Lab3_Quantum teleportation and superdense coding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](../QSS/Qulabs.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 3: Quantum teleportation and superdense coding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lab, you will learn to create `teleportation circuit`, `superdense coding` and how to simulates the circuits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the begining, you will need to import some packgees into your notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit, execute, transpile, IBMQ\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.tools import job_monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibmq_lima\n",
      "ibmq_qasm_simulator\n"
     ]
    }
   ],
   "source": [
    "# Load IBM account\n",
    "if not IBMQ.active_account():\n",
    "    IBMQ.load_account()\n",
    "# Select provider\n",
    "provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')\n",
    "# Select backend/ simulator\n",
    "backend = provider.get_backend('ibmq_lima')\n",
    "print(backend)\n",
    "sim = provider.get_backend('ibmq_qasm_simulator')\n",
    "print(sim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QuantumCircuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore `QuantumCircuit` once more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 229.49x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_r = QuantumRegister(1, name = 'alice')\n",
    "b_r = QuantumRegister(1, name = 'bob')\n",
    "quant_crc = QuantumCircuit(b_r, a_r)\n",
    "# Create Bell state\n",
    "quant_crc.h(0)\n",
    "quant_crc.cx(0, 1)\n",
    "quant_crc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 233.242x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anc = QuantumRegister(1, 'state')\n",
    "quant_crc.add_register(anc)\n",
    "quant_crc.x(2)\n",
    "quant_crc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 478.079x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_r = ClassicalRegister(2, name = 'meas')\n",
    "\n",
    "quant_crc.add_register(c_r)\n",
    "\n",
    "# Measure in Bell basis (Alice + ancilla)\n",
    "quant_crc.cx(2, 1)\n",
    "quant_crc.h(2)\n",
    "quant_crc.measure([1, 2], [0, 1])\n",
    "quant_crc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 598.479x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_crc.x(0).c_if(c_r[0], 1) # Apply gates if meas[0] output is 1\n",
    "quant_crc.z(0).c_if(c_r[1], 1) # Apply gates if meas[1] output is 1\n",
    "\n",
    "quant_crc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.441x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_r = ClassicalRegister(1, name = 'meas_bob')\n",
    "\n",
    "quant_crc.add_register(cb_r)\n",
    "\n",
    "quant_crc.measure(0, 2)\n",
    "quant_crc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = execute(quant_crc, sim).result().get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = {}\n",
    "for key in counts.keys():\n",
    "    if key[0] not in count.keys():\n",
    "        count[key[0]] = counts[key]\n",
    "    else:\n",
    "        count[key[0]] += counts[key]\n",
    "plot_histogram(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum teleportation in real backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 961.786x445.48 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = transpile(quant_crc, backend)\n",
    "trans.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
      "  Input \u001b[0;32mIn [19]\u001b[0m in \u001b[0;35m<cell line: 2>\u001b[0m\n",
      "    result = backend.run(trans).result()\n",
      "\u001b[1;36m  File \u001b[1;32m/opt/conda/lib/python3.8/site-packages/qiskit/providers/ibmq/job/ibmqjob.py:290\u001b[1;36m in \u001b[1;35mresult\u001b[1;36m\u001b[0m\n",
      "\u001b[1;33m    raise IBMQJobFailureError(\u001b[0m\n",
      "\u001b[1;31mIBMQJobFailureError\u001b[0m\u001b[1;31m:\u001b[0m 'Unable to retrieve result for job 638a2251c505e10d16657710. Job has failed: Instruction bfunc is not supported. Error code: 7001.'\n",
      "\n",
      "Use %tb to get the full traceback.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".button {\n",
       "  border: none;\n",
       "  color: white;\n",
       "  padding: 4px 8px;\n",
       "  text-align: center;\n",
       "  text-decoration: none;\n",
       "  display: inline-block;\n",
       "  font-size: 12px;\n",
       "  margin: 4px 2px;\n",
       "  transition-duration: 0.2s;\n",
       "  cursor: pointer;\n",
       "}\n",
       ".iqx-button {\n",
       "  background-color: #0f62fe; \n",
       "  color: white; \n",
       "}\n",
       ".iqx-button:hover {\n",
       "  background-color: #0043ce;\n",
       "  color: white;\n",
       "}\n",
       "</style>\n",
       "<a href=\"https://quantum-computing.ibm.com/docs/manage/errors#error7001\" target='_blank'><button class='button iqx-button'>View solution for error #7001</button></a>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try it by yourself\n",
    "result = backend.run(trans).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708.441x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tel_crc = QuantumCircuit(anc, a_r, b_r, cb_r)\n",
    "# Bell State preparation\n",
    "tel_crc.h(1)\n",
    "tel_crc.cx(1, 2)\n",
    "# Bell measurement\n",
    "tel_crc.barrier()\n",
    "tel_crc.cx(0, 1)\n",
    "tel_crc.h(0)\n",
    "# tel_crc.measure([1, 2], [0, 0])\n",
    "tel_crc.barrier()\n",
    "# Apply gates on Bob's qubit\n",
    "tel_crc.cx(1, 2)\n",
    "tel_crc.cz(0, 2)\n",
    "# Measure Bob's qubit\n",
    "tel_crc.measure(2, 0)\n",
    "tel_crc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.25+0.j 0.25+0.j 0.25+0.j 0.25+0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.25+0.j 0.25+0.j 0.25+0.j 0.25+0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.25+0.j 0.25+0.j 0.25+0.j 0.25+0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.25+0.j 0.25+0.j 0.25+0.j 0.25+0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j 0.  +0.j]]\n",
      "\n",
      " [[1.+0.j 0.+0.j]\n",
      " [0.+0.j 0.+0.j]]\n",
      "\n",
      "                   ░      ┌───┐ ░         \n",
      "state: ───────────░───■──┤ H ├─░───────■─\n",
      "       ┌───┐      ░ ┌─┴─┐└───┘ ░       │ \n",
      "alice: ┤ H ├──■───░─┤ X ├──────░───■───┼─\n",
      "       └───┘┌─┴─┐ ░ └───┘      ░ ┌─┴─┐ │ \n",
      "  bob: ─────┤ X ├─░────────────░─┤ X ├─■─\n",
      "            └───┘ ░            ░ └───┘   \n"
     ]
    }
   ],
   "source": [
    "tel_crc.remove_final_measurements()\n",
    "\n",
    "from qiskit.quantum_info import DensityMatrix, partial_trace\n",
    "\n",
    "DM = DensityMatrix.from_instruction(tel_crc)\n",
    "print(DM.data)\n",
    "\n",
    "PT = partial_trace(DM, [0, 1])\n",
    "print('\\n', PT.data)\n",
    "\n",
    "print('\\n', tel_crc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment\n",
    "#### Read the instructions carefully. You are NOT ALLOWED to make any change outside the instructed place.\n",
    "#### Naming format for the submitted file should be Lab3_\\<Your_Name\\>.ipynb. If you use any other format that submission will not be considered!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab - 3 Assignment - 1 [5 (2+.5+.5+1+1) Points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def teleportation(r1, r2, r3):\n",
    "    \n",
    "    '''Perform teleportation considering r2 as the initial state\n",
    "    and r3 as the register where final state to be produced'''\n",
    "    \n",
    "    tel_crc = QuantumCircuit(r2, r1, r3)\n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "    \n",
    "    # Bell State preparation\n",
    "    tel_crc.h(r3) \n",
    "    tel_crc.cx(r3,r1)\n",
    "    # Bell measurement\n",
    "    tel_crc.barrier()\n",
    "    tel_crc.cx(r2, r1)\n",
    "    tel_crc.h(r2)\n",
    "    # Apply gates on Bob's qubit\n",
    "    tel_crc.cx(r1, r3)\n",
    "    tel_crc.cz(r2, r3)\n",
    "    # Measure Bob's qubit\n",
    "    #tel_crc.measure(r3, r1)\n",
    "    #tel_crc.draw('mpl')\n",
    "            \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    tel_crc.remove_final_measurements()\n",
    "    return tel_crc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [-0.0625+0.j -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j -0.0625+0.j -0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [-0.0625+0.j -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j -0.0625+0.j -0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [-0.0625+0.j -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j -0.0625+0.j -0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]\n",
      " [-0.0625+0.j -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j -0.0625+0.j -0.0625+0.j\n",
      "  -0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j]\n",
      " [ 0.0625+0.j  0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j -0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j  0.0625+0.j  0.0625+0.j\n",
      "   0.0625+0.j  0.0625+0.j -0.0625+0.j  0.0625+0.j]]\n",
      "\n",
      " [[0.25+0.j 0.  +0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.25+0.j 0.  +0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.25+0.j 0.  +0.j]\n",
      " [0.  +0.j 0.  +0.j 0.  +0.j 0.25+0.j]]\n",
      "\n",
      "          ┌───┐      ░            ░                 \n",
      "  alice: ┤ H ├──■───░────────────░─────────────────\n",
      "         └───┘┌─┴─┐ ░            ░ ┌──────────────┐\n",
      "   bob0: ─────┤ X ├─░────────────░─┤0             ├\n",
      "              └───┘ ░ ┌───┐      ░ │              │\n",
      "   bob1: ───────────░─┤ H ├──■───░─┤1 circuit-173 ├\n",
      "                    ░ └───┘┌─┴─┐ ░ │              │\n",
      "charlie: ───────────░──────┤ X ├─░─┤2             ├\n",
      "                    ░      └───┘ ░ └──────────────┘\n",
      "\n",
      "          ┌─────────┐      ░                  ░                           \n",
      "  alice: ┤ U2(0,π) ├──■───░──────────────────░───────────────────────────\n",
      "         └─────────┘┌─┴─┐ ░                  ░            ░      ┌───┐   \n",
      "   bob0: ───────────┤ X ├─░──────────────────░────────────░───■──┤ H ├─■─\n",
      "                    └───┘ ░ ┌─────────┐      ░      ┌───┐ ░ ┌─┴─┐└───┘ │ \n",
      "   bob1: ─────────────────░─┤ U2(0,π) ├──■───░──────┤ X ├─░─┤ X ├──■───┼─\n",
      "                          ░ └─────────┘┌─┴─┐ ░ ┌───┐└─┬─┘ ░ └───┘┌─┴─┐ │ \n",
      "charlie: ─────────────────░────────────┤ X ├─░─┤ H ├──■───░──────┤ X ├─■─\n",
      "                          ░            └───┘ ░ └───┘      ░      └───┘   \n"
     ]
    }
   ],
   "source": [
    "### imports\n",
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "\n",
    "### set up the qubits and classical bits\n",
    "alice = QuantumRegister(1, name = 'alice')\n",
    "bob0 = QuantumRegister(1, name = 'bob0')\n",
    "bob1 = QuantumRegister(1, name = 'bob1')\n",
    "charlie = QuantumRegister(1, name = 'charlie')\n",
    "\n",
    "ghz = QuantumCircuit(alice, bob0, bob1, charlie)\n",
    "\n",
    "### WRITE YOUR CODE BELLOW ACCORDING TO THE GIVEN INSTRUCTIONS\n",
    "# Create |Φ+⟩ state between alice and bob0\n",
    "ghz.h(0)\n",
    "ghz.cx(0,1)\n",
    "\n",
    "ghz.barrier()\n",
    "# Create |Φ+⟩ state between bob1 and charlie\n",
    "ghz.h(2)\n",
    "ghz.cx(2,3)\n",
    "\n",
    "ghz.barrier()\n",
    "### NOTE THAT THERE IS NO ENTANGLEMENT BETWEEN ALICE AND CHARLIE\n",
    "ghz.append(teleportation(bob1, bob0, charlie), [1, 2, 3])   ### DO NOT MAKE ANY CHANGE HERE\n",
    "# Trace out bob0 and bob1 and print the partial trace ONLY\n",
    "\n",
    "DM = DensityMatrix.from_instruction(ghz)\n",
    "print(DM.data)\n",
    "\n",
    "PT = partial_trace(DM, [0, 1])\n",
    "\n",
    "print('\\n', PT.data)\n",
    "print('\\n', ghz)\n",
    "\n",
    "# DON'T TOUCH BELOW\n",
    "print('\\n', ghz.decompose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ONLY enter the elements of the sv vector in the following cell. DO NOT make any other change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\\text{Statevector = }\n",
       "\\begin{bmatrix}\n",
       "\\tfrac{1}{4} & 0 & 0 & 0  \\\\\n",
       " 0 & \\tfrac{1}{4} & 0 & 0  \\\\\n",
       " 0 & 0 & \\tfrac{1}{4} & 0  \\\\\n",
       " 0 & 0 & 0 & \\tfrac{1}{4}  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[[0.0625+0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.0625+0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.0625+0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.0625+0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]]\n",
      "\n",
      "\n",
      " [[[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.0625+0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.0625+0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.0625+0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.0625+0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]]\n",
      "\n",
      "\n",
      " [[[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.0625+0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.0625+0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.0625+0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.0625+0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]]\n",
      "\n",
      "\n",
      " [[[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.    +0.j]]\n",
      "\n",
      "  [[0.0625+0.j 0.    +0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.0625+0.j 0.    +0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.0625+0.j 0.    +0.j]\n",
      "   [0.    +0.j 0.    +0.j 0.    +0.j 0.0625+0.j]]]]\n"
     ]
    }
   ],
   "source": [
    "# Print  (use array_to_latex)\n",
    "# NOTE THAT THIS IS THE STATE VECTOR BETWEEN ALICE CHARLIE\n",
    "# SO ALICE AND CHARLIE ARE NOW ENTANGLED!!!\n",
    "from qiskit.visualization import array_to_latex\n",
    "from numpy import tensordot\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "sv = [[0.25+0.j,0.+0.j,0.+0.j,0.+0.j],[0.+0.j,0.25+0.j,0.+0.j,0.+0.j],[0.+0.j,0.+0.j,0.25+0.j,0.+0.j],[0.+0.j,0.+0.j,0.+0.j,0.25+0.j]] # complete the state vector corresponding to the partial trace above\n",
    "display(array_to_latex(sv, prefix='\\\\text{Statevector = }'))\n",
    "DM = tensordot(sv, sv, 0)\n",
    "print(DM) # this should be same as above partial trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Superdense Coding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you have to send a classical message from Alice's end to Bob's end using shared entanglement. You have to perform the following:\n",
    "\n",
    "1. Creation of entanglement between Alice and Bob (one qubit belongs to Alice, another to Bob).\n",
    "2. Alice applies single-qubit gates as required.\n",
    "3. Alice sends her qubit to Bob (This step is not possible in qiskit).\n",
    "4. Bob applies Bell measurement to collect result.\n",
    "\n",
    "To perform this fill the gaps in following cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab - 3 Assignment - 2 [5 (1+2+2) Points]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the state to be teleported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entangle_qubits(given_circuit, qubit_Alice, qubit_Bob):\n",
    "    \n",
    "    '''Creates required entanglement between Alice and Bob's register\n",
    "    qubit_Alice and qubit_Bob are Alice and Bob's register respectively in given_circuit'''\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "    \n",
    "    qubit_Alice = QuantumRegister(1)\n",
    "    qubit_Bob = QuantumRegister(1)\n",
    "    cqubit = ClassicalRegister(2)\n",
    "    given_circuit = QuantumCircuit(qubit_Alice, qubit_Bob, cqubit)\n",
    "    given_circuit.h(qubit_Alice)\n",
    "    given_circuit.cx(qubit_Alice, qubit_Bob)\n",
    "    \n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    given_circuit.barrier()\n",
    "    return given_circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_gates(given_circuit, qubit, message):\n",
    "    \n",
    "    '''Apply single-qubit gates (x if mesage is [0, 1] or [1, 1]and z if message is [1, 0] or [1, 1]) as required\n",
    "    qubit is the register where single-qubit gates to be applied\n",
    "    and message is a list containing two binary values'''\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - START\n",
    "\n",
    "    if message[1] == 1:\n",
    "        given_circuit.x(qubit)\n",
    "    if message[0] == 1:\n",
    "        given_circuit.z(qubit)\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    given_circuit.barrier()\n",
    "    return given_circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, there will be Bell measurement [Note that now both the qubits belong to Bob]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bell_meas_Bob_qubits(given_circuit, qubit1_Bob, qubit2_Bob):\n",
    "    \n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES (DO NOT ADD MEASUREMENT) - START\n",
    "    given_circuit.cx(qubit1_Bob,qubit2_Bob)\n",
    "    given_circuit.h(qubit1_Bob)\n",
    "\n",
    "    ### WRITE YOUR CODE BETWEEN THESE LINES - END\n",
    "    given_circuit.barrier()\n",
    "    return given_circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next lines of code puts everything together. [LEAVE AS IT IS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 581.178x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### imports\n",
    "from qiskit import QuantumRegister, ClassicalRegister\n",
    "from numpy.random import randint\n",
    "\n",
    "### set up the qubits and classical bits\n",
    "Alice_reg = QuantumRegister(1)\n",
    "Bob_reg = QuantumRegister(1)\n",
    "meas = ClassicalRegister(2)\n",
    "\n",
    "# Generate a 2-bit classical message as an array of size 2\n",
    "message = randint(2, size = 2)\n",
    "\n",
    "### quantum teleportation circuit here\n",
    "# Create Quantum Cirtuit\n",
    "mycircuit = QuantumCircuit(Alice_reg, Bob_reg, meas)\n",
    "# Create Bell state\n",
    "entangle_qubits(mycircuit, Alice_reg, Bob_reg)\n",
    "# Apply quantum gates\n",
    "apply_gates(mycircuit, Alice_reg, message)\n",
    "# Measure in Bell basis\n",
    "bell_meas_Bob_qubits(mycircuit, Alice_reg, Bob_reg)\n",
    "mycircuit.measure([0, 1], [1, 0])\n",
    "### Look at the complete circuit\n",
    "mycircuit.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run above circuit and plot histogram. Also print message. In histogram message should be there with probability 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(execute(mycircuit, sim).result().get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1]\n"
     ]
    }
   ],
   "source": [
    "print(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}