BVA_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bernstein Vazirani Algorithm Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.visualization import plot_histogram\n",
    "from qiskit_aer import AerSimulator\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ask user to input a number and simulate it is unknown\n",
    "\n",
    "# Input number\n",
    "number_to_guess = int(input(\"Enter an integer to guess: \"))\n",
    "\n",
    "ntgb = bin(number_to_guess)[2:]     # It's a string again, delete the \"0b\" in front\n",
    "ntgb = ntgb[::-1]                   # Reverse the binary number to match bit positions\n",
    "\n",
    "# Length of input\n",
    "bin_length = len(ntgb)\n",
    "\n",
    "# Convert to list to integers and prepare number of qubits\n",
    "number_to_guess_binary = []\n",
    "qubit_number = []\n",
    "for ix, i in enumerate(ntgb):\n",
    "    number_to_guess_binary.append(int(i))\n",
    "    qubit_number.append(int(ix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 956.385x618.722 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantum Circuit\n",
    "\n",
    "circuit = QuantumCircuit(bin_length+1, bin_length)\n",
    "\n",
    "circuit.h(qubit_number)\n",
    "circuit.x(bin_length)\n",
    "circuit.h(bin_length)\n",
    "\n",
    "for ix, i in enumerate(number_to_guess_binary):\n",
    "    if i and 1:\n",
    "        circuit.cx(ix, bin_length)\n",
    "\n",
    "circuit.h(qubit_number)\n",
    "\n",
    "circuit.measure(qubit_number, qubit_number)\n",
    "\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The secret number is: 20\n"
     ]
    }
   ],
   "source": [
    "# Simulation, guess the number in only one try\n",
    "\n",
    "simulator = AerSimulator(shots=1)\n",
    "\n",
    "# Result and Plot\n",
    "result = simulator.run(circuit).result()\n",
    "counts = result.get_counts(circuit)\n",
    "\n",
    "#print(counts.int_outcomes().keys())\n",
    "# CHECK how to read values from counts.int_outcomes().keys(), data type = dict_keys([])\n",
    "for i in counts.int_outcomes().keys():\n",
    "    print (f\"The secret number is: {i}\")\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qiskit",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}