QiskitTutorial Video -2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "beginning-slovak",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "modified-monday",
   "metadata": {},
   "outputs": [],
   "source": [
    "qr = QuantumRegister(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "capable-pipeline",
   "metadata": {},
   "outputs": [],
   "source": [
    "cr = ClassicalRegister(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "antique-island",
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(qr,cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ready-framework",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "grateful-lecture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 99.4326x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "seasonal-actor",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'q' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-504c4c88f23a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcircuit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'q' is not defined"
     ]
    }
   ],
   "source": [
    "circuit.h(q[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "coordinate-pursuit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1d32b109b80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h(qr[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "reflected-pollution",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 159.633x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "criminal-outline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1d32b0fe220>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.cx(qr[0],qr[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exotic-federation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 219.833x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "worth-vinyl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1d39b637fd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.measure(qr,cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dirty-dairy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 340.233x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "spiritual-search",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "contrary-newton",
   "metadata": {},
   "outputs": [],
   "source": [
    "result= execute(circuit , backend = simulator ).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "proved-purchase",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "reduce() arg 2 must support iteration",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-175ff43569b9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_histogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\softwares\\anaconda3\\envs\\quantum2\\lib\\site-packages\\qiskit\\visualization\\counts_visualization.py\u001b[0m in \u001b[0;36mplot_histogram\u001b[1;34m(data, figsize, color, number_to_keep, sort, target_string, legend, bar_labels, title, ax)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     labels = list(sorted(\n\u001b[1;32m--> 131\u001b[1;33m         functools.reduce(lambda x, y: x.union(y.keys()), data, set())))\n\u001b[0m\u001b[0;32m    132\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnumber_to_keep\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'rest'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: reduce() arg 2 must support iteration"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "solved-worry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "demanding-bhutan",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\softwares\\anaconda3\\envs\\quantum2\\lib\\site-packages\\qiskit\\providers\\ibmq\\ibmqfactory.py:192: UserWarning: Timestamps in IBMQ backend properties, jobs, and job results are all now in local time instead of UTC.\n",
      "  warnings.warn('Timestamps in IBMQ backend properties, jobs, and job results '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "humanitarian-macedonia",
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.get_provider('ibm-q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "sound-gateway",
   "metadata": {},
   "outputs": [],
   "source": [
    "qcomp = provider.get_backend('ibmq_16_melbourne')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "pointed-supply",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = execute(circuit , backend=qcomp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "stuffed-buying",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.tools.monitor import job_monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "weird-elimination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "job_monitor(job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "pediatric-economics",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "altered-appointment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(result.get_counts(circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "friendly-butter",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Thank You For Watching :)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}