Bernstein-Vazirani.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algoritmo de Bernstein-Vazirani"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 791.028x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### BV - procedimento que constrói o circuito de Bernstein-Vazirani\n",
    "### n  - número de qubits do primeiro registrador\n",
    "### s  - número inteiro na notação decimal da cadeia de bits a ser encontrada\n",
    "def BV(n,s):\n",
    "    global q,c,circ\n",
    "    if 2**n<=s:\n",
    "        raise ValueError(\"Expecting s<2**n\")\n",
    "    # define o circuito com n+1 qubits\n",
    "    q = QuantumRegister(n+1)\n",
    "    c = ClassicalRegister(n+1)\n",
    "    circ = QuantumCircuit(q,c)\n",
    "    # portas X e Hadamard\n",
    "    circ.x(q[0])\n",
    "    circ.h(q[0])\n",
    "    for i in range(1,n+1):\n",
    "        circ.h(q[i])\n",
    "    # oráculo\n",
    "    s2 = list(bin(s))[2:][::-1]\n",
    "    for i in range(len(s2)):\n",
    "        if s2[i]=='1':\n",
    "            circ.cx(i+1,0)\n",
    "    # portas X e Hadamard   \n",
    "    circ.h(q[0])\n",
    "    circ.x(q[0])\n",
    "    for i in range(1,n+1):\n",
    "        circ.h(q[i])\n",
    "    # medição dos n qubits\n",
    "    circ.measure(q[1:],c[1:])\n",
    "    # q[0] sempre retorna 0\n",
    "\n",
    "# EXEMPLO\n",
    "BV(4,11)\n",
    "\n",
    "circ.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "### simulating in qasm\n",
    "simulator = BasicAer.get_backend('qasm_simulator')\n",
    "job1 = execute(circ, simulator, shots=8024)\n",
    "result1 = job1.result()\n",
    "counting1 = result1.get_counts()\n",
    "plot_histogram(counting1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Este comando deve ser usado uma única vez, pois o valor do token ficará gravado so seu laptop.\n",
    "### Vá na página do IBM Q Experience, clique no seu usuário (boneco no alto a direita) para entrar na sua conta. \n",
    "### Copie o 'API Token' e cole no comando abaixo.\n",
    "from qiskit import IBMQ\n",
    "#IBMQ.save_account('API_Token')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_london') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_burlington') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_armonk') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_rome') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### computadores quânticos disponíveis para a sua conta\n",
    "from qiskit import IBMQ\n",
    "provedor = IBMQ.load_account()\n",
    "provedor.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Escolha um dos nomes dos computadores\n",
    "machine_name = 'ibmq_ourense'   #'ibmqx2'  #'ibmq_vigo'\n",
    "maquina = provedor.get_backend(machine_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'00110': 65,\n",
       " '10010': 56,\n",
       " '10100': 106,\n",
       " '11010': 9,\n",
       " '00100': 105,\n",
       " '01010': 2,\n",
       " '01110': 3,\n",
       " '11100': 8,\n",
       " '10110': 535,\n",
       " '00000': 39,\n",
       " '11110': 19,\n",
       " '11000': 4,\n",
       " '01000': 3,\n",
       " '00010': 40,\n",
       " '10000': 27,\n",
       " '01100': 3}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job2 = execute(circ, backend = maquina, shots = 1024)\n",
    "job_monitor(job2)\n",
    "result2 = job2.result()\n",
    "counting2 = result2.get_counts()\n",
    "counting2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = plot_histogram([counting1,counting2],legend=['Simulator', machine_name])\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.5 64-bit",
   "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": "3.10.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "bebf9730dc43cfbc35fb329a82a69476f49cde6de038a2bbfc26f12404ad85fb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}