qdk-maxcut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing Q# environment...\n",
      ".."
     ]
    }
   ],
   "source": [
    "import qsharp\n",
    "from MaxCut import PrepareAnsatzes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import reduce\n",
    "\n",
    "def maxcut_obj(x: str):\n",
    "    return reduce(lambda obj, e: obj - (1 if x[e[0]] != x[e[1]] else 0), [(0, 1), (1, 2), (2, 3), (3, 0)], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def compute_expectation(cuts: Counter):\n",
    "    sum, count = reduce(lambda t, c: (t[0] + (maxcut_obj(c) * cuts[c]), t[1] + cuts[c]), cuts, (0, 0))\n",
    "    return sum / count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_expectation(shots=512):\n",
    "    def execute_circ(params: list[float]):\n",
    "        results = PrepareAnsatzes.simulate(theta=params, shots=shots)\n",
    "        return compute_expectation(Counter(results))\n",
    "\n",
    "    return execute_circ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     fun: -3.91015625\n   maxcv: 0.0\n message: 'Optimization terminated successfully.'\n    nfev: 43\n  status: 1\n success: True\n       x: array([1.01270031, 1.11100261, 1.15376313, 0.93766603])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "expectation = get_expectation()\n",
    "res = minimize(expectation, [1.0, 1.0, 1.0, 1.0], method='COBYLA')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 10 artists>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "results = PrepareAnsatzes.simulate(theta=res.x, shots=512)\n",
    "counter = Counter(results)\n",
    "\n",
    "plt.bar(counter.keys(), counter.values())"
   ]
  }
 ],
 "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}