{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 3: Two-qubit equality circuit\n",
    "Create a circuit that checks whether two qubits are equal in the computational basis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from qiskit import Aer, execute\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 2-qubit input register and a 1-qubit output register\n",
    "# Create a 1-bit classical register for the measurement`\n",
    "# Create a circuit (qc) including those registers\n",
    "\n",
    "qin = QuantumRegister(2)  # input\n",
    "qout = QuantumRegister(1) # output\n",
    "c = ClassicalRegister(1)\n",
    "qc = QuantumCircuit(qin,qout,c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a python function that takes a circuit, an input register, and an output register\n",
    "# The function adds gates to the circuit to implement the equals circuit\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7f658d3f3510>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build a circuit that initializes the input bits to |0> or |1> (using an X gate)\n",
    "# Calls the equal2 function and then measures the output\n",
    "\n",
    "\n",
    "qc.measure(qout,c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 490.028x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 512}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simulate and show results\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "job = execute(qc, backend, shots=512)  # shots default = 1024\n",
    "result = job.result()\n",
    "print(result.get_counts())\n",
    "plot_histogram(result.get_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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}