PlotTreshold.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2a2c9e2-2247-4631-8c25-384b81af794c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from QEC.repetition import bitfliprepetitioncode\n",
    "from Algorithm.noise import construct_bitflip_noise_model,construct_phaseflip_noise_model,construct_gate_bitflip_noise_model\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2524df9-10cf-4e15-a592-78d59ad4a046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n"
     ]
    }
   ],
   "source": [
    "number_qubits=[3,5,7]\n",
    "#physical_errorrates=[0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008]\n",
    "physical_errorrates=list(np.linspace(0,0.02,30))\n",
    "\n",
    "three_fidelity=[]\n",
    "three_fidelity_notcorrect=[]\n",
    "five_fidelity=[]\n",
    "five_fidelity_notcorrect=[]\n",
    "seven_fidelity=[]\n",
    "seven_fidelity_notcorrect=[]\n",
    "nine_fidelity=[]\n",
    "nine_fidelity_notcorrect=[]\n",
    "for num in number_qubits:\n",
    "    for errorrate in physical_errorrates:\n",
    "        rep=bitfliprepetitioncode(num)\n",
    "        rep.set_benchmarkwidth(30)\n",
    "        #noisemode=construct_gate_bitflip_noise_model(errorrate)\n",
    "        noisemode=construct_bitflip_noise_model(errorrate,errorrate,3*errorrate)\n",
    "        rep.construct_syndrome_table()\n",
    "        #rep.show_syndrome_table()    \n",
    "        rep.add_noise_model(noisemode)\n",
    "        \n",
    "        fidelity=rep.show_noise_effect(shots=100)    \n",
    "        if num==3:\n",
    "            three_fidelity.append(fidelity)\n",
    "        if num==5:\n",
    "            five_fidelity.append(fidelity)\n",
    "        if num==7:\n",
    "            seven_fidelity.append(fidelity)\n",
    "        if num==9:\n",
    "            nine_fidelity.append(fidelity)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59beb825-dd4e-4d1f-aae4-a48bc7fcc8a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZI', 'IZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIII', 'IZZII', 'IIZZI', 'IIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n",
      "['ZZIIIII', 'IZZIIII', 'IIZZIII', 'IIIZZII', 'IIIIZZI', 'IIIIIZZ']\n"
     ]
    }
   ],
   "source": [
    "        \n",
    "for num in number_qubits:\n",
    "    for errorrate in physical_errorrates:\n",
    "        rep=bitfliprepetitioncode(num)\n",
    "        rep.set_benchmarkwidth(10)\n",
    "        rep.set_mutecorrection(True)\n",
    "        #noisemode=construct_gate_bitflip_noise_model(errorrate)\n",
    "        noisemode=construct_bitflip_noise_model(errorrate,0,errorrate)\n",
    "        rep.construct_syndrome_table()\n",
    "        #rep.show_syndrome_table()    \n",
    "        rep.add_noise_model(noisemode)\n",
    "        \n",
    "        fidelity=rep.show_noise_effect(shots=100)    \n",
    "        if num==3:\n",
    "            three_fidelity_notcorrect.append(fidelity)\n",
    "        if num==5:\n",
    "            five_fidelity_notcorrect.append(fidelity)\n",
    "        if num==7:\n",
    "            seven_fidelity_notcorrect.append(fidelity)\n",
    "        if num==9:\n",
    "            nine_fidelity_notcorrect.append(fidelity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ac3cb43-0f1b-45a2-863c-a4456585c990",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4e811b56-e4a4-40d2-a9f1-731bba0926a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a37ee62f10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(physical_errorrates,three_fidelity,label=\"Three\")\n",
    "plt.plot(physical_errorrates,five_fidelity,label=\"Five\")\n",
    "plt.plot(physical_errorrates,seven_fidelity,label=\"Seven\")\n",
    "#plt.plot(physical_errorrates,nine_fidelity,label=\"Nine\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2745e19-fa82-41e1-99c7-815a53835a25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a37b3775d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(physical_errorrates,five_fidelity,label=\"Five\")\n",
    "plt.plot(physical_errorrates,five_fidelity_notcorrect,label=\"Five not correct\")\n",
    "#plt.plot(physical_errorrates,five_fidelity,label=\"Five\")\n",
    "#plt.plot(physical_errorrates,seven_fidelity,label=\"Seven\")\n",
    "#plt.plot(physical_errorrates,nine_fidelity,label=\"Nine\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ddcda9c-162a-4d52-bfb1-664b01c828c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZZI', 'IZZ']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\73747\\PycharmProjects\\DynamicCircuit\\yalehack\\Lib\\site-packages\\qiskit\\visualization\\circuit\\matplotlib.py:266: FutureWarning: The default matplotlib drawer scheme will be changed to \"iqp\" in a following release. To silence this warning, specify the current default explicitly as style=\"clifford\", or the new default as style=\"iqp\".\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
     ]
    },
    {
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kyJG11t/q4JNF16+++kqSdNNNN6lx48aVOmbZsmWyWCxq27btRV/Xv39/WSwWzZs3z/5YZGSkLBaLDh06pG3btumee+5RVFSUQkJC1KNHD/373/++aJunT5/W5MmTFRsbq6CgIHXq1ElvvPGGrFarduzYIcm5yNe+fXtZLBYlJSVp5cqVGjVqlCIiImSxWPTRRx/ZX5eTk6OZM2eqV69eatasmRo1aqQ//OEPeu+992S1Wl32x9b2nj17tHbtWo0ZM0YREREKDQ3V9ddfb++TJG3YsEG33nqrWrVqpbCwMA0ePFi7d+92+16Lior0wQcf6Prrr1fz5s3VsGFDderUSc8995xycnIuOk6uZGRkaNKkSerUqZOCgoLUpk0bTZw4Ubm5uZowYYIsFoveeecdh2MOHToki8Wi1q1bu223Z8+eslgsWr16tcPj5Xe6rlu3zj42wcHB6tu3r/1y1hcqP182ixYtksVi0TPPPCNJev/992WxWOx/XnvtNY/HAwAAAAAAoLaUWqU1+2r/vL8dlnIKav+8rqzfL5WU1u45i0qkzQdr95yovKeeekr//e9/1a5dO0m//3w2NjZWAwcO1OTJk/XFF19o7dq12rBhg5YtW6a33npLd999t9q0aaM777xTGzdutLf32GOP6cCBAxRcAfiEt956S2+99ZYSExP1+OOP68svv9QTTzyhb7/9VhaLxf669PR0bd68WTfddJNxna0CH7j6v7Njx45JKvsCVFm9evWSxWLR0aNHdfLkSbVs2dLpNV988YXWrFmj3r1766677pIknThxQidOnFBoaKi+/vprPfPMM2revLnatm2rgoICbd++XX/84x8VHh6uIUOGOLW5bds23XjjjUpPT1dwcLDi4+N17NgxTZo0SUeOHHG50zUzM1OHDx9Wo0aNtGDBAr388stq3ry52rdvLz8/P/ulb7dv366bb75ZBw4cUKNGjdS5c2dlZWVp27Ztevjhh7V+/Xqny93a2m7YsKG+++47Pffcc2rdurXatGmjxMRELV++XIMHD9aePXs0d+5cPffcc4qMjFRUVJQSExO1bNkyDR48WMnJyU4F75SUFN10003avXu3AgIC1LFjR7Vs2VIpKSmaMWOG/vvf/+rXX391+o0Gd7Zt26Zhw4bp+PHjCgkJsY/d3//+d+3fv19nzpxxGjtJ+u233yS531laXFysXbt2yWKxOBS7c3JytH//fvn7+2v58uWaMmWKWrRooejoaO3du1fr1q3TsGHD9NVXX2nUqFFOYxocHKzOnTvbH8/NzVW/fv20ZcsWFRQU6PLLL3d473379q3UOAAAAAAAgPonJCRE/fr1U0hIiNFdqbL9J6WM7No/7/lSafMhaeCltX/uC63fb8x516VIV3cx5tzVxRfWwIWeeeYZvfHGG/Z/L1++XBMmTNDhw4crdXxxcbEWLFigL774Qo899pimT5+u4OBgNWnSRJKUnZ3tUwVXX8wA4AmzrYH4+Hg9/vjjWrRokW699Vb74wcPHtSsWbN05513av78+fbHv/76a/3P//yPQkJClJuba0SXPeaTO107dOggSfr888/10ksv6dChQxUe07RpU8XFxUmSNm/e7PR8QUGBJk+eLEn63//9X3vF3VYUtd20fN68eTp+/Lg2bdqktLQ0jRgxQpL04YcfOrV56tQpjRgxQunp6frLX/6i48ePa+vWrTp+/Lhef/11vfvuu9q0qeymGOULh7bdloWFhXr99df13nvv2c+Znp6uyy+/XAcPHtQNN9ygAwcOaOLEiUpPT9e2bdt08OBBrVixQsHBwfrkk0/09ddfO/TJ1nZJSYmmT5+uxYsXKzU1VVu3btXOnTvVtGlTnTx5Uvfcc49effVVffnll/bnd+/erWbNmik9PV1Lly51aPfMmTMaNmyYdu/erfvvv19paWnau3ev9uzZo8TERMXFxWnr1q166623KpwrqWyH68iRI3X8+HE9/fTTSk9Pt4/djBkztHTpUm3atEkWi8Xp/qu2oqu7Szbv3r1bRUVF6tChg/0bGqlsrm27g6dNm6YPP/xQx44d02+//ab09HRdd911Ki0t1VNPPeWwi9g2pt26dZOf3+9L7t5779WqVavsj61YsUKrV6+2/xk4cGClxgIAAAAAANQ/7du315w5c9S+fXuju1Jlh06Z89w2WfnSac8v3FYt0jOlgmJjzl1dfGENlHfrrbc6FFynTp2qwYMHV7rgWp7VatWcOXPsP8e0OXz4sMufXddXvpYBwFNmWwN//OMf5efnp7ffftvh8Q8++EC5ubm6++67HR7/5ptvFBQUpKFDh9ZiL73jk0XXKVOmKCgoSMXFxXr55ZfVoUMHdenSRQ888ICWLFmi8+fPuzzuyiuvlOS66PrWW2/p0KFDuu2229SvXz/747aia3FxsT7//HPdcccd9oJsUFCQHn74YUly+cX1scceU1pamh5//HH97W9/cyjwPffcc+rRo4cKCwvVtm1bNWvWzOmcpaWl+uc//6kHH3xQ/v7+kiQ/Pz/5+flp/PjxOnHihJ566im9/fbbatq0qf34gQMH6qGHHpIkp0sf29ouKSnRv//9b40ePdr+XGxsrP3fP/zwg+bPn68xY8bYn4+JibFfX/vgQcdrnEycOFEpKSmaOHGiPvjgA4edxLGxsZo1a5YkORWB3XniiSeUmpqqxx57TG+++abD2E2aNEmXXXaZzp8/r/bt2ys0NNThWFsR1N1OV9s9Vi8sypYfm3/9618aN26cfdybNm2qOXPmyM/PTwcOHFBycrLTca6KvMnJycrLy1O7du0UFhZWqfcOAAAAAADqv5KSEuXk5KikpMTorlTZ0TPmPLdN6mnjzm2VlFYHxsAbvrAGbFq2bKl//vOf9n+/8MIL+n//7/+5vb1bRRo1aqRvvvnG/nPo0tKya1h37dpVkyZN8r7DdYQvZQCoCrOtgV69eqmkpMThEupS2QbDbdu2qVevXg6PJyYmat++ffXqEsM+WXTt1auXtm/frvvvv99erExOTtacOXM0evRoderUScuWLXM67qqrrpIk++5SmxMnTuj1119Xw4YNNWPGDIfnbAW1W2+9VcOGDXNqs6io7K72FxbUdu3apS+//FLNmjXTq6++6vJ99O7dW5Lz/VxtRcNrr71W48ePdzpu1apVWrlypcLCwvTKK6+4bPsPf/iDJOfiqK3tO+64w36fgPICAwMlSbfccovL3y4ICCi7YnX5y+Tu3r1bn3/+uSIjI/X666+77I+tIJmWluby+fL27NmjBQsWqEWLFm7b69mzpyTnsZMq3ulqK7q6G/dhw4Y5FJtt2rdvr6ioKKf3YTvOVV9sz7nry8GDBzVq1Cg1adJEzZo107333qvTpw38jh4AAAAAAFSLpKQk9erVS0lJSUZ3pcqOnTXu3KdzjN/pmZZp7PlTDRz/6uALa8Dm3XffVYsWLSRJX375pV577bUqt2UruJa/h+v9999vL8q89NJLSkhI8L7TdYAvZQCoCrOtgUsuuUQZGRn2ull5aWlpioiIsNegbL755hvdeOON9g1wdZ1P3tNVkjp37qwPPvhA7733njZu3Kjly5fr66+/1pYtW3T48GHdfPPNSkxMVHR0tP0Y207XLVu2OLT1wgsvKDs7W88++6z90sU2tqLrHXfc4bIfe/futfenvH//+9+yWq364x//6LBLszxbuC4syNkKdffcc4/L4xYsWCBJGj9+vNtrgTdo0EBS2W5cV23feeedLo9LTEy86PP79u2TJHXp8vtNJebPn6/S0lKVlJS4LORKvxenK3Ptclt7Y8eOdbpvrE2jRo0kORc6T58+rdTUVDVp0kSdOnVyeaytKOuu6GrbveyKbVdt+X5drLB6seeys7M1aNAghYeHa/78+crPz9ekSZM0YsQIrVmzxuFSxZ6KjY316ngAAAAAAOBo3LhxHr0+PT1dkvT99987XULUnY8//tjDXtWskf+zQw1Dwl0+95ehUmgj98eGBv3+35ecf7fd7ly+9L8/un6u62VXqODc8Ur2tvp1u/Gv6nLNQy6fq673L7kfg2mvv6Gxy/+vkr2teb66Bmy7TN3p0aOH/d6Ep06d0qOPPlrlc7kquNru4dq5c2c999xzatCggf7617/qj3/8o9t2jPrZn69moKbcOGWTgpu2VvrxdEVH96r4AB/ji+/fV9dARZ+DlRUcHKzCwkKXzxUUFNhfk5WVZX/8m2++0TPPPKMBAwbo559/rvS5PP0cLP8e+/fvX+n5uJDPFl1t/P391adPH/Xp00d//etfNXv2bP35z39Wdna2vv76az322GP213bt2lWNGzdWenq60tLSFBUVpe3bt+ujjz5SRESEXnjhBYe2CwsL7b+B4O7+m+6KarZwXH/99W77fuDAAadji4uL7YXP4cOHuzxu7dq1kqTrrrvObdu2nZjt2rVz2faAAQOcjiktLdWOHTvcPm+1Wu3Pl+/zihUrJJV903Hq1MVvuFGZa5fb2hs0aJDb16SmpkpyLpzaFkr37t3tl4Eur7S01OXlgEtKSrRr1y75+fm5neuSkhIdOXJEktS2bVtJv4+pn5+f071lpYsXXWfPnq20tDStWrXK3l50dLT69u2rJUuWeLWl3vZhDgAAAAAAqkdeXp5Hr7f9cLGgoKDSx1bmCmG1ySrnn63YhDaSwoLdPm3n51e517ly8uRJZZ82bkw6XmTeauP9Z+fk1qlMmHENSNIjjzxi//vUqVMr/PmnOxcruEplO1z/9Kc/qWXLlrrlllvUqlUrnThxwmVbRv3sz6wZqCrb7uWSkhKfel+V5YvvnzVwcXl5eQ63nizPtkHwwnGw5cTTna7efA66+2ytDJ8vul7owQcf1DPPPKPs7GyncPr7++uKK67QL7/8ok2bNikqKkpPPfWUSktL9fLLLzvcF1Uq2/V5/vx5tW3bVs2bN3d5PneXsj169KgkOe2ctSkoKNDq1audjt2zZ4+KiorUrl0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      "text/plain": [
       "<Figure size 2422.9x1956.5 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rep=bitfliprepetitioncode(3)\n",
    "rep.construct_syndrome_table()\n",
    "rep.construct_circuit()\n",
    "rep.circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22b87ae2-cc59-4252-841c-8a9b41c7b4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(physical_errorrates,three_fidelity,label=\"Three\")\n",
    "plt.plot(physical_errorrates,three_fidelity_notcorrect,label=\"Three not correct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48094663-c90a-4a9d-866a-ce8f5a26c1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZZI', 'IZZ']\n",
      "The result is: 01 110\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'XXI'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m rep\u001b[38;5;241m.\u001b[39mset_mutecorrection(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      4\u001b[0m rep\u001b[38;5;241m.\u001b[39mset_benchmarkwidth(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m \u001b[43mrep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtest_stabilizer_circuit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mXXI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\PycharmProjects\\DynamicCircuit\\QEC\\QECCode.py:284\u001b[0m, in \u001b[0;36mQECCode.test_stabilizer_circuit\u001b[1;34m(self, errorstr)\u001b[0m\n\u001b[0;32m    280\u001b[0m result\u001b[38;5;241m=\u001b[39mresult[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    281\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe result is: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39mresult)\n\u001b[1;32m--> 284\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe expected rsult is \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_symdrome_table\u001b[49m\u001b[43m[\u001b[49m\u001b[43merrorstr\u001b[49m\u001b[43m]\u001b[49m)\n\u001b[0;32m    286\u001b[0m plot_histogram(counts)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'XXI'"
     ]
    }
   ],
   "source": [
    "rep=bitfliprepetitioncode(3)\n",
    "rep.construct_syndrome_table()\n",
    "rep.set_mutecorrection(True)\n",
    "rep.set_benchmarkwidth(0)\n",
    "rep.test_stabilizer_circuit(\"XXI\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02314fbe-5083-4c1d-8066-9b985d513b3c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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