notebook_cirq/superdense.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cirq\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_initial_state(qubits, zbasis):\n",
    "    instructions = []\n",
    "    for i, qubit in enumerate(qubits):\n",
    "        if zbasis[i] == 1:\n",
    "            instructions.append(cirq.X(qubit))\n",
    "    return instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_array_to_string(array):\n",
    "    return ''.join([str(int(i)) for i in array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def superdense(x):\n",
    "    e1 = cirq.NamedQubit('e1')\n",
    "    e2 = cirq.NamedQubit('e2')\n",
    "    a  = cirq.NamedQubit('a')\n",
    "    b  = cirq.NamedQubit('b')\n",
    "\n",
    "    circuit = cirq.Circuit(\n",
    "        cirq.Moment(*prepare_initial_state([e1, e2], x)),\n",
    "        cirq.H(a), cirq.CNOT(a, b),\n",
    "        cirq.CZ(e1, a), cirq.CNOT(e2, a),\n",
    "        cirq.CNOT(a, b),\n",
    "        cirq.H(a),\n",
    "        cirq.measure(a, b, key='m')\n",
    "    )\n",
    "    sim = cirq.Simulator()\n",
    "    result = sim.run(circuit, repetitions=1)\n",
    "    return circuit, result.histogram(key='m', fold_func=join_array_to_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: ────H───@───@───X───@───H───M('m')───\n",
      "           │   │   │   │       │\n",
      "b: ────────X───┼───┼───X───────M────────\n",
      "               │   │\n",
      "e1: ───────────@───┼────────────────────\n",
      "                   │\n",
      "e2: ───X───────────@────────────────────\n"
     ]
    }
   ],
   "source": [
    "circuit, _ = superdense([0, 1])\n",
    "print(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states = [\n",
    "    [0,0],\n",
    "    [0,1],\n",
    "    [1,0],\n",
    "    [1,1]\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(1,4, figsize=(16, 4))\n",
    "for i, state in enumerate(states):\n",
    "    _, res = superdense(state)\n",
    "    cirq.plot_state_histogram(res, ax=ax[i])\n",
    "    ax[i].set_title(f'Input: {state}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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