMUqR9NcmPLjHnjkmu2vE4AAAAADCbJinSzk/y2Kq680KDVXWXJEeM5wEAAADAqjJJkXZikpsmOa+qfrWqDqqqm42/PzPJeUnWJfnj5QgKAAAAACtp3dCJ3f2Bqvq1JH8y/prvxiS/3t3vn1Y4AAAAAJgVg4u0JOnuU6rq/CS/muT+SfbPaE20DyR5dXf/2/QjAgAAAMDKm6hIS5Lu/niSZyxDFgAAAACYWZOskQYAAAAAa9bEd6RVVSW5U5L1SfZcaE53X7CTuQAAAABgpkxUpFXV7yX5rYxKtMUsWLABAAAAwO5qcJFWVb+V5CVJvpHkjUkuT3L9MuUCAAAAgJkyyR1pv5LkyiT37e4vLVMeAAAAAJhJk2w28MNJ/lGJBgAAAMBaNEmR9qVY+wwAAACANWqSIu0fkjyiqm62XGEAAAAAYFZNUqT9fpKvJPn7qjpwmfIAAAAAwEyaZLOBi5PcNMn9k/xUVX0tyVULzOvuvss0wgEAAADArJikSLt5ks5o585t9ppuHAAAAACYTYOLtO6+/XIGAQAAAIBZNskaaQAAAACwZu1wkVZV+1TVhmmGAQAAAIBZNVGRVlU3r6rjq+rzGW00cPmcsftV1duratO0QwIAAADAShu8RlpV7ZPkfUnuleTfklydZO7unJ9IckiSSzPa4RMAAAAAVo1J7kg7NqMS7endfa8kb5o72N3/leTcJIdOLx4AAAAAzIZJirSfTvKe7v7L8eteYM6WJFPd3bOq7lZVZ1fVNVV1ZVW9qKr2nOD8ParqoqrqqnrMNLMBAAAAsHYMfrQzo4LsLUvM+WaS/XY8zn9XVeuTnJXkkiRHJLljkldkVAAeO/AyT8+Uyz0AAAAA1p5J7kj7ZpJbLzHnR5J8dcfjfJ9jkuyV5MjuPrO7T0nywiTPrqp9lzp5XMS9JMnzp5gJAAAAgDVokiLtwiSPqapbLDRYVQckOTzJBdMINnZ4kjO6++o5x07LqFw7eMD5f5jk/CRnTzETAAAAAGvQJEXaq5LcKsk7q+pOcwfGr/8+o4LrVdOLl7tmtAvod3X355JcMx7brqq6V5JfSvKcKeYBAAAAYI0avEZad59eVS/OaG2yS5N8O0mq6osZPfJZSZ7f3edNMd/6JFctcHzreGwxf5Lk5O6+rKo2LvVGVXV0kqOTZMOGDbn44osnSzojnnjQDSsdgTVqd/2ZAQAAgKEm2Wwg3f0HVfW+JM9K8oAkNxt/vSfJid195vQjTq6qfjbJXZL81NBzuvvUJKcmyebNm3vTpk3LlG55Pe60K1Y6AmvUCUfvnj8zAAAAMNRERVqSjMuyXVWYbc3Cu4CuH499n6q6SZI/SnJ8kj2qav8k2zYm2Luq9unubyxHWAAAAABWr0nWSFsJl2beWmhVdWCSm2fe2mlz7J3k9klOzKhs25rkY+Ox05J8dFmSAgAAALCqTXxH2i52epLnzruL7Kgk1yY5dzvnfDPJw+YdOyDJG5M8L8k5yxEUAAAAgNVtcJFWVd9J0gOmdnffbMcj/TenZLQe21ur6vgkByU5LqP12K6ek+2yJOd299O6+/ok752XfeP4jx/v7g9OKRsAAAAAa8gkd6R9MAsXafsn+dGMNh34eJKrF5izQ7p7a1UdmuTkJO/IaAfPkzIq0+Zal2TPab0vAAAAAMw3uEjr7odsb6yq9k3yqiSbM8FOmQPf95IkhywxZ+MS41uS1PRSAbDTjltoLxkmctzXVzoBTI/PhJ3nMwEAlt1UNhsYP2b5tIzuWHvJNK4JAAAAALNkart2dvcNSf45yeOndU0AAAAAmBVTK9LGbppk/ZSvCQAAAAArbmpFWlXdKcnPJPnPaV0TAAAAAGbF4M0GqurURa5xYJKfGP/5d6aQCwAAAABmyuAiLcnTlxi/LMkfdfdrdiIPAAAAAMykSYq0O23n+I1Jtnb3VVPIAwAAAAAzaXCR1t3WPgMAAABgzZr2rp0AAAAAsCpNstnAg3b0Tbr7gh09FwAAAABmwSRrpJ2XpHfwffbcwfMAAAAAYCZMUqS9NMl9kxyWZEuS85N8MckBSR6cZGOSdyf58FQTAgAAAMAMmKRIe3uS3xp/vaq7b9g2UFV7JvnNJH+Y5AXdfeFUUwIAAADACpukSHtxknO6+6T5A+NS7RVVdWhGZdojp5QPAAAAYLfzkY985LB169a9oLsPiM0edwc3VtUXr7/++hfe5z73OWN7kyYp0u6X5OQl5nw0yTMnuCYAAADAqvKRj3zksJvd7GYnb9y48bq99tpr6x577LGja86zi9x444117bXX7rdly5aTP/KRj/za9sq0SRrRPZIctMScgya8JgAAAMCqsm7duhds3Ljxur333vtaJdruYY899ui999772o0bN163bt26F2x33gTXfH+SJ1TVgo9tVtWjkjwhyQWTRQUA+P/s3XuUnWV9L/DvL4SQgCFErkGUyEVRUBFSqLaCgoi3HhQqtHisKByUczx4tNbKqUpQjwJVAbVe8IYXFKtSWuUoFRS81HqjgDcU1EAhiOIJIJAASZ7zx96jwzjJvJPMZHYmn89ae+28z+XdvyFr9sr68rzPAwAwfbTWdpozZ86Kqa6D8ZszZ86K/uO4oxrPo52vTXJFkour6rIkX01ya5Idkxyc5JAk9yb5u3UvFwAAAGCjN8NKtI1T/+9tjQvPOgdprbXvVNXhST6U5Kn9V0tS/SE/S/Li1tr31r1cAAAAABhM41mRltba16rqEUmelGS/JPOS3JHkyiRfa61JWwEAAACYlsZ9MEDr+Wpr7ezW2mn9968K0QAAAACmr+985zuzq2r/z3/+83O7znnrW9+63cc+9rFtJrOuDWlcK9KGVNWcJHskeVBr7ZsTWxIAAADA9LPwNRfvPxWfu+T0Z03ZNlznnXfe9o985COXv+AFL7h9qmqYSONakVZVC6rqU0luT3JVkq8N6/uTqrqmqg6a4BoBAAAAYMp1DtKqaqck305yVJJLknwrvz9oIP2+hyQ5eiILBAAAAGDDO/3007ffaaedHjtnzpzHH3LIIXvcdNNNs4b3n3rqqTvus88+j5o7d+6+22677eMOOeSQPX7wgx9sMdR/wAEHPPKHP/zhlhdeeOG2VbV/Ve3/jne8Y9skede73rXt/vvv/8h58+btu/XWW+974IEHPuKrX/3qlhv6Zxyv8TzaeWqSBUme3lq7tKpOTXLgUGdr7f6q+loSK9IAAAAANmIf//jHtznllFMeduyxx/76yCOPvP0rX/nK3JNOOmnh8DE33XTTrJe85CW/evjDH37fHXfcMePcc8/d/qCDDtrruuuu+8G222676j3vec8Nz3ve83Z/2MMedu/rXve6W5LkUY961L1JsmTJkll/+Zd/+Zs999zz3nvvvbc++clPPvhpT3vaXldeeeUPHv3oR983BT9yJ+MJ0p6V5F9aa5euZcyNSf50/UoCAAAAYCqdccYZC570pCfdef7559+YJEcdddSdt91228xPfepT2w2N+eAHP/ifQ39euXJljjjiiDt33HHHfT/5yU9u87KXvew3+++//4ott9xy9bbbbrvy0EMPvXv4/d/61rfeMvTnVatW5bnPfe6dj3jEI7b60Ic+tO3wvkEznj3Sdkzy0zHG3Jtkq3UvBwAAAICpdP/99+fHP/7xls9+9rMfcEDAkUceuWz49WWXXbbVE5/4xD232WabfTfffPP9586du98999wz46c//ekWGcOVV145+7DDDtt92223fdzMmTP3nzVr1v5LliyZfd11182e6J9nIo1nRdqyJLuMMWbPJL9c93IAAAAAmEq33HLLzFWrVmXHHXe8f3j7ggULVg79+brrrpt1xBFHPOKxj33s3WedddYNu+yyy31bbLFFe+5zn7vnihUr1rpwa9myZTOe+cxnPmK77ba7/01vetN/7rbbbvfNmTNn9Yknnrjw3nvvrbXNnWrjCdK+keS/VNUOrbVfjeysqt2TPCPJJyaqOAAAAAA2rAULFqzcbLPNcuutt24+vP2WW275XY70z//8z1uvWLFixhe/+MXrt95669VJbyXbHXfcsdlY9//KV77yoFtvvXXzL3zhCz99/OMfv2Ko/be//e2Yc6faeB7tfGuSLZNcXlWHJZmdJFW1Rf/6c0lakrdPeJUAAAAAbBCbb7559tprr3s+//nPbzO8/cILL5w/9Ofly5fPqKq2+eabt6G2D37wgw9etWpVjbhXu/feex+QP91zzz0zkmTOnDmrh9q+9KUvbbV06dIHnAo6iDqvSGutfbOqTkryriRfHNZ1T/99VZLjW2vfn8D6AAAAANjAXv3qV9/ywhe+cPfnP//5DzvqqKNu/8pXvjL38ssvnzfUf/jhh/928eLFdfTRRy884YQTbvv+978/5x/+4R92nDt37qrh99ljjz1WXHHFFVt/9rOf3Xr77bdf+YhHPOLegw8++K4tt9xy9Ytf/OKFr3rVq3554403bn7GGWfsvMMOO9z/h5UMlvE82pnW2vur6mtJ/keSP06ybZI7kvx7kne21n408SUCAAAAbPyWnP6s7011DV391V/91e033XTTjeecc86CCy+8cNsDDjjgt+9+97uXHHXUUXsmyQEHHLD8He94xy9OP/30nY855pj5j3zkI+85//zzf/6CF7xgt+H3Oe2005aecMIJs4477rjd7rrrrs3OOeecJSeffPJvPvKRj/zslFNOeeixxx67x8Me9rAVZ5999o1ve9vbdpqan7a7aq2NPWoTs2jRovbd7353qstYJwtfc/FUl8Amasnpz5rqEtjYLJ439hjWbvEdU10BTBzfCevPdwLApKuq77XWFo017uqrr17yuMc97rYNURMT7+qrr97ucY973MLR+jrvkVZVP62qd0xYVQAAAACwERnPYQMLktw1WYUAAAAAwCAbT5D2oyS7jTkKAAAAAKah8QRp70ryZ1W1z2QVAwAAAACDajyndv4syWVJ/q2q3p3kO0l+meQPTitorf3bxJQHAAAAAINhPEHa19MLzSrJqzNKgDbMZutTFAAAAAAMmvEEaW/O2sMzAAAAAJi2OgdprbXXTmYhAAAAADDIxnPYAAAAAABsstYapFXV66vqoA1VDAAAAAAMqrEe7Vzcf311qKGqXp7k5a213SavLAAAAIBpZvG8/afmc+/43pR87jjdcccdM7bZZpvHn3POOUtOPvnk30x1PaNZl0c7t0my60QXAgAAAACDbOD3SKuqR1fVZVV1T1Utrao3VNVmY8z5o6r6cFVd35/3k6o6tapmb6i6AQAAAKaLlStXZsWKFTXVdUy1gQ7Sqmp+kkuTtCRHJHlDkr9OctoYU49JsnuSM5I8M8k/JHllkvMnrVgAAACAaeKoo45auM8++zzqYx/72DZ77LHH3rNnz97v8ssv3+p5z3vewl122eUxs2fP3m/hwoX7nHzyyTsPD9h+8pOfzKqq/T/wgQ/MP/bYY3edO3fuvjvuuONjX/GKV+y8atWqB3zGeeedt83ChQv3mT179n6LFi165NVXX/0HC6AEc1xwAAAgAElEQVRWrlyZV77ylTsvWLDgMbNmzdpvjz322Pu9733vg0er9YILLpi3++677z1nzpzHP/nJT97j1ltv3ewHP/jBFgceeOAj5syZ8/h99tnnUd/61rfmrM9/l7H2SJtqL00yJ8mRrbU7k3ypqrZOsriqzuy3jeb01tptw64vr6oVSd5XVbu21m6Y5LoBAAAANmo333zzrNe97nW7vPrVr1668847358k8+fPX/mWt7zlPx/84AevvPbaa2efccYZO992222bf+ITn3hA1nLqqafu8sxnPnPZRz/60Z9/6Utfmnv22Wcv2HvvvZefcMIJy5Lk61//+pYnnHDC7ocddtiyM88888bvf//7c4499tjdR9bwile84iHvec97dnzlK195y4EHHnj3Zz7zmfknnXTSw6sqL3nJS/7f0LilS5fOeuMb37jz61//+pvvvvvuGa95zWse9sIXvnDXm266aYsXvvCFv/7rv/7rX77+9a/f5dhjj93tuuuu++GMGeu2tqxLkLZNVT1s+HWSVNVDk4y6pK+1duM6VfOHnpHkkhGB2QXprTQ7OMnn1vD5t43S/B/9952TCNIAAAAA1uL222+fefHFF//0iU984vKhtqc//el3Df35aU972l1bbbXV6pe//OULV6xYcePs2bPbUN8BBxzw2/e///03Jclzn/vcO7/85S/Pu+iii+YPBWlvfvObd9p1111XXHzxxT+fMWNGjj766Dvvu+++OvPMMx8ydI9bb711sw984AM7vPzlL7/lzDPPvCVJjjrqqDuXLl26+Vve8padhwdpd95558yvfe1r1+699973Jsk111yz5fve974d3/nOdy552cte9pskaa3d/Bd/8Rd7XHXVVbP322+/Fevy36RL/PbyJL8Y9jq5375kRPvQ6+frUsga7JXk2uEN/ZDunn7feDwhyeokP5uY0gAAAACmrx122OH+4SHa6tWr84Y3vGGH3Xfffe/Zs2fvN2vWrP1POumkh9933311/fXXzxo+97DDDnvAU4R77rnn8ltuuWXzoeurr756q8MPP/z24SvDjjnmmNuHz7nyyivnrFixYsaxxx67bHj7n//5ny+74YYbtli6dOnvFojtvPPO9w6FaEmyxx57rEiSZzzjGb+r41GPetSKJLnxxhs3zzoaa0XajentTzZV5ie5fZT2Zf2+TqpqpySvTfKx1tqv1jDmxCQnJsmCBQty1VVXjb/aAXD0bqvGHgSTYGP9nWEKPfS4qa5g4+f3junEd8L6850AwATbbrvt7h9+/cY3vnGHN77xjQ896aSTfvmUpzzlt9tuu+3Kb37zm1udcsopD1u+fPkDnlqcP3/+AwKKWbNmtXvvvfd3qdltt922+Q477LBy+Jihx0eH3HTTTZsnyUMe8pAHtC9YsOD+JPn1r3+92c4777wySbbeeus/+Lz+z/C79i222KIlyfLly9f5zIC1BmmttYXreuNBUVWzkvxjkruSvGJN41pr5yY5N0kWLVrU9t133w1T4AR7zgU3T3UJbKLOPHHj/J1hCl103lRXsPE7/pyprgAmju+E9ec7AYAJVvXAHb0uuuiiBz/96U9f9s53vvN34cM111yzTpv3b7fddvf/6le/ekAutXTp0gesFNtll13uH2rfaaedfheIDa1s23777Tf4aqKBPrUzvZVn80Zpn9/vW6vq/Y1/NMneSZ7ZWhtzDgAAAAB/aMWKFTNmzZq1enjbBRdc8OA1jV+bxz72sXdfcskl26xe/fvbfepTn9pm+Jj99ttv+ezZs1d/4hOfeMBTiZ/97Gfn77rrrvcOrUbbkAb91M5rM2IvtP4hB1tmxN5pa3B2kiOSHNZa6zIeAAAAgFEcfPDBd374wx/e4fTTT797zz33vPfjH//4g2+44YbZ63KvU0455ZdPecpTHvWsZz1rt+OPP/62a665Zs7555+//fAxO+6446oTTjjhV+ecc86CmTNntgMOOOCez3zmM9tcccUV8973vvdN5B79nQ16kPaFJH9TVXNba7/ttx2TZHmSK9Y2sapOSfKyJEe31r4+uWUCAAAAjGHxHd+b6hLWxxlnnLH0tttum/mWt7zlIUny9Kc/fdnf//3f33jsscfuMd57HXTQQfe8//3v//nixYsf8vznP3+PffbZ5+7zzz//Z09+8pMfNXzcWWeddfPMmTPbeeedt8Pb3va2mQ972MPuffe73/2LE088cUqeOqzWpvIsgbWrqvlJfpTkB0nOSLJbkrcnObu19tph465PckVr7fj+9bFJzk9yXpL3jbjtz1prv17b5y5atKh997vfnagfY4Na+JqLp7oENlFLTn/WVJfAxmbxaE/uMy6L75jqCmDi+E5Yf74TACZdVX2vtbZorHFXX331ksc97nG3bYiamHhXX331do973OMWjtY30CvSWmvLqurQJO9K8rn0TvA8K8niEUNnJtls2PXT+u/H9V/DvSi9gA0AAAAAOhvoIC1JWms/SnLIGGMWjrg+Ln8YoAEAAADAOhv0UzsBAAAAYCAI0gAAAACgg3E/2llV2yc5KsmjkmzVWjthWPvDk3y/tbZ8QqsEAAAA2HisXr16dc2YMWNwT3hkVKtXr64kq9fUP64VaVV1fJIlSf4hyf9Mb+P+ITsm+WaSY8ddJQAAAMA0UVW/XL58+eyproPxW758+eyq+uWa+jsHaVV1WJJzk/w0yXOTvGd4f2vtB0l+mOQ561YqAAAAwMZv5cqVpy1ZsmTW3XffPae/wokBt3r16rr77rvnLFmyZNbKlStPW9O48Tza+bdJbklycGvtzqp6/ChjrknyhHHWCgAAADBt7LfffpdceeWVL/vZz352amttp9ijfmOwuqp+uXLlytP222+/S9Y0aDxB2qIkF7TW7lzLmJuS7DSOewIAAABMO/0wZo2BDBun8SSis5LcPcaYbZKsWvdyAAAAAGAwjSdIW5Jk/zHGHJjkJ+tcDQAAAAAMqPE82vnPSV5dVc9rrX16ZGdVvSjJY5P83UQVB8D6Wfiai6e6hDVa4gyj9TbQf7+nP2uqSwAAgAk3niDtzCR/keSTVfXnSeYlSVW9LMmTkhyZ5Lok75zoIgEAAABgqnUO0lpry6rq4CQfTfK8YV3v6L9/LcmxrbWx9lEDAAAAgI3OeFakpbV2Y5InV9VjkzwhybZJ7kjy7621701CfQAAAAAwEMYVpA1prV2T5JoJrgUAAAAABlbnIK2qzkzy4dbajyexHqaxJbOPneoSNnoLV3xiqksAAIDJt3jeVFew8Vt8x1RXANPSjHGMfVWSH1TVt6vqf1TVgyerKAAAAAAYNOMJ0v4yySVJHp/eAQNLq+ozVfVnVbXZpFQHAAAAAAOic5DWWvtUa+2ZSXZJ8rdJrktyZJKL0gvV3l5V+05OmQAAAAAwtcazIi1J0lq7tbX21tbaY5Lsn+RdSSrJ/0ryvaq6aoJrBAAAAIApN+4gbbjW2n+01l6eZOckf5NkZZLHTERhAAAAADBIOp/aOZqqmpfkmCQvTPLH6a1MczQIAAAAANPOuIO0qpqR5PD0wrP/kmSLJC3JZUk+kuTCiSwQAAAAAAZB5yCtqh6T5K+SPD/JjumtPvtpko8m+Whr7aZJqRAAAAAABsB4VqRd3X+/I8kHkpzXWvvmxJcEAAAAAINnPEHavyY5L8k/tdbunZxyAAAAAGAwdQ7SWmtPn8xCAAAAAGCQzZjqAgAAAABgY7DGFWlV9aH0TuP83621W/vXXbTW2vETUh0AAAAADIi1Pdp5XHpB2hlJbu1fd9GSCNIAAAAAmFbWFqQ9vP9+84hrAAAAANjkrDFIa63dsLZrAAAAANiUdD5soKpeX1UHjTHmSVX1+vUvCwAAAAAGy3hO7Vyc5MljjDkoyanrWgwAAAAADKrxBGldbJ5k9QTfEwAAAACm3EQHafsluW2C7wkAAAAAU25tp3amqr48oum4qnryKEM3S/LQJLsm+eTElAYAAAAAg2OtQVoeuCdaS7Kw/xppdZLfJPlUkldMQF0AAAAAMFDWGqS11n736GdVrU6yuLX2hkmvCgAAAAAGzFgr0oZ7UZL/mKxCAAAAAGCQdQ7SWmsfmcxCAAAAAGCQjWdF2u9U1S5JHpJki9H6W2tfXZ+iAAAAAGDQjCtIq6qnJTkryV5jDN1snSsCAAAAgAE0Y+whPVX1x0k+n2SbJO9KUkm+muT9Sa7tX38uicMIAAAAAJh2OgdpSU5JsiLJH7XWXt5v+0pr7aVJ9knypiRPTfKZiS0RAAAAAKbeeIK0JyT5l9ba0pHzW8/rk/w4yWkTWB8AAAAADITxBGnzktw47Pq+JFuNGPONJAetb1HDVdWjq+qyqrqnqpZW1Ruqasw92KpqXlV9uKqWVdUdVXV+VW07kbUBAAAAsOkYz2EDv0oyf8T17iPGbJ5kzvoWNaSq5ie5NMmPkhzR/7y3pRcAvnaM6f+Y5BFJTkiyOskZSS5K8qSJqg8AAACATcd4grSf5oHB2b8neUZVPaK19tOq2inJUUmum8D6XppeMHdka+3OJF+qqq2TLK6qM/ttf6CqnpDkaUkObq19td92c5JvVdVTW2uXTmCNAAAAAGwCxhOkfTHJm6rqwa21/5fknCRHJvmPqvpRkj2TzE3y6gms7xlJLhkRmF2Q3uqyg9M7JXRN824dCtGSpLX27ar6Rb9PkAYAbHIWvubiqS5hjZbMnuoKNn4D/fd7+rOmugQAmBDj2SPtfentf3Z/krTWvpHkeUl+kd6pnbckOam19tEJrG+vJNcOb2it3Zjknn5f53l9Px5jHgAAAACMqvOKtP6qsG+NaPunJP800UUNMz/J7aO0L8sD92sbz7zdJqAuAACAjdpAr2K0SnW9DfTfr1WqbMTG82jntFZVJyY5sX95V1X9ZCrrmY5qqgsY23ZJbpvqItbu2VNdwBrVGVNdARsb3wkTwXcC04fvhIngO4HpYyP4TkgG/nvBd8Ik2XWqC2BqDXqQtizJvFHa5/f71jZv+/HMa62dm+Tc8RbI9FFV322tLZrqOoDB4DsBGM53AjCS7wXYNK0xSKuqn6/jPVtrbfexh3VybUbsaVZVD02yZUbfA234vCeN0r5XkosmqDYAAAAANiFrO2xgRnorasf7Gs8BBmP5QpLDq2rusLZjkixPcsUY83aqqj8daqiqRentj/aFCawPAAAAgE3EGlektdYWbsA61uS9SU5OcmFVnZFeELY4ydv7hx8kSarq+iRXtNaOT5LW2jer6l+TfLSqXpVkdZIzkny9tXbpBv4Z2Hh4tBcYzncCMJzvBGAk3wuwCarW2lTXsFZV9egk70ryhPRO4vxAksWttVXDxixJcnlr7bhhbdskOSvJc9NbJff5JCe31gZ4M0gAAAAABtU6B2lVNT/Jg1pr/zmxJQEAAADA4BnXfmZV9aCqeltV/TK9Y35/MazvwKr6v1W130QXCQAAAABTrXOQVlXzknwzySuSLE3y4/QOFxjy/fROyvzLiSwQAAAAAAbBeFak/V2SvZMc11rbL8mnh3e21u5J7yTNQyeuPAAAAAAYDOMJ0o5Mcklr7aNrGXNDkoesX0kAAAAAMHjGE6TtkuSaMcbclWTeupcDAAAAAINpPEHab5PsMMaYh6d3CAEAAAAATCvjCdK+k+TZVTV3tM6qWpDkmUm+PhGFAQAAAMAgGU+Qdk6SbZP836p61PCO/vWnk8xO8o6JKw8AAAAABkO11roPrjo1yalJWpL7k2yeZFmS+Ukqyd+21v5+EuoEAAAAgCk1riAtSarqKUlOTvLH6a1QuyPJvyc5q7X25QmvEAAAAAAGwLiDNAAAAADYFI1nj7ROqmr7ib4nAAAAAEy1CQvSqmpeVb05yc8m6p4AAAAAMChmdhlUVbsm2T+9Awa+3Vq7dVjf7CSvSPKq9A4duGcS6gQAAACAKTXmirSqekd6q8w+neSiJEuq6r/3+56c5CdJ3pRkyyTnJNltsooFAAAAgKmy1sMGquqFST6cZHWSa/vNe/Xfj0/yviSbJXl/kje11pZOXqkAAAAAMHXGWpF2XJL7kjyptbZPa22fJIckWZXkg0l+mWS/1tp/F6IBAIyuqhZXVeuv5gcAYCM1VpD22CT/1Fr75lBDa+2r6T3iWUle3Fr7/iTWBwCwXqpq66o6u6q+VlVLq2pFVf2qqr5dVf+rqraa6ho3pKp6cj/UW9Pr9KmuEQBgUI112MC8JNeP0n5d//2bo/QBAAySByc5Mcm3k1yc5Nfp/RvnkCRnJflvVfWE1tqdU1filLgiyeWjtH99A9cBALDRGCtIm5HeSZ0j3Z8krbXlE14RAMDE+s8k81prf/Bvmqr6eJLnJ3lpkjM3dGFT7PLW2uKpLgIAYGMy5qmdSdZ8GgEAsEmoqgdV1X1V9Y0R7XP6j0q2qnrBiL6T+u0v3rDVPlBrbdVoIVrfp/vve07EZ1XV/lX1xar6bVXdWVWXVtUTJuLeAABMvbFWpCXJ4qpaPFpHVa0apbm11rrcFwDYSLTW7qqqbyc5sKrmttZ+2+/6kyRb9P98aJKPDZt2aP/9sg1U5rr4s/77Net7o6p6YpJLk8xKcmF622Psm97jk19e3/tPgj2q6mVJtk7vAKmvtdauG2MOAMAmrUvgVeO853jHAwAbhy+nF5wdlN5eY0kvLFuV3n5bQ8FZqmpGkqck+Xlr7YaxblxV2yT5X+Os56LW2lVdB1fVzCSv7V8+OMmT0gu6vpLk/eP87JH3riQfSjInyXNaa/88rO/lSc4e5/32TfKccZZxdmvt9nGMf37/NfxzP5vkv7XWlo3zswEANgnVmic3AYCxVdXB6a2uOqu19sp+27fT2wbio0neleSRrbWfVtV+Sb6X5P2ttRM73Hthkl+Ms6QXtdbOG0f9s5OM3N/1Y0n+e2vtrnF+9sh7/0l6m/R/tbV28Ii+zZL8JMnuSZ7SWru8w/2OS/LhcZbx8Nbakg733jvJs9MLQ5ckmZ1kUZI3J3l8km8kOai1tnqcnw8AMO112SMNACDpnda9PP2VZ1U1L8l+6T26OfTo4tCqtEP6750eaWytLWmt1Thf542n+NbaitZapffvn12SHJfkqUm+2w/y1sd+/fcrRvncVRnnSZittfPW4b/Hko73/mFr7YzW2g9aa3e11m5rrX0xyZPTCzP/JL9/5BUAgGEEaQBAJ621+9ILhB5TVdunF7xsluSy1tqPk9yS3wdph6a3Um3g9gZrPTe31j6S5Mgkj0xvNd36mNd/v3UN/b9cz/tPutbanUk+0b88aCprAQAYVA4FAADG48tJDksvKHtikhXpPQo41PeMqtoivf3Hftha+1WXm26IPdJG01r796q6Pb1QcH3c0X/fcQ39O43nZhtoj7TR/Lr/vtV63gcAYFoSpAEA4zF0AuehSZ6Q5N9aayuG9T0/yUnpBTHjOa1zmySnjrOWJUnWK0irqrnpnVr527HGjuHK/vvBIzv6e6T96Tjvt2/G/9/jvCTrG6T9cf/95+t5HwCAacmjnQDAeFyZ3uqrI5LsnQeGZUOPcZ4y4npMk7lHWlU9pn/QwMj2Wek90jkjvz+FdHh/q6qupzL9W3oHChxUVUeM6HtZegcNdDaZe6RV1aI1tP/XJMckuS/JP46nXgCATYUVaQBAZ621VVV1eXpBWjIsSGut3VBVP0svNFqVUTbenyLHJ3lRVX0jyQ3prdraOcnT0nvk8idJXjV8QlUN/c/GVV0+oLXWqur4JF9K8tmqujDJ9emtLDs0yReTPH39f5QJ8ZmqWpnku0luSu/Uzj9KckCSlUle0jWUAwDY1AjSAIDxuiy9IO3O9MKYkX27J/lea+2OkROnyKeTPCi9R1GfkGRuerX/KMnbkry7tXbPiDmP6b9f0PVDWmvfqKonJfk/SZ7Rb/5WevuvHZ7BCdLek95ppX+SZLskleTm9B4NPbu1dvXUlQYAMNiqta5PLGx4VbVHkr9J7x+9eyf5WmvtyR3mzUtydnqb9M5I8vkkJ7fWfjN51QIA00VVnZzevyUe01r74VTXAwDAYBj0FWl7J3lmkn9Psvk45v1jkkckOSHJ6iRnJLkovRPEAADGcnCSfxGiAQAw3KCvSJvRWlvd//Nnkmw31oq0qnpCehv+Htxa+2q/7YD0Hq04rLV26eRWDQAAAMB0NNCndg6FaOP0jCS3DoVo/ft8O8kv8vv9SgAAAABgXAY6SFtHeyW5dpT2H/f7AAAAAGDcBn2PtHUxP71j7UdalmS3NU2qqhOTnJgkc+bM2X/hwoWTUhwAAACwcfrxj398W2tt+6mug6kzHYO0ddJaOzfJuUmyaNGi9t3vfneKKwIAAAAGSVXdMNU1MLWm46Ody5LMG6V9fr8PAAAAAMZtOgZp12b0vdDWtHcaAAAAAIxpOgZpX0iyU1X96VBDVS1Kb3+0L0xZVQAAAABs1AZ6j7Sq2jLJM/uXD0mydVX9ef/6/7bW7qmq65Nc0Vo7Pklaa9+sqn9N8tGqelWS1UnOSPL11tqlG/hHAAAAAGCaGOggLckOST49om3o+uFJlqT3M2w2YswxSc5K8qH0Vt19PsnJk1YlAAAAANPeQAdprbUlSWqMMQtHabs9yYv6LwAAAABYb9NxjzQAAAAAmHCCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQwcypLoCJtfA1F091CWyilpz+rKkuAQAAACaVFWkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0MfJBWVY+uqsuq6p6qWlpVb6iqzTrMW1RV/1pV/6//urSqDtwQNQMAAAAw/Qx0kFZV85NcmqQlOSLJG5L8dZLTxpj30P68mUle0H/NTPKlqtp1MmsGAAAAYHqaOdUFjOGlSeYkObK1dmd6QdjWSRZX1Zn9ttE8K8ncJM9trd2RJFX1b0luS/LMJO+Z/NIBAAAAmE4GekVakmckuWREYHZBeuHawWuZt3mSlUnuHtZ2V7+tJrpIAAAAAKa/QQ/S9kpy7fCG1tqNSe7p963JZ/tj3lZVO1TVDknOSrIsyacnqVYAAAAAprFBf7RzfpLbR2lf1u8bVWttaVU9Jcnnk5zcb74lyeGttV+PNqeqTkxyYpIsWLAgV1111frUPWWO3m3VVJfAJmpj/Z0BAACArgY9SFsnVbUgvZVn30tyQr/5fyS5uKqe2F/V9gCttXOTnJskixYtavvuu++GKndCPeeCm6e6BDZRZ564cf7OAAAAQFeDHqQtSzJvlPb5/b41+Zv09kn789ba/UlSVV9Ocl2SV+X3q9QAAAAAoJNB3yPt2ozYC62qHppky4zYO22EvZL8cChES5LW2n1Jfphk90moEwAAAIBpbtCDtC8kObyq5g5rOybJ8iRXrGXeDUn2qapZQw1VtUWSfZIsmYQ6AQAAAJjmBj1Ie2+Se5NcWFVP7R8IsDjJ21trdw4Nqqrrq+qDw+Z9IMnOSf6pqp5VVc9OclGSBenvgwYAAAAA4zHQQVprbVmSQ5NsluRzSU5LclaSU0cMndkfMzTve0menmRuko8l+Wh6j4Me1lq7evIrBwAAAGC6GfTDBtJa+1GSQ8YYs3CUtsuSXDZJZQEAAACwiRnoFWkAAAAAMCgEaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6GDgg7SqenRVXVZV91TV0qp6Q1Vt1nHukVX1napaXlW/qaovVtVWk10zAAAAANPPQAdpVTU/yaVJWpIjkrwhyV8nOa3D3BOSfCLJF5I8I8kJSa5LMnOy6gUAAABg+hr0UOmlSeYkObK1dmeSL1XV1kkWV9WZ/bY/UFXbJTkryf9srb1/WNc/TXrFAAAAAExLA70iLb2VZJeMCMwuSC9cO3gt847uv39ksgoDAAAAYNMy6EHaXkmuHd7QWrsxyT39vjU5MMlPkhxfVTdV1f1V9a2qeuLklQoAAADAdDboQdr8JLeP0r6s37cmOyV5ZJLXJvnbJH+W5O4kX6yqHSe6SAAAAACmv0HfI21dVZIHJXlea+2LSVJV/5bkhiQvS/K6P5hQdWKSE5NkwYIFueqqqzZctRPo6N1WTXUJbKI21t8ZAAAA6GrQg7RlSeaN0j6/37e2eS3J5UMNrbU7q+p7SR492oTW2rlJzk2SRYsWtX333XcdS55az7ng5qkugU3UmUCGs5AAACAASURBVCdunL8zAAAA0NWgP9p5bUbshVZVD02yZUbsnTbCj9NblVYj2ivJ6oksEAAAAIBNw6AHaV9IcnhVzR3WdkyS5UmuWMu8z/ffnzLUUFXzkuyf5OqJLhIAAACA6W/Qg7T3Jrk3yYVV9dT+PmaLk7y9tXbn0KCqur6qPjh03Vr7bpJ/TvLBqnphVT0ryb8kuT/JP2zIHwAAAACA6WGgg7TW2rIkhybZLMnnkpyW5Kwkp44YOrM/Zrj/muSiJG9P8pn0QrRD+vcEAAAAgHEZ9MMG0lr7UZJDxhizcJS2u5Kc1H8BAAAAwHoZ6BVpAAAAADAoBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQx8kFZVj66qy6rqnqpaWlVvqKrNxjF/RlV9t6paVT17MmsFAAAAYPqaOdUFrE1VzU9yaZIfJTkiye5J3pZeAPjajrc5Ickuk1IgAAAAAJuMQV+R9tIkc5Ic2Vr7UmvtvUlOS/LKqtp6rMn9IO7/JPm7yS0TAAAAgOlu0IO0ZyS5pLV257C2C9IL1w7uMP+NSb6R5LJJqA0AAACATcigB2l7Jbl2eENr7cYk9/T71qiqHpvkxUleNWnVAQAAALDJGOg90pLMT3L7KO3L+n1r884k72qtXV9VC8f6oKo6McmJSbJgwYJcddVV46t0QBy926qpLoFN1Mb6OwMAAABdDXqQtk6q6i+SPDLJn3Wd01o7N8m5SbJo0aK27777TlJ1k+s5F9w81SWwiTrzxI3zdwYAAAC6GvRHO5clmTdK+/x+3x+oqs2T/H2SM5LMqKptkgwdTLBVVc2djEIBAAAAmN4GPUi7NiP2QquqhybZMiP2ThtmqyS7JHl7emHbsiRX9/suSPIfk1IpAAAAANPaoD/a+YUkf1NVc1trv+23HZNkeZIr1jDnriRPGdG2U5JPJvnfSb48GYUCAAAAML0NepD23iQnJ7mwqs5IsluSxUne3lq7c2hQVV2f5IrW2vGttZVJLh9+k2GHDXy/tfatyS8bAAAAgOlmoIO01tqyqjo0ybuSfC69EzzPSi9MG25mks02bHUAAAAAbEoGOkhLktbaj5IcMsaYhWP0L0lSE1cVAOtt8WhnyTAui++Y6gpg4vhOWH++EwBg0g36YQMAAAAAMBAEaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6ECQBgAAAAAdCNIAAAAAoANBGgAAAAB0IEgDAAAAgA4EaQAAAADQgSANAAAAADoQpAEAAABAB4I0AAAAAOhAkAYAAAAAHQjSAAAAAKADQRoAAAAAdCBIAwAAAIAOBGkAAAAA0IEgDQAAAAA6EKQBAAAAQAeCNAAAAADoQJAGAAAAAB0I0gAAAACgA0EaAAAAAHQgSAMAAACADgRpAAAAANCBIA0AAAAAOhCkAQAAAEAHgjQAAAAA6GDgg7SqenRVXVZV91TV0qp6Q1VtNsacP6qqD1fV9f15P6mqU6tq9oaqGwAAAIDpZeZUF7A2VTU/yaVJfpTkiCS7J3lbegHga9cy9Zj+2DOSXJfksUne2H8/ahJLBgAAAGCaGuggLclLk8xJcmRr7c4kX6qqrZMsrqoz+22jOb21dtuw68urakWS91XVrq21Gya5bgAAAACmmUF/tPMZSS4ZEZhdkF64dvCaJo0I0Yb8R/9954krDwAAAIBNxaAHaXsluXZ4Q2vtxiT39PvG4wlJVif52cSUBgAAAMCmZNAf7Zyf5PZR2pf1+zqpqp3S21PtY621X61hzIlJTkySBQsW5Kqrrhp/tQPg6N1WTXUJbKI21t8ZptBDj5vqCjZ+fu+YTnwnrD/fCQAw6QY9SFtvVTUryT8muSvJK9Y0rrV2bpJzk2TRokVt33333TAFTrDnXHDzVJfAJurMEzfO3xmm0EXnTXUFG7/jz5nqCmDi+E5Yf74TAGDSDXqQtizJvFHa5/f71qqqKslHk+yd5E9aa2POAQAAAIDRDHqQdm1G7IVWVQ9NsmVG7J22BmcnOSLJYa21LuMBAAAAYFSDftjAF5IcXlVzh7Udk2R5kivWNrGqTknysiT/tbX29ckrEQAAAIBNwaAHae9Ncm+SC6vqqf0DARYneXtr7c6hQVV1fVV9cNj1sUnenN5jnTdX1R8Pe22/YX8EAAAAAKaDgX60s7W2rKoOTfKuJJ9L7wTPs9IL04abmWSzYddP678f138N96Ik501spQAAAABMdwMdpCVJa+1HSQ4ZY8zCEdfH5Q8DNAAAAABYZ4P+aCcAAAAADARBGgAAAMD/Z+/+oy2v63qPv97MqIDBOIoFJjlBGguzWDX9QCUUVEJsgWRSVitT1yy9mf1Qu3YvLQf6sYTix11xiyh/ZOUlLaXQRpIhMSU1SPQmjIo5cAPT0IERB/wBn/vH/p7cHc6Z+czMOWfvc87jsdZZe87n+/3u8z5z1tmMT7/f74YOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOiwdtIDALB4NrzmXZMeYV7bD5z0BMvfVP98X3fapEcAAIAF54w0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoMPaSQ/A6rH9wBdMeoRlb8N9b5n0CAAAsPg2r5v0BMvf5rsnPQGsSM5IAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAh6kPaVV1bFVtrapdVXVHVZ1bVWs6jltXVW+sqh1VdXdV/XlVPWopZgYAAABg5Vk76QF2p6rWJ7k6yU1JTk9ydJILMgqAZ+/h8LcmeUKSlyR5IMl5Sa5IcsJizQsAAADAyjXVIS3JS5MclOTM1trOJO+pqkOTbK6q84e1B6mq45M8K8mJrbX3DWu3J/lQVT2jtXb1Es0PAAAAwAox7SHt1CRXzQpml2d0dtmJSa7czXGfm4loSdJa+3BVfWbYJqQBAKvOhte8a9IjzGv7gZOeYPmb6p/v606b9AgAsCCm/R5pxyTZNr7QWrstya5hW/dxg5v3cBwAAAAAzGnaz0hbn+SuOdZ3DNv25bij5jqgqjYl2TR8ek9VfWIv5qRDTXqAPTssyZ2THmL3njPpAeZV5016ApYbrwkLwWsCK4fXhIXgNQGW2HS/LpyzDF5Zl6fHTXoAJmvaQ9qSaa1dluSySc/B5FTV9a21jZOeA5gOXhOAcV4TgNm8LsDqNO2Xdu5Ism6O9fXDtoU+DgAAAADmNO0hbVtm3dOsqo5McnDmvgfavMcN5rt3GgAAAADs1rSHtC1JTqmqQ8bWzkpyb5Jr93Dc4VX11JmFqtqY0f3RtizGoKwILu0FxnlNAMZ5TQBm87oAq1C11iY9w7yqan2Sm5L8S5LzMgphFya5uLV29th+tyS5trX24rG1q5I8PsmrkjwwHP/51toJS/cdAAAAALBSTPUZaa21HUlOTrImyZVJzklyUZLXztp17bDPuLMyOmvtDUnenOSGJM9dzHkBAAAAWLmm+ow0AAAAAJgWU31GGgAAAABMCyENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBACyAqtpcVa2qnjbpWQAAWBxCGgCw7FXVoVV1cVX9Q1XdUVX3VdXnq+rDVfVLVfXwSc+4lKrqEVX16qr686q6qaq+PkS+Z+zhuDVV9ctV9bGqureqvlhVf1tVT16q2QEAppmQBgCsBI9MsinJ/UneleTCJG9LckiSi5J8uKoOndx4S25DkvOTvCCjv4M793RAVVWSyzP6u3tokkuSvCPJDyd5X1WdvljDAgAsF2snPQAAwAL4f0nWtda+NntDVf1Zkp9K8tKM4tJqcGuSZyT5SGvti1X1piQ/u4djfiLJ85Jcl+Tk1tp9SVJVlyZ5f5I/qqprWmtfWryxAQCmmzPSAIAkSVV9U1V9tao+MGv9oOFSyVZVPzNr28uG9Rct7bT/VWvt/rki2uBtw+PjF+JrVdX3VdW7q+pLVbWzqq6uquMX4rkXSmttR2tta2vti3tx2MuGx7NnItrwXP+U5C+SPDqj0AYAsGoJaQBAkqS1dk+SDyf5gao6ZGzTU5I8bPjzybMOm/l86yKPtz9+dHj82P4+0XCvsH/I6GyvLRld/vjVJO9N8oP7+/yTUlUHJnlykl0ZfX+zbRkeT1qyoQAAppBLOwGAcddkFM5+OKN7jSWjWHZ/kmszFtKq6oAkT0/yr621W/f0xFX1iCS/tJfzXNFau7F356pam+Ts4dNHJjkhyXFJ/j7JH+3l15793JXkDUkOSnJGa+2vx7b9YpKL9/L5jktyxl6OcXFr7a69PKbH0UnWZPSz/Poc2z81PD5hEb42AMCyIaQBAOO2Jvn1jILZeEi7Icnbk1xSVU9orX0yo0D1yCR/1fncj0jy2r2cZ3uS7pCW0b9tZn+NP03y38YvV9xHT07ynUneNx7RBpck+YWMglSv47L3fx9vSrIYIW3d8Hj3PNtn1h+xCF8bAGDZcGknADDuH5Pcm+HMs6pal+R7Mwps1wz7zJyVNnOZ3zXp0Frb3lqrvfx4094M31q7r7VWGf0b57FJXpjRZZjXV9WGvXmuOXzv8HjtHF/3/oxuyN+ttfamffj72L6f3wMAAPtBSAMA/lNr7asZBaEnVdWjkzwto0v+trbWbk7y2XwjpJ2cpKUzpC2lNnJ7a+1PkpyZ0Zlkl+zn086ctfW5ebb/+34+/yTNnHG2bp7tM+uLcTYcAMCy4dJOAGC2a5I8M6NQ9uQk9yX5wNi2U6vqYRndf+zjrbXP9zzpUtwjbS6ttQ9W1V0ZRcH9MRObvmWe7YfvzZNN2T3SPp3RffCOqqq1c9wnbeYdTz+5CF8bAGDZENIAgNlm3oHz5CTHJ7lu7P5iW5P8VJKXJXl49u7dOpfiHmkPMrwD6aFJvrQ/z5Pkn4fHE+f4GmuSPHUvn29q7pHWWruvqq7LKI6ekNGbM4w7dXicurMPAQCWkks7AYDZ/jmjs69OT/LE/NdYNhNSfm3W53u0mPdIq6onVdWBc6w/NKNLOg/IN948YXx7q6rW+S1cl+QTSX64qk6fte3l2bs3GpjGe6T9wfD4m+N/l1X1/UnOSvIf6X9jCQCAFala6/23IwCwWlTVFRmFtCT5odbah8a23ZJRNLo/yaNaa/O90+OSqaqLk/xcRpeg3prRWVuPSfKsjC65/ESSp7fWPjt2zAEZfQ/3t9a6ztKvqqckeU+Sh2b0Lqa3ZHRm2ckZRcUfGb7OexfkG9sPVfW7SQ4bPn1qRj+zv8voPnfJ6LLZK8b2ryRvTfK8JNuSXJnkURlFtAOT/Ngc71YKALCquLQTAJjL1oxC2s4k18+x7egkN0xDRBu8Lck3ZXQp6vFJDslo9puSXJDk91tru2Yd86Th8fLeL9Ja+0BVnZDkt/KNyx0/lNH9107JKKRNi+cledystWeN/Xl7kv8Maa21VlU/mdGZdy9K8gsZ3R/vfUl+s7V23aJOCwCwDEz1GWlV9R1JXp3RP4ifmOQfWmtP6zhuXZKLM7qB7wFJ3pnkFa21LyzetADAclJVr8jo3wtPaq19fNLzAAAw/ab9jLQnJnl2kg8mecheHPfWJE9I8pIkDyQ5L6P/x/WEhR4QAFi2TkzyNyIaAAC9pv2MtANaaw8Mf/7LJIft6Yy0qjo+o0sSTmytvW9Y+4GMLrt4Zmvt6sWdGgAAAICVaKrftXMmou2lU5N8biaiDc/z4SSfyTfuZQIAAAAAe2WqQ9o+Oiajd5qa7eZhGwAAAADstWm/R9q+WJ/RW97PtiPJUfMdVFWbkmxKkoMOOuj7NmzYsCjDAQAAAMvTzTfffGdr7dGTnoPJWYkhbZ+01i5LclmSbNy4sV1//fUTnggAAACYJlV166RnYLJW4qWdO5Ksm2N9/bANAAAAAPbaSgxp2zL3vdDmu3caAAAAAOzRSgxpW5IcXlVPnVmoqo0Z3R9ty8SmAgAAAGBZm+p7pFXVwUmePXz6rUkOrarnDZ//bWttV1XdkuTa1tqLk6S19o9V9XdJ3lxVr0ryQJLzkry/tXb1En8LAAAAAKwQUx3SknxzkrfNWpv5/NuTbM/oe1gza5+zklyU5A0ZnXX3ziSvWLQpAQAAAFjxpjqktda2J6k97LNhjrW7kvzc8AEAAAAA+20l3iMNAAAAABackAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6TH1Iq6pjq2prVe2qqjuq6tyqWtNx3Maq+ruq+uLwcXVV/eBSzAwAAADAyjPVIa2q1ie5OklLcnqSc5O8Msk5ezjuyOG4tUl+ZvhYm+Q9VfW4xZwZAAAAgJVp7aQH2IOXJjkoyZmttZ0ZhbBDk2yuqvOHtbmcluSQJM9trd2dJFV1XZI7kzw7yR8s/ugAAAAArCTTHtJOTXLVrGB2eZLzkpyY5Mp5jntIkq8n+fLY2j3DWi3CnFNjw2veNekRWKW2v+60SY8AAAAAi2qqL+1MckySbeMLrbXbkuwats3nr4Z9Lqiqb66qb05yUZIdSd62SLMCAAAAsIJN+xlp65PcNcf6jmHbnFprd1TV05O8M8krhuXPJjmltfYfcx1TVZuSbEqSI444IjfeeOP+zD0xzz/q/kmPwCq1XH9nAAAAoNe0h7R9UlVHZHTm2Q1JXjIs/3ySd1XVk4ez2v6L1tplSS5Lko0bN7bjjjtuqcZdUGdcfvukR2CVOn/T8vydAQAAgF7THtJ2JFk3x/r6Ydt8Xp3RfdKe11r7WpJU1TVJPpXkVfnGWWoAAAAA0GXa75G2LbPuhVZVRyY5OLPunTbLMUk+PhPRkqS19tUkH09y9CLMCQAAAMAKN+0hbUuSU6rqkLG1s5Lcm+Ta3Rx3a5LvqqqHzixU1cOSfFeS7YswJwAAAAAr3LSHtEuTfCXJ26vqGcMbAmxOcmFrbefMTlV1S1W9fuy4P07ymCTvqKrTquo5Sa5IckSG+6ABAAAAwN6Y6pDWWtuR5OQka5JcmeScJBclee2sXdcO+8wcd0OSH0lySJI/TfLmjC4HfWZr7aOLPzkAAAAAK820v9lAWms3JTlpD/tsmGNta5KtizQWAAAAAKvMVJ+RBgAAAADTQkgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHaY+pFXVsVW1tap2VdUdVXVuVa3pPPbMqvqnqrq3qr5QVe+uqocv9swAAAAArDxTHdKqan2Sq5O0JKcnOTfJK5Oc03HsS5K8JcmWJKcmeUmSTyVZu1jzAgAAALByTXtUemmSg5Kc2VrbmeQ9VXVoks1Vdf6w9iBVdViSi5L8Qmvtj8Y2vWPRJwYAAABgRZrqM9IyOpPsqlnB7PKM4tqJuznu+cPjnyzWYAAAAACsLtMe0o5Jsm18obV2W5Jdw7b5/GCSTyR5cVX9W1V9rao+VFVPXrxRAQAAAFjJpj2krU9y1xzrO4Zt8zk8yXcmOTvJf0/yo0m+nOTdVfUtCz0kAAAAACvftN8jbV9Vkm9K8uOttXcnSVVdl+TWJC9P8usPOqBqU5JNSXLEEUfkxhtvXLppF9Dzj7p/0iOwSi3X3xkAAADoNe0hbUeSdXOsrx+27e64luS9MwuttZ1VdUOSY+c6oLV2WZLLkmTjxo3tuOOO28eRJ+uMy2+f9AisUudvWp6/MwAAANBr2i/t3JZZ90KrqiOTHJxZ906b5eaMzkqrWeuV5IGFHBAAAACA1WHaQ9qWJKdU1SFja2cluTfJtbs57p3D49NnFqpqXZLvS/LRhR4SAAAAgJVv2kPapUm+kuTtVfWM4T5mm5Nc2FrbObNTVd1SVa+f+by1dn2Sv07y+qr62ao6LcnfJPlakv+9lN8AAAAAACvDVIe01tqOJCcnWZPkyiTnJLkoyWtn7bp22GfcTye5IsmFSf4yo4h20vCcAAAAALBXpv3NBtJauynJSXvYZ8Mca/ckednwAQAAAAD7ZarPSAMAAACAaSGkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0mPqQVlXHVtXWqtpVVXdU1blVtWYvjj+gqq6vqlZVz1nMWQEAAABYudZOeoDdqar1Sa5OclOS05McneSCjALg2Z1P85Ikj12UAQEAAABYNab9jLSXJjkoyZmttfe01i5Nck6SX6mqQ/d08BDifivJ/1zcMQEAAABY6aY9pJ2a5KrW2s6xtcszimsndhz/G0k+kGTrIswGAAAAwCoy7SHtmCTbxhdaa7cl2TVsm1dVfXeSFyV51aJNBwAAAMCqMdX3SEuyPsldc6zvGLbtzu8luaS1dktVbdjTF6qqTUk2JckRRxyRG2+8ce8mnRLPP+r+SY/AKrVcf2cAAACg17SHtH1SVT+R5DuT/GjvMa21y5JcliQbN25sxx133CJNt7jOuPz2SY/AKnX+puX5OwMAAAC9pv3Szh1J1s2xvn7Y9iBV9ZAkv5PkvCQHVNUjksy8McHDq+qQxRgUAAAAgJVt2kPatsy6F1pVHZnk4My6d9qYhyd5bJILM4ptO5J8dNh2eZKPLMqkAAAAAKxo035p55Ykr66qQ1prXxrWzkpyb5Jr5znmniRPn7V2eJL/k+R/JLlmMQYFAAAAYGWb9pB2aZJXJHl7VZ2X5Kgkm5Nc2FrbObNTVd2S5NrW2otba19P8t7xJxl7s4H/21r70OKPDQAAAMBKM9UhrbW2o6pOTnJJkiszegfPizKKaePWJlmztNMBAAAAsJpMdUhLktbaTUlO2sM+G/awfXuSWripANhvm+d6Lxn2yua7Jz0BLByvCfvPawIALLppf7MBAAAAAJgKQhoA9dTZlwAAGlRJREFUAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoMPUhraqOraqtVbWrqu6oqnOras0ejvn+qnpjVd0yHPeJqnptVR24VHMDAAAAsLKsnfQAu1NV65NcneSmJKcnOTrJBRkFwLN3c+hZw77nJflUku9O8hvD448t4sgAAAAArFBTHdKSvDTJQUnObK3tTPKeqjo0yeaqOn9Ym8vrWmt3jn3+3qq6L8kfVtXjWmu3LvLcAAAAAKww035p56lJrpoVzC7PKK6dON9BsyLajI8Mj49ZuPEAAAAAWC2mPaQdk2Tb+EJr7bYku4Zte+P4JA8k+fTCjAYAAADAajLtl3auT3LXHOs7hm1dqurwjO6p9qettc/Ps8+mJJuS5IgjjsiNN96499NOgecfdf+kR2CVWq6/M0zQkS+c9ATLn987VhKvCfvPawIALLppD2n7raoemuStSe5J8svz7ddauyzJZUmycePGdtxxxy3NgAvsjMtvn/QIrFLnb1qevzNM0BVvmvQEy9+L/9ekJ4CF4zVh/3lNAIBFN+0hbUeSdXOsrx+27VZVVZI3J3likqe01vZ4DAAAAADMZdpD2rbMuhdaVR2Z5ODMunfaPC5OcnqSZ7bWevYHAAAAgDlN+5sNbElySlUdMrZ2VpJ7k1y7uwOr6teSvDzJT7fW3r94IwIAAACwGkx7SLs0yVeSvL2qnjG8IcDmJBe21nbO7FRVt1TV68c+f0GS387oss7bq+qHxj4evbTfAgAAAAArwVRf2tla21FVJye5JMmVGb2D50UZxbRxa5OsGfv8WcPjC4ePcT+X5E0LOykAAAAAK91Uh7Qkaa3dlOSkPeyzYdbnL8yDAxoAAAAA7LNpv7QTAAAAAKbC1J+RBsC+2/Cad016hHltP3DSEyx/U/3zfd1pkx4BAAAWnDPSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAECHtZMegNVj+4EvmPQIy96G+94y6REAAGDxbV436QmWv813T3oCWJGckQYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQYe2kBwAAYGlseM27Jj3CvLYfOOkJlr+p/vm+7rRJjwAAC2Lqz0irqmOramtV7aqqO6rq3Kpa03Hcuqp6Y1XtqKq7q+rPq+pRSzEzAAAAACvPVJ+RVlXrk1yd5KYkpyc5OskFGQXAs/dw+FuTPCHJS5I8kOS8JFckOWGx5gUAAABg5ZrqkJbkpUkOSnJma21nkvdU1aFJNlfV+cPag1TV8UmeleTE1tr7hrXbk3yoqp7RWrt6ieYHAAAAYIWY9ks7T01y1axgdnlGce3EPRz3uZmIliSttQ8n+cywDQAAAAD2yrSHtGOSbBtfaK3dlmTXsK37uMHNezgOAAAAAOY07Zd2rk9y1xzrO4Zt+3LcUXMdUFWbkmwaPr2nqj6xF3PSoSY9wJ4dluTOSQ+xe8+Z9ADzqvMmPQHLjdeEheA1gZXDa8JC8JoAS2y6XxfOWQavrMvT4yY9AJM17SFtybTWLkty2aTnYHKq6vrW2sZJzwFMB68JwDivCcBsXhdgdZr2Szt3JFk3x/r6YdtCHwcAAAAAc5r2kLYts+5pVlVHJjk4c98Dbd7jBvPdOw0AAAAAdmvaQ9qWJKdU1SFja2cluTfJtXs47vCqeurMQlVtzOj+aFsWY1BWBJf2AuO8JgDjvCYAs3ldgFWoWmuTnmFeVbU+yU1J/iXJeRmFsAuTXNxaO3tsv1uSXNtae/HY2lVJHp/kVUkeGI7/fGvthKX7DgAAAABYKab6jLTW2o4kJydZk+TKJOckuSjJa2ftunbYZ9xZGZ219oYkb05yQ5LnLua8AAAAAKxcU31GGgAAAABMi6k+Iw2WQlUdW1Vbq2pXVd1RVedW1ewzHIFVoKq+o6r+sKo+VlX3V9V7Jz0TMDlV9eNV9TdVdXtV3VNVN1TVT056LmAyqup5VXVdVX2hqu6rqk9U1dlV9dBJzwYsnbWTHgAmabgP39UZ3Yvv9CRHJ7kgo8h89m4OBVamJyZ5dpIPJnnIhGcBJu9XknwmyS8nuTOj14e3VNVhrbXfm+hkwCQ8Ksk1SX4nyV1JfiDJ5iSHJ3n55MYClpJLO1nVqurXkvxqkse11nYOa7+a4T+IM2vA6lBVB7TWHhj+/JdJDmutPW2yUwGTMgSzO2etvSXJ8a21b5/QWMAUqarfSvLzSdY3/+MaVgWXdrLanZrkqlnB7PIkByU5cTIjAZMyE9EAkmR2RBt8JMljlnoWYGp9IYlLO2EVEdJY7Y5Jsm18obV2W5JdwzYAgHHHJ/nkpIcAJqeq1lTVwVX11CSvSPIHzkaD1cM90ljt1md0f4PZdgzbAACSJFV1cpIzkrxo0rMAE/XlJA8b/vzmJK+e4CzAEnNGGgAA7EFVbUjyliR/3Vp700SHASbtyUlOSPLKjN6w7JLJjgMsJWeksdrtSLJujvX1wzYAYJWrqkcm2ZLk1iQ/NeFxgAlrrf3z8Mf3V9WdSf6kqi5orX16knMBS8MZaax22zLrXmhVdWSSgzPr3mkAwOpTVQcneWdGNxN/Tmtt14RHAqbLTFTzTr6wSghprHZbkpxSVYeMrZ2V5N4k105mJABgGlTV2iRvS/L4JD/SWvv8hEcCps9ThsfPTHQKYMm4tJPV7tKM3mnn7VV1XpKjkmxOcmFrbeckBwOW3nDmybOHT781yaFV9bzh8791JgqsOr+f0WvCLyZ5VFU9amzbR1prX5nMWMAkVNW7k1yd5ONJ7s8oor0yyV+4rBNWj/Iuvax2VXVsRjcIPT6jd/D84ySbW2v3T3QwYMkNNxOf7/9R/vbW2vYlGwaYuKranuRx82z2mgCrTFX9RpLnJtmQ5OtJ/jXJG5Nc2lr72gRHA5aQkAYAAAAAHdwjDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAAAAAoIOQBgAAAAAdhDQAAAAA6CCkAQAAAEAHIQ0AAAAAOghpAAAAANBBSAMAAACADkIaAAAAAHQQ0gAAAACgg5AGAAAAAB2ENAAAAADoIKQBAAAAQAchDQAAAAA6CGkAAAAA0EFIAwAAAIAOQhoAAAAAdBDSAAAAAKCDkAYAAAAAHYQ0AAAAAOggpAEAAABAByENAAAAADoIaQAAAADQQUgDAAAAgA5CGgAAAAB0ENIAgG5V9cKqalX1wknPMk2q6t+q6pYFeJ4/G/5+H7sQcy20qlpXVZdU1faq+vow63dNei4AgKUipAFAhyEYtD3ss33Yb8PSTEVVHVZVD1TVv8+z/fiZn11VPX2efW4dtn/b4k67OBYq4nW6IMnPJ/lokt9Ock6Sz+/ugKp6/9jPYL6Ps5dgdgCA/bZ20gMAAMvKO5J8MMlnJz1IkrTW7qyqjyX5nqp6Ymvt47N2OXlm1yQnJfn78Y1V9R1Jvi3Jp1prt+3HKCcOX2Ole06Sm1prp+/DsW9MMt/f8fv2fSQAgKUjpAEA3Vprdye5e9JzzHJNku/JKJTNDmknJfl0kp3Dn399ju1JsnV/BmitfXp/jl8OqmpNkm9J8i/7+BRvaK29fwFHAgBYci7tBIBFVlVnDPe++mRVfXn4uKGqXlFVD/pvcdX/b+/eg62syjiOf38aeIkAMRU1RYcYtdto5i08XkDHLLO0wSIzL8NoMCpmUomWYoOjqYVW3hJzSC1rSDQTNQQUNS8IM06GA1qogEgDISJy9emPtV552bz7nH3gwEHP7zPD7Dlrve+67cMMPq71LN2Rj7vtLelcSf+StDwfHR0uSfm5AZKeze0tyLmrtqtoLyRNlrSLpNslvZnfeUpSU37mo5KuycccV0h6UdKAirYqc6Tlsc0utfNabudlST8qxlzzjiQNLc1vbp5Dt6K9Bpe4CIL1KxdK2hY4jLQLbRJwkKQuNe/WDaRJOl7SeEkL81xekfRzSV0rnq08Ximpu6Qb8tyWS5oh6QJJffI63lZnTpI0RNI/83vzJd1c7lvSMfm48e5A75qjkvXare1kN0k3lb73BZLGSjqg5rkngNX5x/6lfiY00k9rFPOSdKmkQyU9KGmRSrnjivXOvyuj8vhXqXRENK/91ZJm5TVcJOkhSf02pE8zMzMz8I40MzOzzeEq4D3gGWAu0I0UwLkeOAg4rc571wJHAX8FHgFOBEYCnSUtyu2OA6YAx5JyV20NDK5oqzvwJPA28AegB/At4GFJhwG35LIHgE7AQOAeSa9HxNMNzrMT8DCwGzCeFHj5eh7ntqR8WmW/yWOdB9wKrMxzPDi3tarBfh/PfR0laauIeC+X9839TszzvhA4AngQUqQKOJp0JLP2yOcVpN1rC0nr/1/SrrdhwJckfTEiljY3KEnb53b3B6YBvwd2AC4jHQVtznWk7/QB0pr2B84BeudygH+T1vTCPP8bSu9Pa6F9JPUGngB6AhOAu0nHXAcAX5F0UkSMz4/fTlrHnwD/AcaUxrCpHA78lPT9jgZ2Zt3fiW2ByUBX4CHSdzwbQFIP0u/7vsCzwFhgJ+AUYIKksyOiKtjYUp9mZmbWwSmiI6TzMDMz2zhae9FAbTCo7AJSkGzviJhderd37dE/pZ1ovwO+CxwaEc+U6u4ATgdeBfpGxNxc3h14GdgOWAYcEREzct02wHRSoGWPiFhQaq8Y+y3AkCLQJOk0UkDkf6Sgw4CIWJ7rmkjBhHERcVKprTPyuM+MiDtK5bOBXqQA2jci4t1cvjMwMz+2U0Ssqml/JnBIRCzO5Z1JQZ0m4NWI2Kv+cq+znk+Rdp8dFBFTc9lIYDiwa16vRcCoiLgo138WeAGYHhGfL7V1LClw+QRwQj7OWtQNAn4LXBsRw0rlc4DlEfHJUtkIUlDmLuC0yP/oktSLFOjqAYyOiEGld+4ETiUFhJoiYk4u7wQ8lud4YERMK72zXt8NrtmjpIDujyPi6lJ5EylAtQjoFRHLcvlHSEGlRyPimFb08wQpqNlcjrQbi99ZSccAf8/lgyJidEWbc0g78R4GTi7GWKofDZwF3BQRQ0rl+wLPkQK1fSLi9Ub7NDMzMwMf7TQzM2uty5r5063qhar8WTmYdX3+8bg6ff2sCKLldxYD9wPbkwIEM0p1K4B7gM7AfhVtLQOGlXZrQdqBtJq0S2poEUTL7U0hBXP2rzO2es4vgmi5nQXAfaS12af03On5c2QRRMvPrwQubmWfUH28sx8wIyLmR8QSUvCqtr787vtzyJ+DykG0PL7bSDnCTm1gTKcDa4CLiyBabuNV1t09VmVEEUTL76wiBaIg7djbKEo3y/Yj7S67rlyXv/s/AR8n7ShsK2dS/+/OzhXPT20goPWDiiDaNsC3SXnxhpfrIuIl4NfANlTvBG2kTzMzM+vAHEgzMzNrhYhQvT+kHWTrkbSjpKskvSBpaZFfCng+P7J7ne6mVpTNy5/PV9QVQbeqnE4zI+LtmrmsAd4EFkdE1RG9uXXaquetiFgvTxjwev7coVRW5OCqSj7/NGvzcTVqYv7sByDpY8AXWPfI5iTS7Z49ys+yfiDtMGAFMFDS5bV/SKkxdpVUGTjN/e9A2qH3WrHrqUZLSfervvuqddxQxfo/HhFVaz2x5rm20NTM35+qCwyebaG9dypuaQX4FOnY5/RykLakubm11KeZmZl1cM6RZmZmtgnl45jPAXuT/iN9DOnI3GpS3rKhpN0xVapux1zdQF2nBtsq3mmurjX/VqgKWpTHtXWprAhCvVn7cESskbSwFf0CPAW8CzTlY5BHksY+sfTMZOCHwNGSxuVnVpKOmJb1AETaKdWcLtRfu7rza6G8ULWWVeu4oYrxvVGnvijv3gZ9baj5LdTXW8ONmVtLfZqZmVkH50CamZnZpjWIFEQbERGXlytykv+h7TGoLcCS/LkLNQnrJW0N7MjaHXYtiogVOU9af+BQ0m6zIAXPClNIwah+pN1d3Ug7spat2xpLgJURUXXcsFHl+VWpV765FAHAnnXqd615rj20lMi3Xv3GzM3Jg83MzKxZPtppZma2aRUJ4MdW1LV0c+OH2fT8eXhF3aFs2P/sK+dJ6we8EBHv72zLt2xOLdWX3yl7GthJ0j4VdQ2JiEWkxPp7Stqj4pGqeW+oNbR+l1qx/k05cFnr6PzZ4u2fW6AZpKO5B0jqWlH/QZ6bmZmZtTMH0szMzDat2fnzqHKhpAPYsKT6HxZj8ucl5Vxj+dbOKzewzeIY5wDgc6ybH60wCdiXtZcFVAXSfpE/b5O0a22lpC6SDmlgPGNIAa4rJan0/p6svdCgLSwEds5J9huSb5WdRLrl9bxynaS+wDdzu/e13TA3j3xpxt2kHYdXlOsk9QHOJR3pvXPzj87MzMw+6Hy008zMbNMaAwwDRkk6GpgF9AFOAP5CClh0OBHxmKRbgbOBFyWNBVYBXyUduZsHvNdME1Wm5nc/nX+eWPHMJFIA8zPAUiqSy0fEI5IuBX4GzJI0nnS7ZRdgL9JOwkmk77A5VwFfA74D7CdpAikv1ynAY6QbMVs7xyqPkhLnPyRpCilIND0i/tbCe+eQLj34paTjSRdY7EkKRK4GzoiId9pgfIWzJB1Tp25aRNzfhn0NI+36GyrpYNJ670Ra+y7A4Ih4rQ37MzMzsw7CgTQzM7NNKCLmSWoiBVUOB44DXgKGABPooIG0bDBpLc4BvkfaAXUvMByYA7zSmsbyJQWPASeSjjvWXiIA8CQp0NSZlB9tVZ22Ruag1PlAX1JA7K08rpuBuxoYzzuSjiQF5E4Gvk/KB3cF8AwpkLakfgsNGwF0JQX2mki74EYDzQbSImKWpAOBS4Evk448LsnvXRkRVTeHbowzm6kbDbRZIC0iFuZdg8OBk4ALgWXAP4BrImJCW/VlZmZmHYsinFPVzMzMthz5+N1M4I8RMbC9x7MpSBoM3AgMiojR7T0eMzMzM2uMc6SZmZlZu5DUU9JWNWXbA6Pyj/du/lG1LUm7VZT1Ai4hHWVt6filmZmZmW1BfLTTzMzM2ssFwEBJk4E3gJ5Af+ATwHjgz+03tDZzX75nYBqwGNibdARzO2BYRMxvx7GZmZmZWSv5aKeZmZm1C0n9gYuA/YEepAT3M0k3Lo6ql7/sg0TSeaQbQvuQ8pgtJQXVfhUR49pzbGZmZmbWeg6kmZmZmZmZmZmZNcA50szMzMzMzMzMzBrgQJqZmZmZmZmZmVkDHEgzMzMzMzMzMzNrgANpZmZmZmZmZmZmDXAgzczMzMzMzMzMrAEOpJmZmZmZmZmZmTXg/3vqYY2t9XrtAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -1043,7 +1029,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1192,7 +1178,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -1294,7 +1280,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1367,7 +1353,7 @@ "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, "execution_count": 39, @@ -1376,7 +1362,7 @@ }, { "data": { - "image/png": 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/Lz7BAf6FNALbT4qTkRfUUV93lpD2KB4DDiIFSS0bSXtN1XalRsc4pVHirgX+mtRbehLwaUlv70edLcHhMXT15dN5a0T8lHSoMLmY/IqxJ/jjf3JIwdTdrv8y0hB5tab/PCLaqn52iYi/KWq4LyJOIR3S3EQxxF6xV/OpiNiPNPDyJyXVPdhycSjxVknfA35LOpT7EmlM0+5G+HqYqi/kLg5zRgC/qfHaycBvIuJ/ivfx16Q9uZb4sur+cHi0KEmnSDqtGHNCko4iDdXXObTdQtJJwp0k7U8axarTzcCekj5eXNEYJanzG+uvAL4o6YBivYcqjZ96M2mckTOVxrgYLulISQcrfZ3DGZJ2i4iXSAP8do7tcbLSeBoiHTpsoc4xLCTtTjpf8s9Fu/aPiHdGxI+6OQ/U6RrgLyQdX5yruRj4QefJ5C7uBw4oLtdKafT1k4EH6qmxpcUgGIXZP9v+0GV09i7zlgIn9vR60uhaPyWNebGB9Kn66ar5Y0jjqm4gjQx+Ea8coX1ysXwHaTyOC4rpw4DPAk8Wy95HMXI5aXSvW0iDIK0h7elMJe3u31asq3ME9+OKZT5RtOc5UhD8Y5d2nUWXEcmr5u1Cupyd8/6+h7Sn8hzw30B71bxbgQurnr+bNK7JhqLGLwPblf03UvaPx/NoEZI2kU7ofT0iWuJbySTNAY4mjejeEt8b00ocHmaWxec8zCyLw8PMsjg8zCxLzU5Vg82YMWNi4sSJZZdh9qozf/781RExtta8IREeEydOZN68eWWXYfaqI6nrtxO+zIctZpbF4WFmWRweZpZlSJzzMCvLSy+9xPLly9m0aVPZpTTVyJEjGT9+PMOHD697GYeHWQ+WL1/OqFGjmDhxIqnvXuuJCNasWcPy5cvZd999617Ohy1mPdi0aROjR49u2eAAkMTo0aP7vHfVMnselfZ21nV0lF3GNtoqFTrWru33etrb2+kYhO2rVCqs7Wf7BnPb7rrrrn4Hx/3338+WLVsaVFXjDBs2jMMPPxwgq40tEx7rOjq4YfGKssvYxoyDxjVkPR0dHQzGToyN+ERu5bYBbNmyhWnTpmUvP2zYMKZMmfLy85tuuonubppcsWIF559/Ptdffz1z587lK1/5CjfffHPN1/b33qmWCQ+zVrXjjjuycOHCul47btw4rr/++iZXlPich1mDtLe3I2mbnxNPPLHh21q6dCnHH388RxxxBEcccQR33333y9MnT57cy9KN4T0Pswbp7vCrv4c/L7zwAlOnTgVg33335cYbb2T33Xdnzpw5jBw5kiVLlnD66acPeBcOh4fZIFfrsOWll17iIx/5CAsXLmTYsGH85je1xm5uLoeH2RA0a9Ys9thjDxYtWsTWrVsZOXLkgNfgcx5mQ9D69evZc8892W677bjqqqtKuRTs8DAbgj70oQ9x5ZVXcthhh7F48WJ23nnnAa/Bhy1mDVKpVGqeHN1tt936td6NGzduM+2AAw7ggQf++NUxX/7yl4E09s1DDz0EwPTp05k+fXq/tt0Th4dZg3R3p22rDmTlwxYzy+LwMLMsDg8zy+LwMLMsTQsPSd+StFLSQ1XT2iXNkbSk+LfSrO2bWXM1c8/jv4CTuky7APhpRBxA+gb2C5q4fbOWMGzYMKZOncrkyZN517vexfPPP192SUATwyMi7gC6Xrs6BbiyeHwlcGqztm/WKjr7tjz00EPssMMOzJ49u+ySgIE/57FHRPyuePx7YI8B3r5Z01S66ZJ/wpvf3LBtHH/88Tz22GMAnHrqqbzuda9j0qRJXH755UAaeOiss85i8uTJTJkyhVmzZgHw9a9/nUMOOYRDDz2U0047rSG1lHaTWESEpG6Hj5J0HnAewIQJEwasLrNc3Y1m16jR5DZv3sytt97KSSelswHf+ta3aG9v54UXXuDII49kxowZLF26lKeffvrlu0zXrVsHwCWXXMKTTz7JiBEjXp7WXwO95/GMpD0Bin9XdvfCiLg8IqZFxLSxY2t+VabZq0LneB7Tpk1jwoQJnHPOOUDamzjssMM4+uijWbZsGUuWLGG//fbjiSee4KMf/Si33XYbu+66KwCHHnooZ5xxBldffTXbb9+YfYaBDo8fAu8tHr8X+O8B3r7ZkNN5zmPhwoV84xvfYIcddmDu3Lncfvvt3HPPPSxatIjDDz+cTZs2UalUWLRoEdOnT2f27Nmce+65ANxyyy18+MMfZsGCBRx55JFs3ry533U17bBF0veA6cAYScuBzwOXANdJOgd4Cnh3s7Zv1srWr19PpVJhp512YvHixdx7770ArF69mh122IEZM2Zw4IEHMnPmTLZu3cqyZct405vexHHHHce1115bs7NdXzUtPCLi9G5mNe7skdmr1EknncTs2bM5+OCDOfDAAzn66KMBePrppzn77LPZunUrAF/60pfYsmULM2fOZP369UQE559/Pm1tbf2uwb1qzRqkrVKpeXJ0VHHeIVetvYQRI0Zw66231nz9ggULtpl255139quGWhweZg3S3Zd7uUu+mVkVh4eZZXF4mPViMH4VZqPltNHhYdaDkSNHsmbNmpYOkIhgzZo1ff76Bp8wNevB+PHjWb58OatWrcpex+rVq3n00UcbWFVjVNc1cuRIxo8f36flHR5mPRg+fDj77rtvv9ZxyCGHDMo9l/7W5cMWM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLC1zqba7Ho1la6s05tsluvsS5bJVGtC+Vm5b53pasX0tEx7d9WhsFd19iXIraOW2Qeu2z4ctZpbF4WFmWRweZpbF4WFmWRweZpbF4WFmWRweZpbF4WFmWRweZpbF4WFmWRweZpbF4WFmWRweZpbF4WFmWRweZpalZcbzqLS3s66jo+wyttFWqTRkrJFWbl97ezsdg7BtlUqlIWNxtGr7WiY81nV0cMPiFWWXsY1GjW7Wyu3r6OgYlF+K1KjRv1q1fT5sMbMsDg8zy+LwMLMsDg8zy+LwMLMsDg8zy+LwMLMsDg8zy+LwMLMsDg8zy+LwMLMsDg8zy+LwMLMspfSqlbQU2ABsATZHxLQy6jCzfGV2yX9TRKwucftm1g8+bDGzLGWFRwA/kTRf0nkl1WBm/VDWYctxEfG0pN2BOZIWR8Qd1S8oQuU8gAkTJpRRo5n1oJQ9j4h4uvh3JXAjcFSN11weEdMiYtrYsWMHukQz68WAh4eknSWN6nwMvAV4aKDrMLP+KeOwZQ/gxmLw1e2B70bEbSXUYWb9MODhERFPAIcN9HbNrLF8qdbMsjg8zCyLw8PMsjg8zCyLw8PMsjg8zCyLw8PMsjg8zCyLw8PMsjg8zCyLw8PMsjg8zCyLw8PMsjg8zCxLmaOnN1RbpcKMg8aVXcY22iqVhq2nVdtXqVQoxncZVCoN+t21avtaJjw61q4tu4SmauX2rW3htkHrts+HLWaWxeFhZlkcHmaWxeFhZlkcHmaWxeFhZlkcHmaWxeFhZlkcHmaWxeFhZlkcHmaWxeFhZlkcHmaWxeFhZllapkt+pb2ddR0dZZexjbZKpSHd6Vu5fe3t7XQMwrZVKpWGdKdv1fa1THis6+jghsUryi5jG40awKeV29fR0UFENKCaxmrUAD6t2j4ftphZFoeHmWVxeJhZFoeHmWWp64SppBHADGBi9TIRcXFzyjKzwa7eqy3/DawH5gMvNq8cMxsq6g2P8RFxUlMrMbMhpd5zHndLmtLUSsxsSOlxz0PSg0AUrztb0hOkwxYBERGHNr9EMxuMejtsOXlAqjCzIafH8IiIpwAkXRURZ1bPk3QVcGbNBc2s5dV7zmNS9RNJw4DXNb4cMxsqegwPSZ+RtAE4VNKzkjYUz1eSLt+a2atUj+EREV+KiFHAv0TErhExqvgZHRGf6c+GJQ2TdL+km/uzHjMrR733eVwo6Z3AcaSrL7+IiJv6ue2PAY8Cu/ZzPWZWgnrPeVwGfBB4EHgI+KCky3I3Kmk88Hbgitx1mFm56t3zOAE4OIoRTSRdCTzcj+1+Ffg0MKof6zCzEtW75/EYMKHq+d7FtD6TdDKwMiLm9/K68yTNkzRv1apVOZsysyaqNzxGAY9KmivpZ8AjwK6Sfijph33c5rHAOyQtBa4FTpB0ddcXRcTlETEtIqaNHTu2j5sws2ar97Dlc43aYHGV5jMAkqYDfxsRMxu1fjMbGHWFR0T8XNI+wAERcbukHYHtI2JDc8szs8GqrsMWSe8Hrgf+vZg0HujvpVoiYm5EuP+M2RBU7zmPD5POVTwLEBFLgN2bVZSZDX71hseLEfGHzieStifdLGZmr1L1hsfPJV0I7Cjpz4DvAz9qXllmNtjVGx4XAKtId5h+APgx8NlmFWVmg1+9V1u2SroJuCkifMeWmfXaJV+SLpK0Gvg18GtJqyQ17L4PMxuaejts+QTpKsuREdEeEe3A64FjJX2i6dWZ2aDVW3icCZweEU92ToiIJ4CZwF83szAzG9x6C4/hEbG668TivMfw5pRkZkNBb+Hxh8x5ZtbiervacpikZ2tMFzCyCfWY2RDR21cvDBuoQsxsaKn3JjEzs1dweJhZlnoHAxr02ioVZhw0ruwyttFWqTRsPa3avkqlgqQGVNNYlQb97lq1fS0THh1r15ZdQlO1cvvWtnDboHXb58MWM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLA4PM8vi8DCzLC3TJb/S3s66jo6yy9hGW6XSkO707e3tdAzC9lUqlX53OW/ltkHrtq9lwmNdRwc3LF5RdhnbaNQAPh0dHUREQ9bVSI0Y5KaV2wat2z4ftphZFoeHmWVxeJhZFoeHmWVxeJhZFoeHmWVxeJhZFoeHmWVxeJhZFoeHmWVxeJhZFoeHmWVxeJhZlgEPD0kjJf1K0iJJD0v6wkDXYGb9V0aX/BeBEyJio6ThwJ2Sbo2Ie0uoxcwyDXh4RBrYYGPxdHjxM/gGOzCzHpVyzkPSMEkLgZXAnIj4ZRl1mFm+UsIjIrZExFRgPHCUpMldXyPpPEnzJM1btWrVwBdpZj0q9WpLRKwDfgacVGPe5RExLSKmjR07duCLM7MelXG1ZayktuLxjsCfAYsHug4z658yrrbsCVwpaRgpvK6LiJtLqMPM+qGMqy0PAIcP9HbNrLF8h6mZZXF4mFkWh4eZZXF4mFkWh4eZZXF4mFkWh4eZZXF4mFkWh4eZZXF4mFkWh4eZZXF4mFkWh4eZZXF4mFmWMsbzaIq2SoUZB40ru4xttFUqDVlPpVJBUkPW1UiVBrSvldvWuZ5WbF/LhEfH2rVll9BUa1u4fa3cNmjd9vmwxcyyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yytMx4HpX2dtZ1dJRdxjbaKpWGjDXS3t5OxyBsX6VS6fd4Fa3cNmjd9rVMeKzr6OCGxSvKLmMbjRrdrKOjg4hoyLoaqREjZLVy26B12+fDFjPL4vAwsywODzPL4vAwsywODzPL4vAwsywODzPL4vAwsywODzPL4vAwsywODzPL4vAwsywODzPLMuDhIWlvST+T9IikhyV9bKBrMLP+K6NL/mbgUxGxQNIoYL6kORHxSAm1mFmmAd/ziIjfRcSC4vEG4FFgr4Guw8z6p9RzHpImAocDvyyzDjPru9LCQ9IuwA3AxyPi2Rrzz5M0T9K8VatWDXyBZtajUsJD0nBScFwTET+o9ZqIuDwipkXEtLFjxw5sgWbWqzKutgj4T+DRiLh0oLdvZo1Rxp7HscCZwAmSFhY/byuhDjPrhwG/VBsRdwKNGZbazErjO0zNLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yyODzMLIvDw8yylDF6elO0VSrMOGhc2WVso61Sach6KpUKaRylwaXSgPa1cts619OK7WuZ8OhYu7bsEppqbQu3r5XbBq3bPh+2mFkWh4eZZXF4mFkWh4eZZXF4mFkWh4eZZVFElF1DryStAp4awE2OAVYP4PYGWiu3r5XbBgPfvn0iouZXNnbkjSwAAAORSURBVA6J8BhokuZFxLSy62iWVm5fK7cNBlf7fNhiZlkcHmaWxeFR2+VlF9Bkrdy+Vm4bDKL2+ZyHmWXxnoeZZXF4FCTtLelnkh6R9LCkj5VdUyNJGinpV5IWFe37Qtk1NYOkYZLul3Rz2bU0mqSlkh6UtFDSvLLraZku+Q2wGfhURCyQNAqYL2lORDxSdmEN8iJwQkRslDQcuFPSrRFxb9mFNdjHgEeBXcsupEneFBGD4j4W73kUIuJ3EbGgeLyB9Ae4V7lVNU4kG4unw4ufljrhJWk88HbgirJreTVweNQgaSJwOPDLcitprGKXfiGwEpgTES3VPuCrwKeBrWUX0iQB/ETSfEnnlV2Mw6MLSbsANwAfj4hny66nkSJiS0RMBcYDR0maXHZNjSLpZGBlRMwvu5YmOi4ijgDeCnxY0p+WWYzDo0pxLuAG4JqI+EHZ9TRLRKwDfgacVHYtDXQs8A5JS4FrgRMkXV1uSY0VEU8X/64EbgSOKrMeh0dBaYTa/wQejYhLy66n0SSNldRWPN4R+DNgcblVNU5EfCYixkfEROA04H8jYmbJZTWMpJ2LE/lI2hl4C/BQmTX5assfHQucCTxYnBcAuDAiflxiTY20J3ClpGGkD43rIqLlLme2sD2AG4tR2LcHvhsRt5VZkO8wNbMsPmwxsywODzPL4vAwsywODzPL4vAwsywOD6uLpFmSPl71/H8kXVH1/F8lXSjp+m6WnytpWvH4wqrpEyWVer+C5XF4WL3uAo4BkLQdaRTvSVXzjyHdmPWXdazrwt5fYoOdw8PqdTfwhuLxJNLdjRskVSSNAA4G1nbuRUjaUdK1kh6VdCOwYzH9EmDHYkyKa4r1DZP0H8U4Iz8p7oC1Qc7hYXWJiBXAZkkTSHsZ95B6Hb8BmAY8CPyhapG/AZ6PiIOBzwOvK9ZzAfBCREyNiDOK1x4AXBYRk4B1wIwBaJL1k8PD+uJuUnB0hsc9Vc/v6vLaPwWuBoiIB4AHeljvkxHR2SVgPjCxcSVbszg8rC86z3tMIR223Eva8ziGFCy5Xqx6vAX3uRoSHB7WF3cDJwNri7FB1gJtpADpGh53AO8BKMYNObRq3kvF8Ac2hDk8rC8eJF1lubfLtPU1xtX8v8Aukh4FLiYdjnS6HHig6oSpDUHuVWtmWbznYWZZHB5mlsXhYWZZHB5mlsXhYWZZHB5mlsXhYWZZHB5mluX/A5YB2rLSH+hKAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] @@ -1394,17 +1380,17 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 63, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, @@ -1448,7 +1434,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1484,7 +1470,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1690,8 +1676,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.011\n", - "The estimated product of the one and two qubit fidelity is F = 0.989\n" + "The estimated error is p = 0.0109\n", + "The estimated product of the one and two qubit fidelity is F = 0.9891\n" ] } ], @@ -1718,7 +1704,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -1754,7 +1740,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1817,7 +1803,7 @@ { "data": { "text/plain": [ - "array([0.05881116, 0.00176676])" + "array([0.05919906, 0.00115833])" ] }, "execution_count": 57, @@ -1838,11 +1824,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.88270907 0.83079593 0.78193585 0.73594929]\n", - " [0.88114954 0.82932811 0.78055436 0.73464905]\n", - " [0.87959276 0.82786289 0.77917531 0.7333511 ]\n", - " [0.87803874 0.82640026 0.7777987 0.73205545]\n", - " [0.87030969 0.81912576 0.77095203 0.72561144]]\n" + "[[0.8830571 0.83078094 0.78159949 0.73532953]\n", + " [0.88203423 0.82981863 0.78069414 0.73447778]\n", + " [0.88101254 0.82885742 0.77978984 0.73362701]\n", + " [0.87999204 0.82789733 0.77888658 0.73277723]\n", + " [0.87490722 0.82311354 0.77438598 0.72854306]]\n" ] } ], @@ -1859,7 +1845,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] diff --git a/forest/benchmarking/tests/test_volumetrics.py b/forest/benchmarking/tests/test_volumetrics.py new file mode 100644 index 00000000..2c7fbee0 --- /dev/null +++ b/forest/benchmarking/tests/test_volumetrics.py @@ -0,0 +1,31 @@ +import numpy as np +from pyquil.numpy_simulator import NumpyWavefunctionSimulator + +from forest.benchmarking.volumetrics import * + +np.random.seed(1) + + +def test_ideal_sim_heavy_probs(qvm): + qvm.qam.random_seed = 1 + + qv_ckt_template = get_quantum_volume_template() + depths = [2, 3] + dimensions = {d: [d] for d in depths} + + num_ckt_samples = 40 + qv_progs = generate_volumetric_program_array(qvm, qv_ckt_template, dimensions, + num_circuit_samples=num_ckt_samples) + wfn_sim = NumpyWavefunctionSimulator(len(qvm.qubits())) + heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs) + + num_shots = 50 + results = acquire_volumetric_data(qvm, qv_progs, num_shots=num_shots) + + probs_by_width_depth = get_success_probabilities(results, heavy_outputs) + num_successes = [sum(probs_by_width_depth[depth][depth]) * num_shots for depth in depths] + + qv_success_probs = [calculate_success_prob_est_and_err(n_success, num_ckt_samples, num_shots)[0] + for n_success in num_successes] + target_probs = [0.788765, 0.852895] + np.testing.assert_allclose(qv_success_probs, target_probs, atol=.05) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index f6a50b2b..0c682668 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -568,26 +568,23 @@ def func(graph, qc, sequence, **kwargs): # ================================================================================================== # Data acquisition # ================================================================================================== -def sample_random_connected_graphs(graph: nx.Graph, widths: List[int], num_ckts_per_width): +def sample_random_connected_graphs(graph: nx.Graph, width: int, num_ckts: int): """ - Helper to uniformly randomly sample `num_ckts_per_width` many connected induced subgraphs of - `graph` for each width in `widths` + Helper to uniformly randomly sample `num_ckts` many connected induced subgraphs of + `graph` of `width` many qubits. :param graph: - :param widths: - :param num_ckts_per_width: + :param width: + :param num_ckts: :return: """ - samples = {w: [] for w in widths} - for w in widths: - connected_subgraphs = generate_connected_subgraphs(graph, w) - random_indices = np.random.choice(range(len(connected_subgraphs)), size=num_ckts_per_width) - samples[w] = [connected_subgraphs[idx] for idx in random_indices] - return samples + connected_subgraphs = generate_connected_subgraphs(graph, width) + random_indices = np.random.choice(range(len(connected_subgraphs)), size=num_ckts) + return [connected_subgraphs[idx] for idx in random_indices] -def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, widths: List[int], - depths: List[int], num_circuit_samples: int, +def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, + dimensions: Dict[int, List[int]], num_circuit_samples: int, graphs: Dict[int, List[nx.Graph]] = None): """ Creates a dictionary containing random circuits sampled from the input `ckt` family for each @@ -595,17 +592,17 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, :param qc: :param ckt: - :param widths: - :param depths: + :param dimensions :param num_circuit_samples: :param graphs: :return: """ if graphs is None: - graphs = sample_random_connected_graphs(qc.qubit_topology(), widths, - len(depths)*num_circuit_samples) + graphs = {w: sample_random_connected_graphs(qc.qubit_topology(), w, + len(depths)*num_circuit_samples) + for w, depths in dimensions.items()} - programs = {width: {depth: [] for depth in depths} for width in widths} + programs = {width: {depth: [] for depth in depths} for width, depths in dimensions.items()} for width, depth_array in programs.items(): circuit_number = 0 @@ -698,7 +695,7 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, # Note that probabilities are ordered lexicographically with qubit 0 leftmost. # we need to restrict attention to the subset `qubits` probs = abs(wfn_sim.wf)**2 - probs = probs.reshape([2]*wfn_sim.n_qubits) + probs = probs.reshape([2] * wfn_sim.n_qubits) marginal = probs for q in reversed(range(wfn_sim.n_qubits)): if q in qubits: @@ -805,18 +802,19 @@ def get_success_probabilities(noisy_results, ideal_results): prob_success = {width: {depth: [] for depth in depth_array.keys()} for width, depth_array in noisy_results.items()} + assert set(noisy_results.keys()) == set(ideal_results.keys()) + for width, depth_array in prob_success.items(): - for depth, samples in depth_array.items(): + for depth in depth_array.keys(): noisy_ckt_sample_results = noisy_results[width][depth] ideal_ckt_sample_results = ideal_results[width][depth] # iterate over circuits - for noisy_shots, ideal_results in zip(noisy_ckt_sample_results, + for noisy_shots, targets in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): - targets = ideal_results - if not isinstance(ideal_results[0], int): - targets = [bit_array_to_int(res) for res in ideal_results] + if not isinstance(targets[0], int): + targets = [bit_array_to_int(res) for res in targets] pr_success = 0 # determine if each result bitstring is a success, i.e. matches an ideal_result From 7550c22b906671bf0f835e31791324cfd96cdcc2 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Tue, 12 Nov 2019 14:46:32 -0500 Subject: [PATCH 41/49] Remove QAOA helpers. --- forest/benchmarking/volumetrics.py | 51 ------------------------------ 1 file changed, 51 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 0c682668..8e93665f 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -327,24 +327,6 @@ def random_su4_pairs(graph: nx.Graph, idx_label: int) -> Program: return prog -def maxcut_cost_unitary(graph: nx.Graph, idx_label: int) -> Program: - """ - Creates a parameterized program used in QAOA that enacts commuting parameterized 2 qubit - gates on every edge of the graph. - - :param graph: - :param idx_label: a label that uniquely identifies the set of gate definitions used in the - output program. This prevents subsequent calls to this method from producing a program - with definitions that overwrite definitions in previously generated programs. - :return: - """ - prog = Program() - theta = prog.declare('theta_' + str(idx_label), memory_type='REAL') - for edge in graph.edges: - exponential_map(sZ(edge[0]) * sZ(edge[1]))(theta) - return prog - - ### # Sequence Transforms ### @@ -532,39 +514,6 @@ def get_quantum_volume_template(): return template -def get_param_local_RX_template(): - # remember that RX(theta) = e^(i theta X/2) - def func(graph, sequence, **kwargs): - prog = Program() - theta = prog.declare('theta_' + str(len(sequence)), memory_type='REAL') - for node in graph.nodes: - prog += H(node) - prog += RZ(theta, node) - prog += H(node) - return prog - return CircuitTemplate([func]) - - -def get_param_maxcut_graph_cost_template(graph_family: Callable[[int], nx.Graph] = None): - if graph_family is None: - def default_func(graph, qc, sequence, **kwargs): - prog = maxcut_cost_unitary(graph, len(sequence)) - native_quil = qc.compiler.quil_to_native_quil(prog) - # remove gate definition and HALT - return Program([instr for instr in native_quil.instructions][:-1]) - return CircuitTemplate([default_func]) - else: - def func(graph, qc, sequence, **kwargs): - maxcut_graph = graph_family(len(graph.nodes)) - if len(maxcut_graph.nodes) > len(graph.nodes): - raise ValueError("The maxcut graph must have fewer nodes than the number of " - "qubits.") - prog = maxcut_cost_unitary(maxcut_graph, len(sequence)) - native_quil = graph_restricted_compilation(qc, graph, prog) - # remove gate definitions and HALT - return Program([instr for instr in native_quil.instructions][:-1]) - return CircuitTemplate([func]) - # ================================================================================================== # Data acquisition # ================================================================================================== From c062b756925f069de7c2651f5bc68c6fa5ce9e27 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Tue, 12 Nov 2019 15:10:08 -0500 Subject: [PATCH 42/49] Docstring and whitespace adjustments. --- forest/benchmarking/volumetrics.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index 8e93665f..dd64b328 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -1,4 +1,4 @@ -from typing import Tuple, Sequence, Callable, Dict, List, Union +from typing import Tuple, Sequence, Callable, Dict, List, Union, Optional from copy import copy import networkx as nx import numpy as np @@ -15,7 +15,6 @@ from pyquil.quil import Program, address_qubits, merge_programs from pyquil.api import QuantumComputer, BenchmarkConnection from pyquil.gates import * -from pyquil.paulis import exponential_map, sX, sZ from pyquil.numpy_simulator import NumpyWavefunctionSimulator from rpcq.messages import TargetDevice from rpcq._utils import RPCErrorError @@ -435,6 +434,8 @@ def compile_merged_sequence(qc: QuantumComputer, sequence: List[Program], graph: native_quil = graph_restricted_compilation(qc, graph, merged) # remove gate definitions and terminous HALT return [Program([instr for instr in native_quil.instructions][:-1])] + + ### # Templates ### @@ -534,7 +535,8 @@ def sample_random_connected_graphs(graph: nx.Graph, width: int, num_ckts: int): def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, dimensions: Dict[int, List[int]], num_circuit_samples: int, - graphs: Dict[int, List[nx.Graph]] = None): + graphs: Dict[int, List[nx.Graph]] = None) \ + -> Dict[int, Dict[int, List[Program]]]: """ Creates a dictionary containing random circuits sampled from the input `ckt` family for each width and depth. @@ -565,10 +567,10 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, return programs -def acquire_volumetric_data(qc: QuantumComputer, program_array:Dict[int, Dict[int, List[Program]]], - num_shots: int = 500, +def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[int, List[Program]]], + num_shots: int = 500, measure_qubits: Dict[int, Dict[int, List[int]]] = None, - use_active_reset: bool = False, use_compiler: bool = False)\ + use_active_reset: bool = False, use_compiler: bool = False) \ -> Dict[int, Dict[int, List[np.ndarray]]]: """ Runs each program in `program_array` on the qc and stores the results, organized again by @@ -615,8 +617,10 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array:Dict[int, Dict[in return results -def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, - measure_qubits: Dict[int, Dict[int, List[int]]] = None): +def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, + program_array: Dict[int, Dict[int, List[Program]]], + measure_qubits: Optional[Dict[int, Dict[int, List[int]]]] = None) \ + -> Dict[int, Dict[int, List[List[int]]]]: """ Collects and returns those 'heavy' bitstrings which are output with greater than median probability among all possible bitstrings on the given qubits. @@ -625,6 +629,10 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array, from the output of the circuit comprised of the given permutations and gates. :param wfn_sim: a NumpyWavefunctionSimulator that can simulate the provided program + :param program_array: a collection of PyQuil Programs sampled from the circuit family for + each (width, depth) pair. + :param measure_qubits: optional list of qubits to measure for each Program in + `program_array`. By default all qubits in the Program are measured :return: a list of the heavy outputs of the circuit, represented as ints """ heavy_output_array = {w: {d: [] for d in d_arr.keys()} for w, d_arr in program_array.items()} From 4cadd0ea85769db3513345d3a93510b42d2bbfa4 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 15:54:21 -0500 Subject: [PATCH 43/49] Cosmetic formatting changes. --- forest/benchmarking/volumetrics.py | 65 +++++++++++++++++------------- 1 file changed, 38 insertions(+), 27 deletions(-) diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py index dd64b328..7a176ca6 100644 --- a/forest/benchmarking/volumetrics.py +++ b/forest/benchmarking/volumetrics.py @@ -70,8 +70,8 @@ class CircuitTemplate: arXiv:1811.12926v1 (2018). https://arxiv.org/abs/1811.12926 """ - generators: List[Callable] = field(default_factory=lambda : []) - sequence_transforms: List[Callable] = field(default_factory=lambda : []) + generators: List[Callable] = field(default_factory=lambda: []) + sequence_transforms: List[Callable] = field(default_factory=lambda: []) def append(self, other): """ @@ -212,7 +212,7 @@ def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program return program -def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: +def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: """ Create a program to randomly place two qubit gates on edges of the specified graph. @@ -382,7 +382,7 @@ def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **k """ paulis = [I, X, Y, Z] random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] - new_sequence = [None for _ in range(2*len(sequence) + 1)] + new_sequence = [None for _ in range(2 * len(sequence) + 1)] new_sequence[::2] = random_paulis new_sequence[1::2] = sequence return new_sequence @@ -447,8 +447,10 @@ def get_rand_1q_template(gates: Sequence[Gate]): :param gates: :return: """ + def func(graph, **kwargs): return random_single_qubit_gates(graph, gates=gates) + return CircuitTemplate([func]) @@ -460,8 +462,10 @@ def get_rand_2q_template(gates: Sequence[Gate]): :param gates: :return: """ + def func(graph, **kwargs): return random_two_qubit_gates(graph, gates=gates) + return CircuitTemplate([func]) @@ -470,8 +474,10 @@ def get_rand_1q_cliff_template(bm: BenchmarkConnection): Creates a CircuitTemplate representing the family of circuits generated by repeated layers of random single qubit Clifford gates. """ + def func(graph, **kwargs): return random_single_qubit_cliffords(bm, graph) + return CircuitTemplate([func]) @@ -480,8 +486,10 @@ def get_rand_2q_cliff_template(bm: BenchmarkConnection): Creates a CircuitTemplate representing the family of circuits generated by repeated layers of random two qubit Clifford gates. """ + def func(graph, **kwargs): return random_two_qubit_cliffords(bm, graph) + return CircuitTemplate([func]) @@ -490,8 +498,10 @@ def get_dagger_previous(n: int = 1): Creates a CircuitTemplate that can be appended to another template to generate families of circuits with repeated (layer, inverse-layer) units. """ + def func(sequence, **kwargs): return dagger_previous(sequence, n) + return CircuitTemplate([func]) @@ -501,8 +511,10 @@ def get_rand_su4_template(): Haar-random two qubit gates acting on random pairs of qubits. This is the generator used in quantum volume [QVol]_ . """ + def func(graph, sequence, **kwargs): return random_su4_pairs(graph, len(sequence)) + return CircuitTemplate([func]) @@ -550,7 +562,7 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, """ if graphs is None: graphs = {w: sample_random_connected_graphs(qc.qubit_topology(), w, - len(depths)*num_circuit_samples) + len(depths) * num_circuit_samples) for w, depths in dimensions.items()} programs = {width: {depth: [] for depth in depths} for width, depths in dimensions.items()} @@ -569,8 +581,8 @@ def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[int, List[Program]]], num_shots: int = 500, - measure_qubits: Dict[int, Dict[int, List[int]]] = None, - use_active_reset: bool = False, use_compiler: bool = False) \ + measure_qubits: Dict[int, Dict[int, List[int]]] = None, + use_active_reset: bool = False, use_compiler: bool = False) \ -> Dict[int, Dict[int, List[np.ndarray]]]: """ Runs each program in `program_array` on the qc and stores the results, organized again by @@ -602,8 +614,8 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[i qubits = sorted(list(program.get_qubits())) ro = prog.declare('ro', 'BIT', len(qubits)) - for idx, q in enumerate(qubits): - prog += MEASURE(q, ro[idx]) + for ro_idx, q in enumerate(qubits): + prog += MEASURE(q, ro[ro_idx]) prog.wrap_in_numshots_loop(num_shots) @@ -619,7 +631,7 @@ def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[i def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, program_array: Dict[int, Dict[int, List[Program]]], - measure_qubits: Optional[Dict[int, Dict[int, List[int]]]] = None) \ + measure_qubits: Optional[Dict[int, Dict[int, List[int]]]] = None) \ -> Dict[int, Dict[int, List[List[int]]]]: """ Collects and returns those 'heavy' bitstrings which are output with greater than median @@ -651,7 +663,7 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, # Note that probabilities are ordered lexicographically with qubit 0 leftmost. # we need to restrict attention to the subset `qubits` - probs = abs(wfn_sim.wf)**2 + probs = abs(wfn_sim.wf) ** 2 probs = probs.reshape([2] * wfn_sim.n_qubits) marginal = probs for q in reversed(range(wfn_sim.n_qubits)): @@ -664,7 +676,8 @@ def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, median_prob = median(probabilities) # store the integer indices, which implicitly represent the bitstring outcome. - heavy_outputs = [idx for idx, prob in enumerate(probabilities) if prob > median_prob] + heavy_outputs = [idx for idx, prob in enumerate(probabilities) if + prob > median_prob] heavy_output_array[w][d].append(heavy_outputs) return heavy_output_array @@ -709,13 +722,13 @@ def get_error_hamming_weight_distributions(noisy_results: Dict[int, Dict[int, Li noisy_shots] # Hamming weight distribution - hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) + hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) samples.append(np.asarray(hamm_wt_distr)) return distrs def get_single_target_success_probabilities(noisy_results, ideal_results, - allowed_errors: Union[int, Callable[[int], int]] = 0): + allowed_errors: Union[int, Callable[[int], int]] = 0): """ For circuit results of various width and depth, calculate the fraction of noisy results that match the single ideal result for each circuit. @@ -731,14 +744,15 @@ def get_single_target_success_probabilities(noisy_results, ideal_results, :return: """ if isinstance(allowed_errors, int): - error_func = lambda num_bits: allowed_errors + def error_func(num_bits): + return allowed_errors else: error_func = allowed_errors hamming_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results) - return {w: {d: [sum(distr[0:error_func(w)+1]) for distr in distrs] - for d, distrs in d_distrs.items()} + return {w: {d: [sum(distr[0:error_func(w) + 1]) for distr in distrs] + for d, distrs in d_distrs.items()} for w, d_distrs in hamming_distrs.items()} @@ -768,8 +782,7 @@ def get_success_probabilities(noisy_results, ideal_results): ideal_ckt_sample_results = ideal_results[width][depth] # iterate over circuits - for noisy_shots, targets in zip(noisy_ckt_sample_results, - ideal_ckt_sample_results): + for noisy_shots, targets in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): if not isinstance(targets[0], int): targets = [bit_array_to_int(res) for res in targets] @@ -779,7 +792,7 @@ def get_success_probabilities(noisy_results, ideal_results): # convert result to int for comparison with heavy outputs. output = bit_array_to_int(result) if output in targets: - pr_success += 1 / len(noisy_shots) + pr_success += 1 / len(noisy_shots) prob_success[width][depth].append(pr_success) return prob_success @@ -804,8 +817,8 @@ def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_ # Eq. (C3) of [QVol]. Assume that num_heavy/num_shots is worst-case binomial with param # num_circuits and take gaussian approximation. Get 2 sigma one-sided confidence interval. - one_sided_confidence_interval = prob_sample_heavy - \ - 2 * np.sqrt(num_success * (num_shots - num_success / num_circuits)) / total_sampled_outputs + sigma = np.sqrt(num_success * (num_shots - num_success / num_circuits)) / total_sampled_outputs + one_sided_confidence_interval = prob_sample_heavy - 2 * sigma return prob_sample_heavy, one_sided_confidence_interval @@ -820,15 +833,13 @@ def determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt): :return: """ return {w: {d: calculate_success_prob_est_and_err( - sum(np.asarray(succ_probs) * num_shots_per_ckt), - len(succ_probs), - num_shots_per_ckt)[1] + sum(np.asarray(succ_probs) * num_shots_per_ckt), len(succ_probs), num_shots_per_ckt)[1] for d, succ_probs in d_ckt_succ_probs.items()} for w, d_ckt_succ_probs in ckt_success_probs.items()} def determine_successes(ckt_success_probs: Dict[int, Dict[int, List[float]]], num_shots_per_ckt, - success_threshold: float = 2 / 3): + success_threshold: float = 2 / 3): """ Indicate whether the collection of circuit success probabilities for given width and depth recorded in `ckt_success_probs` is considered a success with respect to the specified @@ -1100,7 +1111,7 @@ def plot_pareto_frontier(successes, title, widths=None, depths=None): continue # this width was not measured, so leave the boundary open # horizontal line for this width - if failure_idx < len(depths): # measured a failure + if failure_idx < len(depths): # measured a failure ax.plot((w_idx - margin, w_idx + margin), (failure_idx - margin, failure_idx - margin), color='black') From c695d73cafe69f731bfbd039fa833e37c597a753 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 16:38:00 -0500 Subject: [PATCH 44/49] Restructure into multiple files under volumetrics folder. --- forest/benchmarking/volumetrics.py | 1171 ----------------- forest/benchmarking/volumetrics/__init__.py | 0 .../benchmarking/volumetrics/_generators.py | 143 ++ forest/benchmarking/volumetrics/_main.py | 403 ++++++ forest/benchmarking/volumetrics/_templates.py | 95 ++ .../benchmarking/volumetrics/_transforms.py | 163 +++ forest/benchmarking/volumetrics/plotting.py | 218 +++ .../volumetrics/quantum_volume.py | 163 +++ 8 files changed, 1185 insertions(+), 1171 deletions(-) delete mode 100644 forest/benchmarking/volumetrics.py create mode 100644 forest/benchmarking/volumetrics/__init__.py create mode 100644 forest/benchmarking/volumetrics/_generators.py create mode 100644 forest/benchmarking/volumetrics/_main.py create mode 100644 forest/benchmarking/volumetrics/_templates.py create mode 100644 forest/benchmarking/volumetrics/_transforms.py create mode 100644 forest/benchmarking/volumetrics/plotting.py create mode 100644 forest/benchmarking/volumetrics/quantum_volume.py diff --git a/forest/benchmarking/volumetrics.py b/forest/benchmarking/volumetrics.py deleted file mode 100644 index 7a176ca6..00000000 --- a/forest/benchmarking/volumetrics.py +++ /dev/null @@ -1,1171 +0,0 @@ -from typing import Tuple, Sequence, Callable, Dict, List, Union, Optional -from copy import copy -import networkx as nx -import numpy as np -import random -import itertools -from scipy.spatial.distance import hamming -from scipy.special import comb -from dataclasses import dataclass, field -import matplotlib.pyplot as plt -from statistics import median - -from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate -from pyquil.quilatom import QubitPlaceholder -from pyquil.quil import Program, address_qubits, merge_programs -from pyquil.api import QuantumComputer, BenchmarkConnection -from pyquil.gates import * -from pyquil.numpy_simulator import NumpyWavefunctionSimulator -from rpcq.messages import TargetDevice -from rpcq._utils import RPCErrorError - -from forest.benchmarking.randomized_benchmarking import get_rb_gateset -from forest.benchmarking.distance_measures import total_variation_distance as tvd -from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary -from forest.benchmarking.utils import bit_array_to_int -from forest.benchmarking.compilation import basic_compile - - -@dataclass -class CircuitTemplate: - """ - This dataclass enables us to specify various families of circuits and sample from a specified - family random circuits of various width and depth acting on different groups of qubits. - - 'Width' is simply the number of qubits measured at then end of the circuit. 'Depth' is not - simply circuit depth, but rather the number of repeated structured groups of gates, - each of which constitutes some distinct unit. A depth d circuit could consist of d - consecutive rounds of random single qubit, then two qubit gates. It could also mean d - consecutive random Cliffords followed by the d conjugated Cliffords that invert the first d - gates. - - Because these families of circuits are quite diverse, specifying the family and drawing - samples can potentially require a wide variety of parameters. The compiler may be required to - map an abstract circuit into native quil; a sample acting on a specific qubit topology - may be desired; the sequence of 'layers' generated so far may be necessary to compute an - inverse. - - We represent each sampled circuit as a list of PyQuil Programs, which we call a 'sequence' - since each element of the list holds a distinctly structured group of gates that, - when applied altogether in series, constitute the circuit. This core functionality is found in - :func:`sample_sequence`. In this function `generators` are applied in series in a loop - `repetitions` number of times. Each call to a generator will contribute an element to the - output sequence (some combination of which will constitute a unit of depth). After a - sequence is generated from the output of the various `generators`, each `sequence_transform` - is then applied in series on the generated sequence to create a final output sequence. The - sequence transforms account for any features of the circuit that do increase with depth, - cannot neatly be fit into repeated units, or otherwise require performing a global - transformation on the sequence. See :func:`sample_sequence` for more information. - - This functionality is intended to enable creation and use of any of a wide variety of - 'volumetric benchmarks' described in the sources below. - - .. [Vol] A volumetric framework for quantum computer benchmarks. - Blume-Kohout and Young. - arXiv:1904.05546v2 (2019) - https://arxiv.org/pdf/1904.05546.pdf - - .. [QVol] Validating quantum computers using randomized model circuits. - Cross et al. - arXiv:1811.12926v1 (2018). - https://arxiv.org/abs/1811.12926 - """ - generators: List[Callable] = field(default_factory=lambda: []) - sequence_transforms: List[Callable] = field(default_factory=lambda: []) - - def append(self, other): - """ - Mutates the CircuitTemplate object by appending new generators. - TODO: The behavior of sequence_transforms may not conform with expectations. - """ - if isinstance(other, list): - self.generators += other - elif isinstance(other, CircuitTemplate): - self.generators += other.generators - self.sequence_transforms += other.sequence_transforms - else: - raise ValueError(f'Cannot append type {type(other)}.') - - def __add__(self, other): - """ - Concatenate two circuits together, returning a new one. - """ - ckt = CircuitTemplate() - ckt.append(self) - ckt.append(other) - return ckt - - def __iadd__(self, other): - """ - Concatenate two circuits together using +=, returning a new one. - """ - self.append(other) - return self - - def sample_sequence(self, graph: nx.Graph, repetitions: int, qc: QuantumComputer = None, - width: int = None, sequence: List[Program] = None): - """ - The sequence_transforms are distinct from generators in that they take in a sequence and - output a new sequence. These are applied in series after the entire sequence has been - generated. A family of interest that motivates this distinction is - - C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t - - where C_j is a clifford, P_j is a random local Pauli. We can specify this family by a - generator of random Cliffords, a conjugation sequence transform, and a Pauli frame - randomization transform. - - :param graph: the qubit topology on which the circuit should act. Unless width is - specified, the number of qubits in the graph should be considered circuit width. - :param repetitions: the number of times the loop of generators should be applied. - :param qc: a quantum computer, likely the one on which the circuit will be run, providing - access to the full chip topology and associated compiler. - :param width: the number of qubits that will be measured at the end of the circuit. If - the supplied graph contains more qubits, an induced subgraph of width-many qubits - will be selected uniformly at random from the graph. - :param sequence: an optional initialization of a sequence to build off of/append to. - :return: the list of programs whose sum constitutes a circuit sample from the family of - circuits specified by the generators and sequence_transforms. - """ - if width is not None: - graph = random.choice(generate_connected_subgraphs(graph, width)) - - if sequence is None: - sequence = [] - - # run through the generators 'repetitions' many times; append each generated program to - # the sequence. - for _ in range(repetitions): - for generator in self.generators: - sequence.append(generator(graph=graph, qc=qc, width=width, sequence=sequence)) - - for sequence_transform in self.sequence_transforms: - sequence = sequence_transform(graph=graph, qc=qc, width=width, sequence=sequence) - - return sequence - - def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None): - return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) - - -def graph_restricted_compilation(qc: QuantumComputer, graph: nx.Graph, - program: Program) -> Program: - """ - A useful helper that temporarily modifies the supplied qc's qubit topology to match the - supplied graph so that the given program may be compiled onto the graph topology. - - :param qc: a qc object with a compiler where the given graph is a subraph of the qc's qubit - topology. - :param graph: The desired subraph of the qc's full topology on which we wish to run a program. - :param program: a program we wish to run on a particular graph on the qc. - :return: the program compiled into native quil gates respecting the graph topology. - """ - qubits = list(graph.nodes) - - # restrict compilation to chosen qubits - isa_dict = qc.device.get_isa().to_dict() - single_qs = isa_dict['1Q'] - two_qs = isa_dict['2Q'] - - new_1q = {} - for key, val in single_qs.items(): - if int(key) in qubits: - new_1q[key] = val - new_2q = {} - for key, val in two_qs.items(): - q1, q2 = key.split('-') - if (int(q1), int(q2)) in graph.edges: - new_2q[key] = val - - new_isa = {'1Q': new_1q, '2Q': new_2q} - - new_compiler = copy(qc.compiler) - new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) - # try to compile with the restricted qubit topology - try: - native_quil = new_compiler.quil_to_native_quil(program) - except RPCErrorError as e: - if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ - in str(e): - raise ValueError("The program could not be compiled onto the given subgraph.") - raise - - return native_quil - - -# ================================================================================================== -# Generators -# ================================================================================================== -def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: - """ - Create a program comprised of random single qubit gates acting on the qubits of the - specified graph; each gate is chosen uniformly at random from the list provided. - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param gates: A list of gates e.g. [I, X, Z] or [I, X]. - :return: A program that randomly places single qubit gates on a graph. - """ - program = Program() - for q in graph.nodes: - gate = random.choice(gates) - program += gate(q) - return program - - -def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: - """ - Create a program to randomly place two qubit gates on edges of the specified graph. - - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] - :return: A program that has two qubit gates randomly placed on the graph edges. - """ - program = Program() - # TODO: two coloring with pragmas - for a, b in graph.edges: - gate = random.choice(gates) - program += gate(a, b) - return program - - -def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: - """ - Create a program comprised of single qubit Clifford gates randomly placed on the nodes of - the specified graph. Each uniformly random choice of Clifford is implemented in the native - gateset. - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :return: A program that randomly places single qubit Clifford gates on a graph. - """ - num_qubits = len(graph.nodes) - - q_placeholder = QubitPlaceholder() - gateset_1q = get_rb_gateset([q_placeholder]) - - # the +1 is because the depth includes the inverse - clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) - rand_cliffords = clif_n_inv[0:num_qubits] - - prog = Program() - for q, clif in zip(graph.nodes, rand_cliffords): - gate = address_qubits(clif, qubit_mapping={q_placeholder: q}) - prog += gate - return prog - - -def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: - """ - Write a program to place random two qubit Clifford gates on edges of the graph. - - :param bm: A benchmark connection that will do the grunt work of generating the Cliffords - :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. - :return: A program that has two qubit gates randomly placed on the graph edges. - """ - num_2q_gates = len(graph.edges) - q_placeholders = QubitPlaceholder.register(n=2) - gateset_2q = get_rb_gateset(q_placeholders) - - # the +1 is because the depth includes the inverse - clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) - rand_cliffords = clif_n_inv[0:num_2q_gates] - - prog = Program() - # TODO: two coloring with PRAGMAS? - # TODO: longer term, fence to be 'simultaneous'? - for edges, clif in zip(graph.edges, rand_cliffords): - gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], - q_placeholders[1]: edges[1]}) - prog += gate - return prog - - -def dagger_previous(sequence: List[Program], n: int = 1) -> Program: - """ - Create a program which is the inverse (conjugate transpose; adjoint; dagger) of the last n - layers of the provided sequence. - - :param sequence: a sequence of PyQuil programs whose elements are layers in a circuit - :param n: the number of layers at the end of the sequence that will be inverted - :return: a program that inverts the last n layers of the provided sequence. - """ - return merge_programs(sequence[-n:]).dagger() - - -def random_su4_pairs(graph: nx.Graph, idx_label: int) -> Program: - """ - Create a program that enacts a Haar random 2 qubit gate on random pairs of qubits in the - graph, irrespective of graph topology. - - If the graph contains an odd number of nodes, then one random qubit will not be acted upon by - any gate. - - The output program will need to be compiled into native gates. - - This generator is the repeated unit of the quantum volume circuits described in [QVol]_. Note - that the qubit permutation is done implicitly--the compiler will have to figure out how to - move potentially distant qubits onto a shared edge in order to enact the random two qubit gate. - - :param graph: a graph containing qubits that will be randomly paired together. Note that - the graph topology (the edges) are ignored. - :param idx_label: a label that uniquely identifies the set of gate definitions used in the - output program. This prevents subsequent calls to this method from producing a program - with definitions that overwrite definitions in previously generated programs. - :return: a program with random two qubit gates between random pairs of qubits. - """ - qubits = list(graph.nodes) - qubits = [qubits[idx] for idx in np.random.permutation(range(len(qubits)))] - prog = Program() - # ignore the edges in the graph - for q1, q2 in zip(qubits[::2], qubits[1::2]): - matrix = haar_rand_unitary(4) - gate_definition = DefGate(f"LYR{idx_label}_RSU4_{q1}_{q2}", matrix) - RSU4 = gate_definition.get_constructor() - prog += gate_definition - prog += RSU4(q1, q2) - return prog - - -### -# Sequence Transforms -### -def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs) -> List[Program]: - """ - Insert a Hadamard gate on each qubit at the beginning and end of the sequence. - - This can be viewed as switching from the computational Z basis to the X basis. - - :param sequence: the sequence to be sandwiched by Hadamards - :param graph: the graph containing the qubits to be acted on by Hadamards - :param kwargs: extraneous arguments - :return: a new sequence which is the input sequence with new starting and ending layers of - Hadamards. - """ - prog = Program() - for node in graph.nodes: - prog.inst(H(node)) - return [prog] + sequence + [prog.copy()] - - -def dagger_sequence(sequence: List[Program], **kwargs): - """ - Returns the original sequence with its layer-by-layer inverse appended on the end. - - The net result of the output sequence is the Identity. - - .. CAUTION:: - Merging this sequence and compiling the resulting program will result in a trivial - empty program. To avoid this, consider using a sequence transform to compile each - element of the sequence first, then combine the result. For example, see - :func:`compile_individual_sequence_elements`. Using :func:`compile_merged_sequence` - with `use_basic_compile` set to True will also avoid this issue, but will not compile - gate definitions and will not compile gates onto the chip topology. - - :param sequence: the sequence of programs comprising a circuit that will be inverted and - appended to the sequence. - :param kwargs: extraneous arguments - :return: a new sequence the input sequence and its inverse - """ - return sequence + [prog.dagger() for prog in reversed(sequence)] - - -def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **kwargs) \ - -> List[Program]: - """ - Inserts random single qubit Pauli gates on each qubit in between elements of the input sequence. - - :param sequence: - :param graph: a graph containing qubits that will be randomly paired together. Note that - the graph topology (the edges) are ignored. - :param kwargs: extraneous arguments - :return: - """ - paulis = [I, X, Y, Z] - random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] - new_sequence = [None for _ in range(2 * len(sequence) + 1)] - new_sequence[::2] = random_paulis - new_sequence[1::2] = sequence - return new_sequence - - -def compile_individual_sequence_elements(qc: QuantumComputer, sequence: List[Program], - graph: nx.Graph, **kwargs) -> List[Program]: - """ - Returns the sequence where each element is individually compiled into native quil in a way - that respects the given graph topology. - - :param qc: - :param sequence: - :param graph: - :param kwargs: extraneous arguments - :return: - """ - compiled_sequence = [] - for prog in sequence: - native_quil = graph_restricted_compilation(qc, graph, prog) - # remove gate definitions and HALT - compiled_sequence.append(Program([instr for instr in native_quil.instructions][:-1])) - return compiled_sequence - - -def compile_merged_sequence(qc: QuantumComputer, sequence: List[Program], graph: nx.Graph, - use_basic_compile: bool = False, **kwargs) -> List[Program]: - """ - Merges the sequence into a Program and returns a 'sequence' comprised of the corresponding - compiled native quil program that respects the given graph topology. - - .. CAUTION:: - The option to only use basic_compile will only result in native quil if the merged - sequence contains no gate definitions and if all multi-qubit gates already respect - the graph topology. If this is not the case, the output program may not be able to be - converted properly to an executable that can be run on the qc. - - :param qc: - :param sequence: - :param graph: - :param use_basic_compile: - :param kwargs: extraneous arguments - :return: - """ - merged = merge_programs(sequence) - if use_basic_compile: - return [basic_compile(merged)] - else: - native_quil = graph_restricted_compilation(qc, graph, merged) - # remove gate definitions and terminous HALT - return [Program([instr for instr in native_quil.instructions][:-1])] - - -### -# Templates -### -def get_rand_1q_template(gates: Sequence[Gate]): - """ - Creates a CircuitTemplate representing the family of circuits generated by repeated layers of - random single qubit gates pulled from the input set of gates. - - :param gates: - :return: - """ - - def func(graph, **kwargs): - return random_single_qubit_gates(graph, gates=gates) - - return CircuitTemplate([func]) - - -def get_rand_2q_template(gates: Sequence[Gate]): - """ - Creates a CircuitTemplate representing the family of circuits generated by repeated layers of - random two qubit gates pulled from the input set of gates. - - :param gates: - :return: - """ - - def func(graph, **kwargs): - return random_two_qubit_gates(graph, gates=gates) - - return CircuitTemplate([func]) - - -def get_rand_1q_cliff_template(bm: BenchmarkConnection): - """ - Creates a CircuitTemplate representing the family of circuits generated by repeated layers of - random single qubit Clifford gates. - """ - - def func(graph, **kwargs): - return random_single_qubit_cliffords(bm, graph) - - return CircuitTemplate([func]) - - -def get_rand_2q_cliff_template(bm: BenchmarkConnection): - """ - Creates a CircuitTemplate representing the family of circuits generated by repeated layers of - random two qubit Clifford gates. - """ - - def func(graph, **kwargs): - return random_two_qubit_cliffords(bm, graph) - - return CircuitTemplate([func]) - - -def get_dagger_previous(n: int = 1): - """ - Creates a CircuitTemplate that can be appended to another template to generate families of - circuits with repeated (layer, inverse-layer) units. - """ - - def func(sequence, **kwargs): - return dagger_previous(sequence, n) - - return CircuitTemplate([func]) - - -def get_rand_su4_template(): - """ - Creates a CircuitTemplate representing the family of circuits generated by repeated layers of - Haar-random two qubit gates acting on random pairs of qubits. This is the generator used in - quantum volume [QVol]_ . - """ - - def func(graph, sequence, **kwargs): - return random_su4_pairs(graph, len(sequence)) - - return CircuitTemplate([func]) - - -def get_quantum_volume_template(): - """ - Creates a quantum volume CircuitTemplate. See [QVol]_ . - """ - template = get_rand_su4_template() - template.sequence_transforms.append(compile_merged_sequence) - return template - - -# ================================================================================================== -# Data acquisition -# ================================================================================================== -def sample_random_connected_graphs(graph: nx.Graph, width: int, num_ckts: int): - """ - Helper to uniformly randomly sample `num_ckts` many connected induced subgraphs of - `graph` of `width` many qubits. - - :param graph: - :param width: - :param num_ckts: - :return: - """ - connected_subgraphs = generate_connected_subgraphs(graph, width) - random_indices = np.random.choice(range(len(connected_subgraphs)), size=num_ckts) - return [connected_subgraphs[idx] for idx in random_indices] - - -def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, - dimensions: Dict[int, List[int]], num_circuit_samples: int, - graphs: Dict[int, List[nx.Graph]] = None) \ - -> Dict[int, Dict[int, List[Program]]]: - """ - Creates a dictionary containing random circuits sampled from the input `ckt` family for each - width and depth. - - :param qc: - :param ckt: - :param dimensions - :param num_circuit_samples: - :param graphs: - :return: - """ - if graphs is None: - graphs = {w: sample_random_connected_graphs(qc.qubit_topology(), w, - len(depths) * num_circuit_samples) - for w, depths in dimensions.items()} - - programs = {width: {depth: [] for depth in depths} for width, depths in dimensions.items()} - - for width, depth_array in programs.items(): - circuit_number = 0 - for depth, prog_list in depth_array.items(): - for _ in range(num_circuit_samples): - graph = graphs[width][circuit_number] - circuit_number += 1 - prog = ckt.sample_program(graph, repetitions=depth, width=width, qc=qc) - prog_list.append(prog) - - return programs - - -def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[int, List[Program]]], - num_shots: int = 500, - measure_qubits: Dict[int, Dict[int, List[int]]] = None, - use_active_reset: bool = False, use_compiler: bool = False) \ - -> Dict[int, Dict[int, List[np.ndarray]]]: - """ - Runs each program in `program_array` on the qc and stores the results, organized again by - width and depth. - - :param qc: - :param program_array: - :param num_shots: - :param measure_qubits: - :param use_active_reset: - :param use_compiler: - :return: - """ - reset_prog = Program() - if use_active_reset: - reset_prog += RESET() - - results = {width: {depth: [] for depth in depth_array.keys()} - for width, depth_array in program_array.items()} - - for width, depth_array in program_array.items(): - for depth, prog_list in depth_array.items(): - for idx, program in enumerate(prog_list): - prog = program.copy() - - if measure_qubits is not None: - qubits = measure_qubits[width][depth][idx] - else: - qubits = sorted(list(program.get_qubits())) - - ro = prog.declare('ro', 'BIT', len(qubits)) - for ro_idx, q in enumerate(qubits): - prog += MEASURE(q, ro[ro_idx]) - - prog.wrap_in_numshots_loop(num_shots) - - if use_compiler: - prog = qc.compiler.quil_to_native_quil(prog) - - exe = qc.compiler.native_quil_to_executable(prog) - shots = qc.run(exe) - results[width][depth].append(shots) - - return results - - -def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, - program_array: Dict[int, Dict[int, List[Program]]], - measure_qubits: Optional[Dict[int, Dict[int, List[int]]]] = None) \ - -> Dict[int, Dict[int, List[List[int]]]]: - """ - Collects and returns those 'heavy' bitstrings which are output with greater than median - probability among all possible bitstrings on the given qubits. - - The method uses the provided wfn_sim to calculate the probability of measuring each bitstring - from the output of the circuit comprised of the given permutations and gates. - - :param wfn_sim: a NumpyWavefunctionSimulator that can simulate the provided program - :param program_array: a collection of PyQuil Programs sampled from the circuit family for - each (width, depth) pair. - :param measure_qubits: optional list of qubits to measure for each Program in - `program_array`. By default all qubits in the Program are measured - :return: a list of the heavy outputs of the circuit, represented as ints - """ - heavy_output_array = {w: {d: [] for d in d_arr.keys()} for w, d_arr in program_array.items()} - - for w, d_progs in program_array.items(): - for d, ckts in d_progs.items(): - for idx, ckt in enumerate(ckts): - wfn_sim.reset() - for gate in ckt: - wfn_sim.do_gate(gate) - - if measure_qubits is not None: - qubits = measure_qubits[w][d][idx] - else: - qubits = sorted(list(ckt.get_qubits())) - - # Note that probabilities are ordered lexicographically with qubit 0 leftmost. - # we need to restrict attention to the subset `qubits` - probs = abs(wfn_sim.wf) ** 2 - probs = probs.reshape([2] * wfn_sim.n_qubits) - marginal = probs - for q in reversed(range(wfn_sim.n_qubits)): - if q in qubits: - continue - marginal = np.sum(marginal, axis=q) - - probabilities = marginal.reshape(-1) - - median_prob = median(probabilities) - - # store the integer indices, which implicitly represent the bitstring outcome. - heavy_outputs = [idx for idx, prob in enumerate(probabilities) if - prob > median_prob] - heavy_output_array[w][d].append(heavy_outputs) - - return heavy_output_array - - -# ================================================================================================== -# Analysis -# ================================================================================================== -def get_error_hamming_weight_distributions(noisy_results: Dict[int, Dict[int, List[np.ndarray]]], - ideal_results: Dict[int, Dict[int, List[np.ndarray]]]): - """ - Calculate the hamming distance to the ideal for each noisy shot of each circuit sampled for - each width and depth. - - Note that this method is only appropriate when the ideal result for each circuit is a single - deterministic (circuit-dependent) output; therefore, ideal_results should only contain one - shot per circuit. - - :param noisy_results: - :param ideal_results: - :return: - """ - distrs = {width: {depth: [] for depth in depth_array.keys()} - for width, depth_array in noisy_results.items()} - - for width, depth_array in distrs.items(): - for depth, samples in depth_array.items(): - - noisy_ckt_sample_results = noisy_results[width][depth] - ideal_ckt_sample_results = ideal_results[width][depth] - - # iterate over circuits - for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, - ideal_ckt_sample_results): - if len(ideal_result) > 1: - raise ValueError("You have provided ideal results with more than one shot; " - "this method is intended to analyze results where the ideal " - "result is deterministic, which makes multiple shots " - "unnecessary.") - - hamm_dist_per_shot = [hamming_distance(ideal_result, shot) for shot in - noisy_shots] - - # Hamming weight distribution - hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) - samples.append(np.asarray(hamm_wt_distr)) - return distrs - - -def get_single_target_success_probabilities(noisy_results, ideal_results, - allowed_errors: Union[int, Callable[[int], int]] = 0): - """ - For circuit results of various width and depth, calculate the fraction of noisy results - that match the single ideal result for each circuit. - - Note that this method is only appropriate when the ideal result for each circuit is a single - deterministic (circuit-dependent) output. - - :param noisy_results: noisy shots from each circuit sampled for each width and depth - :param ideal_results: a single ideal result for each circuit - :param allowed_errors: either a number indicating the maximum hamming distance from the ideal - result is still considered a success, or a function which returns the max hamming - distance allowed for a given width. - :return: - """ - if isinstance(allowed_errors, int): - def error_func(num_bits): - return allowed_errors - else: - error_func = allowed_errors - - hamming_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results) - - return {w: {d: [sum(distr[0:error_func(w) + 1]) for distr in distrs] - for d, distrs in d_distrs.items()} - for w, d_distrs in hamming_distrs.items()} - - -def get_success_probabilities(noisy_results, ideal_results): - """ - For circuit results of various width and depth, calculate the fraction of noisy results - that are also found in the collection of ideal results for each circuit. - - Quantum volume employs this method to calculate success_probabilities where the ideal_results - are the heavy hitters of each circuit. - - :param noisy_results: noisy shots from each circuit sampled for each width and depth - :param ideal_results: a collection of ideal results for each circuit; membership of a noisy - shot from a particular circuit in the corresponding set of ideal_results constitutes a - success. - :return: the estimated success probability for each circuit. - """ - prob_success = {width: {depth: [] for depth in depth_array.keys()} - for width, depth_array in noisy_results.items()} - - assert set(noisy_results.keys()) == set(ideal_results.keys()) - - for width, depth_array in prob_success.items(): - for depth in depth_array.keys(): - - noisy_ckt_sample_results = noisy_results[width][depth] - ideal_ckt_sample_results = ideal_results[width][depth] - - # iterate over circuits - for noisy_shots, targets in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): - if not isinstance(targets[0], int): - targets = [bit_array_to_int(res) for res in targets] - - pr_success = 0 - # determine if each result bitstring is a success, i.e. matches an ideal_result - for result in noisy_shots: - # convert result to int for comparison with heavy outputs. - output = bit_array_to_int(result) - if output in targets: - pr_success += 1 / len(noisy_shots) - prob_success[width][depth].append(pr_success) - - return prob_success - - -def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_shots: int) \ - -> Tuple[float, float]: - """ - Helper to calculate the estimate for the probability of sampling a successful output at a - particular depth as well as the 2 sigma one-sided confidence interval on this estimate. - - :param num_success: total number of successful outputs sampled at particular depth across all - circuits and shots - :param num_circuits: the total number of model circuits of a particular depth and width whose - output was sampled - :param num_shots: the total number of shots taken for each circuit - :return: estimate for the probability of sampling a successful output at a particular depth as - well as the 2 sigma one-sided confidence interval on this estimate. - """ - total_sampled_outputs = num_circuits * num_shots - prob_sample_heavy = num_success / total_sampled_outputs - - # Eq. (C3) of [QVol]. Assume that num_heavy/num_shots is worst-case binomial with param - # num_circuits and take gaussian approximation. Get 2 sigma one-sided confidence interval. - sigma = np.sqrt(num_success * (num_shots - num_success / num_circuits)) / total_sampled_outputs - one_sided_confidence_interval = prob_sample_heavy - 2 * sigma - - return prob_sample_heavy, one_sided_confidence_interval - - -def determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt): - """ - Wrapper around `calculate_success_prob_est_and_err` to determine success lower bounds for a - collection of circuits at various depths and widths. - - :param ckt_success_probs: - :param num_shots_per_ckt: - :return: - """ - return {w: {d: calculate_success_prob_est_and_err( - sum(np.asarray(succ_probs) * num_shots_per_ckt), len(succ_probs), num_shots_per_ckt)[1] - for d, succ_probs in d_ckt_succ_probs.items()} - for w, d_ckt_succ_probs in ckt_success_probs.items()} - - -def determine_successes(ckt_success_probs: Dict[int, Dict[int, List[float]]], num_shots_per_ckt, - success_threshold: float = 2 / 3): - """ - Indicate whether the collection of circuit success probabilities for given width and depth - recorded in `ckt_success_probs` is considered a success with respect to the specified - `success_threshold` and given the number of shots used to estimate each success probability. - - :param ckt_success_probs: - :param num_shots_per_ckt: - :param success_threshold: - :return: - """ - lower_bounds = determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt) - return {w: {d: lb > success_threshold for d, lb in d_lower_bounds.items()} - for w, d_lower_bounds in lower_bounds.items()} - - -def average_distributions(distrs): - """ - E.g. take in output of :func:`get_error_hamming_weight_distributions` or - :func:`get_single_target_success_probabilities` - - :param distrs: - :return: - """ - return {w: {d: sum([np.asarray(distr) for distr in distr_list]) / len(distr_list) - for d, distr_list in d_arr.items()} - for w, d_arr in distrs.items()} - - -def get_total_variation_dist(distr1, distr2): - return tvd(np.asarray([distr1]).T, np.asarray([distr2]).T) - - -def hamming_distance(arr1, arr2): - """ - Compute the hamming distance between arr1 and arr2, or the total number of indices which - differ between them. - - The hamming distance is equivalently the hamming weight of the 'error vector' between the - two arrays. - - :return: hamming distance between arr1 and arr2 - """ - n_bits = np.asarray(arr1).size - if not n_bits == np.asarray(arr2).size: - raise ValueError('Arrays must be equal size.') - - return hamming(arr1, arr2) * n_bits - - -def get_hamming_wt_distr_from_list(wt_list, n_bits): - """ - Get the distribution of the hamming weight of the error vector. - - :param wt_list: a list of length num_shots containing the hamming weight. - :param n_bits: the number of bit in the original binary strings. The hamming weight is an - integer between 0 and n_bits. - :return: the relative frequency of observing each hamming weight - """ - num_shots = len(wt_list) - - if n_bits < max(wt_list): - raise ValueError("Hamming weight can't be larger than the number of bits in a string.") - - # record the fraction of shots that resulted in an error of the given weight - return [wt_list.count(weight) / num_shots for weight in range(n_bits + 1)] - - -def get_random_hamming_wt_distr(num_bits: int): - """ - Return the distribution of Hamming weight for randomly drawn bitstrings of length num_bits. - - This is equivalent to the error distribution, e.g. from - :func:`get_error_hamming_weight_distributions` where the `noisy_results` are entirely random. - Comparing real data against this distribution may be a useful benchmark in determining - whether the real data contains any actual information. - - :param num_bits: number of bits in string - returns: list of hamming weights - """ - # comb(N, k) = N choose k - return [comb(num_bits, num_ones) / (2 ** num_bits) for num_ones in range(0, num_bits + 1)] - - -def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], widths=None, - depths=None, plot_rand_distr=False): - """ - For each width and depth plot the distribution of errors provided in distr_arr. - - :param distr_arr: - :param widths: - :param depths: - :param plot_rand_distr: - :return: - """ - if widths is None: - widths = list(distr_arr.keys()) - - if depths is None: - depths = list(list(distr_arr.values())[0].keys()) - - legend = ['data'] - if plot_rand_distr: - legend.append('random') - - fig = plt.figure(figsize=(18, 6 * len(depths))) - axs = fig.subplots(len(depths), len(widths), sharex='col', sharey=True) - - for w_idx, w in enumerate(widths): - x_labels = np.arange(0, w + 1) - depth_distrs = distr_arr[w] - - if plot_rand_distr: - rand_distr = get_random_hamming_wt_distr(w) - - for d_idx, d in enumerate(depths): - distr = depth_distrs[d] - - idx = d_idx * len(widths) + w_idx - if len(widths) == len(depths) == 1: - ax = axs - else: - ax = axs.flatten()[idx] - ax.bar(x_labels, distr, width=0.61, align='center') - - if plot_rand_distr: - ax.bar(x_labels, rand_distr, width=0.31, align='center') - - ax.set_xticks(x_labels) - ax.grid(axis='y', alpha=0.75) - ax.set_title(f'w = {w}, d = {d}', size=20) - - for tick in ax.xaxis.get_major_ticks(): - tick.label.set_fontsize(15) - - for tick in ax.yaxis.get_major_ticks(): - tick.label.set_fontsize(15) - - fig.legend(legend, loc='right', fontsize=15) - plt.ylim(0, 1) - fig.text(0.5, 0.05, 'Hamming Weight of Error', ha='center', va='center', fontsize=20) - fig.text(0.06, 0.5, 'Relative Frequency of Occurrence', ha='center', va='center', - rotation='vertical', fontsize=20) - plt.subplots_adjust(wspace=0, hspace=.15, left=.1) - - return fig, axs - - -def plot_success(successes, title, widths=None, depths=None, boxsize=1500): - """ - Plot the given successes at each width and depth. - - If a given (width, depth) is not recorded in successes then nothing is plotted for that - point. Successes are displayed as filled boxes while failures are simply box outlines. - - :param successes: - :param title: - :param widths: - :param depths: - :param boxsize: - :return: - """ - if widths is None: - widths = list(successes.keys()) - - if depths is None: - depths = list(set(d for w in successes.keys() for d in successes[w].keys())) - - fig_width = min(len(widths), 15) - fig_depth = min(len(depths), 15) - - fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) - - margin = .5 - ax.set_xlim(-margin, len(widths) + margin - 1) - ax.set_ylim(-margin, len(depths) + margin - 1) - ax.set_xticks(range(len(widths))) - ax.set_xticklabels(widths) - ax.set_yticks(range(len(depths))) - ax.set_yticklabels(depths) - ax.set_xlabel('Width') - ax.set_ylabel('Depth') - - colors = ['white', 'lightblue'] - - for w_idx, w in enumerate(widths): - if w not in successes.keys(): - continue - depth_succ = successes[w] - for d_idx, d in enumerate(depths): - if d not in depth_succ.keys(): - continue - color = colors[0] - if depth_succ[d]: - color = colors[1] - ax.scatter(w_idx, d_idx, marker='s', s=boxsize, color=color, - edgecolors='black') - - # legend - labels = ['Fail', 'Pass'] - for color, label in zip(colors, labels): - plt.scatter([], [], marker='s', c=color, label=label, edgecolors='black') - ax.legend() - - ax.set_title(title) - - return fig, ax - - -def plot_pareto_frontier(successes, title, widths=None, depths=None): - """ - Given the successes at measured widths and depths, draw the frontier that separates success - from failure. - - Specifically, the frontier is drawn as follows:: - - For a given width, draw a line separating all low-depth successes from the minimum - depth failure. For each depth smaller than the minimum failure depth, draw a line - separating the neighboring (width +/- 1, depth) cell if depth is less than the - minimum depth failure for that neighboring width. - - If a requested (width, depth) cell is not specified in successes then no lines will be drawn - around that cell. - - :param successes: - :param title: - :param widths: - :param depths: - :return: - """ - if widths is None: - widths = list(successes.keys()) - - if depths is None: - depths = list(set(d for w in successes.keys() for d in successes[w].keys())) - - fig_width = min(len(widths), 15) - fig_depth = min(len(depths), 15) - - fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) - - margin = .5 - ax.set_xlim(-margin, len(widths) + margin - 1) - ax.set_ylim(-margin, len(depths) + margin - 1) - ax.set_xticks(range(len(widths))) - ax.set_xticklabels(widths) - ax.set_yticks(range(len(depths))) - ax.set_yticklabels(depths) - ax.set_xlabel('Width') - ax.set_ylabel('Depth') - - min_depth_idx_failure_at_width = [] - for w_idx, w in enumerate(widths): - if w not in successes.keys(): - min_depth_idx_failure_at_width.append(None) - continue - - depth_succ = successes[w] - min_depth_failure = len(depths) - for d_idx, d in enumerate(depths): - if d not in depth_succ.keys(): - continue - if not depth_succ[d]: - min_depth_failure = d_idx - break - min_depth_idx_failure_at_width.append(min_depth_failure) - - for w_idx, failure_idx in enumerate(min_depth_idx_failure_at_width): - if failure_idx is None: - continue # this width was not measured, so leave the boundary open - - # horizontal line for this width - if failure_idx < len(depths): # measured a failure - ax.plot((w_idx - margin, w_idx + margin), (failure_idx - margin, failure_idx - margin), - color='black') - - # vertical lines - if w_idx < len(widths) - 1: # check not at max width - for d_idx in range(len(depths)): - # check that the current depth was measured for this width - if depths[d_idx] not in [d for d in successes[widths[w_idx]].keys()]: - continue # do not plot line if this depth was not measured - - # if the adjacent width is not measured leave the boundary open - if min_depth_idx_failure_at_width[w_idx + 1] is None: - continue - - # check if in the interior but adjacent to exterior - # or if in the exterior but adjacent to interior - if failure_idx > d_idx >= min_depth_idx_failure_at_width[w_idx + 1] \ - or failure_idx <= d_idx < min_depth_idx_failure_at_width[w_idx + 1]: - ax.plot((w_idx + margin, w_idx + margin), (d_idx - margin, d_idx + margin), - color='black') - - ax.set_title(title) - return fig, ax - - -def basement_log_function(number: float): - return basement_function(np.log2(number)) - - -def basement_function(number: float): - """ - Return the floor of the number, or 0 if the number is negative. - - :param number: the basement function is applied to this number. - :returns: basement of the number - """ - return max(int(np.floor(number)), 0) - - -# ================================================================================================== -# Graph tools -# ================================================================================================== -def generate_connected_subgraphs(graph: nx.Graph, n_vert: int): - """ - Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all - subgraphs with n_vert connect vertices. - - :params n_vert: number of vertices of connected subgraph. - :params graph: networkx graph - :returns: list of subgraphs with n_vert connected vertices - """ - subgraph_list = [] - for sub_nodes in itertools.combinations(graph.nodes(), n_vert): - subg = graph.subgraph(sub_nodes) - if nx.is_connected(subg): - subgraph_list.append(subg) - return subgraph_list diff --git a/forest/benchmarking/volumetrics/__init__.py b/forest/benchmarking/volumetrics/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/forest/benchmarking/volumetrics/_generators.py b/forest/benchmarking/volumetrics/_generators.py new file mode 100644 index 00000000..6fd283d6 --- /dev/null +++ b/forest/benchmarking/volumetrics/_generators.py @@ -0,0 +1,143 @@ +from typing import Sequence, List +import networkx as nx +import numpy as np +import random + +from pyquil.quilbase import Pragma, Gate, DefGate, DefPermutationGate +from pyquil.quilatom import QubitPlaceholder +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.api import BenchmarkConnection +from pyquil.gates import * + +from forest.benchmarking.randomized_benchmarking import get_rb_gateset +from forest.benchmarking.operator_tools.random_operators import haar_rand_unitary + + +def random_single_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: + """ + Create a program comprised of random single qubit gates acting on the qubits of the + specified graph; each gate is chosen uniformly at random from the list provided. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I, X, Z] or [I, X]. + :return: A program that randomly places single qubit gates on a graph. + """ + program = Program() + for q in graph.nodes: + gate = random.choice(gates) + program += gate(q) + return program + + +def random_two_qubit_gates(graph: nx.Graph, gates: Sequence[Gate]) -> Program: + """ + Create a program to randomly place two qubit gates on edges of the specified graph. + + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :param gates: A list of gates e.g. [I otimes I, CZ] or [CZ, SWAP, CNOT] + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + program = Program() + # TODO: two coloring with pragmas + for a, b in graph.edges: + gate = random.choice(gates) + program += gate(a, b) + return program + + +def random_single_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: + """ + Create a program comprised of single qubit Clifford gates randomly placed on the nodes of + the specified graph. Each uniformly random choice of Clifford is implemented in the native + gateset. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that randomly places single qubit Clifford gates on a graph. + """ + num_qubits = len(graph.nodes) + + q_placeholder = QubitPlaceholder() + gateset_1q = get_rb_gateset([q_placeholder]) + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_qubits + 1), gateset=gateset_1q, seed=None) + rand_cliffords = clif_n_inv[0:num_qubits] + + prog = Program() + for q, clif in zip(graph.nodes, rand_cliffords): + gate = address_qubits(clif, qubit_mapping={q_placeholder: q}) + prog += gate + return prog + + +def random_two_qubit_cliffords(bm: BenchmarkConnection, graph: nx.Graph) -> Program: + """ + Write a program to place random two qubit Clifford gates on edges of the graph. + + :param bm: A benchmark connection that will do the grunt work of generating the Cliffords + :param graph: The graph. Nodes are used as arguments to gates, so they should be qubit-like. + :return: A program that has two qubit gates randomly placed on the graph edges. + """ + num_2q_gates = len(graph.edges) + q_placeholders = QubitPlaceholder.register(n=2) + gateset_2q = get_rb_gateset(q_placeholders) + + # the +1 is because the depth includes the inverse + clif_n_inv = bm.generate_rb_sequence(depth=(num_2q_gates + 1), gateset=gateset_2q, seed=None) + rand_cliffords = clif_n_inv[0:num_2q_gates] + + prog = Program() + # TODO: two coloring with PRAGMAS? + # TODO: longer term, fence to be 'simultaneous'? + for edges, clif in zip(graph.edges, rand_cliffords): + gate = address_qubits(clif, qubit_mapping={q_placeholders[0]: edges[0], + q_placeholders[1]: edges[1]}) + prog += gate + return prog + + +def dagger_previous(sequence: List[Program], n: int = 1) -> Program: + """ + Create a program which is the inverse (conjugate transpose; adjoint; dagger) of the last n + layers of the provided sequence. + + :param sequence: a sequence of PyQuil programs whose elements are layers in a circuit + :param n: the number of layers at the end of the sequence that will be inverted + :return: a program that inverts the last n layers of the provided sequence. + """ + return merge_programs(sequence[-n:]).dagger() + + +def random_su4_pairs(graph: nx.Graph, idx_label: int) -> Program: + """ + Create a program that enacts a Haar random 2 qubit gate on random pairs of qubits in the + graph, irrespective of graph topology. + + If the graph contains an odd number of nodes, then one random qubit will not be acted upon by + any gate. + + The output program will need to be compiled into native gates. + + This generator is the repeated unit of the quantum volume circuits described in [QVol]_. Note + that the qubit permutation is done implicitly--the compiler will have to figure out how to + move potentially distant qubits onto a shared edge in order to enact the random two qubit gate. + + :param graph: a graph containing qubits that will be randomly paired together. Note that + the graph topology (the edges) are ignored. + :param idx_label: a label that uniquely identifies the set of gate definitions used in the + output program. This prevents subsequent calls to this method from producing a program + with definitions that overwrite definitions in previously generated programs. + :return: a program with random two qubit gates between random pairs of qubits. + """ + qubits = list(graph.nodes) + qubits = [qubits[idx] for idx in np.random.permutation(range(len(qubits)))] + prog = Program() + # ignore the edges in the graph + for q1, q2 in zip(qubits[::2], qubits[1::2]): + matrix = haar_rand_unitary(4) + gate_definition = DefGate(f"LYR{idx_label}_RSU4_{q1}_{q2}", matrix) + RSU4 = gate_definition.get_constructor() + prog += gate_definition + prog += RSU4(q1, q2) + return prog diff --git a/forest/benchmarking/volumetrics/_main.py b/forest/benchmarking/volumetrics/_main.py new file mode 100644 index 00000000..dd39abf9 --- /dev/null +++ b/forest/benchmarking/volumetrics/_main.py @@ -0,0 +1,403 @@ +from typing import Callable, Dict, List, Union +import networkx as nx +import numpy as np +import random +import itertools +from scipy.spatial.distance import hamming +from scipy.special import comb +from dataclasses import dataclass, field + +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.api import QuantumComputer + +from forest.benchmarking.distance_measures import total_variation_distance as tvd + + +@dataclass +class CircuitTemplate: + """ + This dataclass enables us to specify various families of circuits and sample from a specified + family random circuits of various width and depth acting on different groups of qubits. + + 'Width' is simply the number of qubits measured at then end of the circuit. 'Depth' is not + simply circuit depth, but rather the number of repeated structured groups of gates, + each of which constitutes some distinct unit. A depth d circuit could consist of d + consecutive rounds of random single qubit, then two qubit gates. It could also mean d + consecutive random Cliffords followed by the d conjugated Cliffords that invert the first d + gates. + + Because these families of circuits are quite diverse, specifying the family and drawing + samples can potentially require a wide variety of parameters. The compiler may be required to + map an abstract circuit into native quil; a sample acting on a specific qubit topology + may be desired; the sequence of 'layers' generated so far may be necessary to compute an + inverse. + + We represent each sampled circuit as a list of PyQuil Programs, which we call a 'sequence' + since each element of the list holds a distinctly structured group of gates that, + when applied altogether in series, constitute the circuit. This core functionality is found in + :func:`sample_sequence`. In this function `generators` are applied in series in a loop + `repetitions` number of times. Each call to a generator will contribute an element to the + output sequence (some combination of which will constitute a unit of depth). After a + sequence is generated from the output of the various `generators`, each `sequence_transform` + is then applied in series on the generated sequence to create a final output sequence. The + sequence transforms account for any features of the circuit that do increase with depth, + cannot neatly be fit into repeated units, or otherwise require performing a global + transformation on the sequence. See :func:`sample_sequence` for more information. + + This functionality is intended to enable creation and use of any of a wide variety of + 'volumetric benchmarks' described in the sources below. + + .. [Vol] A volumetric framework for quantum computer benchmarks. + Blume-Kohout and Young. + arXiv:1904.05546v2 (2019) + https://arxiv.org/pdf/1904.05546.pdf + + .. [QVol] Validating quantum computers using randomized model circuits. + Cross et al. + arXiv:1811.12926v1 (2018). + https://arxiv.org/abs/1811.12926 + """ + generators: List[Callable] = field(default_factory=lambda: []) + sequence_transforms: List[Callable] = field(default_factory=lambda: []) + + def append(self, other): + """ + Mutates the CircuitTemplate object by appending new generators. + TODO: The behavior of sequence_transforms may not conform with expectations. + """ + if isinstance(other, list): + self.generators += other + elif isinstance(other, CircuitTemplate): + self.generators += other.generators + self.sequence_transforms += other.sequence_transforms + else: + raise ValueError(f'Cannot append type {type(other)}.') + + def __add__(self, other): + """ + Concatenate two circuits together, returning a new one. + """ + ckt = CircuitTemplate() + ckt.append(self) + ckt.append(other) + return ckt + + def __iadd__(self, other): + """ + Concatenate two circuits together using +=, returning a new one. + """ + self.append(other) + return self + + def sample_sequence(self, graph: nx.Graph, repetitions: int, qc: QuantumComputer = None, + width: int = None, sequence: List[Program] = None): + """ + The sequence_transforms are distinct from generators in that they take in a sequence and + output a new sequence. These are applied in series after the entire sequence has been + generated. A family of interest that motivates this distinction is + + C_0 P_0 C_1 P_1 ... P_{N-1} C_N P_N C_N^t P_{N+1} ... C_1^t P_{2N-1} C_0^t + + where C_j is a clifford, P_j is a random local Pauli. We can specify this family by a + generator of random Cliffords, a conjugation sequence transform, and a Pauli frame + randomization transform. + + :param graph: the qubit topology on which the circuit should act. Unless width is + specified, the number of qubits in the graph should be considered circuit width. + :param repetitions: the number of times the loop of generators should be applied. + :param qc: a quantum computer, likely the one on which the circuit will be run, providing + access to the full chip topology and associated compiler. + :param width: the number of qubits that will be measured at the end of the circuit. If + the supplied graph contains more qubits, an induced subgraph of width-many qubits + will be selected uniformly at random from the graph. + :param sequence: an optional initialization of a sequence to build off of/append to. + :return: the list of programs whose sum constitutes a circuit sample from the family of + circuits specified by the generators and sequence_transforms. + """ + if width is not None: + graph = random.choice(generate_connected_subgraphs(graph, width)) + + if sequence is None: + sequence = [] + + # run through the generators 'repetitions' many times; append each generated program to + # the sequence. + for _ in range(repetitions): + for generator in self.generators: + sequence.append(generator(graph=graph, qc=qc, width=width, sequence=sequence)) + + for sequence_transform in self.sequence_transforms: + sequence = sequence_transform(graph=graph, qc=qc, width=width, sequence=sequence) + + return sequence + + def sample_program(self, graph, repetitions, qc=None, width=None, sequence=None): + return merge_programs(self.sample_sequence(graph, repetitions, qc, width, sequence)) + + +def generate_volumetric_program_array(qc: QuantumComputer, ckt: CircuitTemplate, + dimensions: Dict[int, List[int]], num_circuit_samples: int, + graphs: Dict[int, List[nx.Graph]] = None) \ + -> Dict[int, Dict[int, List[Program]]]: + """ + Creates a dictionary containing random circuits sampled from the input `ckt` family for each + width and depth. + + :param qc: + :param ckt: + :param dimensions + :param num_circuit_samples: + :param graphs: + :return: + """ + if graphs is None: + graphs = {w: sample_random_connected_graphs(qc.qubit_topology(), w, + len(depths) * num_circuit_samples) + for w, depths in dimensions.items()} + + programs = {width: {depth: [] for depth in depths} for width, depths in dimensions.items()} + + for width, depth_array in programs.items(): + circuit_number = 0 + for depth, prog_list in depth_array.items(): + for _ in range(num_circuit_samples): + graph = graphs[width][circuit_number] + circuit_number += 1 + prog = ckt.sample_program(graph, repetitions=depth, width=width, qc=qc) + prog_list.append(prog) + + return programs + + +def sample_random_connected_graphs(graph: nx.Graph, width: int, num_ckts: int): + """ + Helper to uniformly randomly sample `num_ckts` many connected induced subgraphs of + `graph` of `width` many qubits. + + :param graph: + :param width: + :param num_ckts: + :return: + """ + connected_subgraphs = generate_connected_subgraphs(graph, width) + random_indices = np.random.choice(range(len(connected_subgraphs)), size=num_ckts) + return [connected_subgraphs[idx] for idx in random_indices] + + +def generate_connected_subgraphs(graph: nx.Graph, n_vert: int): + """ + Given a lattice on the QPU or QVM, specified by a networkx graph, return a list of all + subgraphs with n_vert connect vertices. + + :params n_vert: number of vertices of connected subgraph. + :params graph: networkx graph + :returns: list of subgraphs with n_vert connected vertices + """ + subgraph_list = [] + for sub_nodes in itertools.combinations(graph.nodes(), n_vert): + subg = graph.subgraph(sub_nodes) + if nx.is_connected(subg): + subgraph_list.append(subg) + return subgraph_list + + +def acquire_volumetric_data(qc: QuantumComputer, program_array: Dict[int, Dict[int, List[Program]]], + num_shots: int = 500, + measure_qubits: Dict[int, Dict[int, List[int]]] = None, + use_active_reset: bool = False, use_compiler: bool = False) \ + -> Dict[int, Dict[int, List[np.ndarray]]]: + """ + Runs each program in `program_array` on the qc and stores the results, organized again by + width and depth. + + :param qc: + :param program_array: + :param num_shots: + :param measure_qubits: + :param use_active_reset: + :param use_compiler: + :return: + """ + reset_prog = Program() + if use_active_reset: + reset_prog += RESET() + + results = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in program_array.items()} + + for width, depth_array in program_array.items(): + for depth, prog_list in depth_array.items(): + for idx, program in enumerate(prog_list): + prog = program.copy() + + if measure_qubits is not None: + qubits = measure_qubits[width][depth][idx] + else: + qubits = sorted(list(program.get_qubits())) + + ro = prog.declare('ro', 'BIT', len(qubits)) + for ro_idx, q in enumerate(qubits): + prog += MEASURE(q, ro[ro_idx]) + + prog.wrap_in_numshots_loop(num_shots) + + if use_compiler: + prog = qc.compiler.quil_to_native_quil(prog) + + exe = qc.compiler.native_quil_to_executable(prog) + shots = qc.run(exe) + results[width][depth].append(shots) + + return results + + +def get_error_hamming_weight_distributions(noisy_results: Dict[int, Dict[int, List[np.ndarray]]], + ideal_results: Dict[int, Dict[int, List[np.ndarray]]]): + """ + Calculate the hamming distance to the ideal for each noisy shot of each circuit sampled for + each width and depth. + + Note that this method is only appropriate when the ideal result for each circuit is a single + deterministic (circuit-dependent) output; therefore, ideal_results should only contain one + shot per circuit. + + :param noisy_results: + :param ideal_results: + :return: + """ + distrs = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in noisy_results.items()} + + for width, depth_array in distrs.items(): + for depth, samples in depth_array.items(): + + noisy_ckt_sample_results = noisy_results[width][depth] + ideal_ckt_sample_results = ideal_results[width][depth] + + # iterate over circuits + for noisy_shots, ideal_result in zip(noisy_ckt_sample_results, + ideal_ckt_sample_results): + if len(ideal_result) > 1: + raise ValueError("You have provided ideal results with more than one shot; " + "this method is intended to analyze results where the ideal " + "result is deterministic, which makes multiple shots " + "unnecessary.") + + hamm_dist_per_shot = [hamming_distance(ideal_result, shot) for shot in + noisy_shots] + + # Hamming weight distribution + hamm_wt_distr = get_hamming_wt_distr_from_list(hamm_dist_per_shot, width) + samples.append(np.asarray(hamm_wt_distr)) + return distrs + + +def get_single_target_success_probabilities(noisy_results, ideal_results, + allowed_errors: Union[int, Callable[[int], int]] = 0): + """ + For circuit results of various width and depth, calculate the fraction of noisy results + that match the single ideal result for each circuit. + + Note that this method is only appropriate when the ideal result for each circuit is a single + deterministic (circuit-dependent) output. + + :param noisy_results: noisy shots from each circuit sampled for each width and depth + :param ideal_results: a single ideal result for each circuit + :param allowed_errors: either a number indicating the maximum hamming distance from the ideal + result is still considered a success, or a function which returns the max hamming + distance allowed for a given width. + :return: + """ + if isinstance(allowed_errors, int): + def error_func(num_bits): + return allowed_errors + else: + error_func = allowed_errors + + hamming_distrs = get_error_hamming_weight_distributions(noisy_results, ideal_results) + + return {w: {d: [sum(distr[0:error_func(w) + 1]) for distr in distrs] + for d, distrs in d_distrs.items()} + for w, d_distrs in hamming_distrs.items()} + + +def average_distributions(distrs): + """ + E.g. take in output of :func:`get_error_hamming_weight_distributions` or + :func:`get_single_target_success_probabilities` + + :param distrs: + :return: + """ + return {w: {d: sum([np.asarray(distr) for distr in distr_list]) / len(distr_list) + for d, distr_list in d_arr.items()} + for w, d_arr in distrs.items()} + + +def get_total_variation_dist(distr1, distr2): + return tvd(np.asarray([distr1]).T, np.asarray([distr2]).T) + + +def hamming_distance(arr1, arr2): + """ + Compute the hamming distance between arr1 and arr2, or the total number of indices which + differ between them. + + The hamming distance is equivalently the hamming weight of the 'error vector' between the + two arrays. + + :return: hamming distance between arr1 and arr2 + """ + n_bits = np.asarray(arr1).size + if not n_bits == np.asarray(arr2).size: + raise ValueError('Arrays must be equal size.') + + return hamming(arr1, arr2) * n_bits + + +def get_hamming_wt_distr_from_list(wt_list, n_bits): + """ + Get the distribution of the hamming weight of the error vector. + + :param wt_list: a list of length num_shots containing the hamming weight. + :param n_bits: the number of bit in the original binary strings. The hamming weight is an + integer between 0 and n_bits. + :return: the relative frequency of observing each hamming weight + """ + num_shots = len(wt_list) + + if n_bits < max(wt_list): + raise ValueError("Hamming weight can't be larger than the number of bits in a string.") + + # record the fraction of shots that resulted in an error of the given weight + return [wt_list.count(weight) / num_shots for weight in range(n_bits + 1)] + + +def get_random_hamming_wt_distr(num_bits: int): + """ + Return the distribution of Hamming weight for randomly drawn bitstrings of length num_bits. + + This is equivalent to the error distribution, e.g. from + :func:`get_error_hamming_weight_distributions` where the `noisy_results` are entirely random. + Comparing real data against this distribution may be a useful benchmark in determining + whether the real data contains any actual information. + + :param num_bits: number of bits in string + returns: list of hamming weights + """ + # comb(N, k) = N choose k + return [comb(num_bits, num_ones) / (2 ** num_bits) for num_ones in range(0, num_bits + 1)] + + +def basement_log_function(number: float): + return basement_function(np.log2(number)) + + +def basement_function(number: float): + """ + Return the floor of the number, or 0 if the number is negative. + + :param number: the basement function is applied to this number. + :returns: basement of the number + """ + return max(int(np.floor(number)), 0) diff --git a/forest/benchmarking/volumetrics/_templates.py b/forest/benchmarking/volumetrics/_templates.py new file mode 100644 index 00000000..d9d36821 --- /dev/null +++ b/forest/benchmarking/volumetrics/_templates.py @@ -0,0 +1,95 @@ +from typing import Sequence + +from pyquil.quilbase import Gate +from pyquil.api import BenchmarkConnection +from forest.benchmarking.volumetrics._main import CircuitTemplate +from forest.benchmarking.volumetrics._generators import * +from forest.benchmarking.volumetrics._transforms import * + + +def get_rand_1q_template(gates: Sequence[Gate]): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random single qubit gates pulled from the input set of gates. + + :param gates: + :return: + """ + + def func(graph, **kwargs): + return random_single_qubit_gates(graph, gates=gates) + + return CircuitTemplate([func]) + + +def get_rand_2q_template(gates: Sequence[Gate]): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random two qubit gates pulled from the input set of gates. + + :param gates: + :return: + """ + + def func(graph, **kwargs): + return random_two_qubit_gates(graph, gates=gates) + + return CircuitTemplate([func]) + + +def get_rand_1q_cliff_template(bm: BenchmarkConnection): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random single qubit Clifford gates. + """ + + def func(graph, **kwargs): + return random_single_qubit_cliffords(bm, graph) + + return CircuitTemplate([func]) + + +def get_rand_2q_cliff_template(bm: BenchmarkConnection): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + random two qubit Clifford gates. + """ + + def func(graph, **kwargs): + return random_two_qubit_cliffords(bm, graph) + + return CircuitTemplate([func]) + + +def get_dagger_previous(n: int = 1): + """ + Creates a CircuitTemplate that can be appended to another template to generate families of + circuits with repeated (layer, inverse-layer) units. + """ + + def func(sequence, **kwargs): + return dagger_previous(sequence, n) + + return CircuitTemplate([func]) + + +def get_rand_su4_template(): + """ + Creates a CircuitTemplate representing the family of circuits generated by repeated layers of + Haar-random two qubit gates acting on random pairs of qubits. This is the generator used in + quantum volume [QVol]_ . + """ + + def func(graph, sequence, **kwargs): + return random_su4_pairs(graph, len(sequence)) + + return CircuitTemplate([func]) + + +def get_quantum_volume_template(): + """ + Creates a quantum volume CircuitTemplate. See [QVol]_ . + """ + template = get_rand_su4_template() + template.sequence_transforms.append(compile_merged_sequence) + return template diff --git a/forest/benchmarking/volumetrics/_transforms.py b/forest/benchmarking/volumetrics/_transforms.py new file mode 100644 index 00000000..18a0fde9 --- /dev/null +++ b/forest/benchmarking/volumetrics/_transforms.py @@ -0,0 +1,163 @@ +from typing import List +import networkx as nx +from copy import copy + +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.api import QuantumComputer +from pyquil.gates import * +from rpcq.messages import TargetDevice +from rpcq._utils import RPCErrorError +from forest.benchmarking.compilation import basic_compile +from forest.benchmarking.volumetrics._generators import random_single_qubit_gates + + +def hadamard_sandwich(sequence: List[Program], graph: nx.Graph, **kwargs) -> List[Program]: + """ + Insert a Hadamard gate on each qubit at the beginning and end of the sequence. + + This can be viewed as switching from the computational Z basis to the X basis. + + :param sequence: the sequence to be sandwiched by Hadamards + :param graph: the graph containing the qubits to be acted on by Hadamards + :param kwargs: extraneous arguments + :return: a new sequence which is the input sequence with new starting and ending layers of + Hadamards. + """ + prog = Program() + for node in graph.nodes: + prog.inst(H(node)) + return [prog] + sequence + [prog.copy()] + + +def dagger_sequence(sequence: List[Program], **kwargs): + """ + Returns the original sequence with its layer-by-layer inverse appended on the end. + + The net result of the output sequence is the Identity. + + .. CAUTION:: + Merging this sequence and compiling the resulting program will result in a trivial + empty program. To avoid this, consider using a sequence transform to compile each + element of the sequence first, then combine the result. For example, see + :func:`compile_individual_sequence_elements`. Using :func:`compile_merged_sequence` + with `use_basic_compile` set to True will also avoid this issue, but will not compile + gate definitions and will not compile gates onto the chip topology. + + :param sequence: the sequence of programs comprising a circuit that will be inverted and + appended to the sequence. + :param kwargs: extraneous arguments + :return: a new sequence the input sequence and its inverse + """ + return sequence + [prog.dagger() for prog in reversed(sequence)] + + +def pauli_frame_randomize_sequence(sequence: List[Program], graph: nx.Graph, **kwargs) \ + -> List[Program]: + """ + Inserts random single qubit Pauli gates on each qubit in between elements of the input sequence. + + :param sequence: + :param graph: a graph containing qubits that will be randomly paired together. Note that + the graph topology (the edges) are ignored. + :param kwargs: extraneous arguments + :return: + """ + paulis = [I, X, Y, Z] + random_paulis = [random_single_qubit_gates(graph, paulis) for _ in range(len(sequence) + 1)] + new_sequence = [None for _ in range(2 * len(sequence) + 1)] + new_sequence[::2] = random_paulis + new_sequence[1::2] = sequence + return new_sequence + + +def compile_individual_sequence_elements(qc: QuantumComputer, sequence: List[Program], + graph: nx.Graph, **kwargs) -> List[Program]: + """ + Returns the sequence where each element is individually compiled into native quil in a way + that respects the given graph topology. + + :param qc: + :param sequence: + :param graph: + :param kwargs: extraneous arguments + :return: + """ + compiled_sequence = [] + for prog in sequence: + native_quil = graph_restricted_compilation(qc, graph, prog) + # remove gate definitions and HALT + compiled_sequence.append(Program([instr for instr in native_quil.instructions][:-1])) + return compiled_sequence + + +def compile_merged_sequence(qc: QuantumComputer, sequence: List[Program], graph: nx.Graph, + use_basic_compile: bool = False, **kwargs) -> List[Program]: + """ + Merges the sequence into a Program and returns a 'sequence' comprised of the corresponding + compiled native quil program that respects the given graph topology. + + .. CAUTION:: + The option to only use basic_compile will only result in native quil if the merged + sequence contains no gate definitions and if all multi-qubit gates already respect + the graph topology. If this is not the case, the output program may not be able to be + converted properly to an executable that can be run on the qc. + + :param qc: + :param sequence: + :param graph: + :param use_basic_compile: + :param kwargs: extraneous arguments + :return: + """ + merged = merge_programs(sequence) + if use_basic_compile: + return [basic_compile(merged)] + else: + native_quil = graph_restricted_compilation(qc, graph, merged) + # remove gate definitions and terminous HALT + return [Program([instr for instr in native_quil.instructions][:-1])] + + +def graph_restricted_compilation(qc: QuantumComputer, graph: nx.Graph, + program: Program) -> Program: + """ + A useful helper that temporarily modifies the supplied qc's qubit topology to match the + supplied graph so that the given program may be compiled onto the graph topology. + + :param qc: a qc object with a compiler where the given graph is a subraph of the qc's qubit + topology. + :param graph: The desired subraph of the qc's full topology on which we wish to run a program. + :param program: a program we wish to run on a particular graph on the qc. + :return: the program compiled into native quil gates respecting the graph topology. + """ + qubits = list(graph.nodes) + + # restrict compilation to chosen qubits + isa_dict = qc.device.get_isa().to_dict() + single_qs = isa_dict['1Q'] + two_qs = isa_dict['2Q'] + + new_1q = {} + for key, val in single_qs.items(): + if int(key) in qubits: + new_1q[key] = val + new_2q = {} + for key, val in two_qs.items(): + q1, q2 = key.split('-') + if (int(q1), int(q2)) in graph.edges: + new_2q[key] = val + + new_isa = {'1Q': new_1q, '2Q': new_2q} + + new_compiler = copy(qc.compiler) + new_compiler.target_device = TargetDevice(isa=new_isa, specs=qc.device.get_specs().to_dict()) + # try to compile with the restricted qubit topology + try: + native_quil = new_compiler.quil_to_native_quil(program) + except RPCErrorError as e: + if "Multiqubit instruction requested between disconnected components of the QPU graph:" \ + in str(e): + raise ValueError("The program could not be compiled onto the given subgraph.") + raise + + return native_quil diff --git a/forest/benchmarking/volumetrics/plotting.py b/forest/benchmarking/volumetrics/plotting.py new file mode 100644 index 00000000..f918fe94 --- /dev/null +++ b/forest/benchmarking/volumetrics/plotting.py @@ -0,0 +1,218 @@ +from forest.benchmarking.volumetrics._main import get_random_hamming_wt_distr +from typing import Sequence, Dict +import numpy as np +import matplotlib.pyplot as plt + + +def plot_error_distributions(distr_arr: Dict[int, Dict[int, Sequence[float]]], widths=None, + depths=None, plot_rand_distr=False): + """ + For each width and depth plot the distribution of errors provided in distr_arr. + + :param distr_arr: + :param widths: + :param depths: + :param plot_rand_distr: + :return: + """ + if widths is None: + widths = list(distr_arr.keys()) + + if depths is None: + depths = list(list(distr_arr.values())[0].keys()) + + legend = ['data'] + if plot_rand_distr: + legend.append('random') + + fig = plt.figure(figsize=(18, 6 * len(depths))) + axs = fig.subplots(len(depths), len(widths), sharex='col', sharey=True) + + for w_idx, w in enumerate(widths): + x_labels = np.arange(0, w + 1) + depth_distrs = distr_arr[w] + + if plot_rand_distr: + rand_distr = get_random_hamming_wt_distr(w) + + for d_idx, d in enumerate(depths): + distr = depth_distrs[d] + + idx = d_idx * len(widths) + w_idx + if len(widths) == len(depths) == 1: + ax = axs + else: + ax = axs.flatten()[idx] + ax.bar(x_labels, distr, width=0.61, align='center') + + if plot_rand_distr: + ax.bar(x_labels, rand_distr, width=0.31, align='center') + + ax.set_xticks(x_labels) + ax.grid(axis='y', alpha=0.75) + ax.set_title(f'w = {w}, d = {d}', size=20) + + for tick in ax.xaxis.get_major_ticks(): + tick.label.set_fontsize(15) + + for tick in ax.yaxis.get_major_ticks(): + tick.label.set_fontsize(15) + + fig.legend(legend, loc='right', fontsize=15) + plt.ylim(0, 1) + fig.text(0.5, 0.05, 'Hamming Weight of Error', ha='center', va='center', fontsize=20) + fig.text(0.06, 0.5, 'Relative Frequency of Occurrence', ha='center', va='center', + rotation='vertical', fontsize=20) + plt.subplots_adjust(wspace=0, hspace=.15, left=.1) + + return fig, axs + + +def plot_success(successes, title, widths=None, depths=None, boxsize=1500): + """ + Plot the given successes at each width and depth. + + If a given (width, depth) is not recorded in successes then nothing is plotted for that + point. Successes are displayed as filled boxes while failures are simply box outlines. + + :param successes: + :param title: + :param widths: + :param depths: + :param boxsize: + :return: + """ + if widths is None: + widths = list(successes.keys()) + + if depths is None: + depths = list(set(d for w in successes.keys() for d in successes[w].keys())) + + fig_width = min(len(widths), 15) + fig_depth = min(len(depths), 15) + + fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) + + margin = .5 + ax.set_xlim(-margin, len(widths) + margin - 1) + ax.set_ylim(-margin, len(depths) + margin - 1) + ax.set_xticks(range(len(widths))) + ax.set_xticklabels(widths) + ax.set_yticks(range(len(depths))) + ax.set_yticklabels(depths) + ax.set_xlabel('Width') + ax.set_ylabel('Depth') + + colors = ['white', 'lightblue'] + + for w_idx, w in enumerate(widths): + if w not in successes.keys(): + continue + depth_succ = successes[w] + for d_idx, d in enumerate(depths): + if d not in depth_succ.keys(): + continue + color = colors[0] + if depth_succ[d]: + color = colors[1] + ax.scatter(w_idx, d_idx, marker='s', s=boxsize, color=color, + edgecolors='black') + + # legend + labels = ['Fail', 'Pass'] + for color, label in zip(colors, labels): + plt.scatter([], [], marker='s', c=color, label=label, edgecolors='black') + ax.legend() + + ax.set_title(title) + + return fig, ax + + +def plot_pareto_frontier(successes, title, widths=None, depths=None): + """ + Given the successes at measured widths and depths, draw the frontier that separates success + from failure. + + Specifically, the frontier is drawn as follows:: + + For a given width, draw a line separating all low-depth successes from the minimum + depth failure. For each depth smaller than the minimum failure depth, draw a line + separating the neighboring (width +/- 1, depth) cell if depth is less than the + minimum depth failure for that neighboring width. + + If a requested (width, depth) cell is not specified in successes then no lines will be drawn + around that cell. + + :param successes: + :param title: + :param widths: + :param depths: + :return: + """ + if widths is None: + widths = list(successes.keys()) + + if depths is None: + depths = list(set(d for w in successes.keys() for d in successes[w].keys())) + + fig_width = min(len(widths), 15) + fig_depth = min(len(depths), 15) + + fig, ax = plt.subplots(figsize=(fig_width, fig_depth)) + + margin = .5 + ax.set_xlim(-margin, len(widths) + margin - 1) + ax.set_ylim(-margin, len(depths) + margin - 1) + ax.set_xticks(range(len(widths))) + ax.set_xticklabels(widths) + ax.set_yticks(range(len(depths))) + ax.set_yticklabels(depths) + ax.set_xlabel('Width') + ax.set_ylabel('Depth') + + min_depth_idx_failure_at_width = [] + for w_idx, w in enumerate(widths): + if w not in successes.keys(): + min_depth_idx_failure_at_width.append(None) + continue + + depth_succ = successes[w] + min_depth_failure = len(depths) + for d_idx, d in enumerate(depths): + if d not in depth_succ.keys(): + continue + if not depth_succ[d]: + min_depth_failure = d_idx + break + min_depth_idx_failure_at_width.append(min_depth_failure) + + for w_idx, failure_idx in enumerate(min_depth_idx_failure_at_width): + if failure_idx is None: + continue # this width was not measured, so leave the boundary open + + # horizontal line for this width + if failure_idx < len(depths): # measured a failure + ax.plot((w_idx - margin, w_idx + margin), (failure_idx - margin, failure_idx - margin), + color='black') + + # vertical lines + if w_idx < len(widths) - 1: # check not at max width + for d_idx in range(len(depths)): + # check that the current depth was measured for this width + if depths[d_idx] not in [d for d in successes[widths[w_idx]].keys()]: + continue # do not plot line if this depth was not measured + + # if the adjacent width is not measured leave the boundary open + if min_depth_idx_failure_at_width[w_idx + 1] is None: + continue + + # check if in the interior but adjacent to exterior + # or if in the exterior but adjacent to interior + if failure_idx > d_idx >= min_depth_idx_failure_at_width[w_idx + 1] \ + or failure_idx <= d_idx < min_depth_idx_failure_at_width[w_idx + 1]: + ax.plot((w_idx + margin, w_idx + margin), (d_idx - margin, d_idx + margin), + color='black') + + ax.set_title(title) + return fig, ax diff --git a/forest/benchmarking/volumetrics/quantum_volume.py b/forest/benchmarking/volumetrics/quantum_volume.py new file mode 100644 index 00000000..11d4c552 --- /dev/null +++ b/forest/benchmarking/volumetrics/quantum_volume.py @@ -0,0 +1,163 @@ +from typing import Tuple, Dict, List, Optional +import numpy as np +from statistics import median + +from pyquil.quil import Program, address_qubits, merge_programs +from pyquil.gates import * +from pyquil.numpy_simulator import NumpyWavefunctionSimulator + +from forest.benchmarking.utils import bit_array_to_int +from forest.benchmarking.volumetrics._templates import get_quantum_volume_template + + +def collect_heavy_outputs(wfn_sim: NumpyWavefunctionSimulator, + program_array: Dict[int, Dict[int, List[Program]]], + measure_qubits: Optional[Dict[int, Dict[int, List[int]]]] = None) \ + -> Dict[int, Dict[int, List[List[int]]]]: + """ + Collects and returns those 'heavy' bitstrings which are output with greater than median + probability among all possible bitstrings on the given qubits. + + The method uses the provided wfn_sim to calculate the probability of measuring each bitstring + from the output of the circuit comprised of the given permutations and gates. + + :param wfn_sim: a NumpyWavefunctionSimulator that can simulate the provided program + :param program_array: a collection of PyQuil Programs sampled from the circuit family for + each (width, depth) pair. + :param measure_qubits: optional list of qubits to measure for each Program in + `program_array`. By default all qubits in the Program are measured + :return: a list of the heavy outputs of the circuit, represented as ints + """ + heavy_output_array = {w: {d: [] for d in d_arr.keys()} for w, d_arr in program_array.items()} + + for w, d_progs in program_array.items(): + for d, ckts in d_progs.items(): + for idx, ckt in enumerate(ckts): + wfn_sim.reset() + for gate in ckt: + wfn_sim.do_gate(gate) + + if measure_qubits is not None: + qubits = measure_qubits[w][d][idx] + else: + qubits = sorted(list(ckt.get_qubits())) + + # Note that probabilities are ordered lexicographically with qubit 0 leftmost. + # we need to restrict attention to the subset `qubits` + probs = abs(wfn_sim.wf) ** 2 + probs = probs.reshape([2] * wfn_sim.n_qubits) + marginal = probs + for q in reversed(range(wfn_sim.n_qubits)): + if q in qubits: + continue + marginal = np.sum(marginal, axis=q) + + probabilities = marginal.reshape(-1) + + median_prob = median(probabilities) + + # store the integer indices, which implicitly represent the bitstring outcome. + heavy_outputs = [idx for idx, prob in enumerate(probabilities) if + prob > median_prob] + heavy_output_array[w][d].append(heavy_outputs) + + return heavy_output_array + + +def get_success_probabilities(noisy_results, ideal_results): + """ + For circuit results of various width and depth, calculate the fraction of noisy results + that are also found in the collection of ideal results for each circuit. + + Quantum volume employs this method to calculate success_probabilities where the ideal_results + are the heavy hitters of each circuit. + + :param noisy_results: noisy shots from each circuit sampled for each width and depth + :param ideal_results: a collection of ideal results for each circuit; membership of a noisy + shot from a particular circuit in the corresponding set of ideal_results constitutes a + success. + :return: the estimated success probability for each circuit. + """ + prob_success = {width: {depth: [] for depth in depth_array.keys()} + for width, depth_array in noisy_results.items()} + + assert set(noisy_results.keys()) == set(ideal_results.keys()) + + for width, depth_array in prob_success.items(): + for depth in depth_array.keys(): + + noisy_ckt_sample_results = noisy_results[width][depth] + ideal_ckt_sample_results = ideal_results[width][depth] + + # iterate over circuits + for noisy_shots, targets in zip(noisy_ckt_sample_results, ideal_ckt_sample_results): + if not isinstance(targets[0], int): + targets = [bit_array_to_int(res) for res in targets] + + pr_success = 0 + # determine if each result bitstring is a success, i.e. matches an ideal_result + for result in noisy_shots: + # convert result to int for comparison with heavy outputs. + output = bit_array_to_int(result) + if output in targets: + pr_success += 1 / len(noisy_shots) + prob_success[width][depth].append(pr_success) + + return prob_success + + +def calculate_success_prob_est_and_err(num_success: int, num_circuits: int, num_shots: int) \ + -> Tuple[float, float]: + """ + Helper to calculate the estimate for the probability of sampling a successful output at a + particular depth as well as the 2 sigma one-sided confidence interval on this estimate. + + :param num_success: total number of successful outputs sampled at particular depth across all + circuits and shots + :param num_circuits: the total number of model circuits of a particular depth and width whose + output was sampled + :param num_shots: the total number of shots taken for each circuit + :return: estimate for the probability of sampling a successful output at a particular depth as + well as the 2 sigma one-sided confidence interval on this estimate. + """ + total_sampled_outputs = num_circuits * num_shots + prob_sample_heavy = num_success / total_sampled_outputs + + # Eq. (C3) of [QVol]. Assume that num_heavy/num_shots is worst-case binomial with param + # num_circuits and take gaussian approximation. Get 2 sigma one-sided confidence interval. + sigma = np.sqrt(num_success * (num_shots - num_success / num_circuits)) / total_sampled_outputs + one_sided_confidence_interval = prob_sample_heavy - 2 * sigma + + return prob_sample_heavy, one_sided_confidence_interval + + +def determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt): + """ + Wrapper around `calculate_success_prob_est_and_err` to determine success lower bounds for a + collection of circuits at various depths and widths. + + :param ckt_success_probs: + :param num_shots_per_ckt: + :return: + """ + return {w: {d: calculate_success_prob_est_and_err( + sum(np.asarray(succ_probs) * num_shots_per_ckt), len(succ_probs), num_shots_per_ckt)[1] + for d, succ_probs in d_ckt_succ_probs.items()} + for w, d_ckt_succ_probs in ckt_success_probs.items()} + + +def determine_successes(ckt_success_probs: Dict[int, Dict[int, List[float]]], num_shots_per_ckt, + success_threshold: float = 2 / 3): + """ + Indicate whether the collection of circuit success probabilities for given width and depth + recorded in `ckt_success_probs` is considered a success with respect to the specified + `success_threshold` and given the number of shots used to estimate each success probability. + + :param ckt_success_probs: + :param num_shots_per_ckt: + :param success_threshold: + :return: + """ + lower_bounds = determine_prob_success_lower_bounds(ckt_success_probs, num_shots_per_ckt) + return {w: {d: lb > success_threshold for d, lb in d_lower_bounds.items()} + for w, d_lower_bounds in lower_bounds.items()} From fcca3d30810544e78723cdded0b12233861c5040 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 16:43:33 -0500 Subject: [PATCH 45/49] Import main and templates from init. --- forest/benchmarking/volumetrics/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/forest/benchmarking/volumetrics/__init__.py b/forest/benchmarking/volumetrics/__init__.py index e69de29b..9b19a372 100644 --- a/forest/benchmarking/volumetrics/__init__.py +++ b/forest/benchmarking/volumetrics/__init__.py @@ -0,0 +1,2 @@ +from forest.benchmarking._main import * +from forest.benchmarking._templates import * From 55fa2f5774f37ee89510709bf9935af7dd3d5330 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 16:45:47 -0500 Subject: [PATCH 46/49] Correct init import. --- forest/benchmarking/volumetrics/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/forest/benchmarking/volumetrics/__init__.py b/forest/benchmarking/volumetrics/__init__.py index 9b19a372..dd6982cd 100644 --- a/forest/benchmarking/volumetrics/__init__.py +++ b/forest/benchmarking/volumetrics/__init__.py @@ -1,2 +1,2 @@ -from forest.benchmarking._main import * -from forest.benchmarking._templates import * +from forest.benchmarking.volumetrics._main import * +from forest.benchmarking.volumetrics._templates import * From e0abf9414ff418321e9856c17a9e395a9b7c47d4 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 17:11:30 -0500 Subject: [PATCH 47/49] replace missing reset and measure imports in main. --- forest/benchmarking/volumetrics/_main.py | 1 + 1 file changed, 1 insertion(+) diff --git a/forest/benchmarking/volumetrics/_main.py b/forest/benchmarking/volumetrics/_main.py index dd39abf9..c16f9c9f 100644 --- a/forest/benchmarking/volumetrics/_main.py +++ b/forest/benchmarking/volumetrics/_main.py @@ -9,6 +9,7 @@ from pyquil.quil import Program, address_qubits, merge_programs from pyquil.api import QuantumComputer +from pyquil.gates import MEASURE, RESET from forest.benchmarking.distance_measures import total_variation_distance as tvd From 19794a205b1d632a045f565da0993e4732edc8b2 Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 17:11:49 -0500 Subject: [PATCH 48/49] Update notebook with new import scheme. --- docs/examples/volumetrics.ipynb | 854 ++++++++++++++++++-------------- 1 file changed, 487 insertions(+), 367 deletions(-) diff --git a/docs/examples/volumetrics.ipynb b/docs/examples/volumetrics.ipynb index 0c676750..c0d5d8b1 100644 --- a/docs/examples/volumetrics.ipynb +++ b/docs/examples/volumetrics.ipynb @@ -88,7 +88,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -178,7 +178,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 0\n", + "RX(pi/2) 0\n", "\n" ] } @@ -204,37 +204,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "Z 0\n", - "X 1\n", - "I 2\n", - "I 3\n", - "X 4\n", + "X 0\n", + "I 1\n", + "X 2\n", + "Z 3\n", + "Z 4\n", "I 5\n", "X 6\n", "X 7\n", "I 8\n", - "CZ 0 3\n", "I 0\n", + "I 3\n", + "CZ 0 1\n", "I 1\n", - "CZ 1 4\n", - "I 1\n", + "I 4\n", + "CZ 1 2\n", "I 2\n", - "CZ 2 5\n", + "I 5\n", "CZ 3 6\n", "I 3\n", "I 4\n", - "CZ 4 7\n", + "I 4\n", + "I 7\n", "I 4\n", "I 5\n", "CZ 5 8\n", "I 6\n", "I 7\n", - "CZ 7 8\n", + "I 7\n", + "I 8\n", "\n" ] } ], "source": [ + "from forest.benchmarking.volumetrics._generators import random_single_qubit_gates, random_two_qubit_gates\n", "prog1 = random_single_qubit_gates(G, one_q_gates)\n", "prog2 = random_two_qubit_gates(G, two_q_gates)\n", "print(prog1+prog2)" @@ -249,29 +253,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-pi) 0\n", - "RZ(pi/2) 1\n", - "RX(-pi) 1\n", - "RZ(-pi/2) 2\n", + "RX(-pi/2) 0\n", + "RZ(-pi/2) 0\n", + "RX(-pi/2) 0\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 1\n", "RX(-pi) 2\n", + "RZ(-pi/2) 3\n", "RX(pi/2) 3\n", - "RZ(pi/2) 3\n", "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(pi/2) 5\n", - "RX(-pi) 5\n", + "RX(-pi) 4\n", + "RX(pi/2) 5\n", + "RZ(-pi) 5\n", + "RX(pi/2) 6\n", "RZ(pi/2) 6\n", - "RX(-pi/2) 6\n", - "RX(-pi/2) 7\n", - "RZ(-pi/2) 7\n", - "RX(-pi/2) 7\n", - "RX(pi/2) 8\n", - "RZ(-pi/2) 8\n", + "RZ(-pi) 7\n", + "RZ(-pi) 7\n", + "RX(-pi/2) 8\n", + "RZ(pi/2) 8\n", + "RX(-pi/2) 8\n", "\n" ] } ], "source": [ + "from forest.benchmarking.volumetrics._generators import random_single_qubit_cliffords\n", + "\n", "rand1qcliff = random_single_qubit_cliffords(bm, G)\n", "print(rand1qcliff)" ] @@ -292,10 +299,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "X 0\n", - "X 3\n", - "X 0\n", - "I 3\n", + "I 5\n", + "X 8\n", + "X 5\n", + "X 8\n", "\n" ] } @@ -314,10 +321,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "I 5\n", - "I 8\n", - "I 5\n", - "I 8\n", + "CNOT 6 7\n", + "CNOT 6 7\n", "\n" ] } @@ -336,17 +341,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(-pi/2) 7\n", - "RX(pi/2) 6\n", - "CZ 6 7\n", - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(pi/2) 6\n", - "CZ 6 7\n", - "RX(pi/2) 7\n", - "RZ(pi/2) 7\n", - "RX(-pi/2) 7\n", - "RX(-pi/2) 6\n", + "CZ 4 5\n", + "RZ(pi/2) 5\n", + "RX(pi/2) 5\n", + "RX(pi/2) 4\n", + "CZ 4 5\n", + "RX(pi/2) 5\n", + "RZ(-pi/2) 5\n", + "RX(-pi/2) 4\n", + "CZ 4 5\n", + "RX(-pi/2) 5\n", + "RZ(pi/2) 4\n", + "RX(pi/2) 4\n", + "CZ 4 5\n", + "RX(-pi/2) 5\n", + "RX(pi/2) 4\n", + "CZ 4 5\n", + "RX(-pi/2) 5\n", "\n" ] } @@ -366,13 +377,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "DEFGATE LYR0_RSU4_7_8:\n", - " -0.17133680286283015+0.5771855029770466i, -0.02466156348536916+0.22179234545831975i, 0.12228377642992853+0.14496205601880133i, -0.2297884080173649+0.7063501439074774i\n", - " -0.11960102370866607-0.3304433900630011i, -0.11032673672938412-0.42880536505067257i, 0.7357619987654005+0.3068282033583267i, 0.06383311224299526+0.2022196818708112i\n", - " -0.26202392947509057+0.06665775229723633i, -0.3849027928296902-0.5194528926129671i, -0.5291269370112102+0.144305312790629i, 0.3979354224512658+0.22308473201253587i\n", - " -0.042541944416586486-0.6626414895218656i, -0.19559242672520571+0.545816881274874i, -0.1609407499642989+0.04292029081848708i, 0.14224663636027113+0.418265217456327i\n", + "DEFGATE LYR0_RSU4_8_5:\n", + " -0.06740625349879759-0.014045321406165294i, 0.2968943368678356-0.4765007530477127i, 0.15175567111229893-0.30564943412758033i, -0.6954532809045382-0.2827601188259824i\n", + " 0.6638702337657884+0.026452398485650015i, -0.09700059763023283+0.11342360586736261i, 0.24893503615880685-0.6703191115524492i, 0.15689771231565453+0.019730619752048928i\n", + " -0.36190874503911585-0.3828986284927636i, -0.07124472148124422-0.5342898321092627i, 0.34887965587582004-0.20161203347299655i, 0.5142180741895144+0.07130646099736504i\n", + " 0.4754516917187752-0.2240359316589525i, -0.49516048709318333-0.35609185859768144i, 0.021039783792687297+0.4576518079672631i, -0.05393976351873825-0.37278803964631857i\n", "\n", - "LYR0_RSU4_7_8 7 8\n", + "LYR0_RSU4_8_5 8 5\n", "\n" ] } @@ -398,23 +409,21 @@ "name": "stdout", "output_type": "stream", "text": [ + "X 2\n", + "X 3\n", "X 4\n", - "I 5\n", - "I 6\n", - "X 7\n", - "CNOT 4 7\n", - "I 4\n", - "I 5\n", - "I 6\n", - "I 7\n", + "X 5\n", + "CNOT 2 5\n", + "CNOT 3 4\n", + "CNOT 4 5\n", + "X 2\n", + "I 3\n", "I 4\n", - "I 5\n", - "X 6\n", - "I 7\n", - "CNOT 4 7\n", + "X 5\n", + "CNOT 2 5\n", + "CNOT 3 4\n", "I 4\n", "I 5\n", - "CNOT 6 7\n", "\n" ] } @@ -442,23 +451,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "H 2\n", - "H 5\n", - "Z 2\n", - "Z 5\n", - "I 2\n", - "I 5\n", - "Z 2\n", - "I 5\n", - "H 2\n", - "CZ 2 5\n", - "H 2\n", - "I 2\n", - "I 5\n", - "I 2\n", - "I 5\n", - "H 2\n", - "H 5\n", + "H 0\n", + "H 1\n", + "I 0\n", + "I 1\n", + "H 0\n", + "CZ 0 1\n", + "H 0\n", + "Z 0\n", + "I 1\n", + "H 0\n", + "CZ 0 1\n", + "H 0\n", + "I 0\n", + "Z 1\n", + "I 0\n", + "I 1\n", + "H 0\n", + "H 1\n", "\n" ] } @@ -490,64 +500,72 @@ "name": "stdout", "output_type": "stream", "text": [ - "RX(pi/2) 0\n", - "RZ(-pi) 0\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 1\n", - "CZ 0 1\n", - "RX(-pi/2) 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "CZ 0 1\n", "RX(-pi/2) 1\n", - "RX(-pi/2) 0\n", - "CZ 0 1\n", - "RX(-pi/2) 0\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", + "RX(pi/2) 4\n", + "RZ(pi/2) 4\n", + "RX(-pi/2) 4\n", + "CZ 1 4\n", + "RZ(-pi/2) 4\n", + "RX(pi/2) 4\n", "RX(pi/2) 1\n", - "RZ(-pi/2) 1\n", + "CZ 1 4\n", + "RZ(-pi/2) 4\n", + "RZ(pi/2) 1\n", + "RZ(-pi) 1\n", + "RZ(-pi/2) 4\n", + "RX(-pi) 4\n", + "RX(-pi/2) 4\n", + "CZ 1 4\n", "RX(-pi/2) 1\n", + "CZ 1 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi/2) 4\n", + "RX(-pi/2) 4\n", + "RZ(-pi) 1\n", + "RZ(pi/2) 1\n", + "RX(-pi) 4\n", + "RX(-pi/2) 4\n", "RX(pi/2) 1\n", - "CZ 0 1\n", + "CZ 1 4\n", + "RX(pi/2) 4\n", "RZ(-pi/2) 1\n", - "RX(pi/2) 0\n", - "RZ(-pi/2) 0\n", - "RZ(pi/2) 1\n", - "RX(-pi) 1\n", - "CZ 0 1\n", + "RX(pi/2) 1\n", + "CZ 1 4\n", + "RX(-pi/2) 4\n", "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(pi/2) 0\n", - "DAGGER RZ(pi/2) 0\n", - "DAGGER CZ 0 1\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 0 1\n", - "DAGGER RX(-pi) 1\n", - "DAGGER RZ(pi/2) 1\n", - "DAGGER RZ(-pi/2) 0\n", - "DAGGER RX(pi/2) 0\n", - "DAGGER RZ(-pi/2) 1\n", - "DAGGER CZ 0 1\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER CZ 1 4\n", "DAGGER RX(pi/2) 1\n", - "DAGGER RX(-pi/2) 1\n", "DAGGER RZ(-pi/2) 1\n", + "DAGGER RX(pi/2) 4\n", + "DAGGER CZ 1 4\n", "DAGGER RX(pi/2) 1\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RZ(-pi/2) 0\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER CZ 0 1\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 0 1\n", - "DAGGER RX(-pi/2) 0\n", - "DAGGER RZ(-pi/2) 0\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER RX(-pi) 4\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RZ(-pi) 1\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER RZ(-pi/2) 4\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER CZ 1 4\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER CZ 0 1\n", - "DAGGER RZ(-pi/2) 1\n", + "DAGGER CZ 1 4\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER RX(-pi) 4\n", + "DAGGER RZ(-pi/2) 4\n", + "DAGGER RZ(-pi) 1\n", + "DAGGER RZ(pi/2) 1\n", + "DAGGER RZ(-pi/2) 4\n", + "DAGGER CZ 1 4\n", + "DAGGER RX(pi/2) 1\n", + "DAGGER RX(pi/2) 4\n", + "DAGGER RZ(-pi/2) 4\n", + "DAGGER CZ 1 4\n", + "DAGGER RX(-pi/2) 4\n", + "DAGGER RZ(pi/2) 4\n", + "DAGGER RX(pi/2) 4\n", "DAGGER RX(-pi/2) 1\n", - "DAGGER RZ(-pi) 0\n", - "DAGGER RX(pi/2) 0\n", "\n", "This program compiles away to nothing: \n", "HALT\n", @@ -573,6 +591,13 @@ "### Quantum Volume" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You could easily make your own Quantum Volume template:" + ] + }, { "cell_type": "code", "execution_count": 17, @@ -582,257 +607,341 @@ "name": "stdout", "output_type": "stream", "text": [ - "RZ(-2.231268474427302) 1\n", + "RZ(2.4567535228446093) 2\n", + "RX(pi/2) 2\n", + "RZ(1.9079844139625592) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.98387329480434) 2\n", + "RZ(-2.9491179617564063) 5\n", + "RX(pi/2) 5\n", + "RZ(1.8350280635126637) 5\n", + "RX(-pi/2) 5\n", + "RZ(2.9144094282733226) 5\n", + "CZ 2 5\n", + "RZ(-pi/2) 2\n", + "RX(-pi/2) 2\n", + "RZ(pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(2.555017505371151) 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RZ(1.8140489262142276) 2\n", + "RX(pi/2) 2\n", + "RX(pi/2) 5\n", + "RZ(-1.5878683423805606) 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RZ(-1.810401907235648) 8\n", + "RX(pi/2) 8\n", + "RZ(1.5887962435158791) 8\n", + "RX(-pi/2) 8\n", + "RZ(-2.180441355911783) 8\n", + "RZ(2.9971046352051487) 1\n", "RX(pi/2) 1\n", - "RZ(2.0191195178095818) 1\n", - "RX(-pi/2) 1\n", - "RZ(-1.1356120065188826) 1\n", - "RZ(1.888479542178537) 4\n", - "RX(pi/2) 4\n", - "RZ(1.5084431913342584) 4\n", - "RX(-pi/2) 4\n", - "RZ(-2.483042302320226) 4\n", - "CZ 1 4\n", - "RZ(-pi/2) 1\n", + "RZ(1.7590571700067648) 1\n", "RX(-pi/2) 1\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(2.2967439629061275) 4\n", - "RX(-pi/2) 4\n", - "CZ 1 4\n", - "RZ(1.911765822813055) 1\n", + "RZ(-1.4375538292718586) 1\n", + "RZ(-0.2950219757493664) 2\n", + "RX(pi/2) 2\n", + "RZ(1.3247687649811228) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", + "RZ(-1.4015247592496605) 1\n", "RX(pi/2) 1\n", - "RX(pi/2) 4\n", - "RZ(-2.0754631531787293) 4\n", - "RX(-pi/2) 4\n", - "CZ 1 4\n", - "RZ(1.3367732406777537) 5\n", + "RZ(1.7400678943401324) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", + "RX(-pi/2) 1\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(2.713646613685171) 5\n", "RX(pi/2) 5\n", - "RZ(1.2981763099836934) 5\n", + "RZ(1.8323569565381046) 5\n", "RX(-pi/2) 5\n", - "RZ(1.7352147829475575) 5\n", - "RZ(0.22008114899935705) 0\n", - "RX(pi/2) 0\n", - "RZ(1.7971328131102209) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.9352242822990644) 0\n", - "RZ(-2.8993914186145995) 1\n", + "CZ 8 5\n", + "RZ(-1.3009855714721716) 5\n", + "RX(pi/2) 5\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RX(-pi/2) 5\n", + "RX(pi/2) 8\n", + "CZ 8 5\n", + "RZ(-0.6349319473600517) 1\n", "RX(pi/2) 1\n", - "RZ(1.447488760525238) 1\n", - "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RZ(-0.8424737294501448) 0\n", - "RX(pi/2) 0\n", - "RZ(2.2991189241396484) 1\n", + "RZ(1.9708638634728546) 1\n", "RX(-pi/2) 1\n", - "CZ 1 0\n", - "RX(-pi/2) 0\n", - "RX(pi/2) 1\n", - "CZ 1 0\n", - "RZ(-1.8937028386273451) 4\n", - "RX(pi/2) 4\n", - "RZ(1.8351213808969726) 4\n", - "RX(-pi/2) 4\n", - "CZ 5 4\n", - "RZ(-1.2810926274779872) 4\n", - "RX(pi/2) 4\n", + "RZ(1.712394198134794) 1\n", + "RZ(2.161700600790245) 2\n", + "RX(pi/2) 2\n", + "RZ(2.8439336725891353) 2\n", + "RX(-pi/2) 2\n", + "RZ(-2.713982049212584) 2\n", + "RZ(0.7875400069404053) 5\n", + "RX(pi/2) 5\n", + "RZ(2.7680474067191723) 5\n", + "RX(-pi/2) 5\n", + "RZ(-2.8177002556501947) 5\n", + "CZ 2 5\n", + "RZ(-pi/2) 2\n", + "RX(-pi/2) 2\n", "RZ(-pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(2.6034694674389662) 5\n", "RX(-pi/2) 5\n", - "CZ 5 4\n", - "RX(-pi/2) 4\n", + "CZ 2 5\n", + "RZ(1.5071539967088885) 2\n", + "RX(pi/2) 2\n", "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(0.9186884109193686) 0\n", - "RX(pi/2) 0\n", - "RZ(0.9723685883655276) 0\n", - "RX(-pi/2) 0\n", - "RZ(-1.6544074654845327) 0\n", - "RZ(2.9575035555583264) 1\n", - "RX(pi/2) 1\n", - "RZ(2.4047972055723332) 1\n", - "RX(-pi/2) 1\n", - "RZ(-3.03642949604237) 1\n", - "RZ(-2.3831689665638613) 4\n", - "RX(pi/2) 4\n", - "RZ(0.3896425336237815) 4\n", - "RX(-pi/2) 4\n", - "RZ(2.948778430174902) 4\n", - "CZ 1 4\n", - "RZ(-pi/2) 1\n", - "RX(-pi/2) 1\n", - "RZ(pi/2) 4\n", - "RX(pi/2) 4\n", - "RZ(2.5883512304295575) 4\n", - "RX(-pi/2) 4\n", - "CZ 1 4\n", - "RZ(1.271621627309611) 1\n", + "RZ(-2.0240752180248474) 5\n", + "RX(-pi/2) 5\n", + "CZ 2 5\n", + "RZ(-1.9435237706069035) 2\n", + "RX(pi/2) 2\n", + "RZ(1.8402109262991528) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", "RX(pi/2) 1\n", - "RX(pi/2) 4\n", - "RZ(-1.9445778666179079) 4\n", - "RX(-pi/2) 4\n", - "CZ 1 4\n", - "RZ(-2.6117730760463704) 1\n", - "RX(-pi/2) 1\n", - "RZ(0.47543492642538066) 1\n", + "RZ(2.281590186801594) 1\n", "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(-pi/2) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.8821307716867253) 1\n", + "RZ(-0.609309162375796) 2\n", + "RX(-pi/2) 2\n", + "CZ 2 1\n", "RX(pi/2) 1\n", - "RZ(1.9093624007809584) 1\n", + "RZ(-1.6791446794781564) 1\n", "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(2.2280745687358383) 0\n", - "RX(pi/2) 0\n", + "RZ(1.1637589335866698) 2\n", + "RX(pi/2) 2\n", + "CZ 2 1\n", + "RZ(-0.9191936239279155) 5\n", + "RX(pi/2) 5\n", + "RZ(0.9042684144627463) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.1235172543269112) 5\n", + "RZ(2.0631328405059177) 8\n", + "RX(pi/2) 8\n", + "RZ(1.7726950216386586) 8\n", + "RX(-pi/2) 8\n", + "RZ(1.8549693322791247) 8\n", + "CZ 8 5\n", + "RZ(pi/2) 5\n", + "RX(pi/2) 5\n", + "RZ(2.5851228139049667) 5\n", + "RX(-pi/2) 5\n", + "RZ(-pi/2) 8\n", + "RX(-pi/2) 8\n", + "CZ 8 5\n", + "RX(pi/2) 5\n", + "RZ(-1.6323140605487065) 5\n", + "RX(-pi/2) 5\n", + "RZ(1.5013193855621343) 8\n", + "RX(pi/2) 8\n", + "CZ 8 5\n", + "RZ(-0.1795276289121972) 1\n", "RX(pi/2) 1\n", - "RZ(-1.7133806045912134) 1\n", + "RZ(2.2436045644689595) 1\n", "RX(-pi/2) 1\n", - "CZ 0 1\n", - "RZ(0.2103781586053466) 4\n", - "RX(pi/2) 4\n", - "RZ(2.5754503762693504) 4\n", - "RX(-pi/2) 4\n", - "RZ(0.32100749829558906) 4\n", - "RZ(2.4743248049333153) 5\n", + "RZ(3.1360145503011747) 1\n", + "RZ(0.07626773627125427) 2\n", + "RX(-pi/2) 2\n", + "RZ(2.204863451859244) 2\n", + "RX(-pi/2) 2\n", + "RZ(-0.8781969895490516) 2\n", + "RZ(-1.073386212205921) 5\n", "RX(pi/2) 5\n", - "RZ(1.277712615858308) 5\n", + "RZ(1.1316436735509159) 5\n", "RX(-pi/2) 5\n", - "RZ(-0.6741096473055608) 5\n", - "CZ 5 4\n", - "RZ(pi/2) 4\n", + "RZ(-2.4727856505954606) 5\n", + "RZ(3.0002623587069106) 8\n", + "RX(-pi/2) 8\n", + "RZ(2.660816523342417) 8\n", + "RX(-pi/2) 8\n", + "RZ(-2.715960997245383) 8\n", + "\n" + ] + } + ], + "source": [ + "from forest.benchmarking.volumetrics._transforms import compile_merged_sequence\n", + "custom_qv_template = rand_su4_layer\n", + "# we want to compile the output sequences with graph-restricted compilation.\n", + "custom_qv_template.sequence_transforms.append(compile_merged_sequence)\n", + "qv_prog = custom_qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=4)\n", + "print(qv_prog)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "but we also provide this template" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RZ(-0.16578807316550104) 1\n", + "RX(pi/2) 1\n", + "RZ(0.8951760925703465) 1\n", + "RX(-pi/2) 1\n", + "RZ(-3.10079491833949) 1\n", + "RZ(-1.4409737603193395) 4\n", "RX(pi/2) 4\n", - "RZ(2.520547806744591) 4\n", + "RZ(1.3605854011246243) 4\n", "RX(-pi/2) 4\n", - "RZ(-pi/2) 5\n", - "RX(-pi/2) 5\n", - "CZ 5 4\n", - "RX(pi/2) 4\n", - "RZ(-1.6057091437181117) 4\n", + "RZ(-2.4254994565738994) 4\n", + "CZ 4 1\n", + "RZ(pi/2) 1\n", + "RX(pi/2) 1\n", + "RZ(2.595980783014279) 1\n", + "RX(-pi/2) 1\n", + "RZ(-pi/2) 4\n", "RX(-pi/2) 4\n", - "RZ(1.748787745637994) 5\n", - "RX(pi/2) 5\n", - "CZ 5 4\n", - "RZ(-2.914320351960506) 0\n", - "RX(-pi/2) 0\n", - "RZ(0.8327635388561472) 0\n", - "RX(-pi/2) 0\n", - "RZ(2.956528289916145) 0\n", - "RZ(0.5125842665655067) 1\n", + "CZ 4 1\n", "RX(pi/2) 1\n", - "RZ(2.254684286471854) 1\n", + "RZ(-1.6629939074364621) 1\n", "RX(-pi/2) 1\n", - "RZ(-2.2874737604929907) 1\n", - "RZ(2.119938303427798) 4\n", + "RZ(1.804901097823322) 4\n", "RX(pi/2) 4\n", - "RZ(1.597075906524711) 4\n", + "CZ 4 1\n", + "RZ(1.4253771658324803) 1\n", + "RX(pi/2) 1\n", + "RZ(1.912096271646144) 1\n", + "RX(-pi/2) 1\n", + "RZ(2.072941377163399) 1\n", + "RZ(0.7670487358338374) 4\n", "RX(-pi/2) 4\n", - "RZ(1.6057829712702203) 4\n", - "RZ(-0.07947205210704489) 5\n", - "RX(-pi/2) 5\n", - "RZ(1.5330844791589164) 5\n", - "RX(-pi/2) 5\n", - "RZ(-0.309112271404403) 5\n", + "RZ(1.8935724315033482) 4\n", + "RX(-pi/2) 4\n", + "RZ(2.6425255170910775) 4\n", "\n" ] } ], "source": [ - "qv_template = rand_su4_layer\n", - "# we want to compile the output sequences with graph-restricted compilation.\n", - "qv_template.sequence_transforms.append(compile_merged_sequence)\n", - "qv_prog = qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=4)\n", - "print(qv_prog)" + "qv_template = get_quantum_volume_template()\n", + "print(qv_template.sample_program(G, repetitions=2, qc=noisy_qc, width=2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Run quantum volume for one width and depth\n", + "## First, an example of a single width and depth \n", + "\n", + "We'll use the Quantum Volume template to demonstrate.\n", + "\n", + "Run quantum volume for one width and depth\n", "\n", "1. Generate the programs\n", - "2. Determine the heavy outputs\n", - "3. Collect experimental data" + "2. Collect experimental data\n", + "3. Determine the heavy outputs -- this uses the special quantum volume submodule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can be a bit slow" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "start_time = time.time()\n", - "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", - "wfn_sim = NumpyWavefunctionSimulator(9)\n", - "d = 2\n", + "# 1) Generate the programs for this circuit template with the given {width: [depths]} dimensions\n", + "d = 2 # depth = width for quantum volume\n", "dimensions = {d: [d]}\n", "qv_progs = generate_volumetric_program_array(perfect_qc, qv_template, \n", " dimensions, num_circuit_samples=200)\n", - "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)\n", + "\n", + "# 2) Run each of these programs on a quantum resource, here a QVM without noise.\n", "experimental_data = acquire_volumetric_data(perfect_qc, qv_progs)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{2: {2: [0.7340000000000005, 0.8380000000000006, 0.6160000000000004, 0.6560000000000005, 0.8960000000000007, 0.7760000000000006, 0.7300000000000005, 0.9020000000000007, 0.8040000000000006, 0.6800000000000005, 0.7880000000000006, 0.8960000000000007, 0.8320000000000006, 0.8480000000000006, 0.8340000000000006, 0.7780000000000006, 0.8400000000000006, 0.6980000000000005, 0.6380000000000005, 0.8100000000000006, 0.7460000000000006, 0.6700000000000005, 0.6120000000000004, 0.8860000000000007, 0.8240000000000006, 0.8060000000000006, 0.6460000000000005, 0.7560000000000006, 0.6700000000000005, 0.8240000000000006, 0.8020000000000006, 0.8200000000000006, 0.7700000000000006, 0.9380000000000007, 0.8220000000000006, 0.8420000000000006, 0.7640000000000006, 0.8440000000000006, 0.8620000000000007, 0.6460000000000005, 0.7360000000000005, 0.7500000000000006, 0.7260000000000005, 0.7100000000000005, 0.6620000000000005, 0.9120000000000007, 0.6600000000000005, 0.8580000000000007, 0.6780000000000005, 0.7980000000000006, 0.7680000000000006, 0.8980000000000007, 0.7560000000000006, 0.7720000000000006, 0.9040000000000007, 0.5900000000000004, 0.9080000000000007, 0.9760000000000008, 0.8420000000000006, 0.6880000000000005, 0.6960000000000005, 0.8420000000000006, 0.7740000000000006, 0.9200000000000007, 0.8040000000000006, 0.9140000000000007, 0.7160000000000005, 0.8380000000000006, 0.8400000000000006, 0.7200000000000005, 0.7920000000000006, 0.9180000000000007, 0.7600000000000006, 0.7200000000000005, 0.6780000000000005, 0.7080000000000005, 0.6260000000000004, 0.8400000000000006, 0.7900000000000006, 0.9780000000000008, 0.9260000000000007, 0.7000000000000005, 0.8560000000000006, 0.8920000000000007, 0.9400000000000007, 0.8100000000000006, 0.8080000000000006, 0.7280000000000005, 0.9040000000000007, 0.8740000000000007, 0.6340000000000005, 0.8980000000000007, 0.7120000000000005, 0.8160000000000006, 0.8740000000000007, 0.7460000000000006, 0.9160000000000007, 0.7380000000000005, 0.6800000000000005, 0.6480000000000005, 0.7740000000000006, 0.9140000000000007, 0.6120000000000004, 0.6860000000000005, 0.7840000000000006, 0.7960000000000006, 0.8300000000000006, 0.7700000000000006, 0.9800000000000008, 0.6160000000000004, 0.8300000000000006, 0.7560000000000006, 0.8700000000000007, 0.8560000000000006, 0.6400000000000005, 0.7020000000000005, 0.8400000000000006, 0.7940000000000006, 0.9120000000000007, 0.8180000000000006, 0.8120000000000006, 0.9220000000000007, 0.8940000000000007, 0.9640000000000007, 0.7020000000000005, 0.8040000000000006, 0.6920000000000005, 0.6980000000000005, 0.8500000000000006, 0.6380000000000005, 0.9600000000000007, 0.8720000000000007, 0.7480000000000006, 0.9640000000000007, 0.7700000000000006, 0.9120000000000007, 0.7040000000000005, 0.7940000000000006, 0.8380000000000006, 0.9020000000000007, 0.7760000000000006, 0.7400000000000005, 0.6640000000000005, 0.8040000000000006, 0.7880000000000006, 0.8560000000000006, 0.7960000000000006, 0.8100000000000006, 0.8480000000000006, 0.6580000000000005, 0.6920000000000005, 0.8800000000000007, 0.6640000000000005, 0.7700000000000006, 0.8580000000000007, 0.6760000000000005, 0.7100000000000005, 0.9280000000000007, 0.7700000000000006, 0.7840000000000006, 0.8900000000000007, 0.7080000000000005, 0.8800000000000007, 0.7920000000000006, 0.7740000000000006, 0.7920000000000006, 0.8100000000000006, 0.9280000000000007, 0.7260000000000005, 0.7940000000000006, 0.6260000000000004, 0.8300000000000006, 0.8800000000000007, 0.9180000000000007, 0.7880000000000006, 0.7520000000000006, 0.8720000000000007, 0.7180000000000005, 0.8400000000000006, 0.9520000000000007, 0.7960000000000006, 0.6920000000000005, 0.9460000000000007, 0.8300000000000006, 0.9220000000000007, 0.7360000000000005, 0.7900000000000006, 0.7240000000000005, 0.8320000000000006, 0.7840000000000006, 0.6520000000000005, 0.7840000000000006, 0.9080000000000007, 0.9120000000000007, 0.7580000000000006, 0.8980000000000007, 0.7200000000000005, 0.8120000000000006, 0.8220000000000006, 0.9740000000000008]}}\n", - "0.7948200000000007\n" - ] - } - ], + "outputs": [], "source": [ - "qvol_success_probs = get_success_probabilities(experimental_data, heavy_outputs)\n", - "print(qvol_success_probs)\n", - "print(np.average(qvol_success_probs[d][d]))" + "from pyquil.numpy_simulator import NumpyWavefunctionSimulator\n", + "from forest.benchmarking.volumetrics.quantum_volume import collect_heavy_outputs\n", + "\n", + "wfn_sim = NumpyWavefunctionSimulator(9)\n", + "\n", + "# 3) For quantum volume we need to simulate the whole circuit.\n", + "# For this we use a dedicated fumction from the `quantum_volume` sub module\n", + "heavy_outputs = collect_heavy_outputs(wfn_sim, qv_progs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now for this example we can perform the analysis for quantum volume using more functions from the `quantum_volume` module" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: True}}\n", - "42.56635141372681\n", - "{2: {2: 0.7377093761897142}}\n" + "0.7865600000000006\n", + "{2: {2: 0.7286146177163358}}\n", + "{2: {2: True}}\n" ] } ], "source": [ + "from forest.benchmarking.volumetrics.quantum_volume import (get_success_probabilities,\n", + " determine_prob_success_lower_bounds,\n", + " determine_successes)\n", + "\n", + "qvol_success_probs = get_success_probabilities(experimental_data, heavy_outputs)\n", + "print(np.average(qvol_success_probs[d][d]))\n", + "\n", + "print(determine_prob_success_lower_bounds(qvol_success_probs, 500))\n", + "\n", "qvol_successes = determine_successes(qvol_success_probs, 500)\n", - "print(qvol_successes)\n", - "end_time = time.time()\n", - "print(end_time - start_time)\n", - "print(determine_prob_success_lower_bounds(qvol_success_probs, 500))" + "print(qvol_successes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Acquire data for ranges of (width, depth)" + "## Acquire data for ranges of (width, depth)\n", + "\n", + "Here we will use a more generic type of volumetric benchmark whith a single ideal solution. We will run this template on a larger collection of different widths and depths. The basic idea is the same:\n", + "\n", + "1. generate the program samples from the circuit family\n", + "2. collect data for each program\n", + "3. analyse, potentially utilizing ideal simulations (we do here)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" + "{2: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 3: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 4: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}, 5: {2: [, , , , , , , , , , , , , , , , , , , ], 3: [, , , , , , , , , , , , , , , , , , , ], 4: [, , , , , , , , , , , , , , , , , , , ], 5: [, , , , , , , , , , , , , , , , , , , ], 10: [, , , , , , , , , , , , , , , , , , , ]}}\n" ] } ], @@ -845,9 +954,16 @@ "print(prog_array)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are slow" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -857,14 +973,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: [array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[1, 0]]), array([[1, 1]]), array([[0, 1]]), array([[1, 1]]), array([[0, 0]]), array([[1, 1]]), array([[1, 1]]), array([[1, 1]]), array([[0, 1]]), array([[0, 0]]), array([[0, 1]]), array([[1, 0]]), array([[1, 0]]), array([[1, 1]]), array([[0, 0]]), 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"cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -970,12 +1086,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -985,6 +1101,7 @@ } ], "source": [ + "from forest.benchmarking.volumetrics.plotting import plot_error_distributions\n", "fig, axs = plot_error_distributions(avg_err_hamm_distrs, widths=[w], depths=[d], plot_rand_distr=True)" ] }, @@ -997,12 +1114,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1024,12 +1141,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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" ] @@ -1051,16 +1168,16 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{2: {2: 0.8732999999999999, 3: 0.8967, 4: 0.8992000000000001, 5: 0.8772, 10: 0.8844000000000001}, 3: {2: 0.8267999999999999, 3: 0.8299, 4: 0.8211, 5: 0.8446, 10: 0.8200000000000001}, 4: {2: 0.8, 3: 0.7802999999999999, 4: 0.7749, 5: 0.7796000000000001, 10: 0.7811999999999999}, 5: {2: 0.7544000000000001, 3: 0.7302000000000002, 4: 0.7378, 5: 0.7012, 10: 0.7077999999999999}}\n", - "{2: {2: 0.9952000000000002, 3: 0.9968999999999999, 4: 0.9962, 5: 0.9957, 10: 0.9921000000000001}, 3: {2: 0.9881000000000002, 3: 0.9892999999999998, 4: 0.9869, 5: 0.9887, 10: 0.9814}, 4: {2: 0.9971, 3: 0.9984999999999999, 4: 0.9991000000000001, 5: 0.9969999999999999, 10: 0.9948999999999998}, 5: {2: 0.9978, 3: 0.9949999999999999, 4: 0.9930000000000001, 5: 0.9934, 10: 0.9849}}\n", - "{2: {2: 0.6233, 3: 0.6467, 4: 0.6492, 5: 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0.9956000000000002, 4: 0.9948, 5: 0.9938999999999998, 10: 0.9879}}\n", + "{2: {2: 0.6454000000000001, 3: 0.6373, 4: 0.6341, 5: 0.6592, 10: 0.6251}, 3: {2: 0.6863, 3: 0.6939, 4: 0.7001, 5: 0.7014999999999999, 10: 0.6857}, 4: {2: 0.7197999999999999, 3: 0.7399, 4: 0.7206, 5: 0.6874, 10: 0.6717}, 5: {2: 0.7776, 3: 0.7826, 4: 0.7754, 5: 0.7637999999999999, 10: 0.7636}}\n" ] } ], @@ -1120,12 +1237,12 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1173,12 +1290,12 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1211,12 +1328,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1256,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1265,7 +1382,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1275,12 +1392,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1311,7 +1428,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1346,23 +1463,23 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 39, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1372,6 +1489,8 @@ } ], "source": [ + "from forest.benchmarking.volumetrics.plotting import plot_success\n", + "\n", "success_threshold = .8\n", "ckt_success_probs = get_single_target_success_probabilities(noisy_results, ideal_results)\n", "successes = determine_successes(ckt_success_probs, num_shots)\n", @@ -1380,17 +1499,17 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", - " )" + " )" ] }, - "execution_count": 40, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, @@ -1406,7 +1525,8 @@ } ], "source": [ - "fake_successes = successes\n", + "from forest.benchmarking.volumetrics.plotting import plot_pareto_frontier\n", + "\n", "plot_pareto_frontier(successes, 'Pareto Frontier', widths=[2,3,4,5,6], depths = [2,3,4,5,7,10,15])" ] }, @@ -1419,7 +1539,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1429,12 +1549,12 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -1465,12 +1585,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZFgFbuzCIV6/0U9I5OiJWS9oTuFrSHRFxbXEFSYuARQDDe8yqIkazWqjz4VV902GDiFid/64FLiXdk6dxnSURMT8i5g/P3KnsEM1qYaxNp91Ulb5IOpJ2ljRj7DHwMuC2aqMyq68ItZ2q0i+HV08GLpUEKeYLI+Lb1YZkVl9uSJ6kfFvTg6uOw6wfRNS7Tacvko6ZTYQY8dkrMytTlW027TjpmA0YX3tlZuWK1K5TV046ZgPIZ6/MrDThhmQzK5sPr8ysVD57ZWaliXDSMbOS+ZS5mZXKbTpmVppAjPrslZmVqcYVnf4YT8fMJiC6N56OpAWS7pS0UtIZTdY5UdIKScslXdiuTNd0zAZRF6o6koaAxcCxwCrgRklLI2JFYZ15wJnAURHxUB5OuCXXdMwGUJdqOocDKyPirojYDFwELGxY503A4oh4KG031rYrdGBrOlIwfdrWqsPoyO+2bl91CBMyfag/PleAYY1UHULpAhgd7SipzJa0rDC/pOEuK3sD9xbmVwFHNJTxTABJPwKGgA+2G9VzYJOO2ZQVQGc1mQe6cKfcYWAe8CJgDnCtpOdExMPNXuDDK7MBFNF+6sBqYG5hfk5eVrQKWBoRWyLi18D/kJJQU046ZoMoOpjauxGYJ2l/SdsDJwFLG9a5jFTLQdJs0uHWXa0K9eGV2cDpzi1mImKrpNOBq0jtNedFxHJJ5wDLImJpfu5lklYAI8B7ImJ9q3KddMwGUZd6B0bEFcAVDcvOLjwO4J156oiTjtmgCYjOzl5VwknHbCA56ZhZmWp88ZWTjtkgmopJR9J04FXAfsXtRMQ5vdqmmTGRzoGV6GVN55vAI8BNwKYebsfMGkzVQbzmRMSCHpZvZs3U+OxVL3skXy/pOT0s38yaULSfqtL1mo6kW0lHlcPA30q6i3R4JVJfoud2e5tmVtD5ZQ6V6MXh1fE9KNPMOqap1ZAcEXcDSLogIk4pPifpAuCUcV9oZt0zxWo6Yw4qzuShD/+8h9szszGjVQfQXNcbkiWdKWkD8FxJj0rakOfXkk6jm1kvjfXTaTdVpOtJJyL+JSJmAJ+IiJkRMSNPu0fEmZMpW9KQpJslXd6lcM0G0pQ6e1VwlqS/Bo4m5d7/jojLJlnm24DbgZmTDc5soNW4TaeX/XQWA6cBtwK3AadJWrythUmaA7wCOLc74ZlZFXpZ0zkGOCAP8oOk84Hlkyjv34D3AjOarSBpEbAIYNoergzZ1FXl4VM7vazprAT2KczPzcsmTNLxwNqIuKnVehGxJCLmR8T84Vk7bcumzPpfkC6DaDdVpJc1nRnA7ZJ+SvoYDgeWSVoKEBEnTKCso4ATJB0H7ADMlPTliHhdt4M2Gwg1run0Mumc3X6VzuSzXmcCSHoR8G4nHLPm6nx41bOkExE/lLQvMC8ivitpR2A4Ijb0aptmltU46fSsTUfSm4BLgM/lRXNI98iZlIj4QUT4+i6zVrpz36ue6GVD8ltJbTGPAkTEL4E9e7g9M6OzjoGD2jlwU0RsllIruaRhal3pMxsgU3QQrx9KOgvYUdKxwNeA/+rh9swsq3NNp5dJ5wxgHalH8ptJdwl8fw+3Z2Zjatym08uzV6OSLgMui4h1vdqOmTWouCbTTi+GtpCkD0p6ALgTuFPSOkld67djZm3UuKbTi8Ord5DOWh0WEbtFxG7AEcBRkt7Rg+2ZWQONtp+q0oukcwpwckT8emxBRNwFvA54fQ+2Z2Z9pBdtOtMi4oHGhRGxTtK0HmzPzBrVuE2nF0ln8zY+Z2bdUPOG5F4knYMlPTrOcpGuEDezXptKSScihrpdpplN0FRKOmZWLVHt2al2etkj2cyq0MULPiUtkHSnpJWSzmix3qskhaT57cp00jEbRF3oHJhvkLkYeDlwIHCypAPHWW8G6U4tP+kkNCcds0HUnR7JhwMrI+KuiNgMXAQsHGe9DwMfA/7QSaED3KYjosY3kS8aqvMB+Di2q/P5WAM6PnyaLWlZYX5JRCwpzO8N3FuYX0W6uuDx7UjPA+ZGxLckvaeTjQ5w0jGbwjpLOg9ERNs2mGYkbQd8CnjjRF7npGM2aKJrZ69Wk24dNWZOXjZmBvBs4Ad5sL6nAEslnRARxRrUEzjpmA2i7hwB3wjMk7Q/KdmcBLzmj5uIeASYPTYv6QekO7U0TTjghmSzgdSNU+YRsRU4HbgKuB24OCKWSzpH0kTuW/cErumYDaIutfVHxBWkUT+Ly8YdGysiXtRJmU46ZoOm4kG62nHSMRswYupdZW5mFXPSMbNyOemYWamcdMysNFNw5EAzq5qTjpmVqc7XEDvpmA0gH16ZWXncOdDMSuekY2ZlcY/kLpC0A3AtMJ0U8yUR8YFqozKrL43WN+v0RdIBNgHHRMTGfGvi6yRdGRE3VB2YWe24TWfyIiKAjXl2Wp5q/LGaVavOh1d9M4iXpCFJPwfWAldHREe3uzCbkrpzN4ie6JukExEjEXEIaZzWwyU9u3EdSYskLZO0bOsjvys/SLOa6NbN9nqhb5LOmIh4GLgGWDDOc0siYn5EzB+etXP5wZnVhWs6kyNpD0m75sc7AscCd1QblVlN5btBtJuq0hcNycBewPn5NqfbkQaIvrzimMxqyf10uiAibgEOrToOs74R9c06fZF0zGxiXNMxs/K4c6CZlc3j6ZhZqZx0zKw8gRuSzaxcbkg2s3I56ZhZWdw50MzKFeFBvMysZPXNOU46ZoPIh1dmVp4AfHhlZqWqb87pj/F0zGxiujVyoKQFku6UtFLSGeM8/05JKyTdIul7kvZtV6aTjtkA0mi0ndqWkcavWgy8HDgQOFnSgQ2r3QzMj4jnApcAH29XrpOO2aDpZKjSzmo6hwMrI+KuiNgMXAQsfMKmIq6JiMfy7A2kMcxbGtg2ndGATVv64+1FqOoQJmSn4c1Vh9CxaRqpOoTSpc6BHWWV2ZKWFeaXRMSSwvzewL2F+VXAES3KOxW4st1G++NXaWYT09lV5g9ExPxubE7S64D5wAvbreukYzaAOqzptLMamFuYn5OXPXFb0kuB9wEvjIhN7Qp1m47ZoOlem86NwDxJ+0vaHjgJWFpcQdKhwOeAEyJibSeFuqZjNnC6c+1VRGyVdDpwFTAEnBcRyyWdAyyLiKXAJ4BdgK9JArgnIk5oVa6Tjtkg6tIgXhFxBXBFw7KzC49fOtEynXTMBk14uFIzK5uHKzWzUtU35zjpmA0ijdb3+MpJx2zQBJ12DqyEk47ZgBHRrc6BPeGkYzaInHTMrFROOmZWGrfpmFnZfPbKzEoUPrwysxIFTjpmVrL6Hl31x3g6kuZKuiaPOr9c0tuqjsmszhTRdqpKv9R0tgLvioifSZoB3CTp6ohYUXVgZrXkw6vJiYj7gPvy4w2SbicNGu2kY9YoAkbqe3zVF0mnSNJ+wKHAT8Z5bhGwCGB49qxS4zKrlRrXdPqiTWeMpF2ArwNvj4hHG5+PiCURMT8i5g/N2qn8AM3qIqL9VJG+qelImkZKOF+JiG9UHY9ZbQXpxm811RdJR2nE5y8At0fEp6qOx6zeAqK+bTr9cnh1FHAKcIykn+fpuKqDMqulIDUkt5sq0hc1nYi4jnS3VDPrRI0bkvsi6ZjZBDnpmFl5fMGnmZUpAA9tYWalck3HzMrjyyDMrEwBUeN+Ok46ZoPIPZLNrFRu0zGz0kT47JWZlcw1HTMrTxAjI1UH0ZSTjtmg8dAWZla6Gp8y75ehLcysQwHEaLSdOiFpgaQ7Ja2UdMY4z0+X9J/5+Z/k4YRbctIxGzSRB/FqN7UhaQhYDLwcOBA4WdKBDaudCjwUEc8APg18rF25TjpmAyhGRtpOHTgcWBkRd0XEZuAiYGHDOguB8/PjS4CX5JE+m1LU+NTaZEhaB9zdg6JnAw/0oNxe6KdYob/i7VWs+0bEHpMpQNK3SfG1swPwh8L8kohYUijn1cCCiPj7PH8KcEREnF5Y57a8zqo8/6u8TtPPZmAbkie745qRtCwi5vei7G7rp1ihv+Ktc6wRsaDqGFrx4ZWZNbMamFuYn5OXjbuOpGFgFrC+VaFOOmbWzI3APEn7S9oeOAlY2rDOUuAN+fGrge9HmzabgT286qEl7VepjX6KFfor3n6KdZtExFZJpwNXAUPAeRGxXNI5wLKIWEq6NdQFklYCD5ISU0sD25BsZvXkwyszK5WTjpmVykmnA5LmSrpG0gpJyyW9reqYWpG0g6SfSvpFjvdDVcfUjqQhSTdLurzqWNqR9BtJt+Y7zS6rOp5+44bkzmwF3hURP5M0A7hJ0tURsaLqwJrYBBwTERslTQOuk3RlRNxQdWAtvA24HZhZdSAdenGrDnDWnGs6HYiI+yLiZ/nxBtKPY+9qo2ouko15dlqeanvGQNIc4BXAuVXHYr3npDNB+SraQ4GfVBtJa/lw5efAWuDqiKhzvP8GvBeo73gMTxTAdyTdJGlR1cH0GyedCZC0C/B14O0R8WjV8bQSESMRcQipF+nhkp5ddUzjkXQ8sDYibqo6lgk4OiKeR7r6+q2SXlB1QP3ESadDuW3k68BXIuIbVcfTqYh4GLgGqOv1OEcBJ0j6Dekq5mMkfbnakFqLiNX571rgUtLV2NYhJ50O5Ev1vwDcHhGfqjqediTtIWnX/HhH4FjgjmqjGl9EnBkRcyJiP1Jv1u9HxOsqDqspSTvnkwlI2hl4GXBbtVH1F5+96sxRwCnArbmdBOCsiLiiwpha2Qs4Pw/CtB1wcUTU/lR0n3gycGkeMmYYuDAivl1tSP3Fl0GYWal8eGVmpXLSMbNSOemYWamcdMysVE46ZlYqJ50pTNKnJb29MH+VpHML85+UdJakS5q8/geS5ufHZxWW75fvEmD2J5x0prYfAUcCSNqOdNuSgwrPH0nqrPfqDso6q/0qZk46U931wPPz44NIPWs3SHqSpOnAAcCDY7UWSTtKukjS7ZIuBXbMyz8K7JjHl/lKLm9I0ufzeD7fyT2jzZx0prKI+C2wVdI+pFr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\n", 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" ] @@ -1507,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1516,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1540,7 +1660,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1554,7 +1674,7 @@ " 10, 10, 10, 10]])" ] }, - "execution_count": 46, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1589,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1613,7 +1733,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -1637,7 +1757,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -1659,7 +1779,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1669,15 +1789,15 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The estimated error is p = 0.0109\n", - "The estimated product of the one and two qubit fidelity is F = 0.9891\n" + "The estimated error is p = 0.0113\n", + "The estimated product of the one and two qubit fidelity is F = 0.9887\n" ] } ], @@ -1689,7 +1809,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -1699,12 +1819,12 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 61, "metadata": {}, "outputs": [ { "data": { - "image/png": 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3wphZeZHaQerKAcSs5nwVxsxKCTeimtlI+BTGzErzVRgzKyXCAcTMRsCXcc2sNLeBmFkpgRj0VRgzK6vGFZBqBhQysw5F98YDkbRI0u2SVkg6uUma10u6RdJySd9ol6drIGZ114UqiKSJwJnAEcBq4HpJSyPilkKaBcApwEERsT4POdqSayBmNdelGsgBwIqIWBkRW4ALgKMb0rwNODMi1qf9xtp2mfZFDWSCBpk+4dGqi9GR3SZuqroI22Xz5MlVF8FaCGBwsKMAMVvSssL8koanHcwBVhXmVwMHNuTxNABJPwMmAqe3Gy2wLwKI2bgVQGc1jHVdeMLjJGABcBgwF7hK0jMjYkOzDXwKY1ZzEe2nDqwB5hXm5+ZlRauBpRHxWETcAfwPKaA05QBiVnfRwdTe9cACSU+RtAPwBmBpQ5pLSLUPJM0mndKsbJWpT2HMaq07j22IiK2S3glcRmrf+HJELJd0BrAsIpbmda+QdAvpwffvj4gHWuXrAGJWd13qSRYRlwKXNiw7rfA6gPfmqSMOIGZ1FhCdXYWphAOIWe05gJhZWTW+GcYBxKzu+j2ASNoReB2wd3GbiDijN8UyM2B7OpJVotMayLeBh4AbgM29K46ZNRoLAwrNjYhFPS2JmQ2vxldhOu2JerWkZ/a0JGY2LEX7qSotayCSbiadhU0C3iJpJekURqR+J8/qfRHNxrHOu6pXot0pzGtGpRRm1oT6txE1Iu4CkHRuRBxXXCfpXOC4YTc0s+7p4xrIkP2KM3l4tOd3vzhm9gSDVReguZaNqJJOkbQReJakhyVtzPNrSZd2zayXhvqBtJsq0jKARMTHImIa8MmImB4R0/K0W0ScMpIdS5oo6ZeSvjOSfMzGur69ClNwqqQ/Aw4mxcT/iohLRrjvdwO3AtNHmI/Z2FbjNpBO+4GcCZwA3Az8CjhB0plldyppLvBq4Itl8zCz6nVaAzkc2CcPOIKkc4DlI9jvZ4EPANOaJZC0GFgMsPueE0ewK7P+VuUpSjud1kBWAHsV5uflZdtN0muAtRFxQ6t0EbEkIhZGxMKZuzmA2DgVpK7s7aaKdFoDmQbcKuk60r90ALBM0lKAiDhqO/Z5EHCUpCOBnYDpks6LiGO3Iw+z8aPGNZBOA8hp7ZN0Jl+9OQVA0mHA+xw8zJqr8ylMRwEkIn4iaT6wICJ+KGkKMCkiNva2eGZW5xpIR20gkt4GXASclRfNJT1DYkQi4sqI8P02Zq1057kwPdFpI+o7SG0XDwNExK+Btk/uNrOR6aQTWT90JNscEVuk1NoraRK1rliZjSFjYEChn0g6FZgi6Qjg34H/7F2xzGxInWsgnQaQk4H7ST1R3056utWHelUoMyuocRtIp1dhBiVdAlwSEff3uExmNqTiGkY77W7nl6TTJa0Dbgdul3S/pK71CzGzNmpcA2l3CvMe0tWXF0TErIiYBRwIHCTpPT0vnZmhwfZTVdoFkOOAYyLijqEFEbESOBZ4Uy8LZmb1164NZHJErGtcGBH3S5rcozKZWVGN20DaBZAtJdeZWTfUvBG1XQB5tqSHh1ku0p20ZtZr/RpAIsIDcZhVrV8DiJlVS1R7laWdTnuimlkVungznaRFkm6XtELSyS3SvU5SSFrYLk8HELO660JHsvwwuDOBVwH7AsdI2neYdNNIT0y4tpOiOYCY1V13eqIeAKyIiJURsQW4ADh6mHQfAT4BPNpJpn3RBjKRYMaEzVUXoyNbJg130cq6YccJj1VdhEp0eIoyW9KywvySiFhSmJ8DrCrMryb1Kn98P9LzgHkR8V1J7+9kp30RQMzGtc4CyLqIaNtm0YykCcCngeO3ZzsHELM6i65dhVlDehzLkLl52ZBpwP7AlXngsD8Clko6KiKKNZttOICY1V13+oFcDyyQ9BRS4HgD8MY/7CLiIWD20LykK0lPTGgaPMCNqGa1143LuBGxFXgncBnpmdQXRsRySWdI2p7nOm3DNRCzuutST9SIuJQ0mmBx2bBj+0TEYZ3k6QBiVmcVDxjUjgOIWY2J/r4b18wq5gBiZuU5gJhZaQ4gZlZKn49IZmZVcwAxs7LqPKCQA4hZzfkUxszKcUcyMxsRBxAzK8M9URtI2gm4Ctgx7/+iiPjwaJfDrF9osL4RpIoayGbg8IjYlB+P+VNJ34uIayooi1m9uQ1kWxERwKY8OzlPNT5EZtWq8ylMJQMKSZoo6UZgLXB5RHQ0hLzZuNSdUdl7opIAEhEDEfEc0riMB0javzGNpMWSlklatv7BGvekMeuxbj1YqhcqHdIwIjYAVwCLhlm3JCIWRsTCXWd55EUbx1wDeZykJ0mamV9PAY4Abhvtcpj1hTwqe7upKlVchdkDOCc/am8CaXDX71RQDrPacz+QBhFxE/Dc0d6vWd+K+kYQ90Q1qznXQMysHHckM7OR8HggZlaaA4iZlRO4EdXMynMjqpmV5wBiZmW4I5mZlRfhAYXMbATqGz8cQMzqzqcwZlZOAD6FMbPS6hs/qh1QyMza69aIZJIWSbpd0gpJJw+z/r2SbpF0k6QfSZrfLk8HELOa02C0ndrmkcbfORN4FbAvcIykfRuS/RJYGBHPAi4C/rldvg4gZnXWyXCGndVADgBWRMTKiNgCXAAcvc2uIq6IiEfy7DWkMYtb6os2kIkE0ybU+I6ibTzSPkmN7DBpoOoidGzahN9XXYRRlzqSdRQhZktaVphfEhFLCvNzgFWF+dXAgS3yeyvwvXY77YsAYjaudfbbuS4iFnZjd5KOBRYCh7ZL6wBiVnMd1kDaWQPMK8zPzcu23Zf0cuDvgUMjYnO7TN0GYlZn3WsDuR5YIOkpknYA3gAsLSaQ9FzgLOCoiFjbSaaugZjVWnfuhYmIrZLeCVwGTAS+HBHLJZ0BLIuIpcAngV2Af5cEcHdEHNUqXwcQs7rr0oBCEXEpcGnDstMKr1++vXk6gJjVWXhIQzMbCQ9paGal1Td+OICY1Z0G63sO4wBiVmdBpx3JKuEAYlZjIrrVkawnHEDM6s4BxMxKcwAxs1LcBmJmI+GrMGZWUvgUxsxK8sO1zWxE6nsGM/rjgUiaJ+mKPPrzcknvHu0ymPUTRbSdqlJFDWQrcFJE/ELSNOAGSZdHxC0VlMWs/nwK87iIuBe4N7/eKOlW0oCvDiBmjSJgoL7nMJW2gUjaG3gucO0w6xYDiwHmzPHIizaO1bgGUtk3U9IuwLeAEyPi4cb1EbEkIhZGxMJZsxxAbByLaD9VpJIaiKTJpODx9Yj4jyrKYNYX/HDtbSmN1vol4NaI+PRo79+svwREfdtAqjg3OAg4Djhc0o15OrKCcpjVX5AaUdtNFaniKsxPSU/sM7NO1LgR1T1RzerOAcTMyvHNdGZWVgC+nd/MSnMNxMzKcVd2MysrIGrcD8QBxKzu3BPVzEpzG4iZlRLhqzBmNgKugZhZOUEMDFRdiKYcQMzqzLfzm9mI1Pgyrof6MquxAGIw2k6dkLRI0u2SVkg6eZj1O0r6Zl5/bR5ytCUHELM6izygULupDUkTgTOBVwH7AsdI2rch2VuB9RHxVOAzwCfa5esAYlZzMTDQdurAAcCKiFgZEVuAC4CjG9IcDZyTX18EvCyPINhUX7SB3Hzz1nXz5913Vw+yng2s60G+vdBPZYX+Km+vyjp/pBlsZP1lP4yLZneQdCdJywrzSyJiSWF+DrCqML8aOLAhjz+kiYitkh4CdqPFsemLABIRT+pFvpKWRcTCXuTdbf1UVuiv8ta5rBGxqOoytOJTGLPxYQ0wrzA/Ny8bNo2kScAM4IFWmTqAmI0P1wMLJD1F0g7AG4ClDWmWAm/Or/8c+HFE626wfXEK00NL2iepjX4qK/RXefuprKXkNo13ApcBE4EvR8RySWcAyyJiKelxK+dKWgE8SAoyLalNgDEza8qnMGZWmgOImZU27gKIpHmSrpB0i6Tlkt5ddZlakbSTpOsk/Xcu7z9WXaZ2JE2U9EtJ36m6LO1IulPSzfkJicvab2FF47ERdStwUkT8QtI04AZJl0fELVUXrInNwOERsSk/lPynkr4XEddUXbAW3g3cCkyvuiAdemlE9Eunt1oZdzWQiLg3In6RX28kfdDnVFuq5iLZlGcn56m2Ld+S5gKvBr5YdVms98ZdACnKdxs+F7i22pK0lk8JbgTWApdHRJ3L+1ngA0B970HfVgA/kHSDpMVVF6bfjNsAImkX4FvAiRHxcNXlaSUiBiLiOaTegwdI2r/qMg1H0muAtRFxQ9Vl2Q4HR8TzSHepvkPSIVUXqJ+MywCS2xK+BXw9Iv6j6vJ0KiI2AFcAdb0/4iDgKEl3ku72PFzSedUWqbWIWJP/rgUuJt21ah0adwEk3578JeDWiPh01eVpR9KTJM3Mr6cARwC3VVuq4UXEKRExNyL2JvVi/HFEHFtxsZqSNDU3pCNpKvAK4FfVlqq/jMerMAcBxwE353YFgFMj4tIKy9TKHsA5eUCYCcCFEVH7y6N9Ynfg4jzkxSTgGxHx/WqL1F/cld3MSht3pzBm1j0OIGZWmgOImZXmAGJmpTmAmFlpDiBjgKTPSDqxMH+ZpC8W5j8l6VRJFzXZ/kpJC/PrUwvL95bkfhHWlAPI2PAz4MUAkiaQHlOwX2H9i0mduv68g7xObZ/ELHEAGRuuBl6UX+9H6k25UdKuknYE9gEeHKpNSJoi6QJJt0q6GJiSl38cmJLHxvh6zm+ipLPzWCQ/yL1hzQAHkDEhIu4Btkrai1Tb+DnpDuMXAQuBm4EthU3+BngkIvYBPgw8P+dzMvD7iHhORPxlTrsAODMi9gM2AK8bhX/J+oQDyNhxNSl4DAWQnxfmf9aQ9hDgPICIuAm4qUW+d0TEUJf/G4C9u1dk63cOIGPHUDvIM0mnMNeQaiAvJgWXsjYXXg8wPu+fsiYcQMaOq4HXAA/m8UMeBGaSgkhjALkKeCNAHlvkWYV1j+XhDszacgAZO24mXX25pmHZQ8OM9/l5YBdJtwJnkE5NhiwBbio0opo15btxzaw010DMrDQHEDMrzQHEzEpzADGz0hxAzKw0BxAzK80BxMxK+1/uiRQOgrj/nQAAAABJRU5ErkJggg==\n", 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" ] @@ -1735,12 +1855,12 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 62, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1778,7 +1898,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -1788,7 +1908,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -1797,16 +1917,16 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.05919906, 0.00115833])" + "array([0.05877651, 0.00253226])" ] }, - "execution_count": 57, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1817,18 +1937,18 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[0.8830571 0.83078094 0.78159949 0.73532953]\n", - " [0.88203423 0.82981863 0.78069414 0.73447778]\n", - " [0.88101254 0.82885742 0.77978984 0.73362701]\n", - " [0.87999204 0.82789733 0.77888658 0.73277723]\n", - " [0.87490722 0.82311354 0.77438598 0.72854306]]\n" + "[[0.88142068 0.82961385 0.78085204 0.73495628]\n", + " [0.87918869 0.82751305 0.77887472 0.73309518]\n", + " [0.87696236 0.82541757 0.77690241 0.7312388 ]\n", + " [0.87474167 0.8233274 0.77493509 0.72938711]\n", + " [0.86372226 0.81295568 0.76517298 0.72019878]]\n" ] } ], @@ -1840,12 +1960,12 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1882,7 +2002,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 68, "metadata": {}, "outputs": [ { From f7057684eac8056b4cfaa2605917d8d3cca028ad Mon Sep 17 00:00:00 2001 From: Kyle Gulshen Date: Mon, 25 Nov 2019 17:15:49 -0500 Subject: [PATCH 49/49] Update test imports. --- forest/benchmarking/tests/test_volumetrics.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/forest/benchmarking/tests/test_volumetrics.py b/forest/benchmarking/tests/test_volumetrics.py index 2c7fbee0..6e12ee78 100644 --- a/forest/benchmarking/tests/test_volumetrics.py +++ b/forest/benchmarking/tests/test_volumetrics.py @@ -2,6 +2,9 @@ from pyquil.numpy_simulator import NumpyWavefunctionSimulator from forest.benchmarking.volumetrics import * +from forest.benchmarking.volumetrics.quantum_volume import (collect_heavy_outputs, + get_success_probabilities, + calculate_success_prob_est_and_err) np.random.seed(1)