Demo-Oracle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oracle - Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\marshmallow\\schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "from QArithmetic import mult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "from qiskit import BasicAer\n",
    "from qiskit.tools.visualization import iplot_histogram\n",
    "from qiskit.tools.visualization import circuit_drawer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of qubits\n",
    "N = 2\n",
    "a = QuantumRegister(N, 'a')\n",
    "b = QuantumRegister(N, 'b')\n",
    "m = QuantumRegister(2*N, 'res')\n",
    "anc = QuantumRegister(4, 'anc')\n",
    "c = QuantumRegister(N, 'anc2')\n",
    "\n",
    "cr = ClassicalRegister(2*N+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create quantum circuit\n",
    "qc = QuantumCircuit(a, b, m, anc, cr)\n",
    "qc.add_register(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = QuantumCircuit(a, b, m, anc, c, cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1cd08c7c0b8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.h(a)\n",
    "H.h(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mult(qc, a, b, m, N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.extensions.standard.x.XGate at 0x1cd08c93128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.x(m[0])\n",
    "qc.x(m[3])\n",
    "\n",
    "qc.ccx(m[0], m[1], anc[0])\n",
    "qc.ccx(m[2], anc[0], anc[1])\n",
    "qc.ccx(m[3], anc[2], anc[1])\n",
    "qc.cx(anc[1], anc[2])\n",
    "qc.ccx(m[3], anc[2], anc[1])\n",
    "qc.ccx(m[2], anc[0], anc[1])\n",
    "qc.ccx(m[0], m[1], anc[0])\n",
    "\n",
    "qc.x(m[0])\n",
    "qc.x(m[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grover = H + qc "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.measure.Measure at 0x1cd08c935f8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grover.barrier(a)\n",
    "Grover.barrier(b)\n",
    "Grover.measure(a[0], cr[0])\n",
    "Grover.measure(a[1], cr[1])\n",
    "Grover.measure(b[0], cr[2])\n",
    "Grover.measure(b[1], cr[3])\n",
    "Grover.measure(anc[2], cr[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from qiskit import BasicAer\n",
    "\n",
    "backend_sim = BasicAer.get_backend('qasm_simulator')\n",
    "job_sim = execute(Grover, backend_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\marshmallow\\schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "result_sim = job_sim.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00100': 80, '00011': 67, '01000': 66, '01111': 43, '00101': 57, '00000': 54, '11110': 72, '01100': 72, '01101': 69, '11011': 62, '01001': 69, '00010': 74, '00111': 66, '00001': 57, '00110': 56, '01010': 60}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\marshmallow\\schema.py:364: ChangedInMarshmallow3Warning: strict=False is not recommended. In marshmallow 3.0, schemas will always be strict. See https://marshmallow.readthedocs.io/en/latest/upgrading.html#schemas-are-always-strict\n",
      "  ChangedInMarshmallow3Warning\n"
     ]
    }
   ],
   "source": [
    "counts = result_sim.get_counts(Grover)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import plot_histogram, iplot_histogram\n",
    "\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "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
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