qiskit_lesson_9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1637592f940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit\n",
    "from numpy import pi\n",
    "\n",
    "#this is a different implementation of the Quantum Teleportation Protocol, taken from QiskitBlocks\n",
    "#It is written in QASM and automatically transfered to Qiskit by the Circuit Composer on IBM's platform\n",
    "\n",
    "qreg_q = QuantumRegister(3, 'q')\n",
    "creg_c0 = ClassicalRegister(1, 'c0')\n",
    "creg_c1 = ClassicalRegister(1, 'c1')\n",
    "creg_c2 = ClassicalRegister(1, 'c2')\n",
    "circuit = QuantumCircuit(qreg_q, creg_c0, creg_c1, creg_c2)\n",
    "\n",
    "circuit.id(qreg_q[0])\n",
    "circuit.id(qreg_q[1])\n",
    "circuit.id(qreg_q[2])\n",
    "circuit.ry(4.5249899471855e-07, qreg_q[0])\n",
    "circuit.rz(0, qreg_q[0])\n",
    "circuit.h(qreg_q[1])\n",
    "circuit.cx(qreg_q[1], qreg_q[2])\n",
    "circuit.barrier(qreg_q[0])\n",
    "circuit.barrier(qreg_q[1])\n",
    "circuit.barrier(qreg_q[2])\n",
    "circuit.cx(qreg_q[0], qreg_q[1])\n",
    "circuit.h(qreg_q[0])\n",
    "circuit.measure(qreg_q[0], creg_c0[0])\n",
    "circuit.measure(qreg_q[1], creg_c1[0])\n",
    "circuit.z(qreg_q[2]).c_if(creg_c0, 1)\n",
    "circuit.x(qreg_q[2]).c_if(creg_c1, 1)\n",
    "circuit.measure(qreg_q[2], creg_c2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 682.577x385.28 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "circuit.draw(output='mpl')"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}