Grover with 3SAT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, execute, transpile, Aer, IBMQ, QuantumRegister, ClassicalRegister\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.aqua import QuantumInstance\n",
    "from qiskit.aqua.algorithms import Grover\n",
    "from qiskit.aqua.components.oracles import LogicalExpressionOracle, TruthTableOracle\n",
    "# Loading your IBM Q account(s)\n",
    "#provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jfern\\AppData\\Roaming\\Python\\Python38\\site-packages\\qiskit\\aqua\\algorithms\\amplitude_amplifiers\\grover.py:215: DeprecationWarning: The package qiskit.aqua.algorithms.amplitude_amplifiers is deprecated. It was moved/refactored to qiskit.algorithms.amplitude_amplifiers (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/master/README.md#migration-guide>\n",
      "  warn_package('aqua.algorithms.amplitude_amplifiers',\n",
      "C:\\Users\\jfern\\AppData\\Roaming\\Python\\Python38\\site-packages\\qiskit\\aqua\\quantum_instance.py:135: DeprecationWarning: The class qiskit.aqua.QuantumInstance is deprecated. It was moved/refactored to qiskit.utils.QuantumInstance (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/master/README.md#migration-guide>\n",
      "  warn_class('aqua.QuantumInstance',\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'011': 290, '000': 288, '010': 37, '111': 35, '001': 23, '101': 289, '100': 36, '110': 26}\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression = '(~a | ~b | ~c) & (a | ~b | c) & (a | b | ~c) & (a | ~b | ~c) & (~a | b | c)' \n",
    "oracle = LogicalExpressionOracle(expression)\n",
    "grover = Grover(oracle)\n",
    "result = grover.run(QuantumInstance(Aer.get_backend('qasm_simulator'), shots=1024))\n",
    "counts = result.measurement\n",
    "print(counts)\n",
    "\n",
    "plot_histogram(result.measurement)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'001': 23, '110': 26, '111': 35, '100': 36, '010': 37, '000': 288, '101': 289, '011': 290}\n"
     ]
    }
   ],
   "source": [
    "dict1 = counts\n",
    "sorted_tuples = sorted(dict1.items(), key=lambda item: item[1])\n",
    "sorted_dict = {k: v for k, v in sorted_tuples}\n",
    "print(sorted_dict) "
   ]
  }
 ],
 "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": 4
}