Quantum Teleportation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vinu\\Anaconda3\\envs\\QISKitenv\\lib\\site-packages\\marshmallow\\schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "from qiskit import IBMQ\n",
    "IBMQ.load_accounts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Imports needed to create and Visualize the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vinu\\Anaconda3\\envs\\QISKitenv\\lib\\site-packages\\marshmallow\\schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "# useful additional packages \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "# importing Qiskit\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\n",
    "from qiskit import Aer, IBMQ, execute\n",
    "\n",
    "# import basic plot tools\n",
    "from qiskit.tools.visualization import matplotlib_circuit_drawer as circuit_drawer\n",
    "from qiskit.tools.visualization import plot_histogram, qx_color_scheme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ciruit Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.extensions.standard.barrier.Barrier at 0x133aeb47f98>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating registers\n",
    "tq = QuantumRegister(3)\n",
    "tc0 = ClassicalRegister(1)\n",
    "tc1 = ClassicalRegister(1)\n",
    "tc2 = ClassicalRegister(1)\n",
    "\n",
    "# Quantum circuit to make the shared entangled state \n",
    "tel = QuantumCircuit(tq, tc0,tc1,tc2)\n",
    "tel.h(tq[1]) #HADAMARD\n",
    "tel.cx(tq[1], tq[2]) #CNOT\n",
    "\n",
    "tel.ry(0.5,tq[0]) #Ry ROTATION OF BLOC SPHERE \n",
    "tel.cx(tq[0],tq[1])\n",
    "tel.h(tq[0])\n",
    "tel.barrier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measurement of Quantum States being undertaken "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.measure.Measure at 0x133aeb47898>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tel.measure(tq[0],tc0[0])\n",
    "tel.measure(tq[1],tc1[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying conditional transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.measure.Measure at 0x133a98bc710>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tel.z(tq[2]).c_if(tc0,1)\n",
    "tel.x(tq[2]).c_if(tc1,1)\n",
    "tel.measure(tq[2],tc2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Vinu\\Anaconda3\\envs\\QISKitenv\\lib\\site-packages\\qiskit\\tools\\visualization\\_circuit_visualization.py:544: DeprecationWarning: The matplotlib_circuit_drawer() function is deprecated and will be removed in a future release. Instead use the circuit_drawer() function with the `output` kwarg set to `mpl`.\n",
      "  '`mpl`.', DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 782.6x379.26 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit_drawer(tel,style=qx_color_scheme())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_backend = Aer.get_backend('qasm_simulator') \n",
    "tel_job = execute(tel, local_backend) \n",
    "tel_result = tel_job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creates Historgram after normalizing the data\n",
    "\n",
    "<b><u>Data is a dictory of probabilities </u></b><br/>\n",
    "Therefore <br/>\n",
    "<b>alice['00'] = data['0 0 0'] + data['1 0 0']<br/>\n",
    "alice['00'] = alice[\"00\"]/sum(alice.values())</b>\n",
    "<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = tel_result.get_counts(tel)\n",
    "alice = {}\n",
    "alice['00'] = data['0 0 0'] + data['1 0 0']\n",
    "alice['10'] = data['0 1 0'] + data['1 1 0']\n",
    "alice['01'] = data['0 0 1'] + data['1 0 1']\n",
    "alice['11'] = data['0 1 1'] + data['1 1 1']\n",
    "plot_histogram(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1 0 1': 14, '0 1 0': 223, '0 1 1': 236, '1 1 1': 9, '1 0 0': 20, '0 0 0': 258, '0 0 1': 252, '1 1 0': 12}\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob = {}\n",
    "bob['0'] = data['0 0 0'] + data['0 1 0'] +  data['0 0 1'] + data['0 1 1']\n",
    "bob['1'] = data['1 0 0'] + data['1 1 0'] +  data['1 0 1'] + data['1 1 1']\n",
    "plot_histogram(bob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}