BB84.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0140eb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from qiskit import *\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit import Aer, execute, assemble\n",
    "from qiskit.visualization import plot_histogram, plot_bloch_vector\n",
    "from qiskit.visualization import plot_state_qsphere, plot_state_city, plot_bloch_multivector\n",
    "from qiskit.visualization import array_to_latex\n",
    "\n",
    "# numpy for linear algebra stuff\n",
    "from numpy.random import randint, shuffle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# To supress the deprecation warnings\n",
    "import warnings\n",
    "import matplotlib.cbook\n",
    "warnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)\n",
    "%run utility_functions.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2f79c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def BB84(TOTAL_BITS = 10, EAVESDROPPED_BITS_RATE = 0.5, TEST_BITS_RATE = 0.5, DISPLAY_CIRCUIT = False, DEBUG = False):\n",
    "    \n",
    "    if (EAVESDROPPED_BITS_RATE > 1) or (EAVESDROPPED_BITS_RATE < 0) or (TEST_BITS_RATE > 1) or (TEST_BITS_RATE < 0):\n",
    "        print(\"EAVESDROPPED_BITS_RATE and TEST_BITS_RATE must be between 0 and 1. Exiting....\")\n",
    "        return\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    alice_bits = generate_bits(TOTAL_BITS)\n",
    "    alice_bases = generate_bits(TOTAL_BITS)\n",
    "    message = encode_bits(alice_bits,alice_bases)\n",
    "    \n",
    "    if DISPLAY_CIRCUIT:\n",
    "        print(\"Circuit after Alice completes encoding:\")\n",
    "        display(message.draw(output='mpl'))\n",
    "        \n",
    "    bob_bases = generate_bits(TOTAL_BITS)\n",
    "    received_encoded_message, eve_bases = received_encode(message,TOTAL_BITS,Eve=True,ERROR_RATE = EAVESDROPPED_BITS_RATE)\n",
    "    \n",
    "    if DISPLAY_CIRCUIT:\n",
    "        print(\"Eve measures \", int(EAVESDROPPED_BITS_RATE * TOTAL_BITS), \" qubits\")\n",
    "        print(\"Circuit after Eve eavesdrops:\")\n",
    "        display(received_encoded_message.draw(output='mpl'))\n",
    "        \n",
    "    received_message, bob_bits = measure_bits(received_encoded_message,bob_bases)\n",
    "    \n",
    "    if DISPLAY_CIRCUIT:\n",
    "        print(\"Circuit after Bob completes decoding and measurement\")\n",
    "        display(received_message.draw(output='mpl'))\n",
    "\n",
    "    if DEBUG:\n",
    "        print(\" Alice bits:\", alice_bits, \"\\n Alice bases:\", alice_bases, \"\\n Eve bases:\", eve_bases, \"\\n Bob bases:\", bob_bases, \"\\n Bob bits:\", bob_bits)\n",
    "        \n",
    "        \n",
    "    agreed_base_indices = sifting(alice_bases,bob_bases)\n",
    "        \n",
    "    if len(agreed_base_indices) > 0:\n",
    "        pass\n",
    "    else:\n",
    "        print(\" No index found to match between Alice and Bob bases. Exiting....\")\n",
    "        return 0\n",
    "\n",
    "    if DEBUG:\n",
    "        print(\" Agreed base indices:\", agreed_base_indices)\n",
    "    \n",
    "    test_error_rate, test_base_indices = qber(alice_bits,bob_bits,agreed_base_indices,frac = TEST_BITS_RATE)\n",
    "    \n",
    "    if DEBUG:\n",
    "        print(\" Test indices are: \", test_base_indices)\n",
    "        print(\" The QBER on test indices is \", test_error_rate)\n",
    "\n",
    "    # TO DO: PRIVACY AMPLIFICATION and RECONCILIATION TO GENERATE FINAL SECURE KEY AND CHECK FINAL ERROR RATE\n",
    "    \n",
    "    return test_error_rate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b361526",
   "metadata": {},
   "source": [
    "### An example BB84 protocol\n",
    "#### TOTAL BITS = 5, EAVESDROPPED BITS RATE = 0.2, TEST BITS RATE = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14c3704a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit after Alice completes encoding:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 267.197x385.28 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eve measures  1  qubits\n",
      "Circuit after Eve eavesdrops:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 327.397x385.28 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit after Bob completes decoding and measurement\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 688.597x385.28 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Alice bits: [0 0 1 0 0] \n",
      " Alice bases: [0 0 1 0 1] \n",
      " Eve bases: [0, -999, -999, -999, -999] \n",
      " Bob bases: [0 1 0 1 1] \n",
      " Bob bits: [0, 0, 1, 0, 0]\n",
      " Agreed base indices: [0, 4]\n",
      " Test indices are:  [0, 4]\n",
      " The QBER on test indices is  0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BB84(TOTAL_BITS=5,EAVESDROPPED_BITS_RATE=0.2,TEST_BITS_RATE=0.5,DISPLAY_CIRCUIT=True,DEBUG=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bca4897",
   "metadata": {},
   "source": [
    "### Calculate avergage QBER on test key for various eavesdropping rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d3bb48d",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_ITER = 100\n",
    "N = [10, 20, 50, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4fa301fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Running on TOTAL_BITS =  10\n",
      " Running on TOTAL_BITS =  20\n",
      " Running on TOTAL_BITS =  50\n",
      " Running on TOTAL_BITS =  100\n",
      "[0.18, 0.2154206349206349, 0.18356904078034106, 0.17350167356352553]\n"
     ]
    }
   ],
   "source": [
    "AVG_TEST_QBER_E0p5_T0p5 = []\n",
    "\n",
    "for n in N:\n",
    "    print(\" Running on TOTAL_BITS = \", n)\n",
    "    TEST_QBER = []\n",
    "    for i in range(MAX_ITER):\n",
    "        error = BB84(TOTAL_BITS=n,EAVESDROPPED_BITS_RATE=0.5,TEST_BITS_RATE=0.5)\n",
    "        TEST_QBER.append(error)\n",
    "    AVG_TEST_QBER_E0p5_T0p5.append(np.mean(TEST_QBER))\n",
    "print(AVG_TEST_QBER_E0p5_T0p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27614a31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Running on TOTAL_BITS =  10\n",
      " Running on TOTAL_BITS =  20\n",
      " Running on TOTAL_BITS =  50\n",
      " Running on TOTAL_BITS =  100\n",
      "[0.0845, 0.09439682539682538, 0.07699011527687999, 0.0711283326871166]\n"
     ]
    }
   ],
   "source": [
    "AVG_TEST_QBER_E0p2_T0p5 = []\n",
    "\n",
    "for n in N:\n",
    "    print(\" Running on TOTAL_BITS = \", n)\n",
    "    TEST_QBER = []\n",
    "    for i in range(MAX_ITER):\n",
    "        error = BB84(TOTAL_BITS=n,EAVESDROPPED_BITS_RATE=0.2,TEST_BITS_RATE=0.5)\n",
    "        TEST_QBER.append(error)\n",
    "    AVG_TEST_QBER_E0p2_T0p5.append(np.mean(TEST_QBER))\n",
    "print(AVG_TEST_QBER_E0p2_T0p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70fe5412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Running on TOTAL_BITS =  10\n",
      " Running on TOTAL_BITS =  20\n",
      " Running on TOTAL_BITS =  50\n",
      " Running on TOTAL_BITS =  100\n",
      "[0.2796666666666667, 0.2782222222222222, 0.25244984182484176, 0.2497810968488899]\n"
     ]
    }
   ],
   "source": [
    "AVG_TEST_QBER_E0p7_T0p5 = []\n",
    "\n",
    "for n in N:\n",
    "    print(\" Running on TOTAL_BITS = \", n)\n",
    "    TEST_QBER = []\n",
    "    for i in range(MAX_ITER):\n",
    "        error = BB84(TOTAL_BITS=n,EAVESDROPPED_BITS_RATE=0.7,TEST_BITS_RATE=0.5)\n",
    "        TEST_QBER.append(error)\n",
    "    AVG_TEST_QBER_E0p7_T0p5.append(np.mean(TEST_QBER))\n",
    "print(AVG_TEST_QBER_E0p7_T0p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c69eb0dd",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(N,AVG_TEST_QBER_E0p2_T0p5,\"-o\",label=\"EAVESDROPPED RATE = 0.2, TEST RATE = 0.5\")\n",
    "plt.plot(N,AVG_TEST_QBER_E0p5_T0p5,\"-o\",label=\"EAVESDROPPED RATE = 0.5, TEST RATE = 0.5\")\n",
    "plt.plot(N,AVG_TEST_QBER_E0p7_T0p5,\"-o\",label=\"EAVESDROPPED RATE = 0.7, TEST RATE = 0.5\")\n",
    "plt.ylim(0,0.5)\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel(\"TOTAL MESSAGE BITS\")\n",
    "plt.ylabel(\"AVERAGE TEST QBER\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "324d5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AVG_TEST_QBER_E0p5_T0p5 = [0.18, 0.2154206349206349, 0.18356904078034106, 0.17350167356352553]\n",
    "# AVG_TEST_QBER_E0p2_T0p5 = [0.0845, 0.09439682539682538, 0.07699011527687999, 0.0711283326871166]\n",
    "# AVG_TEST_QBER_E0p7_T0p5 = [0.2796666666666667, 0.2782222222222222, 0.25244984182484176, 0.2497810968488899]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "025756bb",
   "metadata": {},
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