Bernstein Vazirani Algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe4a9d46-8c3a-4e8e-a413-d627b55cdd5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7024a05-56fe-44d9-8ce6-b91e7879b48a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import*\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a80a48a-81ab-4dff-adba-68d57d250dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "secretnumber = [1000101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69c7c66c-a0fb-4900-8c2a-8e3223eeb1ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = QuantumCircuit(8,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ebd687e-8a42-426c-b1e1-fb4d16ccc16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 206.997x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h([0,1,2,3,4,5,6])\n",
    "#circuit.x(7)\n",
    "#circuit.h(7)\n",
    "circuit.barrier()\n",
    "circuit.draw(output ='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8899d2c9-0cff-49d7-aa73-0b67b2401add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 447.797x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.cx(0,7)\n",
    "circuit.cx(2,7)\n",
    "circuit.cx(6,7)\n",
    "circuit.barrier()\n",
    "circuit.draw(output ='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50f9b49b-1c83-4d79-a45c-ceb39ba33b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 929.397x565.88 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.h([0,1,2,3,4,5,6])\n",
    "circuit.measure([0,1,2,3,4,5,6],[0,1,2,3,4,5,6])\n",
    "circuit.draw(output = 'mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98f79ee6-27c0-4f9f-8822-a04032726393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0000000': 511, '1000101': 513}\n"
     ]
    }
   ],
   "source": [
    "simulator = Aer.get_backend('qasm_simulator')\n",
    "result = execute(circuit,backend = simulator ,shots = 1024).result()\n",
    "count = result.get_counts()\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a140ae26-7fb5-4952-b894-1b45d1716768",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Qiskit v0.34.2 (ipykernel)",
   "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": "3.8.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}