4. Bernstein-Vazirani Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit as q\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "secret = '110010010011'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f4fe44dd290>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit = q.QuantumCircuit(len(secret)+1, len(secret))\n",
    "\n",
    "#circuit.h([0, 1, 2, 3, 4, 5])\n",
    "#circuit.x(6)\n",
    "#circuit.h(6)\n",
    "\n",
    "circuit.h(range(len(secret)))\n",
    "circuit.x(len(secret))\n",
    "circuit.h(len(secret))\n",
    "\n",
    "circuit.barrier()\n",
    "\n",
    "\n",
    "#circuit.cx(5, 6)\n",
    "#circuit.cx(3, 6)\n",
    "#circuit.cx(0, 6)\n",
    "\n",
    "for i , val in enumerate(reversed(secret)):\n",
    "    \n",
    "    if val == '1':\n",
    "        circuit.cx(i, len(secret))\n",
    "\n",
    "circuit.barrier()\n",
    "\n",
    "#circuit.h([0, 1, 2, 3, 4, 5])\n",
    "\n",
    "circuit.h(range(len(secret)))\n",
    "\n",
    "circuit.barrier()\n",
    "\n",
    "circuit.measure(range(len(secret)), range(len(secret)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1612.16x866.88 with 1 Axes>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'110010010011': 1}\n"
     ]
    }
   ],
   "source": [
    "simulator = q.Aer.get_backend('qasm_simulator')\n",
    "result = q.execute(circuit, backend = simulator, shots = 1).result()\n",
    "counts = result.get_counts()\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}