Grover for 4 qubits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import qiskit "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from qiskit import Aer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "# importing Qiskit\n",
    "from qiskit import IBMQ, BasicAer, Aer\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "\n",
    "# import basic plot tools\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Credentials already present. Set overwrite=True to overwrite.\n"
     ]
    }
   ],
   "source": [
    "# IBMQ.save_account('0411be6664ec0308d5f3a8dcb3bf985fa9a7c25e1dd020b245db3804c027977d84ebe17c31bf4dfdf9faaa60ade63ec29446d2b0f16524980cc42719d090c3b8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = QuantumRegister(8)\n",
    "c = ClassicalRegister(5)\n",
    "qc = QuantumCircuit(q, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x12fc8f0d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize in a superposition of all possible states \n",
    "qc.x(q[4])\n",
    "qc.h(q[0:5])\n",
    "qc.barrier(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x12fc94c70>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.barrier(q)\n",
    "\n",
    "# build oracle. ex: to find 1010 state\n",
    "qc.x(q[1])\n",
    "qc.x(q[3])\n",
    "qc.barrier(q)\n",
    "qc.mct([q[0], q[1], q[2], q[3]], q[4], q[5:7])    # control qubits, target qubit, ancilla qubits = number of control qubits - 2\n",
    "qc.barrier(q)\n",
    "qc.x(q[1])\n",
    "qc.x(q[3])\n",
    "\n",
    "qc.barrier(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x12fca4c40>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.barrier(q)\n",
    "qc.h(q[0:4])\n",
    "qc.barrier(q)\n",
    "qc.x(q[0:4])\n",
    "qc.barrier(q)\n",
    "qc.h(q[3])\n",
    "qc.barrier(q)\n",
    "qc.mct([q[0], q[1], q[2]], q[3], q[5:6])\n",
    "qc.barrier(q)\n",
    "qc.h(q[3])\n",
    "qc.barrier(q)\n",
    "qc.x(q[0:4])\n",
    "qc.barrier(q)\n",
    "qc.h(q[0:4])\n",
    "qc.barrier(q)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x12fc8f4c0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.measure(q[0:4], c[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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S7X/zI9H0r9AwhNk8HmnA1dJ3b7o4NiWThiHaL5qGNjQ0NKiyslIjRoxo9bY9e/bUm2++qXfffVe7du3SK6+8optvvjkCKQGg7Xur7qAazpzWtpmLdMZ3TuW1B9yOFLNefP9dfa1/sKPReO6sdn38vqt5hlwTXJHihL8fLqXE6O/eYqHuI78kXdfXmX3dOSq2VnTGQn3/1rhrpS/1uPz9eCTNHB1sRMaiWKt7pPAdAwBo7zi5xIZOnTrJ5/O5HQPt0Pk7HpnG1Nwmam+13vHR/gs/uE7of51er92nG65JbzKvtPo9Tf/DcmVe3V+HThxRZs/+2nz7g9GOGzWh3m+3pCu04ms5kqTV75boni+P1ZJXi1zNecdIqb5B2lsb+vlw7i5708Bgo8ZtlzrGXv6gwvW6ezzS3TcF71pd/UnoOeHUevJ1wQakW0w4rr1e6b6x0qMvSB9/GnpOOLWeNip42qfbYvm4jga+YwAA7R0rDQGDrFq1yu0Itpia20TtrdbHG0/qqsTgsqfOick6fjr0hdtuSb1Wo65J1wt3LdAtqdfq0a+Hvrt9WxHq/Z4826hOHZJ01ndOpR+8p/H93D/HND5O+n6WNLrpz+At8nqCp4JO/0ps3J0zVM0DgUDM1D0pQXrga/Zu1hHvDZ5+OLnpvd+iypTj+spk6f/cau9mMYnxwVPivhojpyXH+nEdaXzHAADaO1YaAgaZM2eOkU0hU3ObqL3VunNiR33aeEqS9GnjKXVJCn3BmarjH2tA556SpJrP6tXnym5Ry+iGL77fI6c+VWbP/pKkte/9WXcNvam5zaMqPk66a3SwmfX7nZdenfW3+veQ7rxB6tc98vm+6PDJ4/rus49axlKu6KwlN2c3Ocbe/vhQTNU9KUGafYu0833pj29Lx062vM3gXsFap3SOfL6WmHRcX5kkzfma9No+aeu70menm5/vkXRdarA52/WKqES0MPm4jiS+YwAA7R1NQ8AgJSUlbkewxdTcJmpvtR7de5D+Y9eLmn7taL30/m7N+vJYnfP79MmpBqVccbHL8d4nNRrWva98fr+8sbAsLcK++H5fPPSuvt7/y5Kkv9TX6p0jh/Qfu17Ue0dr9NjOrcobMcnlxNKwPtLQ3tL+Oumt6uCdletOSGd9wTv19u4ipXaXbvhS8J9u6XVFF71w14Im48/sL29yjMVi3T2e4CnGf9dPqvhIerdG+qBeOvKpdM4fbCz26RpsyI5Kk3rFQLPwPNOOa68neCOT0enSOx8E6/1BvfRJg+QPSMkJUp9uwWsgfiVN6t7JvaymH9eRwncMAKC9o2kIADDW36UMUFJ8gsavX6LhPftr1DUDtf/YYS1741n9ZtIPLsx772iNRvcepEbfWX38+aeqbTimazq13ds7fvH9vlS9W/NH/b0k6RdZMy/MG7d+cUz9gO/xSIN6BR+mCXWMvVl3MGbr7vVKGX2DD1OYelzHx0kjvhR8mMa049ppfMcAANo7moYAAKMtn3Cv5c/vHKnWjC+cLvfQ6G9f+Pe3cv7/qORy0xffb9He1+X1NL2MccnMxVFM1baFOsZuH/QV6u4gjuvo47jmOwYA0L7RNAQMUlFR4XYEW0zNbSJqLd0x+CtuR4g5068d7XaEdom6Rxb1dUd7rzvfMQCA9oSmIWCQDRs2KDs72+0YrWZqbhO1xVon/Gpmy5PaEeoRPdQ6eqh19FBrK+oBAMClNT23AEDMWrRokdsRbDE1t4moNQAAAADACTQNAQAAAAAAAFjQNAQAAAAAAABgQdMQMMjKlSvdjmCLqblNRK0BAAAAAE6gaQgYJCMjw+0Itpia20TUGgAAAADgBJqGgEGysrLcjmCLqblNRK0BAAAAAE6gaQgAAAAAAADAgqYhYJBRo0a5HcEWU3ObiFoDAAAAAJxA0xAwSFlZmdsRbDE1t4moNQAAAADACTQNAQAAAAAAAFjQNAQAAAAAAABgQdMQMMjGjRvdjmCLqblNRK0BAAAAAE6gaQgAAAAAAADAgqYhYJBp06a5HcEWU3ObiFoDAAAAAJxA0xAAAAAAAACAj1n9lAAAIABJREFURbzbAQAAaM7ZH6935XUTfjXTlddtCfUAAMDKje/G9vq9SK2jh1pbUQ93sNIQMEheXp7bEWwxNbeJqDUAAAAAwAk0DYH/x969h0dV3+vfv2dCSKIoh6acEUwICUkJCKJEWxLQIvjT4olQbLVJrfBg6FPoYW+f9uJU2rS1bOJu66nWH7OlgpsEbKVAqQcSrSKQghUwGBERowGRk4ZCkJl5/kgB12SSzAxrZuWbeb+ua67Kmu9aufMhzSQ3a80yyKxZs5yOEBFTc5uIWQMAAAAA7EBpCBhk7NixTkeIiKm5TcSsAQBArPj90uGG838+9Knk8zuXpyPz+aVDn5z/8+GGpvnDfj6fdOD4+T8f+xezRvyiNAQMcujQIacjRMTU3CaK11l/dOK4Oi++U9sP7rNsf+/4IXVefKdqDn/gTDCHMA8AQLT4/NJb9dLSl6SfVEiL/nz+uZ8/K/24XHq8UtpV11S+tAemvi76fNLOOun3G6X/b6X08zXnn1v056b5e16W3j7QfkotU2d9xiv9Y5/00PPS/SulX/7l/HMLnpHmrZaeelXa9zGzbq+YR3RwIxQAgPGqD+xVSqfOGvbFAZbtWw+8o0s6pyizRx+HkjmDeQAAouHAcWn5Jmn/4ZbXnPpM2vVB06NPV+nOPGnAF2KXMRgTXxf3fSyt2CQd/KTlNf86Lb2+v+kxKFWalif1ujR2GYMxcdZv1UtPvyYd/VfLaz49JW19t+mR1UeaerXU/eLYZQzGxFlHE/OIDs40BAySnZ3tdISImJrbRPE6638c3KvhPQeqkzvBsr26/h2N7HW53K74erljHgAAu21+R/r1utYLw0D1x6WyDVLl7ujlCoVpr4svvin994bWC8NA+z5u+vupfjd6uUJh0qx9funZbdIjL7ZeGAbaXS/9aq1U82H0soXCpFnHAvOIDqYGGGTVqlVOR4iIqblNFK+z/seBvbqyd1qz7VsPvKNRQbZ3dMwDAGCn196RVrwmeSO43Njnl/70j6YizCkmvS4+t1N6drsUyRWwZ7zSH1+Vtu61PVbITJm13y89Uy29WBPZ/qc+k/5Q6WxxaMqsY4V5RAeloY2KiorkcrmCPpKTk52Ohw5g3rx5TkeIiKm5TRSvs9528F2N6p1u2ebz+7T94D5d2TtN7xw7qPFP/1TjVixUwYoF+scBB3+ajoG25iFJE1f+XH0fmqHSTc84EREAYIgPjkorN7e+5sFvND1as2a79M5H9uUKhymvi7UHpLX/bH1NKLN+erP1Rh6xZMqst+2TXq5tfU1bs/b6pSf/Lh0P4yxFO5ky61jh94HooDS00dy5c7Vp0ybLY/369XK73Zo8ebLT8dABlJeXOx0hIqbmNlE8zvqDT4/owIljGtnrcsv2f370nho+O6Wr+2SoW9JFKp88RxunzdfDX/2OfrhxmUNpoy+UeUjSHyb9P/pl/p1ORAQAGMLna3pfPTvuiOxX07FOn7nwY4XDlNfFxjNNZ3Pawetreu/JWN/J2pRZf3pKWlVtz7FOfiaVb7XnWOEwZdaxwu8D0cONUGyUnp6u9HRrs/3oo4/K5/OpqKjImVAA0MF90HBEktQt6SLL9qdrXtXVfQZrwKXWd19P6pSohA78niahzqP/JQ6/Kz0AoN2r+VCqO2rf8T5uaLppx1UxvFLQlNfFbfukoyfsO97+w01nLmbF8N4Ppsz6lbebbiJjl5110odHpb7d7TtmW0yZdazw+0D0MKUQ+Xw+LV68WBkZGUpOTtbw4cNVVVWlzMxMTZ8+vcX9PB6P+vbtqwkTJsQwLQDEj6wefdU16SL9esuzOnbqhI6cbNDvX39ej77+nH4+dpplrdfn0/df+B/96OqvOZQ2+sKZBwAArXnl7Sgcs41LQu1myutiNObCrJvz+aRN0fi6jsIxW2PCrGOJ3weihzMNQ3TPPfdo9erVmjt3rkaNGqVXX31V06ZN06FDh/T9738/6D41NTXavHmz7r//fiUkJARdA4SjqqrK6QgRMTW3ieJx1pcmXaQ/3fpD/filp3X5Y7N0UackDe85UH+5/T/1lQFDz63z+/2696+P6cb0K3TD5cMdTBxdoc4DAIDWeH3S2wftP+57h5tuIpGcaP+xgzHhdfFEo71ndJ5Ve6DpEmW3y/5jB2PCrA9+Ih0/af9xaw/Yf8zWmDDrWOL3geihNAzBihUr5PF4VFlZqfz8fEnSuHHjtG3bNq1evVojR44Mup/H45EkLk2GbXbt2qWePXs6HSNspuY2UbzO+tr+Waq6c0Gra2a/4FF6916aMeKrsQnloFDmAQBAaw4elz7zRufY7x+RMnpF59jBtPfXxfePROe4jWekQ59KvS6NzvGDiddZH/pUOnlaSukcneMH095nHWv8PhAdlIYhKC0t1cSJE88VhmcNHjxYiYmJys3NbbaP1+vVsmXLlJeXp8zMTMtzp0+f1v333681a9YoOTlZ3bt310svvRTVz8HlitE/LyFic+bMaXNNWVlZm+vKysrsihQSU3ObyI5Zmzjn0z9cfsHHqNr/pv7wxovK6ztEG9/bpe4pXVQ+ufV5ttfvm3bMQ5Lu/etj2lK/R6e9Z7Slfo/+dNuPWl3fXucBAIiOgbkTdct/rLdsa+uuvS09P/sp659vvv2beuuVp4IvjoAdr41Ovi7m5H9b19/7hGWbXbMefc11en/XixeQzsr0WY+e/BNdM+Vnlm12zbr/5dk68mHNBaSzMn3WdrvQeXSk3wdaEkres2v8/tDulERp2Ia6ujrt3Lkz6C/h+/fvV05OjpKSkpo9t2HDBtXX12vBggXNnvvxj3+sTz/9VLt371ZCQoLq6+ujER0A8G/5l2XrxPe5Q9rnPT5xhtMRAADtWDR/WXa1wxsQOPq6yKxjJqpf125m3Z7x+0BkXP5Q68U49dprrykvL09r167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      "text/plain": [
       "<Figure size 1694.03x2371.88 with 1 Axes>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01010': 35, '01111': 32, '00111': 35, '00000': 49, '01001': 30, '01101': 26, '00110': 28, '00011': 30, '00001': 33, '01110': 33, '00010': 43, '01100': 30, '01011': 30, '01000': 38, '00100': 29, '00101': 499}\n"
     ]
    }
   ],
   "source": [
    "# running and getting results \n",
    "\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "job = execute(qc, backend, shots=1000)\n",
    "# backend = provider.get_backend('ibmq_qasm_simulator')\n",
    "# job = execute(qc, backend=backend, shots=8000, seed_simulator=12345, backend_options={\"fusion_enable\":True})\n",
    "result = job.result()\n",
    "count = result.get_counts()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# backend = provider.get_backend('ibmq_qasm_simulator')\n",
    "# job = backend.retrieve_job('5e4580133003870018864ed8')\n",
    "# counts = job.result().get_counts()\n",
    "# print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(count)"
   ]
  },
  {
   "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}