local-vqe.ipynb

{
 "cells": [ 
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b978f38e-20d8-45ef-925c-4e0521f072b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a251dc4b-8c88-4c2b-bf61-e77514c8e207",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/__init__.py:86: DeprecationWarning: The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua', 'qiskit-terra')\n",
      "/opt/conda/lib/python3.8/site-packages/qiskit/optimization/__init__.py:92: DeprecationWarning: The package qiskit.optimization is deprecated. It was moved/refactored to qiskit_optimization (pip install qiskit-optimization). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('optimization', 'qiskit_optimization', 'qiskit-optimization')\n"
     ]
    }
   ],
   "source": [
    "# useful additional packages\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.axes as axes\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "\n",
    "from qiskit import Aer\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit.optimization.applications.ising import max_cut, tsp\n",
    "from qiskit.aqua.algorithms import VQE, NumPyMinimumEigensolver\n",
    "from qiskit.aqua.components.optimizers import SPSA,COBYLA\n",
    "from qiskit.aqua import aqua_globals\n",
    "from qiskit.aqua import QuantumInstance\n",
    "from qiskit.optimization.applications.ising.common import sample_most_likely\n",
    "from qiskit.optimization.algorithms import MinimumEigenOptimizer\n",
    "from qiskit.optimization.problems import QuadraticProgram\n",
    "\n",
    "# setup aqua logging\n",
    "import logging\n",
    "from qiskit.aqua import set_qiskit_aqua_logging\n",
    "# set_qiskit_aqua_logging(logging.DEBUG)  # choose INFO, DEBUG to see the log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b98b8cfc-1426-4253-904c-6544160cc5ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/optimization/applications/ising/tsp.py:67: DeprecationWarning: The variable qiskit.aqua.aqua_globals is deprecated. It was moved/refactored to qiskit.utils.algorithm_globals (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  aqua_globals.random_seed = seed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "distance\n",
      " [[ 0. 48. 91. 33.]\n",
      " [48.  0. 63. 71.]\n",
      " [91. 63.  0. 92.]\n",
      " [33. 71. 92.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generating a graph of 4 nodes\n",
    "n = 4\n",
    "num_qubits = n ** 2\n",
    "ins = tsp.random_tsp(n, seed=123)\n",
    "print('distance\\n', ins.w)\n",
    "\n",
    "# Draw the graph\n",
    "G = nx.Graph()\n",
    "G.add_nodes_from(np.arange(0, ins.dim, 1))\n",
    "colors = ['r' for node in G.nodes()]\n",
    "\n",
    "for i in range(0, ins.dim):\n",
    "    for j in range(i+1, ins.dim):\n",
    "        G.add_edge(i, j, weight=ins.w[i,j])\n",
    "\n",
    "pos = {k: v for k, v in enumerate(ins.coord)}\n",
    "def draw_graph(G, colors, pos):\n",
    "    default_axes = plt.axes(frameon=True)\n",
    "    nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, ax=default_axes, pos=pos)\n",
    "    edge_labels = nx.get_edge_attributes(G, 'weight')\n",
    "    nx.draw_networkx_edge_labels(G, pos=pos, edge_labels=edge_labels)\n",
    "draw_graph(G, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10300bb5-c481-4e03-b25e-1543cd6e8c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "order = (0, 1, 2, 3) Distance = 236.0\n",
      "Best order from brute force = (0, 1, 2, 3) with total distance = 236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#brute force method\n",
    "from itertools import permutations\n",
    "\n",
    "def brute_force_tsp(w, N):\n",
    "    a=list(permutations(range(1,N)))\n",
    "    last_best_distance = 1e10\n",
    "    for i in a:\n",
    "        distance = 0\n",
    "        pre_j = 0\n",
    "        for j in i:\n",
    "            distance = distance + w[j,pre_j]\n",
    "            pre_j = j\n",
    "        distance = distance + w[pre_j,0]\n",
    "        order = (0,) + i\n",
    "        if distance < last_best_distance:\n",
    "            best_order = order\n",
    "            last_best_distance = distance\n",
    "            print('order = ' + str(order) + ' Distance = ' + str(distance))\n",
    "    return last_best_distance, best_order\n",
    "\n",
    "best_distance, best_order = brute_force_tsp(ins.w, ins.dim)\n",
    "print('Best order from brute force = ' + str(best_order) + ' with total distance = ' + str(best_distance))\n",
    "\n",
    "def draw_tsp_solution(G, order, colors, pos):\n",
    "    G2 = nx.DiGraph()\n",
    "    G2.add_nodes_from(G)\n",
    "    n = len(order)\n",
    "    for i in range(n):\n",
    "        j = (i + 1) % n\n",
    "        G2.add_edge(order[i], order[j], weight=G[order[i]][order[j]]['weight'])\n",
    "    default_axes = plt.axes(frameon=True)\n",
    "    nx.draw_networkx(G2, node_color=colors, edge_color='b', node_size=600, alpha=.8, ax=default_axes, pos=pos)\n",
    "    edge_labels = nx.get_edge_attributes(G2, 'weight')\n",
    "    nx.draw_networkx_edge_labels(G2, pos, font_color='b', edge_labels=edge_labels)\n",
    "\n",
    "draw_tsp_solution(G, best_order, colors, pos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0272907e-fea5-4f2c-8b76-adb9d555246d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offset: 1600796.0\n",
      "Ising Hamiltonian:\n",
      "IIIIIIIIIIIIIIIZ\t(-200086+0j)\n",
      "IIIIIIIIIIZIIIII\t(-200091+0j)\n",
      "IIIIIIIIIIZIIIIZ\t(12+0j)\n",
      "IIIIIIIIIIIIIIZI\t(-200086+0j)\n",
      "IIIIIIIIIZIIIIII\t(-200091+0j)\n",
      "IIIIIIIIIZIIIIZI\t(12+0j)\n",
      "IIIIIIIIIIIIIZII\t(-200086+0j)\n",
      "IIIIIIIIZIIIIIII\t(-200091+0j)\n",
      "IIIIIIIIZIIIIZII\t(12+0j)\n",
      "IIIIIIIIIIIIZIII\t(-200086+0j)\n",
      "IIIIIIIIIIIZIIII\t(-200091+0j)\n",
      "IIIIIIIIIIIZZIII\t(12+0j)\n",
      "IIIIIIZIIIIIIIII\t(-200123+0j)\n",
      "IIIIIIZIIIIIIIIZ\t(22.75+0j)\n",
      "IIIIIZIIIIIIIIII\t(-200123+0j)\n",
      "IIIIIZIIIIIIIIZI\t(22.75+0j)\n",
      "IIIIZIIIIIIIIIII\t(-200123+0j)\n",
      "IIIIZIIIIIIIIZII\t(22.75+0j)\n",
      "IIIIIIIZIIIIIIII\t(-200123+0j)\n",
      "IIIIIIIZIIIIZIII\t(22.75+0j)\n",
      "IIZIIIIIIIIIIIII\t(-200098+0j)\n",
      "IIZIIIIIIIIIIIIZ\t(8.25+0j)\n",
      "IZIIIIIIIIIIIIII\t(-200098+0j)\n",
      "IZIIIIIIIIIIIIZI\t(8.25+0j)\n",
      "ZIIIIIIIIIIIIIII\t(-200098+0j)\n",
      "ZIIIIIIIIIIIIZII\t(8.25+0j)\n",
      "IIIZIIIIIIIIIIII\t(-200098+0j)\n",
      "IIIZIIIIIIIIZIII\t(8.25+0j)\n",
      "IIIIIIIIIIIZIIZI\t(12+0j)\n",
      "IIIIIIIIIIZIIZII\t(12+0j)\n",
      "IIIIIIIIIZIIZIII\t(12+0j)\n",
      "IIIIIIIIZIIIIIIZ\t(12+0j)\n",
      "IIIIIIZIIIIZIIII\t(15.75+0j)\n",
      "IIIIIZIIIIZIIIII\t(15.75+0j)\n",
      "IIIIZIIIIZIIIIII\t(15.75+0j)\n",
      "IIIIIIIZZIIIIIII\t(15.75+0j)\n",
      "IIZIIIIIIIIZIIII\t(17.75+0j)\n",
      "IZIIIIIIIIZIIIII\t(17.75+0j)\n",
      "ZIIIIIIIIZIIIIII\t(17.75+0j)\n",
      "IIIZIIIIZIIIIIII\t(17.75+0j)\n",
      "IIIIIIIZIIIIIIZI\t(22.75+0j)\n",
      "IIIIIIZIIIIIIZII\t(22.75+0j)\n",
      "IIIIIZIIIIIIZIII\t(22.75+0j)\n",
      "IIIIZIIIIIIIIIIZ\t(22.75+0j)\n",
      "IIIIIIIZIIZIIIII\t(15.75+0j)\n",
      "IIIIIIZIIZIIIIII\t(15.75+0j)\n",
      "IIIIIZIIZIIIIIII\t(15.75+0j)\n",
      "IIIIZIIIIIIZIIII\t(15.75+0j)\n",
      "IIZIIIIZIIIIIIII\t(23+0j)\n",
      "IZIIIIZIIIIIIIII\t(23+0j)\n",
      "ZIIIIZIIIIIIIIII\t(23+0j)\n",
      "IIIZZIIIIIIIIIII\t(23+0j)\n",
      "IIIZIIIIIIIIIIZI\t(8.25+0j)\n",
      "IIZIIIIIIIIIIZII\t(8.25+0j)\n",
      "IZIIIIIIIIIIZIII\t(8.25+0j)\n",
      "ZIIIIIIIIIIIIIIZ\t(8.25+0j)\n",
      "IIIZIIIIIIZIIIII\t(17.75+0j)\n",
      "IIZIIIIIIZIIIIII\t(17.75+0j)\n",
      "IZIIIIIIZIIIIIII\t(17.75+0j)\n",
      "ZIIIIIIIIIIZIIII\t(17.75+0j)\n",
      "IIIZIIZIIIIIIIII\t(23+0j)\n",
      "IIZIIZIIIIIIIIII\t(23+0j)\n",
      "IZIIZIIIIIIIIIII\t(23+0j)\n",
      "ZIIIIIIZIIIIIIII\t(23+0j)\n",
      "IIIIIIIIIIIZIIIZ\t(50000+0j)\n",
      "IIIIIIIZIIIIIIIZ\t(50000+0j)\n",
      "IIIIIIIZIIIZIIII\t(50000+0j)\n",
      "IIIZIIIIIIIIIIIZ\t(50000+0j)\n",
      "IIIZIIIIIIIZIIII\t(50000+0j)\n",
      "IIIZIIIZIIIIIIII\t(50000+0j)\n",
      "IIIIIIIIIIZIIIZI\t(50000+0j)\n",
      "IIIIIIZIIIIIIIZI\t(50000+0j)\n",
      "IIIIIIZIIIZIIIII\t(50000+0j)\n",
      "IIZIIIIIIIIIIIZI\t(50000+0j)\n",
      "IIZIIIIIIIZIIIII\t(50000+0j)\n",
      "IIZIIIZIIIIIIIII\t(50000+0j)\n",
      "IIIIIIIIIZIIIZII\t(50000+0j)\n",
      "IIIIIZIIIIIIIZII\t(50000+0j)\n",
      "IIIIIZIIIZIIIIII\t(50000+0j)\n",
      "IZIIIIIIIIIIIZII\t(50000+0j)\n",
      "IZIIIIIIIZIIIIII\t(50000+0j)\n",
      "IZIIIZIIIIIIIIII\t(50000+0j)\n",
      "IIIIIIIIZIIIZIII\t(50000+0j)\n",
      "IIIIZIIIIIIIZIII\t(50000+0j)\n",
      "IIIIZIIIZIIIIIII\t(50000+0j)\n",
      "ZIIIIIIIIIIIZIII\t(50000+0j)\n",
      "ZIIIIIIIZIIIIIII\t(50000+0j)\n",
      "ZIIIZIIIIIIIIIII\t(50000+0j)\n",
      "IIIIIIIIIIIIIIZZ\t(50000+0j)\n",
      "IIIIIIIIIIIIIZIZ\t(50000+0j)\n",
      "IIIIIIIIIIIIIZZI\t(50000+0j)\n",
      "IIIIIIIIIIIIZIIZ\t(50000+0j)\n",
      "IIIIIIIIIIIIZIZI\t(50000+0j)\n",
      "IIIIIIIIIIIIZZII\t(50000+0j)\n",
      "IIIIIIIIIIZZIIII\t(50000+0j)\n",
      "IIIIIIIIIZIZIIII\t(50000+0j)\n",
      "IIIIIIIIIZZIIIII\t(50000+0j)\n",
      "IIIIIIIIZIIZIIII\t(50000+0j)\n",
      "IIIIIIIIZIZIIIII\t(50000+0j)\n",
      "IIIIIIIIZZIIIIII\t(50000+0j)\n",
      "IIIIIIZZIIIIIIII\t(50000+0j)\n",
      "IIIIIZIZIIIIIIII\t(50000+0j)\n",
      "IIIIIZZIIIIIIIII\t(50000+0j)\n",
      "IIIIZIIZIIIIIIII\t(50000+0j)\n",
      "IIIIZIZIIIIIIIII\t(50000+0j)\n",
      "IIIIZZIIIIIIIIII\t(50000+0j)\n",
      "IIZZIIIIIIIIIIII\t(50000+0j)\n",
      "IZIZIIIIIIIIIIII\t(50000+0j)\n",
      "IZZIIIIIIIIIIIII\t(50000+0j)\n",
      "ZIIZIIIIIIIIIIII\t(50000+0j)\n",
      "ZIZIIIIIIIIIIIII\t(50000+0j)\n",
      "ZZIIIIIIIIIIIIII\t(50000+0j)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/optimization/applications/ising/tsp.py:207: DeprecationWarning: The package qiskit.aqua.operators is deprecated. It was moved/refactored to qiskit.opflow (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  return WeightedPauliOperator(paulis=pauli_list), shift\n"
     ]
    }
   ],
   "source": [
    "#getting the hamiltonian\n",
    "qubitOp, offset = tsp.get_operator(ins)\n",
    "print('Offset:', offset)\n",
    "print('Ising Hamiltonian:')\n",
    "print(qubitOp.print_details())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6f1d4469-a1f7-4c6f-a493-be0436a41345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// This file has been generated by DOcplex\n",
      "// model name is: AnonymousModel\n",
      "// single vars section\n",
      "dvar bool x_0;\n",
      "dvar bool x_1;\n",
      "dvar bool x_2;\n",
      "dvar bool x_3;\n",
      "dvar bool x_4;\n",
      "dvar bool x_5;\n",
      "dvar bool x_6;\n",
      "dvar bool x_7;\n",
      "dvar bool x_8;\n",
      "dvar bool x_9;\n",
      "dvar bool x_10;\n",
      "dvar bool x_11;\n",
      "dvar bool x_12;\n",
      "dvar bool x_13;\n",
      "dvar bool x_14;\n",
      "dvar bool x_15;\n",
      "\n",
      "minimize\n",
      " - 200000 x_0 - 200000 x_1 - 200000 x_2 - 200000 x_3 - 200000 x_4 - 200000 x_5\n",
      " - 200000 x_6 - 200000 x_7 - 200000 x_8 - 200000 x_9 - 200000 x_10\n",
      " - 200000 x_11 - 200000 x_12 - 200000 x_13 - 200000 x_14 - 200000 x_15 [\n",
      " 200000 x_0*x_1 + 200000 x_0*x_2 + 200000 x_0*x_3 + 200000 x_0*x_4\n",
      " + 48 x_0*x_5 + 48 x_0*x_7 + 200000 x_0*x_8 + 91 x_0*x_9 + 91 x_0*x_11\n",
      " + 200000 x_0*x_12 + 33 x_0*x_13 + 33 x_0*x_15 + 200000 x_1*x_2\n",
      " + 200000 x_1*x_3 + 48 x_1*x_4 + 200000 x_1*x_5 + 48 x_1*x_6 + 91 x_1*x_8\n",
      " + 200000 x_1*x_9 + 91 x_1*x_10 + 33 x_1*x_12 + 200000 x_1*x_13 + 33 x_1*x_14\n",
      " + 200000 x_2*x_3 + 48 x_2*x_5 + 200000 x_2*x_6 + 48 x_2*x_7 + 91 x_2*x_9\n",
      " + 200000 x_2*x_10 + 91 x_2*x_11 + 33 x_2*x_13 + 200000 x_2*x_14 + 33 x_2*x_15\n",
      " + 48 x_3*x_4 + 48 x_3*x_6 + 200000 x_3*x_7 + 91 x_3*x_8 + 91 x_3*x_10\n",
      " + 200000 x_3*x_11 + 33 x_3*x_12 + 33 x_3*x_14 + 200000 x_3*x_15\n",
      " + 200000 x_4*x_5 + 200000 x_4*x_6 + 200000 x_4*x_7 + 200000 x_4*x_8\n",
      " + 63 x_4*x_9 + 63 x_4*x_11 + 200000 x_4*x_12 + 71 x_4*x_13 + 71 x_4*x_15\n",
      " + 200000 x_5*x_6 + 200000 x_5*x_7 + 63 x_5*x_8 + 200000 x_5*x_9 + 63 x_5*x_10\n",
      " + 71 x_5*x_12 + 200000 x_5*x_13 + 71 x_5*x_14 + 200000 x_6*x_7 + 63 x_6*x_9\n",
      " + 200000 x_6*x_10 + 63 x_6*x_11 + 71 x_6*x_13 + 200000 x_6*x_14 + 71 x_6*x_15\n",
      " + 63 x_7*x_8 + 63 x_7*x_10 + 200000 x_7*x_11 + 71 x_7*x_12 + 71 x_7*x_14\n",
      " + 200000 x_7*x_15 + 200000 x_8*x_9 + 200000 x_8*x_10 + 200000 x_8*x_11\n",
      " + 200000 x_8*x_12 + 92 x_8*x_13 + 92 x_8*x_15 + 200000 x_9*x_10\n",
      " + 200000 x_9*x_11 + 92 x_9*x_12 + 200000 x_9*x_13 + 92 x_9*x_14\n",
      " + 200000 x_10*x_11 + 92 x_10*x_13 + 200000 x_10*x_14 + 92 x_10*x_15\n",
      " + 92 x_11*x_12 + 92 x_11*x_14 + 200000 x_11*x_15 + 200000 x_12*x_13\n",
      " + 200000 x_12*x_14 + 200000 x_12*x_15 + 200000 x_13*x_14 + 200000 x_13*x_15\n",
      " + 200000 x_14*x_15 ] + 800000;\n",
      " \n",
      "subject to {\n",
      "\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "qp = QuadraticProgram()\n",
    "qp.from_ising(qubitOp, offset, linear=True)\n",
    "qp.to_docplex().prettyprint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38f128a4-f55c-4ee5-b8fc-79168000034b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/algorithms/minimum_eigen_solvers/minimum_eigen_solver.py:36: DeprecationWarning: The package qiskit.aqua.algorithms.minimum_eigen_solvers is deprecated. It was moved/refactored to qiskit.algorithms.minimum_eigen_solvers (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua.algorithms.minimum_eigen_solvers',\n",
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/algorithms/eigen_solvers/eigen_solver.py:36: DeprecationWarning: The package qiskit.aqua.algorithms.eigen_solvers is deprecated. It was moved/refactored to qiskit.algorithms.eigen_solvers (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua.algorithms.eigen_solvers',\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimal function value: 236.0\n",
      "optimal value: [0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0.]\n",
      "status: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "# solving Quadratic Program using exact classical eigensolver\n",
    "exact = MinimumEigenOptimizer(NumPyMinimumEigensolver())\n",
    "result = exact.solve(qp)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da71cc86-b0a2-426f-8e27-0872f0a390f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energy: -1600560.0\n",
      "tsp objective: 236.0\n",
      "feasible: True\n",
      "solution: [1, 2, 3, 0]\n",
      "solution objective: 236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making the Hamiltonian in its full form and getting the lowest eigenvalue and eigenvector\n",
    "ee = NumPyMinimumEigensolver(qubitOp)\n",
    "result = ee.run()\n",
    "\n",
    "print('energy:', result.eigenvalue.real)\n",
    "print('tsp objective:', result.eigenvalue.real + offset)\n",
    "x = sample_most_likely(result.eigenstate)\n",
    "print('feasible:', tsp.tsp_feasible(x))\n",
    "z = tsp.get_tsp_solution(x)\n",
    "print('solution:', z)\n",
    "print('solution objective:', tsp.tsp_value(z, ins.w))\n",
    "draw_tsp_solution(G, z, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "887b4054-986e-4386-be05-cef7f747970f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/quantum_instance.py:135: DeprecationWarning: The class qiskit.aqua.QuantumInstance is deprecated. It was moved/refactored to qiskit.utils.QuantumInstance (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_class('aqua.QuantumInstance',\n"
     ]
    }
   ],
   "source": [
    "aqua_globals.random_seed = np.random.default_rng(123)\n",
    "seed = 10598\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b8a3d4e4-6890-44a5-90c1-c8c7b51fcae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/components/optimizers/optimizer.py:49: DeprecationWarning: The package qiskit.aqua.components.optimizers is deprecated. It was moved/refactored to qiskit.algorithms.optimizers (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua.components.optimizers',\n",
      "/opt/conda/lib/python3.8/site-packages/qiskit/aqua/algorithms/vq_algorithm.py:70: DeprecationWarning: The class qiskit.aqua.algorithms.VQAlgorithm is deprecated. It was moved/refactored to qiskit.algorithms.VariationalAlgorithm (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_class('aqua.algorithms.VQAlgorithm',\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "energy: -1352911.2392578125\n",
      "time: 3267.594801902771\n",
      "feasible: True\n",
      "solution: [2, 3, 0, 1]\n",
      "solution objective: 236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#spsa is the optimizer used and ry is the ansatz\n",
    "spsa = SPSA(maxiter=300)\n",
    "ry = TwoLocal(qubitOp.num_qubits, 'ry', 'cz', reps=7, entanglement='linear')\n",
    "vqe = VQE(qubitOp, ry, spsa, quantum_instance=quantum_instance)\n",
    "\n",
    "result = vqe.run(quantum_instance)\n",
    "\n",
    "print('energy:', result.eigenvalue.real)\n",
    "print('time:', result.optimizer_time)\n",
    "x = sample_most_likely(result.eigenstate)\n",
    "print('feasible:', tsp.tsp_feasible(x))\n",
    "z = tsp.get_tsp_solution(x)\n",
    "print('solution:', z)\n",
    "print('solution objective:', tsp.tsp_value(z, ins.w))\n",
    "draw_tsp_solution(G, z, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e2b6e2d1-cbd2-4a57-8523-b95ea4398fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#qasm simulator is used as the backend\n",
    "aqua_globals.random_seed = np.random.default_rng(123)\n",
    "seed = 10598\n",
    "backend = Aer.get_backend('qasm_simulator')\n",
    "quantum_instance = QuantumInstance(backend, seed_simulator=seed, seed_transpiler=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8321ee74-6c5d-41f6-b737-b02ff38ecf13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "optimal function value: 258.0\n",
      "optimal value: [0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.]\n",
      "status: SUCCESS\n",
      "solution: [2, 3, 0, 1]\n",
      "solution objective: 236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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5Rx4Bbr2VM9ulS3md0wnMnWvt+CS4xMTwY0kVC3XqcBF2yRLgrLOA++/nYuz332uxTfzPNkG3Tp3ju4wVFgLPPst/n3aa9t9LxUVFcXF28WLuXHvlFaB/fy7OFRVZPToJVbYJuvXqsUj+wAF+vmoVsGEDm2AvW8YieJGTERbG9YFZs7g+sGQJg/B77wHZ2VaPTkKNbRbSIiOBSy7hanXdulxMe+cdBtzMTJ5AK1IZhgF07MjL779z99vbb3P9oH9/vtsSqSxbLaQBwJQp/IPo2pWXffu446hGDatHJqFo92727P34Y+6EGzyY6SwRb7wtpNkq6B7bSUokkI4c4WLt3LksRxsyBGjdWluLpWQhUb0AAD/+WLzzrKCAJwdotVkCoUYNVs0sXsz0w6hRPN14xQqdUi0VY6ugu3cvsGMH/x0RwS2fmmlIIEVGAr1785y2QYOAd98t/jw/3+rRiR3YKr1wrHXruBU4L4+dpFq0sHpEUhWZJns8TJvGapq+fdkFLy7O6pGJlbylF2xTveDxxx8s5Tl6lLNcw2AVQ3o68PTTxfvwRQLBMIB27XjZupUVDz16sARtwABtM5Z/slXQzc7mhgiXi2/pTj2VDaujooDt27m4sWGD1aOUqiolhc3T9+3jNuNBg9iEZ8gQ4IwzrB6dBAtbBV2Ahet//fXPr8fGKr8rwSExkZssbrqJ/R3uvRdo1IjBt317/Z5WdbYKutHRQKtWbGaeksKWfZmZQFoaayn//W8dySLBIyaGdb39+vEYofHjuftt8GCebhFmq78+8RXbLaT9/jvw4os8wiUyknW7deoAV17JI7tFgpVpsqnOu+9yDWLAAOZ/1ZY09ITM5ohjHTzIPgzR0ayh9HSVErGDDRtY8bBqFdCrF2fDtWtbPSrxlZCqXgA4Y6hdW7+kYl/Nm/Ost127mBrr3Zu9RQYP5gKxhC5bbY7wKCzULiAJDaecAjz0EDdXJCYCt9zC/r4//2z1yMRfbBl0v/0WeOYZq0ch4js1a/LkkyVLWOHwxBOsfvjyS00wQo0tg25MDLs/iYSayEjguus48+3fH5g6lTvcFixgvxGxP1sG3cTEkmt1RUKF08mysnffBUaMAL74gicbT50KZGRYPTqpDFsupHmCrmpyJdQZBnD22bxs2VK8zbh7d5acJSVZPUKpKFvOdKOi2GVM56JJVdK4MfDkkzxWyOFg0H38cWDzZqtHJhVhy6ALcLa7d6/VoxAJvLp1gf/7P/b2bdIEuOce4I472G9a/aWDn22Dbt26x58OLFLVxMSwn8OiRTy66sUXebrxJ5+wKZQEJwVdEZuLiODpxbNnA7fdBnzwAfO+s2cDOTlWj05OZNugq/SCyPEcDuCCC4DJk9kC9aefGIwnTgQOHbJ6dOJh26Crma5I6Vq0AMaOZYnZ0aPAtddyQ5HnuCuxjoKuSAhLTgYeeYQph1q1uMvtgQeAX36xemRVl22DrtILIuVXqxYwfDi3GaemAo8+yj4PX3+tbcaBZtug65npqkRGpPyqV+fhmQsWcLvx5Mn8uGiRthkHim2DblQUO+9rg4RIxTmdPMR1+nR2OfvsMy66vfOO/qb8zZbbgD0SEznb1XHXIifHMIBzzuFl82aetH3NNezzMGAA31GKb9l2pgvwF0KNb0R8o2lTYPRoYOZMpu3692eLybQ0q0cWWhR0ReQ4SUnAffcBCxfyANg77+TpxqtWaQ3FF2wddD3pBRHxvbg44IYbWPFwySU8XmjIEGD5cm0zrgxbB13NdEX8LyKCed65c4FbbwXefx/o2ROYMwfIzbV6dPajoCsi5eJwABdeCLz1FjBmDNMNV18NTJqkbcYVYeugq/SCiDVatQJeeIEB+NAhbjN+9llg506rRxb8bB10PTNdJfdFrJGczOOEPvgAiI8HbrwRePBB4NdfrR5Z8LJ10I2O5luerCyrRyJStdWqBdx+Oxurt23Lfg9DhwLffKNtxieyddAF1PhGJJhERbG+d8ECoFcv4PXXgX79GIy1zZhsH3R1MrBI8AkLAy6/HJgxA7j/fp5mcc01wLRpemdq623AgCoYRIKZYQDt2/OyaRN7PVx9NQNw//6cNPlcejqwYQPw22/A/v0cRFIScNZZQPPmfnrQ8guJoKv0gkjwO+MMlprt2cOtxv36sQRt8GDg9NMreeeFhcCnnwJvvMFg63Qyn+H4+8282w1UqwYUFQHnnsuC406diq8PINsH3cREYP16q0chIuVVrx5TDrfeCsybxwW4M84Arr8eaNeOE9MK2bSJRyJv2cJgGx9f+p243SwwXrWKK37jxgENG1b6OVWE7XO6Si+I2FNcHE+yWLIEuPhi4OmnGXg/++z4ioddu7wcMzR3Llui/fEHg21srPeo7XDw++LieIjcZZcBK1b49HmVJSSCrtILIvYVEcFtxfPmMQjPnMnP584F8vLYfKd37xJ2vc2aBTz8MBAZ6X12WxLDAGrU4Mx42DDg8899+ZS8sn3Q9VQvaIOEiL05HMBFF/EwzdGjgR9+4Az4yy85sRo+nClZAJylPvEEi/UjIk7+QSMjmeu94w5g2zYfPIuy2T7oxsTwY3a2teMQEd9p3Zrp1iZNGGgPHOBk9KGHwC4799zDKF2ZgOsRGckHuffegOzksH3QBZRiEAlFGRnAypVA7dqs+DIMbrZIGzWDZWGxsb57sPh47l3++GPf3WcpbF+9ABSfDJySYvVIRMRXYmO5yGYYzABUqwZUC3ejRrcpPGGzDI/v3o0fc3KQ63ajTlgYhtSujR41apT8zYbB/O6kScAVV/j2iZwgJIKuZroioccweITQcX5cDRw5Uq5Z7o116uDx8HBEOBz4Mz8fQ7dvxxnVquHM0gJ2TAzw++/M7Z52WqXHXxqlF0TEPtat40aIckipVg0Rf29+MAAYhoFd3m5rGLz88kvlx+lFSARdT3pBRELcjz+ysUM5Pbd3L87fuBHXbt2KOmFhON+z8l6awkIGdj8KmfRCAMvsRMQq6elAeHi5v/3hpCQ8WLcu1ufmYk1ODiLKquUND+duDD8KiZmu0gsiVcRJFOQ7DANtoqLwV2Eh5h0+XPYN/HzqZkgEXaUXRKqIuLiTDoouALvKaurrcrFGzY9CIujGxPAFUBskREJcaiqQn1/mtx0qKsLyjAzkuN1wmyb+m5WFT44exbnR0WU/Rps2lR+nFyGR0zWM4sY3qtUVCWGtW5drF5oBYN7hw3hmzx64AdQLD8f9deviwrJKzcLC2HPXj0Ii6ALFPRgUdEVC2Pnnc/tvYaHXBbWaYWGYfOqpFbvvvDzuTGvZspKD9C4k0guAFtNEqoToaKBPH7gzM31/37m5bPLrdPr+vo8RUkFXfXVFQtuePcCYA8Px1+HIcuV2yy0nh7Pc667z3X2WImSCbmKiZroioaqgAHjzTWDgQCCxTX3UeeUJBkpfdAVzuRjAx49n4PWzkAm6mumKhB7TBL7+mk3MN20C3nsPGDoUCB94HdCtG3D0aOUCr8vF+7jxRjbzDQD7L6RlZACrVqHpivXos3QtkJ7NFchGjVhe0rYt0Lix1aMUkQrasQN48UVg925gxAjgvPOOudLh4MzUNNmO8WSameflMY87ZAgfIEAM08sOj9TUVHP16tUBG0yFbNsGTJkCzJ8PmCbMggIUIgIRkQ7+IAoLixtYnHkm285ffrklp3+KSPnl5ABvvQUsXAjccANPDS61UMHtBqZNA559ln/z8fFlL4QVFXGyFh0NPPUUz4Ov8GmY3hmGscY0zdQSr7Nd0HW5+BMZN47/ebGx3htgmCaQlcXbpabydg0aBG68IlIupgksXw5MmMA/1bvuAhISynnj7dt5w6VLGYhNkydCeAJwURFntk4n40Xv3nyAxES/PJfQCbrZ2SzpWLmS29Aq0PgCpslXt2rVeAjTuef6b5wiUiFpacALL3B+9OCDldgUdugQ8MUXwOrVwJo17L1rGECtWsA55zCad+5cfM6Xn4RG0M3PZ+5l9Wqe4nmybwc8K54zZwLt2vl0iCJSMRkZwBtvcIY7dChw7bWhkQH0FnTt8/TGjgVWrapcwAWAqCje/uab+SooIgHndjNn27s3U7Fz5wJ9+oRGwC2LPaoXfvoJePfdip9tX5roaAbckSOZBxKRgPntN+D555lenTCB69xViT2C7ujRfAn05fa8uDhg2TLg9tuBM87w3f2KSIkOHQJeew34/nvgzjt5/mNVmNmeKPif8qZNPBq5jO5Acw4dwuBt29Bh40aMSk8v+34df5eWTZvmo4GKSElcLmD2bO6wjYkB5s0DunevmgEXsEPQXbSICaAy0gp1wsJwc506uLoi2/hiY4EPPvB7p3iRqmrnTmDAAOCrr4DJk4H77vN74UDQC/70wg8/sMyrDJ3j4gAAG3Jzsa+oqHz3HRbG2e6ffwKnn16JQYpUTWvWAElJpZe+16nDVMK//uXz/Qe2FdxB1zSBDRuA0s6p95WNGxV0RSogJwe45RYWFDVtyrraMWP+GVgjI4ELLrBkiEEruNMLhYWsz/Vnf8uiIja8EJFyW7sWyMzkpoY33uDnM2Zwc8OxNLv9p+AOuhb8xNxurttt3RrwhxaxjZo1Odvdvx845RTg+uuBb78F1q+3emTBL7iDblgY35/4c6ErLAxZYTWwdCmT/G3bAl27qqhBxJvCQqBVK2b/AKBvXzb5WrOGn5/ESelVRnAHXU+HsLy8Mr/VZZoocLvhBuAGUOB2w1XOn/wz85uhVy9g0iR2pvc0KNuzR788UnWZJisO1q4t3rzpmf+0aME50apVwK5d/NoVV7DROKC0gjfBHXQBoGPHch3L8daBA+i4aRPeOXgQHx49io6bNuGtAwe836ioCHA4MOa9RnjlFTYciojgL8zGjexrfPHF7LEzdiywYAFTD7m5PnpuIkFq7lz2hlm+HPjPf4CXXmLqzeks/tizJzusLl7M23TtCjRpot31ZQnu6gUAuPpqTkFN0+vL59CEBAwtdx+4v2VmAgMGwBHmwPDhQPv2wLBhzFNNmcJ6wsOHgS1buGCwfj3Lerdt40kVjRvzl6xJE67gJiVV3YJvCQ0uF5vQfPQR8PLLrDyYPp2TEM9+Is+fYceOnIC89hrw3Xes7uzZMyAn3tiaPbqM9eoF/PKLb3+abjeD7rJljJh/O3wYWLGC3Y5K43KxfeeWLcDmzQzIaWlcuW3cmHfnCcaNG7PHjkgwy8sDHn2UHwcM4NpGVBT/RPr0YRfVESOY7atR4/jbHjoEfPopUK8ecOGFlgw/6Ni/tePatfzJx8b6rnzsyBHOoseP9839gZVnnlnx5s3899atQO3axbNhz+y4QQPNiiU4LFwIPPYYcNllwFlnAa+8AsyZw5Ykjz7KmW2zZuw75XZzBgzwe1JTgZQUK0cfnLwF3eBPLwB82b3pJmbpa9asfJY+K4uz5pEjfTO+v8XHA2efzYuH282tkJ4Z8dKl/HdGBvdjeGbEnllxVd8iKYFXrx4wcWLxLHXpUp4XAACPP84CIgBITgZef50z2+xszn7r17dmzHZmj6ALAP/+N1MMP/5YuZ662dmcYr75ZmCOW3YAp57KS5cuxV/PzDx+VrxsGfDHH3xqxwbipk1ZB6lZsfhL+/b8eOgQjwvbu5etF2+77fgDVrZt4wEMnkvDhtaM1+7sE3QjIng22rBh7A1X0dM/TZPv/6tXB955h7NnC8XGcgjHDsPtZvmNJ1f80Ud8q3foEN/CnZgr/rvdRKW53Qzq+/czp6c/pqopPh7o35/dTqdPZ7WOy8XZ7JQpXOt48kmrR2l/9sjpHsvlYkPz559nyVdZZ6WZJqeVbjfPcB471nbvibKzi2fFnsuWLQzcJ+aKk5NPLu2dnQ089BA3hSxZAnTqxH8fPsz/tnbtKnYkndhLScVBXbsCTzzBRbJ9+3gcuhaFy8f+C2kl2bGDKYK5cxlQCwoYFTzvwwsKiqNPy5Y8gr1Ll5Cp2na7uXnDkyv2zI737+es2DMb9syOS8ukeP7Y7riDqY3vv+fCypNP8r/QMFgKdPfdwODBgXyG4k+FhTxusE2bkvtJ/fYbu4NNnFj1TnbwBfsvpJUkOZknSjz0ELfF/PorsG4dZ7VhYYw87drxt+q006werc85HKyAaNCAGzg8cnKYG/bMiFes4MeoKK5Gjxt3/EzYMIBRo/iH1707a5Hj49l0+quvGLA//ZRvL7t1Y6s+oDhYL1wIvP02x/PMM/oDtYOVK3nyboMG/1z03bULeOQRvoDfc49+nv5g36DrER0NXHQRL4KoKE7sW7Ys/pppcla8ezezM56ga5rsEZ+by7eOy5YVV088/TRw773c2rl5M0uJPAEXYMDdsIEz4hdeAH7+maVEr75asVS7BE56On9GGzdyXfqCC45/4+dwsHW1J7Xkz+Z+VZn9g66UyTCYxj4xlZ2ZybePBw/y488/s8oiM5NvHho14mKe01lcm3nsbadPBwYNAi69lH/AzZuHTPYmpOTnM4jOmsWND089Vfq5AHXr8ih08R8VIlVhsbGcmd52G99yLlrEVEKnTpzVvvYaF9WSkoobmXganmzfzqoKT0nRn3+y9OiPPyx5KlIC02SKqE8fpphmzGDj8XIcxCJ+pJluFWYYzPOecQYwZAhnrc89x7RC7drF35edXdzorbCQM9+dO5nK8Hzfjh3ct+J2B/55yD9t386U0Z493G12bL2tWEtBt4ozTV7S04GEBKbI77yTf7C5uQy4LldxytyzOykqqrjbFMDV7po1jw/WAGdY+/ezkiIhQekHf8vJ4buSRYvYJa9vX5X6BRsF3SrOMHhp1Igt/ACgc2eWi/34I3DgAGe/ycnsJJWWxgL6884DHnwQeOAB3mbOHOD++5kT9Cgq4gz4gw+4GGeax++2a9KERSZV5u3uwYM8XmHtWlbaZGWx0qZhQ+Zm2rXjbpmTeGUyTeCTT7iZ5pxzgPffP37hU4KHfet0JeD27wd+/714j/6LL3KfSt26jBtTpjCGlMQ0mQM+tq44LY1vg+vXP36DR9Om7G0cMrPijRv5ivbxx/zc5eIrjdPJ/5jCQr5ChYUxgT58eHFrrxLk5x//QpWWxj0/ng0urVsH4DmJV6G5OUIsc+zupa1beenQgamJiios5CLcsbvt0tI40z5xVnz66cXpDVsoKOBq5OuvMxcTF+e9Dss0mdPJz+crz4QJx7UdBThZ7tYNePZZzmgnTQI++4y743v2VI+OYKGgK7Zz6NDxW583b+asuG7d4tmwZ9ddvXpBOCvOyGBSdd06lomU9hagJJ6t64bBGfKll/7vqjvuYE+E2Fg2Qrr0UvZKUOPw4KKgKyHBkyM+tnF8WhoXj0qaFVvWJyAnBxg4kNv7KtMRLy+Ps9433wQ6dcIXX7DKJDub7xDuuQcYM8anIxcfUdCVkHbkSHHvCc/seOtWVkucmCuuVy8Ab8FHjuTOEV/0fs7NBQwDh+d9jrM618XBg4zj1asz1bJypWa5wSg0ey+I/K1GDZ5gkHrMr7jLxVmxZza8aBE/ZmYyCB/bDKhx45PLR5do1SrgvfcYCX2R86heHThyBM5HH8K557yN8zoYqF+f6eFatdT03o4005UqJSPjn7nirVsZwI5tHN+48Uk2j+/Rg00pfNXsGGCONyODdXkW94GW8tFMV+RvcXEsh23Xrvhrnu5axx6plJbGtEVJRyrFxpZy55s2sdtdOd7vZ7hcGL1nD37IykKNsDDcmZCAy0u7nWFwkG+/raAbAhR0pcpzOLj5Izn5uEIBZGUdPyv+6CP2loiL++fCXXIy4Fi6lMGxHGmF5/buRbhhYHnTpticl4d7du5E08hIpJS2UyQujgMoKFAbN5tT0BUpRUwM2zG3aVP8NbebLTI9Gzw++YSluAcPAi/t+gHtqkWgrI6IuW43VmRmYk5KCqIcDrSJikKn2FgsO3oUdyUmlnwjp5OXLVvYzk1sS0FXpAIcDu6+a9iQ26U9cnIAZ+sNcFQve/fGjoICOAEkHzNjbVKtGn7KyfF+Q5eLu9sUdG1N+1dEfCAq0o1qBVkwytH5O8ftRvQJK3QxDgeyy2rRVlTEBTWxNQVdEV+oQHlYVAkBNruEQFwi7fO1Pf0ERXzBMFgwXFRU5rcmR0TABaYZPDbn55e+iOYRHs4NF2JrCroivtKyZXG3dy+qOxzoHBuLSfv3I9ftxs85OfgqMxNXllVqZhhAs2Y+GqxYRUFXxFc6dizXTBcAHk5KQr7bjS6bN2PE7t14JCnJ+0y3qIhBNyXFR4MVq6h6QcRXrroKGD+edWVl5F7jnE6Ma9iw/PedmclTJXVEr+1ppiviKw0bcrbr6woDTxAfPNi39yuWUNAV8aXHHmOALCz03X0ePcqeDic0NBd7UtAV8aWmTdnoNiuLjWoqKzOTp30+/njl70uCgoKuiK8NHw506cKOOZU5kz4ri2ViU6f6tmuZWEpBV8TXnE4ey3vllUwNlKOM7DhuNwN2ZCQwcybQooVfhinWUNAV8YeICB4sOW4cPz98mEfveOMJthkZbOzw2WdAq1Z+H6oElkrGRPzFMLgAdv75nLG+/TZztJ5aXs8R7EVFDNJuN7/31lv5MehO2xRf0MkRIoFSUMAm5xs38pTgI0cYbE87jTPaVq143LHYnk6OEAkGERHFx1YMGGD1aMQiyumKiASQgq6ISAAp6IqIBJCCrohIACnoiogEkNeSMcMw9gPYHrjhiIiEhFNN00wo6QqvQVdERHxL6QURkQBS0BURCSAFXRGRAFLQFREJIAVdEZEA+n+s8NXmA1VaMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create minimum eigen optimizer based on VQE\n",
    "vqe_optimizer = MinimumEigenOptimizer(vqe)\n",
    "\n",
    "# solve quadratic program\n",
    "result = vqe_optimizer.solve(qp)\n",
    "print(result)\n",
    "\n",
    "z = tsp.get_tsp_solution(x)\n",
    "print('solution:', z)\n",
    "print('solution objective:', tsp.tsp_value(z, ins.w))\n",
    "draw_tsp_solution(G, z, colors, pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f02a995a-5349-466b-b5cb-bb5159623012",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc133781-9abf-473a-bcc6-f8fe3525112f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Qiskit v0.29.0",
   "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": "3.8.10"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}