tequila-qaoa-maxcut.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "from networkx import Graph\n",
    "\n",
    "g = Graph()\n",
    "g.add_nodes_from([0, 1, 2, 3])\n",
    "g.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 0)])\n",
    "nx.draw(g, with_labels=True, alpha=0.8, node_size=500)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from tequila import gates\n",
    "\n",
    "def mixer(g: Graph, beta: float):\n",
    "    return gates.Rx(2 * beta, list(g.nodes()))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "import tequila as tq\n",
    "\n",
    "from functools import reduce\n",
    "\n",
    "def problem(g: Graph, gamma: float):\n",
    "    return reduce(lambda c, e: c + gates.ExpPauli({e[0]: 'Z', e[1]: 'Z'}, gamma), g.edges(), tq.QCircuit())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def create_qaoa_circ(g: Graph, theta: list[float]):\n",
    "    mid = len(theta) // 2\n",
    "    betas = theta[:mid]\n",
    "    gammas = theta[mid:]\n",
    "    c = gates.H(list(g.nodes()))\n",
    "    for i in range(mid):\n",
    "        c += problem(g, gammas[i]) + mixer(g, betas[i])\n",
    "\n",
    "    return c"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# c_tmp = create_qaoa_circ(g, [1.0, 1.0, 1.0, 1.0])\n",
    "# print(c_tmp)\n",
    "# tq.compile(c_tmp, backend=\"qiskit\").circuit.draw(\"mpl\")\n",
    "\n",
    "# measurements = tq.simulate(c_tmp, samples=512, read_out_qubits=[0,1,2,3])\n",
    "# counter = Counter(measurements.items())\n",
    "# print(type(measurements))\n",
    "# print(measurements)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def maxcut_obj(x: str, g: Graph):\n",
    "    return reduce(lambda obj, e : obj - (1 if x[e[0]] != x[e[1]] else 0), g.edges(), 0)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def compute_expectation(cuts: Counter, g: Graph):\n",
    "    sum, count = reduce(lambda t, c: (t[0] + (maxcut_obj(c, g) * cuts[c]), t[1] + cuts[c]), cuts, (0, 0))\n",
    "    return sum / count"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "def get_expectation(g: Graph, shots=512):\n",
    "    def execute_circ(params: list[float]):\n",
    "        qc = create_qaoa_circ(g, params)\n",
    "        results = tq.simulate(qc, samples=shots, read_out_qubits=[0,1,2,3])\n",
    "        counter = Counter({str(k.binary): int(v) for k, v in results.items()})\n",
    "        return compute_expectation(counter, g)\n",
    "\n",
    "    return execute_circ"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "     fun: -3.96875\n   maxcv: 0.0\n message: 'Optimization terminated successfully.'\n    nfev: 43\n  status: 1\n success: True\n       x: array([1.06253475, 1.04608415, 1.05933089, 1.0122466 ])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "expectation = get_expectation(g)\n",
    "res = minimize(expectation, [1.0, 1.0, 1.0, 1.0], method='COBYLA')\n",
    "res"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'1010': 253, '0101': 242, '0011': 7, '1001': 5, '1100': 3, '0110': 2})\n"
     ]
    },
    {
     "data": {
      "text/plain": "<BarContainer object of 6 artists>"
     },
     "execution_count": 10,
     "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",
    "qc = create_qaoa_circ(g, res.x)\n",
    "results = tq.simulate(qc, samples=512, read_out_qubits=[0,1,2,3])\n",
    "counter = Counter({str(k.binary): int(v) for k, v in results.items()})\n",
    "print(counter)\n",
    "plt.bar(counter.keys(), counter.values())"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}