Grover_2qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb9ad168-3465-4b8e-b22b-055ea903df14",
   "metadata": {},
   "source": [
    "## Grover's algorithm for 2 qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "304f7de3",
   "metadata": {},
   "source": [
    "Grover search for state |11>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664562c0-57cc-4fc3-95c0-529d0f8a9b34",
   "metadata": {},
   "source": [
    "Import required packages and initialize the least busy backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "099e2923-0201-40ef-8a21-7f31514d5993",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import IBMQ, Aer, assemble, transpile\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "provider = IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(\"ibm-q\")\n",
    "device = least_busy(provider.backends(filters=lambda x: x.configuration().n_qubits >= 3 and \n",
    "                                   not x.configuration().simulator and x.status().operational==True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c85af9b-ddbb-45cb-be77-8a9cf3061d67",
   "metadata": {},
   "source": [
    "Implementing the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd7e9907-cd51-43a1-ad0a-05af046bb4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2\n",
    "grover_circuit = QuantumCircuit(n)\n",
    "\n",
    "def initialize_s(qc, qubits):\n",
    "    \"\"\"Apply a H-gate to 'qubits' in qc\"\"\"\n",
    "    for q in qubits:\n",
    "        qc.h(q)\n",
    "    return qc\n",
    "\n",
    "# Oracle\n",
    "grover_circuit = initialize_s(grover_circuit, [0,1])\n",
    "grover_circuit.cz(0,1)\n",
    "# Diffusion operator (U_s)\n",
    "grover_circuit.h([0,1])\n",
    "grover_circuit.z([0,1])\n",
    "grover_circuit.cz(0,1)\n",
    "grover_circuit.h([0,1])\n",
    "grover_circuit.draw()\n",
    "\n",
    "grover_circuit.measure_all()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b8b802-75e0-4610-acd9-747f7d667831",
   "metadata": {},
   "source": [
    "Simulating the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d708e3e-7491-41a9-b68a-a294ea2cf9cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_sim = Aer.get_backend('aer_simulator')\n",
    "qobj = assemble(grover_circuit)\n",
    "result = aer_sim.run(qobj).result()\n",
    "counts = result.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72a6fe93-66af-45c7-9e65-41e138fb17d6",
   "metadata": {},
   "source": [
    "Processing of the circuit on quantum hardware"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9875b2b7-8b90-4349-b7da-259b3219de95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run on the least busy backend\n",
    "transpiled_grover_circuit = transpile(grover_circuit, device, optimization_level=3)\n",
    "job = device.run(transpiled_grover_circuit)\n",
    "job_monitor(job, interval=2)\n",
    "\n",
    "results = job.result()\n",
    "answer = results.get_counts(grover_circuit)\n",
    "plot_histogram(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6218e294",
   "metadata": {},
   "source": [
    "As we can see running the circuit on a real quantum computer doesn't give perfect results, as expected, but it is mostly correct (84%)."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "eca16aa7a5f8b35a277530dea12079435ebf8cbf4ee51249fed3c4b16880e759"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}