bit_flip_with_benchmarking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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(1,3):\n",
    "            circuit.initialize([1,0],i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encode\n",
    "def encode():\n",
    "    circuit.cx(0,1)\n",
    "    circuit.cx(0,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error(error_bitflip):\n",
    "    circuit.barrier()\n",
    "    for i in range(3):\n",
    "        if(random.random() < error_bitflip):\n",
    "            circuit.x(i)\n",
    "    circuit.barrier()\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correction\n",
    "def correct():\n",
    "    circuit.cx(0,1)\n",
    "    circuit.cx(0,2)\n",
    "    circuit.ccx(2,1,0)\n",
    "    \n",
    "    circuit.snapshot(\"one_qubit\", qubits=[0])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def run(circuit,error_prob):\n",
    "        initial_state = [complex(random.random(),random.random()),complex(random.random(),random.random())]\n",
    "        #initial_state = [0,1]\n",
    "        initial_state = normalize(initial_state)\n",
    "        initialize(initial_state)\n",
    "        encode()\n",
    "        error(error_prob)\n",
    "        correct()\n",
    "        return initial_state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 0.892, 0.816, 0.705, 0.64, 0.562, 0.491, 0.427, 0.375, 0.325, 0.286, 0.24, 0.224, 0.187, 0.121, 0.121, 0.11, 0.058, 0.048, 0.041, 0.017, 0.018, 0.014, 0.007, 0.008, 0.004, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eab3df5470>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "    \n",
    "p = np.linspace(0,1,30)\n",
    "fidelity_array = []\n",
    "for k in p:\n",
    "    fidelity = 0\n",
    "    shots = 1000\n",
    "    circuit = QuantumCircuit(3,2,name=\"repitition_circ\")\n",
    "    for j in range(shots):\n",
    "        circuit = QuantumCircuit(3,2,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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8849055493784048, 0.7923969220440797, 0.6923029876980294, 0.6258264488820283, 0.5592556930248868, 0.4982704018478705, 0.44038622842613023, 0.4178914932585013, 0.35576572078482643, 0.3259623665337894, 0.2707839222699714, 0.2715847027862138, 0.24134130904599158, 0.21838734486179978, 0.23449778977457222, 0.2269124524500946, 0.23345610307379638, 0.2205209008893954, 0.23997159140962793, 0.2648611596547444, 0.3095918293198636, 0.35027227283534734, 0.38629063553262366, 0.4337408601520501, 0.47707206629656507, 0.5415016343195512, 0.6250610051805569, 0.6822895635775069, 0.7801133767667131, 0.8378793000353291]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eab417fb00>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.linspace(0,1,30)\n",
    "fidelity_array = []\n",
    "for k in p:\n",
    "    fidelity = 0\n",
    "    shots = 1000\n",
    "    circuit = QuantumCircuit(3,2,name=\"repitition_circ\")\n",
    "    for j in range(shots):\n",
    "        circuit = QuantumCircuit(3,2,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": []
  }
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