Entanglement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "501186a1",
   "metadata": {},
   "source": [
    "QUANTUM ENTANGLEMENT\n",
    "\n",
    "A set of qubits whose combined state is |w> is said to be in state of entanglement if |w> cann't be decomposed as |w> = |u> * |v> ,where |u> and |v> are two independent quantum states. ( * represents tensor product )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b42154e",
   "metadata": {},
   "source": [
    "Creating an Entanglement pair (|00> + |11>)/sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62c46a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00': 500, '11': 500}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">       ┌───┐     ┌─┐   \n",
       "q57_0: ┤ H ├──■──┤M├───\n",
       "       └───┘┌─┴─┐└╥┘┌─┐\n",
       "q57_1: ─────┤ X ├─╫─┤M├\n",
       "            └───┘ ║ └╥┘\n",
       " c8: 2/═══════════╩══╩═\n",
       "                  0  1 </pre>"
      ],
      "text/plain": [
       "       ┌───┐     ┌─┐   \n",
       "q57_0: ┤ H ├──■──┤M├───\n",
       "       └───┘┌─┴─┐└╥┘┌─┐\n",
       "q57_1: ─────┤ X ├─╫─┤M├\n",
       "            └───┘ ║ └╥┘\n",
       " c8: 2/═══════════╩══╩═\n",
       "                  0  1 "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the required classes and modules.\n",
    "from qiskit import QuantumRegister, ClassicalRegister\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "#creating quantum register and quantum circuit objects\n",
    "q = QuantumRegister(2)\n",
    "c = ClassicalRegister(2)\n",
    "qc = QuantumCircuit(q,c)\n",
    "\n",
    "#Applying the entangling operation\n",
    "qc.h(q[0])  # Applying Hadamard gate on the first qubit\n",
    "qc.cx(q[0],q[1])  # Applying CNOT gate, where the first qubit is the controlled qubit and second one is target qubit\n",
    "qc.measure(q,c)\n",
    "\n",
    "#Executing the circuit in the simulator\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "qjob = execute(qc, backend, shots = 1000)\n",
    "count = qjob.result().get_counts()\n",
    "print(count)\n",
    "\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7881c649",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Histogram ( It will help us to observe the probabilty of qubits in entagled pair)\n",
    "plot_histogram(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ee20f3",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Creating an Entanglement pair (|000> + |111>)/sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c213d9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'111': 474, '000': 526}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">       ┌───┐          ┌─┐      \n",
       "q64_0: ┤ H ├──■───────┤M├──────\n",
       "       └───┘┌─┴─┐     └╥┘┌─┐   \n",
       "q64_1: ─────┤ X ├──■───╫─┤M├───\n",
       "            └───┘┌─┴─┐ ║ └╥┘┌─┐\n",
       "q64_2: ──────────┤ X ├─╫──╫─┤M├\n",
       "                 └───┘ ║  ║ └╥┘\n",
       " c9: 3/════════════════╩══╩══╩═\n",
       "                       0  1  2 </pre>"
      ],
      "text/plain": [
       "       ┌───┐          ┌─┐      \n",
       "q64_0: ┤ H ├──■───────┤M├──────\n",
       "       └───┘┌─┴─┐     └╥┘┌─┐   \n",
       "q64_1: ─────┤ X ├──■───╫─┤M├───\n",
       "            └───┘┌─┴─┐ ║ └╥┘┌─┐\n",
       "q64_2: ──────────┤ X ├─╫──╫─┤M├\n",
       "                 └───┘ ║  ║ └╥┘\n",
       " c9: 3/════════════════╩══╩══╩═\n",
       "                       0  1  2 "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the required classes and modules.\n",
    "from qiskit import QuantumRegister, ClassicalRegister\n",
    "from qiskit import QuantumCircuit, Aer, execute\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "#creating quantum register and quantum circuit objects\n",
    "q = QuantumRegister(3)\n",
    "c = ClassicalRegister(3)\n",
    "qc = QuantumCircuit(q,c)\n",
    "\n",
    "#Applying the entangling operation\n",
    "qc.h(q[0])  # Applying Hadamard gate on the first qubit\n",
    "qc.cx(q[0],q[1])  # Applying CNOT gate, where the first qubit is the controlled qubit and second one is target qubit\n",
    "qc.cx(q[1],q[2])\n",
    "qc.measure(q,c)\n",
    "\n",
    "#Executing the circuit in the simulator\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "qjob = execute(qc, backend, shots = 1000)\n",
    "count = qjob.result().get_counts()\n",
    "print(count)\n",
    "\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4fb1420e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Histogram ( It will help us to observe the probabilty of qubits in entagled pair)\n",
    "plot_histogram(count)"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}