teleportation_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SQUANCH Test\n",
    "\n",
    "Small test of the SQUANCH (https://github.com/att-innovate/squanch) framework for simulation of quantum channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from squanch import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantum teleportation example, from the paper [Bartlett 2018]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "678c3a51ffce45e484d3af68f7287a72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Alice', max=12500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2662343635147fba05882da4aa96eb0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Bob', max=12500), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class Alice(Agent):\n",
    "    \n",
    "    def distribute_bell_pair(self, a, b):\n",
    "        H(a)\n",
    "        CNOT(a, b)\n",
    "        self.qsend(bob, b)\n",
    "    \n",
    "    def teleport(self, q, a):\n",
    "        CNOT(q, a)\n",
    "        H(q)\n",
    "        \n",
    "        apply_x = a.measure()\n",
    "        apply_z = q.measure()\n",
    "        self.csend(bob, [apply_x, apply_z])\n",
    "    \n",
    "    def run(self):\n",
    "        for qsystem in self.qstream:\n",
    "            q, a, b = qsystem.qubits\n",
    "            self.distribute_bell_pair(a, b)\n",
    "            self.teleport(q, a)\n",
    "\n",
    "class Bob(Agent):\n",
    "    \n",
    "    def run(self):\n",
    "        measurement_results = []\n",
    "        for _ in self.qstream:\n",
    "            b = self.qrecv(alice)\n",
    "            # get corrections necessary for correction on the qubit\n",
    "            apply_x, apply_z = self.crecv(alice)\n",
    "            if apply_x:\n",
    "                X(b)\n",
    "            if apply_z:\n",
    "                Z(b)\n",
    "            \n",
    "            measurement_results.append(b.measure())\n",
    "        self.output(measurement_results)\n",
    "\n",
    "angles = np.linspace(0, 2 * np.pi)\n",
    "num_trials = 250\n",
    "\n",
    "stream = QStream(3, len(angles) * num_trials)\n",
    "for angle in angles:\n",
    "    for _ in range(num_trials):\n",
    "        q, _, _ = stream.next().qubits\n",
    "        RX(q, angle)\n",
    "\n",
    "out = Agent.shared_output()\n",
    "alice = Alice(stream, out=out)\n",
    "bob = Bob(stream, out=out)\n",
    "alice.qconnect(bob)\n",
    "alice.cconnect(bob)\n",
    "\n",
    "Simulation(alice, bob).run()\n",
    "\n",
    "bob_out = out[\"Bob\"]\n",
    "\n",
    "count = 0\n",
    "probabilities = []\n",
    "for angle in angles:\n",
    "    prob = 0\n",
    "    for _ in range(num_trials):\n",
    "        prob += bob_out[count] * 1/num_trials\n",
    "        count +=1\n",
    "    probabilities.append(prob)\n",
    "\n",
    "plt.plot(angles, probabilities)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Bartlett 2018] B. Bartlett, \"A distributed simulation framework for quantum networks and channels\", arXiv 1808.07047, 2018"
   ]
  },
  {
   "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",
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