phase_flip_with_benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "from qiskit import*\n",
    "from qiskit.tools.visualization import plot_bloch_multivector\n",
    "import numpy as np\n",
    "from numpy import linalg as LA\n",
    "import random\n",
    "from qiskit.extensions.simulator import snapshot\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize state vector\n",
    "\n",
    "def normalize(vector):\n",
    "    return vector / LA.norm(vector)\n",
    "\n",
    "def initialize( vector ):\n",
    "    circuit.initialize(vector.tolist(),0)\n",
    "    for i in range(1,5):\n",
    "            circuit.initialize([1,0],i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encode\n",
    "def encode():\n",
    "    for i in range(5):\n",
    "        circuit.h(i)\n",
    "    circuit.cx(0,1)\n",
    "    circuit.cx(0,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#error\n",
    "def error(error_prob):\n",
    "    for i in range(3):\n",
    "        if( abs(random.random()) < error_prob ):\n",
    "            circuit.z(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correction\n",
    "def correct():\n",
    "    circuit.cx(0,3)\n",
    "    circuit.cx(1,3)\n",
    "\n",
    "    circuit.cx(1,4)\n",
    "    circuit.cx(2,4)\n",
    "    \n",
    "    syndrome = [circuit.measure(3,0),circuit.measure(4,1)]\n",
    "    \n",
    "    if(syndrome == [1,0]):\n",
    "        circuit.z(0)\n",
    "    elif(syndrome == [0,1]):\n",
    "        circuit.z(2)\n",
    "    elif(syndrome == [1,1]):\n",
    "        circuit.z(1)    \n",
    "    \n",
    "    circuit.cx(1,2)\n",
    "    circuit.measure(2,0)\n",
    "    circuit.cx(0,1)\n",
    "    circuit.measure(1,0)\n",
    "    circuit.h(0)\n",
    "    circuit.snapshot(\"one_qubit\", qubits=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.22537428701932613, 0.21956844546321633, 0.21738511769736174, 0.21945934008540974, 0.22189835872993258, 0.2177408550391589, 0.2179554246673739, 0.22120921167813432, 0.21504767620690174, 0.22113249507317176, 0.22556865322143518, 0.2234158176614677, 0.21507037811628496, 0.2192425978445736, 0.220602934266914, 0.21889414920570008, 0.21242725246862804, 0.21981826381009, 0.21813049940428164, 0.2126458884674774, 0.2141655961849589, 0.21808054659114076, 0.21376743751708815, 0.21563059734302584, 0.2255632690457152, 0.22341396300885855, 0.21406629141770367, 0.22079429269068843, 0.209465586944633, 0.2145781186244177]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eac8aa3550>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOy9eXRb93nn/XmIlQBIcAMpiYso0XK8SLLk0HI2x1ncxk5auZOmsZ3YbVKn7ps2M33H75lOTt3JnOPpNvE5b9L2pG3SmXTaJK0TJ3WixnbcxvVbu44XydYu2RIli+ImLiDBBSABgvi9fwAXhCCQuNhB4fc5R+cA915c/C5F3uc+2/cRpRQajUajqT3qKr0AjUaj0VQGbQA0Go2mRtEGQKPRaGoUbQA0Go2mRtEGQKPRaGoUa6UXkAttbW2qt7e30svQaDSaDcXrr78+pZTypW/fUAagt7eXQ4cOVXoZGo1Gs6EQkcFM202FgETkThF5S0QGROSLGfY/LCKnROSYiDwnIlsT2/eIyMsicjKx756Uz4iI/KGInBGR0yLyn/K9OI1Go9HkTlYPQEQswNeAnwOGgYMickApdSrlsMNAv1IqJCKfB74M3AOEgF9VSp0VkS3A6yLyrFIqAHwG6AauU0rFRKS9qFem0Wg0mnUx4wHsAwaUUueVUhHgceDu1AOUUs8rpUKJt68AXYntZ5RSZxOvR4EJwIhDfR54VCkVS+yfKPRiNBqNRmMeMwagExhKeT+c2LYWDwLPpG8UkX2AHTiX2NQH3CMih0TkGRHZkelkIvJQ4phDk5OTJpar0Wg0GjOYMQCSYVtGASERuR/oBx5L274Z+BbwWeOJH3AAS0qpfuCvgW9mOqdS6htKqX6lVL/Pd0USW6PRaDR5YsYADBOP1Rt0AaPpB4nIHcAjwH6lVDhleyPwFPD7SqlX0s77g8TrJ4HduS1do9FoNIVgxgAcBHaIyDYRsQP3AgdSDxCRvcDXid/8J1K224nf3P9OKfVE2nl/CHwo8fp24Ex+l6DRaDSafMhqAJRSUeALwLPAaeB7SqmTIvKoiOxPHPYY4AGeEJEjImIYiE8C7wc+k9h+RET2JPb9CfDLInIc+GPgc8W7LM1G4/XBGV4fnKn0MjRXAceGA5ybXKj0MjYEspHmAfT39yvdCHZ1cudXXyAQWubf/+sHsVq0Qokmfz7ylRcQgWd+5zZEMqUwaw8ReT2Rb72MmvhLm19aZngmlP1ATUVYWl7h7MQCl+aW+LczutJLUxjj80u8eWmeo8OzlV5K1VMTBuA3v/U6X/j7w5VehmYN3ro0z0os7on+w2sXK7wazUZmeSVGILQMwD+8qn+XslETBmBbm5u3p4KVXoZmDU6OzgHwizdt4V/fnGBsdrHCK9JsVGaCEQDcdgsHjo4yv7Rc4RVVNzVjAGYXl5O/HJrq4sToLA1OK//l599BTMETh4YrvSTNBmVqIf43/sC7e1lcXuHA0Ssq1jUp1IwBADivvYCq5OToHDduaaSn1cVtO9r47sGhZEhIo8kFfzDegvSh69q5blODDilmoaYMgA4DVR/RlRhvjs1x4xYvAPft62EksMgLZ3UyWJM7/oQH0Oax86lbezgxMsdxnQxek5owAN0tLix1wgVtAKqOc5NBwtEYOzsbAbjj+g5a3XYe109umjyYWoh7AK0eB3fv6cRpq+Pv9e/SmtSEAbBZ6uhpcWkPoAo5ORp/OjM8ALu1jk/0d/HT0xNMzC1VcmmaDYg/GMFmERqdVrz1Nn5h9xYOHBkhGI5WemlVSU0YAIDeVpfOAVQhJ0fncFjr2J4I0wHce0sPKzHFE6/rZLAmN/wLYVrdjmQD2H37eghGVvgnnQzOSM0YgG1tHi5MBdlInc+1wImRWa7f3HhZ9++2Njfv6Wvl8YMXielksCYH/AsRWj325Pube5p4R4dOBq9F7RgAn5vF5RXG58LZD9aUBaUUp8biFUDp3Luvh6HpRV46N1WBlWk2KlMLYVo9juR7EeHefd0cHZ7lxIhOBqdTOwag1SgF1SJR1cLQ9CLzS9Fk/D+Vj9zYQbPLpp/cNDkxtRChzW2/bNt/2NuJw1rH4wf171I6tWMAfLoUtNo4kUgAGxVAqTisFj7xzi7++eQ4k/Paa9NkRymFPxi+LAQE0OSy87Fdm/nh4VFCEZ0MTqVmDMDmRicOa50uBa0iTo7OYqkTru1oyLj/nlt6iMYUP3hDJ4M12QlFVlhajl0WAjK479YeFsJRfnxsrAIrq15qxgDU1Qm9rVoTqJo4OTrHjnYPTpsl4/5r2j3s29bC46/pZLAmO0YTWGtaCAigf2sz17R7dEgxDVMGQETuFJG3RGRARL6YYf/DInJKRI6JyHMisjWxfY+IvCwiJxP77snw2T8XkbIE5re1uXUpaBVxYmQuY/w/lU/t6+GCP8Qr5/1lWpVmozKVkIFoy+ABiAj33tLN4YsBTo/NlXtpVUtWAyAiFuBrwF3ADcB9InJD2mGHgX6l1G7g+8CXE9tDwK8qpW4E7gS+KiJNKefuB5ooE9t8boamQ0RXYtkP1pSUibklphbCGSuAUrlz5ya89Tb+4eBQmVam2agkPQDPlR4AwC/f3IXdUqe7zFMw4wHsAwaUUueVUhHgceDu1AOUUs8rpYyJK68QHxyPUuqMUups4vUoMAH4IGlYHgN+txgXYoZtbW6WVxQjAS03XGkMCehsBsBps/Dxmzt59sQl/As6GaxZG3+KDEQmmt127tq1iX88PMJiZKWcS6tazBiATiD18Ws4sW0tHgSeSd8oIvsAO3AusekLwAGl1LpZGRF5SEQOicihycnCBMK0Kmj1YNRk35DFAEC8mzOyEuMf3xgp9bI0Gxh/cO0cgMF9+3qYX4ry9HGdDAZzBiDTUM2MGTkRuR/oJ/5kn7p9M/At4LNKqZiIbAF+BfjzbF+ulPqGUqpfKdXv8/lMLHdtkqqgk9oAVJqTo3P0trpocNqyHnttRwPv3NrMPxy8qDu5NWsytRDG47CuWVQAcOu2Fra3uXUyOIEZAzAMdKe87wKuENYQkTuAR4D9SqlwyvZG4Cng95VSryQ27wWuAQZE5ALgEpGBvK4gB1rddhqcVi74tQGoNCfHZrMmgFO5b18P5yeDvPb2dAlXpdnIpMtAZEJEuG9fD4cGZzgzPl+mlVUvZgzAQWCHiGwTETtwL3Ag9QAR2Qt8nfjNfyJlux14Evg7pdQTxnal1FNKqU1KqV6lVC8QUkpdU/jlrI+I6PGQVcBsaJmh6UVuzNAAthYf27WZBqdVP7lp1sQfDK8b/jH4+M2d2Cyif5cwYQCUUlHi8fpngdPA95RSJ0XkURHZnzjsMcADPCEiR0TEMBCfBN4PfCax/YiI7Cn+ZZhnW5ub8zoEVFFOjl0uAW2GeruFj+/t5OkTlwiE9GhPzZXEPYDMCeBUWj0OPnLjJv7xjRGWlms7GWyqD0Ap9bRS6lqlVJ9S6g8T276klDqQeH2HUqpDKbUn8W9/Yvu3lVK2lO17lFJHMpzfU8yLWo9tbW5GZxdr/j++kpwyWQGUzr37eohEdTJYk5mphQhtWUJABp/a18Ps4jI/OXGpxKuqbmqmE9hgW5sbpeDidCj7wZqScGJklk2NzowNO+tx/eZG9nQ38Q+v6WSw5nJiMcV0MD4LwAzv2t5Kb6ur5qeF1aQBAHQYqIIYQ+Dz4b593ZydWEj2EWg0AIHFZWIK0x5AXZ1wzy09vPb2NMMztfswWHMGoDdhAHQlUGVYjKxwbnIhbwOwuyveOK49OE0q2ZrAMnFTVzwHNTRdu42hNWcAGp022jwO3QtQIU5fmiOm4MZO8wngVDY1OgG4NKvnBWtWmcoiA5GJtoa4sZis4Q7zmjMAANva9ID4SmFWAmItmlw27JY6xue1AdCs4l9HCG4tfIljp2p43kSNGgCtClopTo3O4q230dlUn9fnRYT2RgcTerSnJoX1pKDXwltvw1on2gOoNba1eZhaCDO/tFzppdQcJ0bm2NnZiEgmhRFzdDQ6GZ/THoBmFf9CmDqJT/8yS12d0OZxaA+g1tjW5gLgwpROJJaT5ZUYb12az6kBLBObGp1c0gZAk8JUMEKL246lLrcHi7YGO1PaA6gttrXF+870gPjyMjCxQGQllnf830CHgDTp+BfM9wCk0uZx6BBQrbG11YWI9gDKjSEBXagH0NHoZCEcZSGsB3xr4pgRgsuEz+Ngar52pUVq0gA4bRa2eOt5W3sAZeXk6Bz1NkuyGS9fOhrjT3oTOgykSeAPmtMBSqetwcHUQrhmZ07XpAEAtCpoBTg1Osf1mxtyjtOm09EQ7wUY12EgTYKpBXNKoOn4PA6iMcXsYm0WhNS0ATg/FdSaMmUiFlOcGptjZ54NYKl0eA0DoD0ADYSjK8wvRU3LQKRiNIPVaiK4pg3A/FKU6WDtxv/KyeB0iIVwtOAEMMRzAKANgCbO6jD43ENARjPYZI2Wgta0AQB0GKhMnBwtTgIYwOOw4rZbdAhIA+TXBGbga4h/plYrgWreAOiO4PJwcnQOm0XY0VGc0Q8djU4tB6EBYCqYuxCcQZv2ALIjIneKyFsiMiAiX8yw/2EROSUix0TkORHZmti+R0ReFpGTiX33pHzmO4lznhCRb4pI9ungRaSruR5rnXBBG4CycGJklh3tDTisaw/szoWORifjWhBOw6oHkE8OwFtvw2aRpJhcrZHVAIiIBfgacBdwA3CfiNyQdthhoF8ptRv4PvDlxPYQ8KtKqRuBO4GvikhTYt93gOuAXUA98LkCryUnrJY6elq0KFw5UEpxqoAZAJnoaHRoD0AD5CcFbSASl4PQHsDa7AMGlFLnlVIR4HHg7tQDlFLPK6WMrqpXgK7E9jNKqbOJ16PABOBLvH9aJQBeMz5TTnQpaHkYnwvjD0aKUgFkENcDCusqLg3+YASHtQ63PT/v0pfoBahFzBiATmAo5f1wYttaPAg8k75RRPYBduBc2nYb8ADwk0wnE5GHROSQiByanJw0sVzzbGtzc8EfrJomkDcvzSW7Za8mVjuAi+cBtDc6iURjNVu/rVllaiFMm8eRt8Bgm0cbgPXI9FPNeMcUkfuBfuCxtO2bgW8Bn1VKxdI+9hfAC0qpFzOdUyn1DaVUv1Kq3+fzmViuebb53Cwtx6pCWEwpxW995w3+n+8drfRSis7J0TlE4jN9i4XRDawrgTT5ykAY+HQIaF2Gge6U913AaPpBInIH8AiwXykVTtneCDwF/L5S6pW0z/x34iGhh3NfeuFsa62eUtCTo3OcnwxydmKe4FWmcXNydJZtbW7cDmvRzpmcDFYFxltTWfzB/LqADdoa7PiDkaqJBJQTMwbgILBDRLaJiB24FziQeoCI7AW+TvzmP5Gy3Q48CfydUuqJtM98DvgIcF8Gr6AsbPNVTynogaNxmxpTXHVhoPgQ+OLF/0E3g2lWiXsAuSeADdo8DlZiiplQ7VUCZTUASqko8AXgWeA08D2l1EkReVRE9icOewzwAE+IyBERMQzEJ4H3A59JbD8iInsS+/4K6ABeTmz/UhGvyxQdDU7qbZaKl4LGYooDR0bZ2xMvkDp+FRmAmWCEkcBiUeP/EE/cgRaEq3WUUoWHgJJyELVnAEz55Eqpp4Gn07Z9KeX1HWt87tvAt9fYV7x4QJ7U1QlbWytfCvrahWkuzS3xex+7nom5Nzk6fPUYgFNj8RnAO4vsAThtFppcNp0DqHHmw1EiKzHa8pgFYGA0g00thHkHDcVa2oagZjuBDbb7Kl8K+qMjo9TbLNxxfTu7Or0cGw5UdD3FpBQVQAZ6MphmVQeocA+gFhPBNW8AtrW5GZoOsbxSkTQEkWiMZ06M8fM3duCyW9nd7WXQH2I2dHWUN54cnWOL10lzAUm6tWhvdOoQUI1TSBOYQaoHUGvUvAHobXUTjSmGZxYr8v0vnp0kEFrm7j1bALipK54HODZydXgBJ0dnubGIDWCpdDQ4dAioxpkqQAjOoNFpxW6p0x5ALbLdZ5SCVmY62I+OjNLssnHbjniPg9Ete6zMeYDzkwu890/+tajhsGA4yvmpYEnCPxCvBJpcCLNSg+V7mjj+hBBcWwEegIjga6jN2cA1bwCMAfFvV2A+cCgS5V9OjXPXrs3YLPH/Cm+9jW1t7rLnAZ49Oc5IYJHXB2eKds43L82jVHEkoDPR4XWyElPJm4Cm9jByAC0FhhjbPHbtAdQizS4b3npbRTyAfzk1zuLyCnfftOWy7fFEcHk9gJ+dmwLgor94HoDhTVzTXhwJ6HQ6Esm78dna+8PVxPEvhPHW27BbC7uVxfWAaq8MtOYNgIjQWyFRuANHRtnsdXJLb8tl23d3eRmbXWKiTGqX4egKBy9MA3DBXzxPaNAfxFIndDbVF+2cqehmMM1UsLAeAINa1QOqeQMAsL3NzduT5TUAM8EI/3Zmkv03baEubUj6Td2JhrAyeQFvDAZYWo7hsNYxOF08A3DBH6Kzqb7gp7O1SBoALQtds/gXwgX1ABj4Ghz4azCfpA0A8VLQ0dkllpZXyvadz5y4RDSm+MW08A/Ea+brhLI1hL00MIWlTvjIjZsYLGIIaNAfZGurq2jnS6fNY6dOtCBcLVNoF7BBm8dBTFGwHMTAxDy//Jc/Y25pY5RxawMA9CbGQ14o4s0vGz86MkKfz52xQsZlt7KjvaFsieCXzk1xU5eXnZ2NBELLRelBUErx9lSQ3oTgXimwWupo8zj0ZLAaxl/EEBAU3gz272eneH1whnMTlakqzBVtAIiHgICyhYFGA4u8dmGau/d0rqlhvrvLy/Hh2ZIPPJlbWubY8CzvvaaNnpb4z2FwuvCfQyC0zPxStKQeAJR/NvDPBqaS+RJNZYmuxJgJRWgtUggICm8GM/qJNoqwnDYArHoAb5fJA/jxsVGUgv0Zwj8Gu7u8+BNCaqXk1fPTrMQU7+lro7ctfrMeLEIi2MgllNIDgMRoyDKFgAb9QT77fw7y0N8duuokuzciM6FllMpvFnA6xjkKNQBDM/Hf+5mgDgFtGDwOK74GR9k8gANHR7mpy5s0PJnYbXQElzgP8NLAFE5bHTdvbaKnxTAAhf8cjHMYRqVUlEsOQinF7//wBCLxG8/fvTxY8u/UrI/R/1GIDIRBsfSAtAewQSnXfOCBiQVOjMyxf896UzXhus0N2CxScgPws3NT3NLbgsNqwWW30t7gKIoHcGEqhAh0NZfWAGxqdOIPRohES6vl9KMjo7x4dorf++j1fOAdPr7xwjntBVQYfxFkIAw8DisOa13BvQBDCc9XG4ANxvYyGYADR0cRgV/cvXnd4xxWC9dtaixpInhifokz4wu895q25Latra7ihID8QbZ463Ha8hvUbRZjNGQpeyYCoQj/48en2NPdxKdv3crvfHiH9gKqgKkiCMEZJOUgCvAAZheXmVuKPxTMbBAxR1MGQETuFJG3RGRARL6YYf/DInJKRI6JyHMisjWxfY+IvCwiJxP77kn5zDYReVVEzorIdxPTwyrGtjY3/mCkpEPGlVIcODLCe/paaU/UsK+HkQgu1ai6nw34AXhvX6oBcBelGupCiUtADdqTzWClywP80dOnmV1c5o8/vgtLnbC3p1l7AVWA4QEUIwcQP09hzWDDM6sPToGrxQMQEQvwNeAu4AbgPhG5Ie2ww0C/Umo38H3gy4ntIeBXlVI3AncCXxWRpsS+/wl8RSm1A5gBHiz0YgohWQpaQi/g+MgsF/yhdZO/qdzU1cR8OFqy8tSXBqbw1tu4IaUUdWuLi4n5MKFIYTe2QX+oLAagoyFuAEqVB3jlvJ/vHRrmc7dtv2yoveEF/O3LF0ryvZrsTC2EsdYJjU5bUc7XVuBw+KHpePzfZbcwHbxKDACwDxhQSp1XSkWAx4G7Uw9QSj2vlDLM3yvEB8ejlDqjlDqbeD0KTAA+idc+foi4sQD4W+CXCr2YQkiWgpbQAPzoyCh2Sx133rh++MdgV1fplEGVUvzsnJ/39LViSelE3pr4OVwsoCN4bmkZfzDC1hJXAMFqCKgUchDh6Aq/9+Rxulvq+Z0P77hsn+EF/PUL51nQXkBF8C9EaHHbr+ikz5e4HlDhHsDOLV4CV1EIqBMYSnk/nNi2Fg8Cz6RvFJF9gB04B7QCgcS8YTPnLDk9rS5ESmcAVmKKfzo6ygfe4cPrMvfEsqPdg9NWx9ES5AEG/SFGAou8JyX+D3EPwNifLxf9Rglo6T2AFrcdm0W4VIIQ0F88f47zk0H+4Jd2UW+/Mpexmgu4UPTv1mTHHwwXJf5v4PPYmQ5G8paDGJ5ZxOOw0tvmuqqSwJnMa8afkIjcD/QTHxKfun0z8C3gs0qpWI7nfEhEDonIocnJSRPLzQ+H1UJnU33JDMCr5/1MzIe5O0v1TypWSx07t3hLogn07wNx9c/39rVett2o2y+kFNQIWZXDAxAR2huKXwo6MLHAX/5/59h/0xZuv9aX8RjtBVSWqYVI0eL/EPcAYoq85cWHZ0J0NdfT7LYzE1wueRNnMTBjAIaB7pT3XcBo+kEicgfwCLBfKRVO2d4IPAX8vlLqlcTmKaBJRIzB8BnPCaCU+oZSql8p1e/zZf5DLBalLAU9cHQUt93Ch69vz+lzu7q8nBidJVrkkZU/OzfFZq+TbWm9CN6EPHYhHoDx2XLkACDRDFbEKqBYTPF7Tx7Haavjv/1CerrrcsrpBbw9FeSjf/piSfNUGwl/MFyUElCD5GjI+fye3oemF+lqdtHsshNZiRGKlE9bLF/MGICDwI5E1Y4duBc4kHqAiOwFvk785j+Rst0OPAn8nVLqCWO7ipvG54FPJDb9GvCjQi6kGGxvc3NhKlh0yx2OrvD08TE+cuOmnMsib+pqYmk5xtkiaovEYoqXz/l57zVtGaUoegssBb0wFaS9wYHLbs1+cBHoaHQWtQroideHeO3taX7vo9cnG4TWopxewOuDM5wam+OrPz1T0u/ZKMSF4IoYAjKawfLIAyilGJ4J0d1ST4srbpQ2QhgoqwFIxOm/ADwLnAa+p5Q6KSKPisj+xGGPAR7gCRE5IiKGgfgk8H7gM4ntR0RkT2LffwUeFpEB4jmB/128y8qPbW1u5sPRog+G+Le3JplbirJ/j7nqn1SMRHAxw0CnxuaYCS3z3mtaM+7vaXUXpAc06A+VXAIilY5GZ9EE4aYWwvzR02+yr7eFT/Z3Z/8A8H/fcW1ZvIDRhCzIj46OMjAxX9LvqnZCkSihyEpRhOAMVj2A3A3ATGiZYGSFrmYXTYkc30aQgzDVB6CUelopda1Sqk8p9YeJbV9SSh1IvL5DKdWhlNqT+Lc/sf3bSilbyvY9SqkjiX3nlVL7lFLXKKV+JTVsVCmMUtCBIiv5HTg6SqvbflnDlVm2tbppcFiLmgh+KRH/f09f5vX0troYmVnMu7u2XD0ABh2NTubD0aLU5P/Bj08RikT5o4/vNF1dsqe7iQ+WwQsYm12kwWGl3mbhqz89W7Lv2QgkewCKIARn0FaAIJxRAdSdyAHAVeIB1BJ7u5uxW+t45sRY0c65EI7y09PjfDRl7m8u1NUJu7q8HB8pngfw0jk/17R7kgNV0tna6iamyEuILhSJMjEfXlfnqNisdgMX9gzx4tlJfnhklM9/4BquaW/I6bO/UwYvYCSwxDafm8+8p5enjo/x1qXa9QL8iTr7YnoAbruFepslr14AowfAyAGANgAbDq/Lxkd3buLJwyNFGw7z46OjLC3H+KW9uYd/DHZ1eTk9Nkc4WviaItEYB9+evqL6JxXj6T2fBrRyJ4ChOKMhFyMrPPLkCba3ufmtD/Tl/PlyeAFjgUW2eOv5jdu247Zb+dPnajcX4C+iDISBiNDWYC/IA+hqqac5GQLSBmDDcc8tPcwvRXn6eOFegFKKb70yyHWbGri5pznv89zU1cTyiuLNscKf+A5fnGFxeWXdcJRx876YRyI4qQJa1hxA4c1gf/avZ7k4HeIP/sPOvPWLDC/gb392Ie91rIVSitHAIpubnDS77Xz2vb08ffwSp0bniv5dG4FiCsGl4vM48koCD82E8NbbaHTGq+hgY+gBaQOQxru2t9Db6uLxg0PZD87CkaEAJ0fnuP9dW9cc/GKG3UZHcBHCQC8NTFEncOv2tT0An8eBy27JywMwhsr3bCAP4MJUkL9+4TyfeGfXmnkRMyS9gBeL7wXMLUYJRlbobKoH4HPv206Do3a9gKmkFHRxDUCbx5FXGejwzCLdLfH/G6ulDm+9bUPoAWkDkIaI8Mlbunnt7WnOTxaWDP7WK4O47RZ+aW9hTc6dTfW0uO0cGyo8EfzSOT+7upqSTymZEBF6Wlx5ewCtbnvR9FnM4HFYcdkteZeCvjgwRTSm+I8fuqbgtfzOHdcSKIEXMDobjzFv9sZvMl6XjQdv28azJ8c5UcT80EbBvxDBZbcUvdS4rSFPD2A6RFfT6kNPs8vGtPYANiafeGcXljrhu4fy9wJmghF+fGyMj9/chcdR2C+piLC7y1uwJtD80jJHhgLrxv8Ntra68vMApsojApeKiCR6AfLzAI4NBWh22ZIDcQqhVF6AUQK6pWk1cf/r79tGo9Nak30B/oVw0Z/+Ie79zoQiOTVexnsAVj0AgCaXXXsAG5X2Bicfvq6dH7w+nHcp5BOvDxGJxrj/XVuLsqbdXU2cnZgvSKXztbfj4x/fZ6IctbfVzdD0Ys66KIP+0g6CX4v2BgcTeXoAx0dm2d3VVFCYLpVSeAGGATBCQACNThu/cdt2fnp6gqNF8A43Ev5gcWYBp9PW4EApclLznFwIE47GLht+1OK26yqgjcy9+7qZWojwr2+O5/zZWEzxnVcvsq+3hXdsyq2ccC12d3qJKThZQNLvpQE/DmsdN2/NnpDuaXURWYlxKYen6qXlFUZnl8qiAZTOJq8zp7UaLEZWODM+n8yzFINSeAGjs0vYLJJsVjL4zHt7aXLZas4LKLYOkIHPk3tJsVECerkHYLt6GsFqkffv8LGp0ZlXMvjFgSkG/SHuf3dxnv4BdnfHb1CFPOn97NwU/b3Npqpc8hGFM8bhlXoOcCaMEFCuMh4nR2eJqdUZzMXis+/dRiC0zBuDM0U53zRiyeIAACAASURBVGhgkU1e5xXNaQ0JL+D5tyZ542Jxvmsj4F8Il8QD8DXkPhw+WQLanJoD0B7AhsZqqeOT/V3825nJpPttlm+9PEibx86dN24q2nraG5xs9jrzbgibnA/z5qV501UuPXnIQl9I9gBUJgQUjsaYW8ztidvIqxTTAwDY0eEB4uWBxWAssJRMAKfza+/ppcVt5yv/UhteQCymmA5GaGsohQcQz7Hk0gxmDILval79/2lx2wlFVorSu1NKtAFYh19JaME8cWjY9GdGAov865vj3HNLN3ZrcX+8uzrzTwT/7Fxc/sFM/B9gS1M9NovklAhe7QGojAcA5KwKemw4QEejY82u6LzX0+DEbqkraLBOKiOBxcvi/6l4HFZ+8/3befHsFIcuTBfl+6qZuaVlojFVohyA4QGYf3ofngnR6rZfVpFk6AFV+2AYbQDWobvFxfuuaeN7h4ZMJ0P/4dWLKOC+fT1FX89N3U28PRXMa27xzwb8NDqt7Ow096RrqRO6m3MrBb3gD+Ktt9HkKv94503e/HoBjo3MsquzuOEfiEt4dDXXMzydu5xGOisxxaW5pcsqgNJ54N1bafPY+UoN5AKMm3MpqoBc9nhJcS4ewND0Il1pFWSGHES1j4bUBiAL99zSzUhgMTlAZT0i0RiPHxziw9e1XxYPLBZGmCKfuu+Xzk3xru2Xj3/MRrwU1LwBiKuAlv/pH1ZnA1/KQRV0bmmZ85NBbipy+Megu8VVFA9gcj7MSkytGQKC+I3r/7q9j5cG/Lx63l/wd1YzhgxEekK8WOQ6GnJ4JkR38+X/N0lF0CrPA2gDkIWfu6GDFred7x68mPXYZ09eYmohzKeLVPqZzq7E03uuyqAX/SGGZxZzViPd2urmot/8fIS4Cmj54/8A7XkIwhmGdFeJDEBPkQzASIYS0Ex8+tat+BocV70XUAohuFTaPOYNwEpMMRJYvOKBryUhUaFDQBsch9XCx/d28i+nxrP+Unz7lUG6W+q5fUdpJpc1uexsbXVxbCg3DyA5/jFnA+AiGFkxFQ+NRGOMzCxWzANw2ix46205hYBWE8DFDwFB3ADMLi4zW+BNwChC2LxOCAig3m7h87f38cr56WTO52okKQRXghwAJPSATD5ITMwvsbyiLisBBR0Cuqq455ZullcU//jG2sngM+PzvPr2NJ++datpHfl82NWZuzT0S+em6Gh00OfL7ek8KQpnYjjM8EyImKpMBZBBR6MjJwNwfHiWrub65NNaselOxIULrQQamzW6gNf3AAA+dWsPHY0OvvIvZzbETNp8mFqIIEJSdbPY5KIImioDncpqEvgqMAAicqeIvCUiAyLyxQz7HxaRUyJyTESeE5GtKft+IiIBEflx2mc+LCJvJKaE/buIFC7EUiJ2dDTwzq3NPH5waM0/qm+/MojdWmd6ilS+3NTVxEhg0fQvaHL8Y1/m8Y/rYdzML0xlv4EZ5aKV6AEw6Gh0cimHbuCjw4Gil3+mYpTSFhoGGg0s0eCwmtJXctos/PYHr+HghRleGrg6cwH+YJhmlx1rHvM1zODzOJkJLbNsQg4idRBMKg6rBbfdUvWKoFl/giJiAb4G3AXcANwnIumTsg8D/Uqp3cD3gS+n7HsMeCDDqf8S+LRSag/w98Dv57788nHvLd2cnwxyKENjTzAc5R/fGOFjuzaX7GnSIKkMajIP8OaleaaDEd6TxzSyruZ6RGDQxA3MKBetrAfgZMKkBzAdjDA8s1iy8A+sdoYWbgAWs4Z/Urnnlm46Gh38nxLIUpthfmmZrz0/YOoGmg/+hUjRZaBTMUpB/SZCn4YHkMk7a3LZq34mgBkTug8YSIxwjACPA3enHqCUel4pZfyWvwJ0pex7DsgkZK+AxsRrLzCa49rLysd2b8bjsPL4a1d2Bv/wyAgL4WjRdH/W48ZOLyKY7gd4KRn/zy4Al47DamGLt95UN/CgP4THYS3pH2Y2OhodTMyHiZko2TXCaLtNlsXmQ4PTRrPLluyQzpfR2UVT4R8Dh9XCLb0tnBmvzMSwfzo6xmPPvsXL50rjgcSHwZfQAHjMj4YcngnR0ejI2F3f7LZdFVVAnUDqXW84sW0tHgSeMXHezwFPi8gwcQ/hTzIdJCIPicghETk0OTlp4rSlwWW3sn/PFp46PnpZHb5Sim+9PMj1mxu5uad0T5MGHoeVa3yeNQ3ASkxxYmSW//XieT73twf56k/P0Odzr1tCuB69bS5T3cDGHOBiCarlQ0ejk5WYSlaJrIchrb2zhCEgKE4l0Og6XcBr0efzMDQTKtpku1w4nJCkKOYY01SmFsJFnQSWji8xG9hMInhoJrRmyXdcDmKDh4CATH/RGR+xROR+oJ942Ccb/xn4qFKqC/gb4P/NdJBS6htKqX6lVL/PV5rqGrPce0s3S8sxDhxddVbeuDjDm5fmeaDAoS+5sLuriWPDAZRSaTf8Q+x99J/5hT//d/7gqdOcmwyyf88W/vy+m/P+rp4Wt2kPoBIqoKnkMhjm2Mgs29vcJZ9b0N3iKsgDWFpeYToYoTOHEBBAX7sHpfIb61kohxPGtVRzCqYWwrSV0NM0BOHMzAUYnlm8Iv5vsBH0gMwI1Q8DqZnNLjKEa0TkDuAR4Hal1Lo/ORHxATcppV5NbPou8BNTK64guzq93LC5ke8evMgDiXDPt1+5iMdh5e49+c/8zZXdXV5+8MYwn/mbgxy+OMPcUlz/prfVxUd3bebdfa3cuq012R1bCL2tLmZCy8wuLq85RCa6EmN4JsRdO4unfZQPqQYgW8fzseEA715nKlqx6Glx8ZMTl1iJqZya8AxW5wDk6gHEjfG5iSDXbWrMcnTxmF1cZmAiPkipFB5AJBpjbilaUg/ACAFl8wCiKzHGZpfW8QBsVZ8DMGMADgI7RGQbMALcC3wq9QAR2Qt8HbhTKTVh4pwzgFdErlVKnQF+Djid08orgIhw775uvvSjk5wYmWWz18lTx8a4b1837gKHvuTCe/riHb0X/EHu2rmZd/W18K7trXmHedYjdT7wWg1TY7PxWujKewDGbOD1/3DH55YYnwuzq4QJYIOeFhfRmGJs9spmITOMJTqbc/2/3d4WF6M7V+BUu1wx1Gpv29HGi2enCIQiRZUGmS5xExjE+yk8DmvWHMDY7BIrsSt7AAya3XbmlqJEV2Ilq1gqlKx3LaVUVES+ADwLWIBvKqVOisijwCGl1AHiIR8P8EQiDHJRKbUfQEReBK4DPIl4/4NKqWdF5DeAH4hIjLhB+PUSXF/RufumTv7wqdM8fvAiXc1xzfxyJH9T2dHRwOlH7yy62FwmjKqewengmgZgtQKociWgEH9yE8keAjLyJ6WSgEilO6UUNB8DYLYLOJ16u4XOpvqyG4DDFwOIwKdv7eHFs1OcGJnjfTvyn7OczlSJm8AM2jz2rB7AUAYZ6FSMZrDA4nLJZCsKxdRjq1LqaeDptG1fSnl9xzqfvW2N7U8CT5pbZvXgddn46K7N/OjwKI31Nm7d1sKOjuIMfcmFctz8wZws9IVkD0BlPQCbpY5Wt4OJLIqgx4cD1AncsKX0oRHj5zc8vQh9uX9+NLCICHR4c7+BbPe5y28AhmbY0e7hXYnw2vGR2aIaACPBX4phMKmY0QMyhP661zAAqc1g1WoAqtMvqXLuvaWb+XCUkcAiDxRx6Es14nZY8TU41k0ED04FcdrqaG+o/C/5Jq8jqyDc0eFZru1oKPpA8Uxs9jqx1EnelUBjgSXaPA4c1uxDfNLp83k4P2ley6lQlFIcGQqwt7uZJped7pb6oieCkzIQJb6hxvWA1o/fD8+EqJO1JTqMnqBqrgTSBiAP9m1rYXubmzaPg5+/obKJz3KwtWV9VdAL/hBbW9wVLQE16GhwrpsDUEpxfGQ2KaxXaqyWOjqb6vM2ALn2AKTS1+4hFFnJa1RmPlzwhwiEltmbKIfe1enlxGixDUDpcwAQ9wCyh4AW2eytx7ZGfN8IAVVzIlgbgDwQEb726Zv55mf6yxaKqSRxVdC1b2CDiR6AaqC90bluCGh4ZpHpYITd3aVPABsU0gswElhkS57VXKmVQOXAqP/f2xOfOX3jFi+D/lBe8yvWYioYxm6po6HERRdtHgezi8vrTvQangldNgUsnY0gCX31371KxPWbG0sqI1BNbG11cWluicXIlX8MsZhicDpU8fi/QUdj3HVfS4agHB3A6eTbC6CUYiywlLcHcI2vvJVAhy8G4o2K7fHvNbysk0UMAxldwKX2No1msPXkIIam16/s0iEgzVXBqirolTexS3NLRKKxqvEANjWuP9P12PAsNotw3ebyJe67W+rxByMshHObVxwILbO4vJK3AfA1OGhwWMtnAIZmuKnbm+x3MAxAMfsB/Avhkod/ILscRDi6wvj80poloAD1Ngt2a532ADQbm2QpaIZE8IXkHOBq8QASk8HWiHsfGw5w3abGvJKq+WJUAuXqBYwaMtB5hoBEhO3tnrIYgMXICqfH5tnb3Zzc1uy209lUz4nRuaJ9jz8YKXkJKKxWGa1lAEYDSyi1dgkoxH/+1d4Mpg2AJivGkJdMpaDGtmrxAJKTwTIYgFgskQAuQ/1/KnkbgED8GvL1ACCeByhHDuD4yCwrMcWetNzKzs7GolYClVoIziCbHtBaMtDpVLsekDYAmqw0uew0Oq0MZhgMc8EfxG6pK0kXcj6sykFc+Yd7wR9kfilalgawVPKdC2B2Eth69Pk8XJpbyjn8lCtGAnhPmiDirk4vb08FmVsq/CaolIrrAJWhpn41BJT56T05CKZl/QefZpe9qofCaAOgMUVvmzuzBzAVorulPi+dm1LQ4rJjs0jGbmAjFr2rs7zJe2+9jQanNa8QkN1SR1sBIY++RCL4fInDQIcvBuhpcV1xc96ZTAQXHgYKRlYIR2NlkRx32iw0OK3regDWOknmnNai2W2r6rGQ2gBoTNHTklkW+oI/WDXxf4C6OqG9wZkxB3B0aBaHtY5rOzxlXZOI5FUKOhpYYnOTs6ARo8lS0FIbgKGZZP1/KkkDUIR+gHI1gRn4PI41FUGHZuL9GdkefOIegA4BaTY4va1uRgKLl5VXKqUY9IcqOgUsE+2NDiYyhICOjwS4cUtjRYS5uptzNwBjgUU2F6jo2tPqwlInJc0DjM0uMj4XZm+G3oo2j4PNXmdRKoGmytQEZtC2TjPY8Exo3Qogg2aXncDisqkhRZVAGwCNKXpaXazEFCMzi8ltk/NhFpdXKjoHOBPxbuDLPYDoSowTI3MV693oaXUxNLOY041gNJB/F7CBw2qhp8VVUg/g8MW4AqjRAJbOzk5vUQyA4QEUEhLLBZ9nbT2goelFupqy/943uWysxBTzS6XNweSLNgAaUxhhntQBIxeSFUDV5QF0NDquMADnJoMsLq+UdAj8enS3uIhEY0yYmDIFcYN1aW6JLUVIrveVWBTu8MUZ7NY6rt+cWVzPSAQXmoj2l0EKOpU2j52pDP9fS8srTC2ETXsAUL3dwNoAaEyRqRlstQegyjwAr5O5pehlncvHhuNPqRXzAIxS0BlzYaCJ+TAxVVgJqEGfz8OFqRArJQpDHL4YYFend01ZlJ2djSgFpwrsB1jNAZTHAPgaHMwtRa8Yq5ksAc1SAQSp3cDaAGg2MO0NDpy2Oi5Mrd7ABv1BrHWSs1Z9qelouHI05LHhWTwOK9srJFmRLAU1MV8ZUieBFT7Vrc/nIZKY2lZsItEYx0dmM8b/DXYWqSN4aiFCg9NatiY+o6IpfcZ0sgQ0Sw8AVL8ekDYAGlOICL2tbi5OXx4C6mqur7ppR5lmAx8bmWVnZ2NBFTWF0NlUj4j5XoCRPEdBZqKvvXSVQG9emiMcja0Z/wdob3DS0egouCHMHyyvrv5azWCrTWDZPYBVRdDqrAQy9ZcrIneKyFsiMiAiX8yw/2EROSUix0TkORHZmrLvJyISEJEfp31GROQPReSMiJwWkf9U+OVoSklPmix0XAW0uuL/kDIaMvGHG4nGOD1auQQwxAf4bG50mu4FWB0FWbgHkBwPWYJKICMBnN4Als6uIiSC/QvhsvQAGCSbwdIMwNDMInZrnSlj1LzRQ0AiYgG+BtwF3ADcJyI3pB12GOhXSu0Gvg98OWXfY8ADGU79GeLD5q9TSl0PPJ7z6jVlpbfNzcXpELGYipeAToWqLv4P8RwArMpBnBmfJ7ISq1gC2KA7h16A0cAijU4rDU5bwd/b7LbT6raXxAM4fHGG9gZHVr2iG7d4OTe5QCiSfyK4XDIQBm0NmQXhDBloM95ko9OKpU42rgEA9gEDSqnzSqkI8Rv13akHKKWeV0oZv9mvAF0p+54D5jOc9/PAo0qpWOI4M8PkNRWkJ1HJcmluielghPlwtCo9gAaHlXqbJTkZ7KiRAC5zB3A6uTSDjRYgA52JPl9pROEODwXY29OUVZ55V6e34ESwPxguWxMYrArCpYeAsslApyIiNNXbqlYPyIwB6ASGUt4PJ7atxYPAMybO2wfcIyKHROQZEdmR6SAReShxzKHJyUkTp9WUit6kKmgoZQ5w9XkAIhIvBU384R4fnqXJZTNVtldKelpcTMyHr6gqyUQxegBS6Wt3c26yuCGg6WCEQX9o3fi/gSHAl28YaCWmmA5GaCtjCMhhtdDotGb0ALKJwKXS7K5ePSAzBiCTac9YTyYi9wP9xMM+2XAAS0qpfuCvgW9mOkgp9Q2lVL9Sqt/n85k4raZUbE2qggaT0tDV6AFAfDKYkQQ+OhwfAVnpkZU9iZ+fmWqc0dnCu4BT2d7mYToYKaouzZGhxAQwE9PVOhqd+BoceRuAQChCTJVPBsLA13C5HMRCOMpMaNm0BwAkJKE3rgcwTDxWb9AFjKYfJCJ3AI8A+5VSZrpdhoEfJF4/Cew28RlNBdnsdWKzCBcSHkCdmCuFqwQdjU4m5pZYWl7hzPh8xeP/sFo3ni0MFIpECYSWi+4BQHFF4Q5fDGCpE9Py2ju3NOYtClduGQiDNo+DqflVo7naA2D+/6bJZd/QOYCDwA4R2SYiduBe4EDqASKyF/g68Zu/2Vj+D4EPJV7fDpwx+TlNhbBa6uhqdnFxOu4BbGmqL+tglVzY1Ojg0twSJ0fnWImpqhjfabYXwJgDUMz+ir4SjIc8fDHAdZsacNnNzefd1enl7MR8xtGi2Ug2gZVJBsIg3QNY7QEw7wG0bGQDoJSKAl8AngVOA99TSp0UkUdFZH/isMcAD/CEiBwRkaSBEJEXgSeAD4vIsIh8JLHrT4BfFpHjwB8DnyvaVWlKxtZWFxem4h5ANamAptPR6GRpOcZLA1MAVeEBtLrt1NssXJxeXPe4scQksGKGgLqaXdgtdUXLA6zEFEcSCWCz7Oz0ElNwaix3L+Bn5/yIlH/wUNwDWDUAZgfBpNLkjieBlao+QThTplsp9TTwdNq2L6W8vmOdz962xvYA8DFzy9RUC1tbXBy6MIPVInxs1+ZKL2dN2hPNYD89PY6vwZFVt70cmJWFHi1iE5iBpU7Y1uYuWgjo3OQCC+HoZSMgs2F0BJ8YmeWdW81/LhiO8q1XBvn5GzqK+jMxg6/BwXw4LgfhtFkYml6k3mZJSjyYodllJxKNEYqs4HaY85bKRXW1cGqqnq2tbhbC8Rh1VXsAiRruY8Oz7K6CBLBBd4srazPYSGAJEdhURA8AilsJZEwAy8UD2Ox10uq255wI/t6hIWYXl3no/X05fa4Y+DyXdwMbMtC5/D61VLEgnDYAmpxIdcGrZQ5wJlJvntUQ/zfoaXExNBNaNxwwFlikvcGBrcgSG30+DxenQ4Sjucfg0zl8MYC33sa2HLSVRISdnd6cJCGiKzH+14tvc0tvc05eQ7Foa7h8OPzQzKIpCYhUDD2gahwMow2AJidSyz57KySsZob2hlQDUPn4v0FPSz2hyMoVAmOpjM4WtwfAoM/nYSWmTAvSrcfhiwH2dGdvAEsnngheMNULAfDU8TFGAosVefoH8Hniv0epHkCulW+GHEQ1jobUBkCTE3H3N/66x4QcbqWot8ebeADTZYrloCeDrHY6o4HizAFIp1iVQPNLy5yZmM8p/GOws7ORlZjitIlEsFKKr//befp8bj58XXs+Sy2YVQ8gwmxomfmlqCkZ6FSqeSaANgCanHBYLWzx1rPZ68Rpq84SUIOORiedTfVlVZDMhhE+WCsPoJRKdAEXP2m9PTkfuLA8wPHhWZRaewLYeqQmgrPx0oCfU2NzPPT+7RVTcTXKTqcWwslZDjl7AFUcAqqulLRmQ3BTt5dYLPtxlebuPVsqduNYC6N+fK0wzHQwQjgaY3MJPAC3w8pmr5NzE4V5AIeHEgqgeeRWOpvqaXbZOGGiIezrL5zD1+Dgl/aupzxTWuzWOppcNibnw8kS0Fx6AAC89XEDUI0hIG0ANDnz1Xv2VnoJpvjChzLKS1WUeruF9gbHmiEgQwa6VOWO24swHvLwxRn6fG68rtyVSo1EcLZKoJOjs7x4dorfvfMdFW82bEvMBjaawHJNAlstdXjrbVWpB6RDQJqcsVvr1hz/p8nOer0AxiCYUk1Zi6uCBvNuSlJKcfhiIK/wj8HOTi9nxufXTQT/9QvncdstfPrWrWseUy58HkfSA2hwWvMyfM2u6lQE1X/FGk2Z6WlxMTyTuRvYaALbXIIcAMQNwEI4ano4fTpD04v4g5G8EsAGuzq9RGOKty5lUomPV9r807Ex7tvXkwyfVJK2hoQHMGNeBjqdatUD0gZAoykz3S0uRmcXiUSvTKSMzS5ht9aVbPJVshIozzzA4aQCaP4ewC4jETyaOQz0zX+/gAC//r5teX9HMUn1AHKRgEilxa0NgEajIW4AlFoN96QyElhki9dZss7lQucDH74YwGW3cG2HJ+81dDXX4623ZawEmg0t8/jBi+y/aUvZZR/Woq3BTjCywoWpUAEeQP6S0CdGZnnkyeNJ77CYaAOg0ZSZnnVkoYs9CCadTY1OXHZL3qWghy/OsKvTi7WALuV4IrgxYyL4268OEoqs8NDt2/M+f7ExyogjK7G8hwo1FxACeuPiDN959SKleCbQBkCjKTPrGYCxIo+CTEdE8h4PubS8wqmxuYISwAY7O728dWn+MlmKpeUV/ualC9x+rY/rNjUW/B3Fwtew2keSrwfQ7LIRiqzkJcNxdnyBBoe1JIKG2gBoNGWmvcGB3Vp3RTPY8kqM8fmlrAPWC6XP5+Z8Hh7AydE5lldUQQlgg12dXpZXFGfHVw3Rk4dHmFoI85tV9PQPq4JwkNsgmFQMOYh8msHOjM+zo8NTkrCgNgAaTZmpqxO6m+uvMADjc0soVboeAIM+n4eRwCKhSDSnzyUVQE2MgMzGzi2XzwiOxRR//cJ5dnV6eff21oLPX0yK4wHkrwd0dmKBHe0NeX1vNkwZABG5U0TeEpEBEflihv0Pi8gpETkmIs+JyNaUfT8RkYCI/HiNc/+5iBRvTJFGswHI1AtgTAIruQFojydwc/UCDg8F6GyqT85aKIStrS4anNakAfiX0+Ocnwrym7dvrxrpbgND+7/ZZcOTp56/oQiaax5gaiHMdDDCjgKS7uuR1QCIiAX4GnAXcANwn4jckHbYYaBfKbUb+D7w5ZR9jwEPrHHufqB6tHo1mjLR3eLiov9yWejVQTClDQGtagKZf+4am13kudPjvO+atqKsQUTYuWVVGvobL5ynu6WeO2/cVJTzFxObpY4Wtz1nEbhUWvIMAZ0Zj/dKXNtROQ9gHzCglDqvlIoAjwN3px6glHpeKWU8zrxCfHC8se854IqOj4RheQz43TzXrtFsWHpaXMyHo8wurt4QRpOjIEvrAfS2uhHJTRTusWffIqbgP374mqKtY1eXlzfH5nn5nJ/XB2f43Pu2F1RdVEr6fG6uLyAxna8i6ECiX6NUBsCMP9MJDKW8HwZuXef4B4FnTJz3C8ABpdTYei6fiDwEPATQ09Nj4rQaTfXTnVIJ1JS4OYwGFvHW20o+NtBps9Dd7DLtARwbDvCPb4zw+Q/05R0Dz8SNWxqJrMR45IfHaXbZ+JX+ruwfqhB/89l9WAsQFkyGgHLMAZwZn6fBaaWjsTSKtmbMbaarzigkIiL3A/3En+zXPqHIFuBXgD/P9uVKqW8opfqVUv0+n8/EcjWa6idTKWipS0BT6fO5TXUDK6X4g6dO0+q281sfKO5QFqMj+PxkkAfe3YvLXr3alB6HtSD5c4fVgttuyVkP6Mz4Atd2NJQsL2LGAAwD3Snvu4DR9INE5A7gEWC/Uiqb0Mhe4BpgQEQuAC4RGTC1Yo3mKqA7gwEYCSzSWeL4v0Gfz8PbU0FWYuuLwj17cpzX3p7m4Z+/lgZncXV5elvdeBxWHNY6fu3dlRd9KzW56gEppTg7Ps+O9tIkgMFcCOggsENEtgEjwL3Ap1IPEJG9wNeBO5VSE9lOqJR6Ckhme0RkQSlVvOCiRlPleBxWWt32pMQwxENAt/S2lOX7+9o9hKMxRgOLayY3w9EV/viZ01zb4eGe/u6MxxRCXZ1w/7u20uSy0VpFQ3tKRbPbllMIaGohwkxomR0liv+DCQOglIqKyBeAZwEL8E2l1EkReRQ4pJQ6QDzk4wGeSLgqF5VS+wFE5EXgOsAjIsPAg0qpZ0tzORrNxqGrxZXsBVgIR5lbipYxBBR/qhyYXFjTAHzr5UEG/SH+9tf3lSw5+8W7rivJeauRuByE+RDQ2WQFUGU9AJRSTwNPp237UsrrO9b57G0mzl+6K9RoqpSeFhdHE9O1xspUAmrQlygFPT8Z5IPvuHL/dDDCnz53ltuv9XH7tTr3VgyaXfY1R4Fm4myJK4BAdwJrNBWjp6WekcAi0ZVYUhm0XB5Ai9tOk8u2ZiXQnz13lmA4yiMfu74s66kFml22nDqBz4zP0+i00t5QuvCYNgAaTYXoaXGxElOMzS6VfBRkOklRWAghWwAADO1JREFUuAyVQAMTC3zrlUHu29dT0qfPWqPZbWduKUp0xdxA7bPjC+woYQUQaAOg0VSM1Eqg0cAidQIdJXzaS6fP587YDPYnz5ym3mbhP//ctWVbSy1gNIOlNv+thVKKMxPzJY3/gzYAGk3FMHoBhqZDjAaW6Gh0lrUTdrvPw9RCmNmUxORLA1P89PQEv/3Ba5I6+JrikIse0ORCmEBouWQicAbaAGg0FWKztx5rnSQ9gM0lloFOJzkecioeBlqJxZu+Opvq+ex7e8u6llrA0AMyUwk0MF76BDBoA6DRVAxLndDZXB83ALOlnQSWCaMSyMgD/OD1YU6PzfHFu64rqOtVk5mkHpCJRPCZMpSAgjYAGk1F6WlxMegPMTa7RGeZDUB3iwubRTg3GSQYjvLYP7/F3p4mfmH35rKuo1bIJQR0ZmKBRqf1slkEpUAbAI2mgnS3uHjz0hyRaKzsISCbpY6trW7OTS7w9X87x+R8mP/2CzdUnR7/1UIuIaCz4/Ml1QAyqF71JY2mBuhpcbG8EtfjKXcICOJhoDcuBnjx7CS/eNMWbi7CvF9NZuptFuzWuqwegFKKM+MLfHRX6T0x7QFoNBWkJ0WGoTIGwMPkfJiYgt/9SIaWYE3REBGaXdn1gCYXwswuLpc8/g/aAGg0FaW7ufIGAODB920raOKVxhxm9IDOlqkCCHQISKOpKIYH4LTV0ewqrtyyGe64voPf/mAfn/+AFuMtB80uO4EsISCjAqiUMtAG2gPQaCqI12Wj0Wlli7e+IslXr8vGf/nIdXkPO9fkRrPbltUDODO+gLfeVvIKINAegEZTcXZ0NNBUX/6nf035aXbZs+YA4hVAnrI8EGgDoNFUmK996mYsBcyb1Wwcml12AovLKKUy3uDjFUDz/MJNW8qyHm0ANJoKs6nM9f+aytHksrESU8wtRfFm8Pom58PMLUW5tgzxfzCZAxCRO0XkLREZEJEvZtj/sIicEpFjIvKciGxN2fcTEQmIyI/TPvOdxDlPiMg3RUT7wBqN5qommxzEmUQFUCnHQKaS1QCIiAX4GnAXcANwn4jckHbYYaBfKbUb+D7w5ZR9jwEPZDj1d4iPitwF1AOfy3n1Go1Gs4FY7QZeywAkKoDK0AMA5jyAfcCAUuq8UioCPA7cnXqAUup5pZQx6+wVoCtl33PAfPpJlVJPqwTAa6mf0Wg0mqsRQw8osEYl0NmJeZpcNnxlkuI2YwA6gaGU98OJbWvxIPCM2QUkQj8PAD9ZY/9DInJIRA5NTk6aPa1Go9FUHUYIaK3RkGfGF7i2vfQaQAZmDECmlaiMB4rcD/QTD/uY5S+AF5RSL2baqZT6hlKqXynV7/Pp4dQajWbj0rxOCEgpxdnx+bKFf8BcFdAw0J3yvgsYTT9IRO4AHgFuV0qFzXy5iPx3wAf8ppnjNRqNZiPT6LRiqZOMIaCJRAVQOTqADcx4AAeBHSKyTUTswL3AgdQDRGQv8HVgv1JqwswXi8jngI8A9ymlzE1J1mg0mg2MiNBUb2M6gwewOgSmPBVAYMIAKKWiwBeAZ4HTwPeUUidF5FER2Z847DHAAzwhIkdEJGkgRORF4AngwyIyLCIfSez6K6ADeDnxmS8V77I0Go2mOml2Z9YDKncJKJhsBFNKPQ08nbbtSymv71jns7etsV03oWk0mpojLgl9ZQjo7Pg8zS4bbR572daixeA0Go2mjDS57BmTwGcnFthRhilgqWgDoNFoNGWkJYMBMDSAypkABm0ANBqNpqw0JSSh4z2wccbnwswvRcuaAAZtADQajaasNLvsRKIxQpGV5LZyS0AYaAOg0Wg0ZaTFdWUzWCVKQEEbAI1GoykrmfSABiYWaHHbaSuTBpCBNgAajUZTRjLJQVQiAQzaAGg0Gk1ZSReEi2sALZQ9/g/aAGg0Gk1ZaU4LAV2aW2I+XP4KINAGQKPRaMqKMQrSCAElJSDatQHQaDSaqxqrpQ5vvS05FvJssgJIh4A0Go3mqqfZFW8GAzg7vkCr205rmSuAQBsAjUajKTupekBnJua5pgIVQKANgEaj0ZSdFnfcACilGBhfqEgCGLQB0Gg0mrLTlJCEHps1KoCq2AMQkTtF5C0RGRCRL2bY/7CInBKRYyLynIhsTdn3ExEJiMiP0z6zTUReFZGzIvLdxLQxjUajueppdsWHwqxqAFWpByAiFuBrwF3ADcB9InJD2mGHgX6l1G7g+8CXU/Y9BjyQ4dT/E/iKUmoHMAM8mPvyNRqNZuPR7LIRjKxwcnQOKL8GkIEZD2AfMKCUOq+UigCPA3enHqCUel4pFUq8fYX44Hhj33PAfOrxEp948CHixgLgb4FfyusKNBqNZoNhyEEcvDBNq9tOi7syARAzBqATGEp5P5zYthYPAs9kOWcrEEjMG173nCLykIgcEpFDk5OTJpar0Wg01Y0hB3HowkxFJCAMzBiATPPJVIZtiMj9QD/xsE9RzqmU+oZSql8p1e/z+bKcVqPRaKofQxF0oUISEAZmBrMPA90p77uA0fSDROQO4BHgdqVUOMs5p4AmEbEmvICM59RoNJqrkdSQT6USwGDOAzgI7EhU7diBe4EDqQeIyF7g68B+pdREthOq+Cy054FPJDb9GvCjXBau0Wg0GxUjBARwbYWawMCEAUg8oX8BeBY4DXxPKXVSRB4Vkf2Jwx4DPMATInJERJIGQkReBJ4APiwiwyLykcSu/wo8LCIDxHMC/7toV6XRaDRVjBECgsp6AGZCQCilngaeTtv2pZTXd6zz2dvW2H6eeIWRRqPR1BQOqwW33UK93VKxCiAwaQA0Go1GU1yaXHZ6WlwVXYM2ABqNRlMBHv65a2lvLL8CaCraAGg0Gk0F+OV3dmU/qMRoMTiNRqOpUbQB0Gg0mhpFGwCNRqOpUbQB0Gg0mhpFGwCNRqOpUbQB0Gg0mhpFGwCNRqOpUbQB0Gg0mhpF4sKcGwMRmQQG8/x4G3EZ6lpCX3NtoK/56qfQ692qlLpioMqGMgCFICKHlFL9lV5HOdHXXBvoa776KdX16hCQRqPR1CjaAGg0Gk2NUksG4BuVXkAF0NdcG+hrvvopyfXWTA5Ao9FoNJdTSx6ARqPRaFLQBkCj0WhqlKvOAIjInSLylogMiMgXM+x3iMh3E/tfFZHe8q+yuJi45odF5JSIHBOR50RkayXWWUyyXXPKcZ8QESUiG7pk0Mz1isgnE//PJ0Xk78u9xmJj4ve6R0SeF5HDid/tj1ZincVERL4pIhMicmKN/SIif5b4mRwTkZsL+kKl1FXzD7AA54DtgB04CtyQdsxvAX+VeH0v8N1Kr7sM1/xBwJV4/flauObEcQ3AC8ArQH+l113i/+Md8P+3dwchVlVxHMe/v5ikhWbiIIgKtnDAmE0RMW4sGBFxMbNpUTDkiLQIahHSqkWSuyJcCVYkpQtFXegjCjcViTiR0KaCYLBhHAoUrdkMVtavxbnEMOm807x37+3d9/+s7n3v8O7/N/e9d+455zKPb4B1xf6GuuuuIPN7wEvF9mPATN11dyH3TuAJ4Nv7PL8X+BQQMAJ81cnxmjYCeAqYtn3N9u/AaWB8SZtx4KNi+xwwKkkV1thtbTPb/tz2QrE7BdT/W3SdyTnPAIeBt4A7VRZXgpy8LwJHbf8CYPtGxTV2W05mAw8X22uBnyqsrxS2vwRuL9NkHDjhZAp4RNLGlR6vaR3AJuD6ov254rF7trF9F5gH1ldSXTlyMi92gHQF0cvaZpb0OLDF9sdVFlaSnHM8BAxJuixpStKeyqorR07mQ8CEpDngE+CVakqr1X/9vC+raT8Kf68r+aX3uea06SXZeSRNAE8CT5daUfmWzSzpAeAIMFlVQSXLOccDpGmgZ0gjvEuShm3/WnJtZcnJ/Dzwoe13JO0AThaZ/yq/vNp09furaSOAOWDLov3N/HtY+E8bSQOkoeNyQ67/u5zMSNoFvA6M2f6totrK0i7zGmAY+ELSDGmutNXDC8G57+sLtv+w/SPwA6lD6FU5mQ8AZwBsXwEeIv3TtCbL+rznaloH8DWwTdKjklaRFnlbS9q0gH3F9rPAZy5WV3pU28zFdMi7pC//Xp8bhjaZbc/bHrS91fZW0rrHmO2r9ZTbsZz39XnSYj+SBklTQtcqrbK7cjLPAqMAkraTOoCblVZZvRbwQnE30Agwb/vnlb5Yo6aAbN+V9DJwkXQXwXHb30l6E7hquwV8QBoqTpOu/J+rr+LOZWZ+G1gNnC3Wu2dtj9VWdIcyMzdGZt6LwG5J3wN/Aq/ZvlVf1Z3JzHwQeF/Sq6RpkMkev5hD0inSNN5gsbbxBvAggO1jpLWOvcA0sADs7+h4Pf73CiGEsEJNmwIKIYSQKTqAEELoU9EBhBBCn4oOIIQQ+lR0ACGE0KeiAwghhD4VHUAIIfSpvwEZdk2Deu6BwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def run(circuit,error_prob):\n",
    "        initial_state = [complex(random.random(),random.random()),complex(random.random(),random.random())]\n",
    "        initial_state = normalize(initial_state)\n",
    "        initialize(initial_state)\n",
    "        encode()\n",
    "        error(error_prob)\n",
    "        correct()\n",
    "        return initial_state\n",
    "\n",
    "    \n",
    "p = np.linspace(0,1,30)\n",
    "fidelity_array = []\n",
    "for k in p:\n",
    "    fidelity = 0\n",
    "    shots = 50\n",
    "    circuit = QuantumCircuit(5,2,name=\"repitition_circ\")\n",
    "    for j in range(shots):\n",
    "        circuit = QuantumCircuit(5,2,name=\"repitition_circ\")\n",
    "        initial_state = run(circuit,k)\n",
    "        simulator = Aer.get_backend('statevector_simulator')\n",
    "        job = execute(circuit,backend = simulator)\n",
    "        result = job.result()\n",
    "        snapshots = result.data()['snapshots']['statevector'].values()\n",
    "        s = []\n",
    "        for v in snapshots:\n",
    "            for i in range(2):\n",
    "                s.append(complex(v[0][i][0],v[0][i][1]))\n",
    "        s = np.array(s)\n",
    "        dot = abs(np.dot(initial_state,s))\n",
    "        fidelity += dot\n",
    "    fidelity = fidelity/shots\n",
    "    fidelity_array.append(fidelity)\n",
    "    \n",
    "print(fidelity_array)\n",
    "%matplotlib inline\n",
    "plt.plot(p,np.array(fidelity_array))\n"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}