Grover_3qubits_error_angle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grover's algorithm dependence on an error rotation angle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of an error in Grover's algorithm to observe the impact on the probability to measure the solution state as function of the angle of rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from numpy import pi\n",
    "from qiskit import Aer, assemble, transpile\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definition of the registers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "qreg_q = QuantumRegister(3, 'q')\n",
    "creg_c = ClassicalRegister(3, 'c')\n",
    "circuit = QuantumCircuit(qreg_q, creg_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa897871240>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.h(qreg_q[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa8c44639c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.cz(qreg_q[2], qreg_q[0])\n",
    "circuit.cz(qreg_q[1], qreg_q[0])\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introducing the error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa8977f5f40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.rx(pi/8, qreg_q[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Diffuser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa8975fcf00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(qreg_q[2])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.x(qreg_q[2])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.ccx(qreg_q[0], qreg_q[1], qreg_q[2])\n",
    "circuit.x(qreg_q[0])\n",
    "circuit.x(qreg_q[1])\n",
    "circuit.x(qreg_q[2])\n",
    "circuit.h(qreg_q[2])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.barrier(qreg_q[0], qreg_q[1], qreg_q[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fa897623b40>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.measure(qreg_q[0], creg_c[0])\n",
    "circuit.measure(qreg_q[1], creg_c[1])\n",
    "circuit.measure(qreg_q[2], creg_c[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simulation of the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_sim = Aer.get_backend('aer_simulator')\n",
    "transpiled_grover_circuit = transpile(circuit, aer_sim)\n",
    "qobj = assemble(transpiled_grover_circuit)\n",
    "results = aer_sim.run(qobj).result()\n",
    "counts = results.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drawing the circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 929.397x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output=\"mpl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 4
}