testanto_qiskit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Código Exemplo\n",
    "\n",
    "Estes são códigos básicos para o estudo do IBM Quantum Experience e do Qiskit. Os códigos foram inspirados de um minicurso \n",
    "do programa de verão do LNCC, dado por Renato Portugal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "configrc.store_credentials:WARNING:2021-02-18 14:20:56,764: Credentials already present. Set overwrite=True to overwrite.\n"
     ]
    }
   ],
   "source": [
    "#executar apenas da primeira vez\n",
    "#digite seu token entre os parenteses\n",
    "from qiskit import *\n",
    "from qiskit import IBMQ\n",
    "#IBMQ.save_account('token') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ibmqfactory.load_account:WARNING:2021-02-18 14:22:01,437: Credentials are already in use. The existing account in the session will be replaced.\n"
     ]
    }
   ],
   "source": [
    "from qiskit import *\n",
    "from qiskit import IBMQ\n",
    "provedor = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.monitor import job_monitor\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     ┌───┐     ┌─┐   \n",
      "q_0: ┤ H ├──■──┤M├───\n",
      "     └───┘┌─┴─┐└╥┘┌─┐\n",
      "q_1: ─────┤ X ├─╫─┤M├\n",
      "          └───┘ ║ └╥┘\n",
      "c: 2/═══════════╩══╩═\n",
      "                0  1 \n"
     ]
    }
   ],
   "source": [
    "#vamos a seguir recriar o circuito para os estados de bell \n",
    "qreg_q = QuantumRegister(2, 'q')\n",
    "creg_c = ClassicalRegister(2, 'c')\n",
    "circuit = QuantumCircuit(qreg_q, creg_c)\n",
    "\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.cx(qreg_q[0], qreg_q[1])\n",
    "circuit.measure(qreg_q[0], creg_c[0])\n",
    "circuit.measure(qreg_q[1], creg_c[1])\n",
    "#exibimos aqui o cicuito criado, igual feito no composer\n",
    "print(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_armonk') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_athens') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_santiago') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_lima') from IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <IBMQBackend('ibmq_quito') from IBMQ(hub='ibm-q', group='open', project='main')>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#listaremos então as máquinas disponíveis \n",
    "provedor.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n",
      "{'00': 543, '01': 17, '10': 18, '11': 446}\n"
     ]
    }
   ],
   "source": [
    "### O comando get_backend seleciona o backend\n",
    "### O comando execute executa o circuito no IBMQX2\n",
    "### O job entra na fila. A espera pode demorar MUITAS HORAS.\n",
    "### O comando job_monitor mostra a posição em tempo real.\n",
    "### Se a conexão com a internet for interrompida, o job é cancelado.\n",
    "maquina = provedor.get_backend('ibmq_lima')\n",
    "job2 = execute(circuit, maquina, shots=1024)\n",
    "job_monitor(job2)\n",
    "resultado2 = job2.result()\n",
    "contagem2 = resultado2.get_counts()\n",
    "print(contagem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(contagem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 4029, '00': 3995}\n"
     ]
    }
   ],
   "source": [
    "#executando agora no simulador qasm\n",
    "simulador = BasicAer.get_backend('qasm_simulator')\n",
    "job1 = execute(circuit, simulador, shots=8024)\n",
    "resultado1 = job1.result()\n",
    "contagem1 = resultado1.get_counts()\n",
    "print(contagem1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(contagem1)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}