test_qubo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b43fc2e-065a-4cb8-b2d9-262cef0b475f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.visualization import plot_histogram\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f5b7629-a6eb-4990-92cf-447fcd75d0fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#backends = provider.backends(simulator=False, operational=True)\n",
    "\n",
    "# # choose IBMQ backend\n",
    "# backend = least_busy(backends)\n",
    "\n",
    "#backend = provider.get_backend(\"ibmq_belem\")\n",
    "\n",
    "#backend.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71968468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "   1.35087445e+02  0.00000000e+00  1.35087445e+02  0.00000000e+00\n",
      "   1.35087445e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [-2.65000000e+01  0.00000000e+00 -2.65000000e+01  0.00000000e+00\n",
      "  -2.65000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   5.00000000e-01  0.00000000e+00  5.00000000e-01  0.00000000e+00\n",
      "   5.00000000e-01  0.00000000e+00  1.14590909e+02  0.00000000e+00\n",
      "   1.14590909e+02  0.00000000e+00  1.14590909e+02  0.00000000e+00\n",
      "   3.19619970e+01  0.00000000e+00  3.19619970e+01  0.00000000e+00\n",
      "   3.19619970e+01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 6.50000000e+00  0.00000000e+00  6.50000000e+00  0.00000000e+00\n",
      "   6.50000000e+00  0.00000000e+00  1.67375000e+02  0.00000000e+00\n",
      "   1.67375000e+02  0.00000000e+00  1.67375000e+02  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  4.61772727e+02  0.00000000e+00\n",
      "   4.61772727e+02  0.00000000e+00  4.61772727e+02  0.00000000e+00\n",
      "   1.39109249e+02  0.00000000e+00  1.39109249e+02  0.00000000e+00\n",
      "   1.39109249e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 2.81666667e+01  0.00000000e+00  2.81666667e+01  0.00000000e+00\n",
      "   2.81666667e+01  0.00000000e+00  3.77722222e+02  0.00000000e+00\n",
      "   3.77722222e+02  0.00000000e+00  3.77722222e+02  0.00000000e+00\n",
      "   9.33888889e+01  0.00000000e+00  9.33888889e+01  0.00000000e+00\n",
      "   9.33888889e+01  0.00000000e+00  1.03939899e+03  0.00000000e+00\n",
      "   1.03939899e+03  0.00000000e+00  1.03939899e+03  0.00000000e+00\n",
      "   3.14351333e+02  0.00000000e+00  3.14351333e+02  0.00000000e+00\n",
      "   3.14351333e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [-2.65000000e+01  0.00000000e+00 -2.65000000e+01  0.00000000e+00\n",
      "  -2.65000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   5.00000000e-01  0.00000000e+00  5.00000000e-01  0.00000000e+00\n",
      "   5.00000000e-01  0.00000000e+00  1.14590909e+02  0.00000000e+00\n",
      "   1.14590909e+02  0.00000000e+00  1.14590909e+02  0.00000000e+00\n",
      "   3.19619970e+01  0.00000000e+00  3.19619970e+01  0.00000000e+00\n",
      "   3.19619970e+01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 6.50000000e+00  0.00000000e+00  6.50000000e+00  0.00000000e+00\n",
      "   6.50000000e+00  0.00000000e+00  1.67375000e+02  0.00000000e+00\n",
      "   1.67375000e+02  0.00000000e+00  1.67375000e+02  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  3.95000000e+01  0.00000000e+00\n",
      "   3.95000000e+01  0.00000000e+00  4.61772727e+02  0.00000000e+00\n",
      "   4.61772727e+02  0.00000000e+00  4.61772727e+02  0.00000000e+00\n",
      "   1.39109249e+02  0.00000000e+00  1.39109249e+02  0.00000000e+00\n",
      "   1.39109249e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 2.81666667e+01  0.00000000e+00  2.81666667e+01  0.00000000e+00\n",
      "   2.81666667e+01  0.00000000e+00  3.77722222e+02  0.00000000e+00\n",
      "   3.77722222e+02  0.00000000e+00  3.77722222e+02  0.00000000e+00\n",
      "   9.33888889e+01  0.00000000e+00  9.33888889e+01  0.00000000e+00\n",
      "   9.33888889e+01  0.00000000e+00  1.03939899e+03  0.00000000e+00\n",
      "   1.03939899e+03  0.00000000e+00  1.03939899e+03  0.00000000e+00\n",
      "   3.14351333e+02  0.00000000e+00  3.14351333e+02  0.00000000e+00\n",
      "   3.14351333e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [-1.65000000e+01  0.00000000e+00 -1.65000000e+01  0.00000000e+00\n",
      "  -1.65000000e+01  0.00000000e+00  4.03333333e+01  0.00000000e+00\n",
      "   4.03333333e+01  0.00000000e+00  4.03333333e+01  0.00000000e+00\n",
      "   3.83333333e+00  0.00000000e+00  3.83333333e+00  0.00000000e+00\n",
      "   3.83333333e+00  0.00000000e+00  1.14893939e+02  0.00000000e+00\n",
      "   1.14893939e+02  0.00000000e+00  1.14893939e+02  0.00000000e+00\n",
      "   3.29629980e+01  0.00000000e+00  3.29629980e+01  0.00000000e+00\n",
      "   3.29629980e+01  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 9.00000000e+00  0.00000000e+00  9.00000000e+00  0.00000000e+00\n",
      "   9.00000000e+00  0.00000000e+00  1.67583333e+02  0.00000000e+00\n",
      "   1.67583333e+02  0.00000000e+00  1.67583333e+02  0.00000000e+00\n",
      "   4.03333333e+01  0.00000000e+00  4.03333333e+01  0.00000000e+00\n",
      "   4.03333333e+01  0.00000000e+00  4.61848485e+02  0.00000000e+00\n",
      "   4.61848485e+02  0.00000000e+00  4.61848485e+02  0.00000000e+00\n",
      "   1.39359499e+02  0.00000000e+00  1.39359499e+02  0.00000000e+00\n",
      "   1.39359499e+02  0.00000000e+00]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [ 2.92777778e+01  0.00000000e+00  2.92777778e+01  0.00000000e+00\n",
      "   2.92777778e+01  0.00000000e+00  3.77814815e+02  0.00000000e+00\n",
      "   3.77814815e+02  0.00000000e+00  3.77814815e+02  0.00000000e+00\n",
      "   9.37592593e+01  0.00000000e+00  9.37592593e+01  0.00000000e+00\n",
      "   9.37592593e+01  0.00000000e+00  1.03943266e+03  0.00000000e+00\n",
      "   1.03943266e+03  0.00000000e+00  1.03943266e+03  0.00000000e+00\n",
      "   3.14462555e+02  0.00000000e+00  3.14462555e+02  0.00000000e+00\n",
      "   3.14462555e+02  0.00000000e+00]]\n",
      "[1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 1. 0. 0. 0. 0. 0.]\n",
      "[0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0.]\n",
      "[0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
      " 0. 0. 1. 0. 0. 0.]\n",
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0.\n",
      " 0. 0. 0. 1. 0. 0.]\n",
      "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 1. 0.]\n",
      "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 0. 1.]\n",
      "[[array([1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
      "       0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]), [1.0, '==']], [array([0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
      "       0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]), [1.0, '==']], [array([0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "       0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0.]), [1.0, '==']], [array([0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0.,\n",
      "       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.]), [1.0, '==']], [array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1.,\n",
      "       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0.]), [1.0, '==']], [array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
      "       1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1.]), [1.0, '==']]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#plot\n",
    "area=[1,2,3]\n",
    "yeild=[0.1,0.2,0.3]\n",
    "soil=[0.1,0.2,0.3]\n",
    "\n",
    "#plant\n",
    "price=[[100,150],[200,150],[300,350],[100,350],[300,350]]\n",
    "fertizer_need=[0.1,-0.2,0.3,0.3,0.2]\n",
    "impactFactor=[0.1,1.2,0.3,3.3,0.999]\n",
    "\n",
    "#enviorment varialbe\n",
    "fertilizer_price=0.1\n",
    "Enviorment = 10\n",
    "\n",
    "#i -> plant\n",
    "#j -> plot\n",
    "#k -> time\n",
    "I,J,K=5,3,2\n",
    "\n",
    "class PlantOpp:\n",
    "    def __init__(self,area,yeild,soil,price,fertizer_need,impactFactor,fertilizer_price,Enviorment,I,J,K):\n",
    "        self.area=area\n",
    "        self.yeild=yeild\n",
    "        self.soil=soil\n",
    "        self.price=price\n",
    "        self.fertizer_need=fertizer_need\n",
    "        self.impactFactor=impactFactor\n",
    "        self.fertilizer_price=fertilizer_price\n",
    "        self.Enviorment=Enviorment\n",
    "        self.I=I\n",
    "        self.J=J\n",
    "        self.K=K\n",
    "    \n",
    "    def find_liner_coffecent(self):\n",
    "        liner=np.zeros((self.I,self.J,self.K))\n",
    "        print(liner)\n",
    "        #find the coffecent of the liner equation\n",
    "        #financial benifit at k=0\n",
    "        k=0\n",
    "        for i in range(self.I):\n",
    "            for j in range(self.J):\n",
    "                    liner[i][j][k]+=self.area[j]*self.yeild[j]*self.price[i][k]\n",
    "                    print(liner)\n",
    "        \n",
    "        #enviormental benifit\n",
    "        for i in range(self.I):\n",
    "            for j in range(self.J):\n",
    "                for k in range(self.K):\n",
    "                    liner[i][j][k]-=(self.Enviorment*self.fertizer_need[i])/(self.soil[j]*self.impactFactor[i])\n",
    "        \n",
    "        # fertizer loss\n",
    "        for i in range(self.I):\n",
    "            for j in range(self.J):\n",
    "                for k in range(self.K):\n",
    "                    liner[i][j][k]-=(self.fertizer_need[i]*self.fertilizer_price)/(self.soil[j]*self.yeild[j])\n",
    "        print(liner)\n",
    "        #np.reshape(liner,(I*J*K))\n",
    "        return np.reshape(liner,(I*J*K))\n",
    "                \n",
    "    def find_quad_coffent(self):\n",
    "        quad=np.zeros((self.I,self.J,self.K,self.I,self.J,self.K))\n",
    "        k=1\n",
    "        for i in range(self.I):\n",
    "            for j in range(self.J):\n",
    "                for Q_i in range(self.I):\n",
    "                    for Q_j in range(self.J):\n",
    "                        Q_k=0\n",
    "                        quad[i,j,k,Q_i,Q_j,Q_k]+=self.area[j]*self.yeild[j]*self.price[i][k]*self.impactFactor[Q_i]\n",
    "        \n",
    "        # fertizer loss\n",
    "        for i in range(self.I):\n",
    "            for j in range(self.J):\n",
    "                for Q_i in range(self.I):\n",
    "                    for Q_j in range(self.J):\n",
    "                        quad[i,j,k,Q_i,Q_j,Q_k]-=((self.fertizer_need[i]*self.fertilizer_price)/(self.soil[j]*self.yeild[j]*self.impactFactor[Q_i]))\n",
    "        \n",
    "        return np.reshape(quad,(I*J*K,I*J*K))\n",
    "\n",
    "    def create_constraints(self):\n",
    "        constraints=[]\n",
    "        for j in range(self.J):\n",
    "            for k in range(self.K):\n",
    "                l=np.zeros((self.I,self.J,self.K)) \n",
    "                for i in range(self.I):\n",
    "                    l[i][j][k] = 1\n",
    "                print(np.reshape(l,(I*J*K)))\n",
    "                constraints.append([np.reshape(l,(I*J*K)),[1.0,\"==\"]])\n",
    "        return constraints\n",
    "    \n",
    "    def plot_graph(self,res):\n",
    "        # reverse l_coff[(((i*I)+j)*J)+k]=liner[i][j][k]\n",
    "        # np.reshape(res.values(),(self.I,self.J,self.K))\n",
    "        return np.reshape(list(res.values()),(self.I,self.J,self.K))\n",
    "\n",
    "    def good_print(self,res):\n",
    "        l=self.plot_graph(res)\n",
    "        for i in range(len(l)):\n",
    "            for j in range(len(l[0])):\n",
    "                for k in range(len(l[0][0])):\n",
    "                    if l[i][j][k]==1:\n",
    "                        print(\"plant {} in plot {} at time {}\".format(i,j,k))\n",
    "            \n",
    "                \n",
    "\n",
    "            \n",
    "f=PlantOpp(area,yeild,soil,price,fertizer_need,impactFactor,fertilizer_price,Enviorment,I,J,K)\n",
    "print(f.find_liner_coffecent())\n",
    "print(f.find_quad_coffent()) \n",
    "print(f.create_constraints())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4e4ca93-f159-47a5-bec5-50db9303d7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.utils import algorithm_globals\n",
    "from qiskit.algorithms.minimum_eigensolvers import QAOA, NumPyMinimumEigensolver\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "from qiskit.primitives import Sampler\n",
    "from qiskit_optimization.algorithms import (\n",
    "    MinimumEigenOptimizer,\n",
    "    RecursiveMinimumEigenOptimizer,\n",
    "    SolutionSample,\n",
    "    OptimizationResultStatus,\n",
    ")\n",
    "from qiskit_optimization import QuadraticProgram\n",
    "from qiskit.visualization import plot_histogram\n",
    "from typing import List, Tuple\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "287ead4c-0743-4864-8506-4d1eebb70ae3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb431094-4392-4e5c-8764-e29b484d6a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      " [[ 30.   0.]\n",
      "  [120.   0.]\n",
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      "[[[ 10.   0.]\n",
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      "\n",
      " [[ 20.   0.]\n",
      "  [ 80.   0.]\n",
      "  [180.   0.]]\n",
      "\n",
      " [[ 30.   0.]\n",
      "  [120.   0.]\n",
      "  [270.   0.]]\n",
      "\n",
      " [[ 10.   0.]\n",
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      "\n",
      " [[ 30.   0.]\n",
      "  [120.   0.]\n",
      "  [270.   0.]]]\n",
      "[[[ -91.         -101.        ]\n",
      "  [ -10.25        -50.25      ]\n",
      "  [  56.55555556  -33.44444444]]\n",
      "\n",
      " [[  38.66666667   18.66666667]\n",
      "  [  88.83333333    8.83333333]\n",
      "  [ 185.77777778    5.77777778]]\n",
      "\n",
      " [[ -73.         -103.        ]\n",
      "  [  69.25        -50.75      ]\n",
      "  [ 236.33333333  -33.66666667]]\n",
      "\n",
      " [[  -2.09090909  -12.09090909]\n",
      "  [  34.70454545   -5.29545455]\n",
      "  [  86.63636364   -3.36363636]]\n",
      "\n",
      " [[   7.97997998  -22.02002002]\n",
      "  [ 109.48998999  -10.51001001]\n",
      "  [ 263.10443777   -6.89556223]]]\n",
      "[1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
      " 1. 0. 0. 0. 0. 0.]\n",
      "[0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      " 0. 1. 0. 0. 0. 0.]\n",
      "[0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
      " 0. 0. 1. 0. 0. 0.]\n",
      "[0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0.\n",
      " 0. 0. 0. 1. 0. 0.]\n",
      "[0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 1. 0.]\n",
      "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1.\n",
      " 0. 0. 0. 0. 0. 1.]\n",
      "Problem name: \n",
      "\n",
      "Maximize\n",
      "  -8.5*x0*x1 + 15.722222222222223*x0*x11 - 26.499999999999993*x0*x13\n",
      "  + 6.500000000000002*x0*x15 + 28.166666666666664*x0*x17\n",
      "  - 26.499999999999993*x0*x19 + 6.500000000000002*x0*x21\n",
      "  + 28.166666666666664*x0*x23 - 16.5*x0*x25 + 9*x0*x27\n",
      "  + 29.277777777777775*x0*x29 + 3.5*x0*x3 + 12.38888888888889*x0*x5 + 21.5*x0*x7\n",
      "  + 11*x0*x9 + 17.166666666666668*x1*x10 + 1.1666666666666665*x1*x12\n",
      "  + 1.1666666666666665*x1*x14 + 1.1666666666666665*x1*x16\n",
      "  + 49.196969696969695*x1*x18 - 8.5*x1*x2 + 49.196969696969695*x1*x20\n",
      "  + 49.196969696969695*x1*x22 + 13.983998998998999*x1*x24\n",
      "  + 13.983998998998999*x1*x26 + 13.983998998998999*x1*x28 - 8.5*x1*x4\n",
      "  + 17.166666666666668*x1*x6 + 17.166666666666668*x1*x8\n",
      "  + 162.1851851851852*x10*x11 + 39.5*x10*x13 + 167.375*x10*x15\n",
      "  + 377.7222222222222*x10*x17 + 39.5*x10*x19 + 167.375*x10*x21\n",
      "  + 377.7222222222222*x10*x23 + 40.333333333333336*x10*x25\n",
      "  + 167.58333333333334*x10*x27 + 377.8148148148148*x10*x29\n",
      "  + 41.24074074074074*x11*x12 + 41.24074074074074*x11*x14\n",
      "  + 41.24074074074074*x11*x16 + 445.56734006734007*x11*x18\n",
      "  + 445.56734006734007*x11*x20 + 445.56734006734007*x11*x22\n",
      "  + 135.08744466688913*x11*x24 + 135.08744466688913*x11*x26\n",
      "  + 135.08744466688913*x11*x28 + 0.5000000000000018*x12*x13 + 39.5*x12*x15\n",
      "  + 93.38888888888887*x12*x17 + 0.5000000000000018*x12*x19 + 39.5*x12*x21\n",
      "  + 93.38888888888887*x12*x23 + 3.833333333333333*x12*x25\n",
      "  + 40.333333333333336*x12*x27 + 93.75925925925924*x12*x29\n",
      "  + 0.5000000000000018*x13*x14 + 0.5000000000000018*x13*x16\n",
      "  + 114.5909090909091*x13*x18 + 114.5909090909091*x13*x20\n",
      "  + 114.5909090909091*x13*x22 + 31.961996996997*x13*x24\n",
      "  + 31.961996996997*x13*x26 + 31.961996996997*x13*x28 + 39.5*x14*x15\n",
      "  + 93.38888888888887*x14*x17 + 0.5000000000000018*x14*x19 + 39.5*x14*x21\n",
      "  + 93.38888888888887*x14*x23 + 3.833333333333333*x14*x25\n",
      "  + 40.333333333333336*x14*x27 + 93.75925925925924*x14*x29 + 39.5*x15*x16\n",
      "  + 461.77272727272725*x15*x18 + 461.77272727272725*x15*x20\n",
      "  + 461.77272727272725*x15*x22 + 139.10924924924927*x15*x24\n",
      "  + 139.10924924924927*x15*x26 + 139.10924924924927*x15*x28\n",
      "  + 93.38888888888887*x16*x17 + 0.5000000000000018*x16*x19 + 39.5*x16*x21\n",
      "  + 93.38888888888887*x16*x23 + 3.833333333333333*x16*x25\n",
      "  + 40.333333333333336*x16*x27 + 93.75925925925924*x16*x29\n",
      "  + 1039.3989898989896*x17*x18 + 1039.3989898989896*x17*x20\n",
      "  + 1039.3989898989896*x17*x22 + 314.3513329996663*x17*x24\n",
      "  + 314.3513329996663*x17*x26 + 314.3513329996663*x17*x28\n",
      "  + 114.5909090909091*x18*x19 + 461.77272727272725*x18*x21\n",
      "  + 1039.3989898989896*x18*x23 + 114.89393939393939*x18*x25\n",
      "  + 461.8484848484849*x18*x27 + 1039.4326599326598*x18*x29\n",
      "  + 114.5909090909091*x19*x20 + 114.5909090909091*x19*x22\n",
      "  + 31.961996996997*x19*x24 + 31.961996996997*x19*x26 + 31.961996996997*x19*x28\n",
      "  + 15.722222222222223*x2*x11 - 26.499999999999993*x2*x13\n",
      "  + 6.500000000000002*x2*x15 + 28.166666666666664*x2*x17\n",
      "  - 26.499999999999993*x2*x19 + 6.500000000000002*x2*x21\n",
      "  + 28.166666666666664*x2*x23 - 16.5*x2*x25 + 9*x2*x27\n",
      "  + 29.277777777777775*x2*x29 + 3.5*x2*x3 + 12.38888888888889*x2*x5 + 21.5*x2*x7\n",
      "  + 11*x2*x9 + 461.77272727272725*x20*x21 + 1039.3989898989896*x20*x23\n",
      "  + 114.89393939393939*x20*x25 + 461.8484848484849*x20*x27\n",
      "  + 1039.4326599326598*x20*x29 + 461.77272727272725*x21*x22\n",
      "  + 139.10924924924927*x21*x24 + 139.10924924924927*x21*x26\n",
      "  + 139.10924924924927*x21*x28 + 1039.3989898989896*x22*x23\n",
      "  + 114.89393939393939*x22*x25 + 461.8484848484849*x22*x27\n",
      "  + 1039.4326599326598*x22*x29 + 314.3513329996663*x23*x24\n",
      "  + 314.3513329996663*x23*x26 + 314.3513329996663*x23*x28\n",
      "  + 32.962997997998*x24*x25 + 139.35949949949952*x24*x27\n",
      "  + 314.46255533311086*x24*x29 + 32.962997997998*x25*x26\n",
      "  + 32.962997997998*x25*x28 + 139.35949949949952*x26*x27\n",
      "  + 314.46255533311086*x26*x29 + 139.35949949949952*x27*x28\n",
      "  + 314.46255533311086*x28*x29 + 71.79166666666667*x3*x10\n",
      "  + 17.166666666666668*x3*x12 + 17.166666666666668*x3*x14\n",
      "  + 17.166666666666668*x3*x16 + 197.92424242424244*x3*x18\n",
      "  + 197.92424242424244*x3*x20 + 197.92424242424244*x3*x22\n",
      "  + 59.689749749749744*x3*x24 + 59.689749749749744*x3*x26\n",
      "  + 59.689749749749744*x3*x28 + 3.5*x3*x4 + 71.79166666666667*x3*x6\n",
      "  + 71.79166666666667*x3*x8 + 15.722222222222223*x4*x11\n",
      "  - 26.499999999999993*x4*x13 + 6.500000000000002*x4*x15\n",
      "  + 28.166666666666664*x4*x17 - 26.499999999999993*x4*x19\n",
      "  + 6.500000000000002*x4*x21 + 28.166666666666664*x4*x23 - 16.5*x4*x25\n",
      "  + 9*x4*x27 + 29.277777777777775*x4*x29 + 12.38888888888889*x4*x5 + 21.5*x4*x7\n",
      "  + 11*x4*x9 + 161.90740740740742*x5*x10 + 40.129629629629626*x5*x12\n",
      "  + 40.129629629629626*x5*x14 + 40.129629629629626*x5*x16\n",
      "  + 445.46632996632997*x5*x18 + 445.46632996632997*x5*x20\n",
      "  + 445.46632996632997*x5*x22 + 134.75377766655546*x5*x24\n",
      "  + 134.75377766655546*x5*x26 + 134.75377766655546*x5*x28\n",
      "  + 161.90740740740742*x5*x6 + 161.90740740740742*x5*x8\n",
      "  + 162.1851851851852*x6*x11 + 39.5*x6*x13 + 167.375*x6*x15\n",
      "  + 377.7222222222222*x6*x17 + 39.5*x6*x19 + 167.375*x6*x21\n",
      "  + 377.7222222222222*x6*x23 + 40.333333333333336*x6*x25\n",
      "  + 167.58333333333334*x6*x27 + 377.8148148148148*x6*x29\n",
      "  + 19.666666666666668*x6*x7 + 72.41666666666667*x6*x9\n",
      "  + 19.666666666666668*x7*x10 + 11.166666666666668*x7*x12\n",
      "  + 11.166666666666668*x7*x14 + 11.166666666666668*x7*x16\n",
      "  + 50.10606060606061*x7*x18 + 50.10606060606061*x7*x20\n",
      "  + 50.10606060606061*x7*x22 + 16.987002002002*x7*x24 + 16.987002002002*x7*x26\n",
      "  + 16.987002002002*x7*x28 + 19.666666666666668*x7*x8 + 162.1851851851852*x8*x11\n",
      "  + 39.5*x8*x13 + 167.375*x8*x15 + 377.7222222222222*x8*x17 + 39.5*x8*x19\n",
      "  + 167.375*x8*x21 + 377.7222222222222*x8*x23 + 40.333333333333336*x8*x25\n",
      "  + 167.58333333333334*x8*x27 + 377.8148148148148*x8*x29\n",
      "  + 72.41666666666667*x8*x9 + 72.41666666666667*x9*x10\n",
      "  + 19.666666666666668*x9*x12 + 19.666666666666668*x9*x14\n",
      "  + 19.666666666666668*x9*x16 + 198.15151515151516*x9*x18\n",
      "  + 198.15151515151516*x9*x20 + 198.15151515151516*x9*x22\n",
      "  + 60.4405005005005*x9*x24 + 60.4405005005005*x9*x26 + 60.4405005005005*x9*x28\n",
      "  - 90.99999999999999*x0 - 100.99999999999999*x1 + 185.77777777777774*x10\n",
      "  + 5.777777777777778*x11 - 73*x12 - 103*x13 + 69.25*x14 - 50.75*x15\n",
      "  + 236.33333333333331*x16 - 33.66666666666667*x17 - 2.090909090909089*x18\n",
      "  - 12.09090909090909*x19 - 10.249999999999993*x2 + 34.70454545454545*x20\n",
      "  - 5.295454545454545*x21 + 86.63636363636363*x22 - 3.363636363636364*x23\n",
      "  + 7.97997997997998*x24 - 22.02002002002002*x25 + 109.48998998999*x26\n",
      "  - 10.51001001001001*x27 + 263.1044377711044*x28 - 6.895562228895563*x29\n",
      "  - 50.24999999999999*x3 + 56.555555555555536*x4 - 33.44444444444445*x5\n",
      "  + 38.66666666666667*x6 + 18.666666666666668*x7 + 88.83333333333333*x8\n",
      "  + 8.833333333333334*x9\n",
      "\n",
      "Subject to\n",
      "  Linear constraints (6)\n",
      "    x0 + x12 + x18 + x24 + x6 == 1  'c0'\n",
      "    x1 + x13 + x19 + x25 + x7 == 1  'c1'\n",
      "    x14 + x2 + x20 + x26 + x8 == 1  'c2'\n",
      "    x15 + x21 + x27 + x3 + x9 == 1  'c3'\n",
      "    x10 + x16 + x22 + x28 + x4 == 1  'c4'\n",
      "    x11 + x17 + x23 + x29 + x5 == 1  'c5'\n",
      "\n",
      "  Binary variables (30)\n",
      "    x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20\n",
      "    x21 x22 x23 x24 x25 x26 x27 x28 x29\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qubo = QuadraticProgram()\n",
    "qubo.binary_var_list(range(I*J*K))\n",
    "qubo.maximize(linear=f.find_liner_coffecent(),quadratic=f.find_quad_coffent())\n",
    "for stuff in f.create_constraints():\n",
    "    qubo.linear_constraint(linear=stuff[0],sense=stuff[1][1],rhs=stuff[1][0])\n",
    "print(qubo.prettyprint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfd6228a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_optimization.converters import LinearEqualityToPenalty\n",
    "lineq2penalty = LinearEqualityToPenalty()\n",
    "qubo = lineq2penalty.convert(qubo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "587d3e60-b3b3-402d-ae3a-32f4d6f3844e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offset: 732608.3558464152\n",
      "operator:\n",
      "-52962.11432955683 * IIIIIIIIIIIIIIIIIIIIIIIIIIIIIZ\n",
      "- 52935.78415859041 * IIIIIIIIIIIIIIIIIIIIIIIIIIIIZI\n",
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      "- 52360.78258216552 * IIIIIIIIIIIIIIIIIIIIIIIIZIIIII\n",
      "- 52355.69650548275 * IIIIIIIIIIIIIIIIIIIIIIIZIIIIII\n",
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      "- 78.61563883327771 * ZZIIIIIIIIIIIIIIIIIIIIIIIIIIII\n"
     ]
    }
   ],
   "source": [
    "op, offset = qubo.to_ising()\n",
    "print(\"offset: {}\".format(offset))\n",
    "print(\"operator:\")\n",
    "print(op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76a79136-2674-4ded-91ef-89675d00f1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "## initializing the MinimumEigensolver we want to use\n",
    "algorithm_globals.random_seed = 10598\n",
    "qaoa_mes = QAOA(sampler=Sampler(), optimizer=COBYLA())#, initial_point=[0.0, 0.0])\n",
    "exact_mes = NumPyMinimumEigensolver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97a3adc6-ad62-4edb-b2c9-6ec8488db43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "qaoa = MinimumEigenOptimizer(qaoa_mes)  # using QAOA\n",
    "exact = MinimumEigenOptimizer(exact_mes)  # using the exact classical numpy minimum eigen solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "184c538f-fa7f-48e9-8ec8-53cc09616983",
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mCanceled future for execute_request message before replies were done"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "exact_result = exact.solve(qubo)\n",
    "print(exact_result.prettyprint())\n",
    "f.good_print(exact_result._variables_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ef1ff470-d8f0-4a87-94b9-fb0647dc94bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "objective function value: 2030.9027777777737\n",
      "variable values: x0=0.0, x1=0.0, x2=0.0, x3=0.0, x4=0.0, x5=0.0, x6=1.0, x7=0.0, x8=1.0, x9=0.0, x10=1.0, x11=0.0, x12=0.0, x13=1.0, x14=0.0, x15=1.0, x16=0.0, x17=1.0\n",
      "status: SUCCESS\n"
     ]
    }
   ],
   "source": [
    "qaoa_result = qaoa.solve(qubo)\n",
    "print(qaoa_result.prettyprint())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "acee4b14-f174-4d80-8b23-d732dc204af8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "solution objective value: {'x0': 0.0, 'x1': 0.0, 'x2': 0.0, 'x3': 0.0, 'x4': 0.0, 'x5': 0.0, 'x6': 1.0, 'x7': 0.0, 'x8': 1.0, 'x9': 0.0, 'x10': 1.0, 'x11': 0.0, 'x12': 0.0, 'x13': 1.0, 'x14': 0.0, 'x15': 1.0, 'x16': 0.0, 'x17': 1.0}\n",
      "[[[0. 0.]\n",
      "  [0. 0.]\n",
      "  [0. 0.]]\n",
      "\n",
      " [[1. 0.]\n",
      "  [1. 0.]\n",
      "  [1. 0.]]\n",
      "\n",
      " [[0. 1.]\n",
      "  [0. 1.]\n",
      "  [0. 1.]]]\n",
      "plant 1 in plot 0 at time 0\n",
      "plant 1 in plot 1 at time 0\n",
      "plant 1 in plot 2 at time 0\n",
      "plant 2 in plot 0 at time 1\n",
      "plant 2 in plot 1 at time 1\n",
      "plant 2 in plot 2 at time 1\n"
     ]
    }
   ],
   "source": [
    "print(\"solution objective value: {}\".format(qaoa_result._variables_dict))\n",
    "#create a 3d array of size I*J*K\n",
    "print(f.plot_graph(qaoa_result._variables_dict))\n",
    "f.good_print(qaoa_result._variables_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6a27c159-bb33-4754-8f50-195bd7d06a90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable order: ['x_p0_l0_t0', 'x_p0_l0_t1', 'x_p0_l1_t0', 'x_p0_l1_t1', 'x_p1_l0_t0', 'x_p1_l0_t1', 'x_p1_l1_t0', 'x_p1_l1_t1']\n",
      "[array([1., 1., 1., 0.]), array([1., 1., 0., 1.])]\n",
      "[array([1., 1., 1., 0.]), array([1., 1., 0., 0.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 1., 0., 1.])]\n",
      "[array([1., 1., 1., 0.]), array([1., 1., 1., 1.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 1., 0., 0.])]\n",
      "[array([1., 1., 1., 0.]), array([1., 1., 1., 0.])]\n",
      "[array([0., 1., 1., 0.]), array([1., 1., 0., 1.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 1., 1., 1.])]\n",
      "[array([0., 1., 1., 0.]), array([1., 1., 0., 0.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 1., 1., 0.])]\n",
      "[array([1., 1., 1., 0.]), array([0., 1., 0., 1.])]\n",
      "[array([0., 1., 1., 1.]), array([1., 1., 0., 1.])]\n",
      "[array([0., 1., 1., 0.]), array([1., 1., 1., 1.])]\n",
      "[array([1., 1., 1., 0.]), array([0., 1., 0., 0.])]\n",
      "[array([0., 1., 1., 1.]), array([1., 1., 0., 0.])]\n",
      "[array([0., 1., 1., 0.]), array([1., 1., 1., 0.])]\n",
      "[array([1., 1., 1., 0.]), array([1., 0., 0., 1.])]\n",
      "[array([1., 1., 1., 1.]), array([0., 1., 0., 1.])]\n",
      "[array([1., 1., 1., 0.]), array([0., 1., 1., 1.])]\n",
      "[array([0., 1., 1., 1.]), array([1., 1., 1., 1.])]\n",
      "[array([1., 1., 1., 0.]), array([1., 0., 0., 0.])]\n",
      "[array([1., 1., 1., 1.]), array([0., 1., 0., 0.])]\n",
      "[array([1., 1., 1., 0.]), array([0., 1., 1., 0.])]\n",
      "[array([0., 1., 1., 1.]), array([1., 1., 1., 0.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 0., 0., 1.])]\n",
      "[array([0., 1., 1., 0.]), array([0., 1., 0., 1.])]\n",
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      "[array([0., 1., 1., 0.]), array([1., 0., 0., 0.])]\n",
      "[array([0., 1., 1., 1.]), array([0., 1., 0., 0.])]\n",
      "[array([1., 1., 0., 0.]), array([1., 1., 0., 0.])]\n",
      "[array([1., 0., 1., 0.]), array([1., 1., 0., 0.])]\n",
      "[array([1., 1., 1., 1.]), array([1., 0., 1., 0.])]\n",
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      "[array([1., 1., 0., 1.]), array([1., 0., 0., 1.])]\n",
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      "[array([0., 1., 0., 0.]), array([1., 0., 0., 0.])]\n",
      "[array([1., 1., 0., 1.]), array([1., 0., 0., 0.])]\n",
      "[array([0., 0., 1., 1.]), array([1., 0., 0., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 1., 0., 0.])]\n",
      "[array([0., 0., 1., 0.]), array([1., 0., 1., 0.])]\n",
      "[array([0., 1., 0., 0.]), array([0., 1., 1., 0.])]\n",
      "[array([1., 1., 0., 1.]), array([0., 1., 1., 0.])]\n",
      "[array([0., 0., 1., 1.]), array([0., 1., 1., 0.])]\n",
      "[array([0., 0., 0., 0.]), array([1., 1., 1., 0.])]\n",
      "[array([1., 1., 0., 0.]), array([0., 0., 0., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 1., 0., 1.])]\n",
      "[array([0., 1., 0., 1.]), array([0., 1., 0., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 1., 0., 1.])]\n",
      "[array([0., 1., 0., 0.]), array([1., 0., 1., 1.])]\n",
      "[array([1., 1., 0., 1.]), array([1., 0., 1., 1.])]\n",
      "[array([0., 0., 1., 1.]), array([1., 0., 1., 1.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 1., 1., 1.])]\n",
      "[array([1., 1., 0., 0.]), array([0., 0., 0., 0.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 1., 0., 0.])]\n",
      "[array([0., 1., 0., 1.]), array([0., 1., 0., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 1., 0., 0.])]\n",
      "[array([0., 1., 0., 0.]), array([1., 0., 1., 0.])]\n",
      "[array([1., 1., 0., 1.]), array([1., 0., 1., 0.])]\n",
      "[array([0., 0., 1., 1.]), array([1., 0., 1., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 1., 1., 0.])]\n",
      "[array([0., 0., 1., 0.]), array([0., 0., 0., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([1., 0., 0., 1.])]\n",
      "[array([0., 1., 0., 1.]), array([1., 0., 0., 1.])]\n",
      "[array([0., 0., 0., 0.]), array([0., 1., 0., 1.])]\n",
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      "[array([1., 0., 0., 0.]), array([0., 1., 1., 1.])]\n",
      "[array([0., 1., 0., 1.]), array([0., 1., 1., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 1., 1., 1.])]\n",
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      "[array([0., 1., 0., 1.]), array([0., 1., 1., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 1., 1., 0.])]\n",
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      "[array([0., 0., 0., 0.]), array([1., 0., 0., 1.])]\n",
      "[array([1., 0., 0., 1.]), array([0., 1., 0., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([1., 0., 1., 1.])]\n",
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      "[array([0., 0., 0., 0.]), array([0., 1., 1., 1.])]\n",
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      "[array([0., 0., 0., 0.]), array([1., 0., 0., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([0., 1., 0., 0.])]\n",
      "[array([1., 0., 0., 0.]), array([1., 0., 1., 0.])]\n",
      "[array([0., 1., 0., 1.]), array([1., 0., 1., 0.])]\n",
      "[array([0., 0., 0., 0.]), array([0., 1., 1., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 0., 0., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([0., 1., 0., 1.])]\n",
      "[array([1., 1., 0., 1.]), array([0., 0., 1., 1.])]\n",
      "[array([0., 0., 0., 0.]), array([1., 0., 1., 1.])]\n",
      "[array([1., 0., 0., 1.]), array([0., 1., 1., 1.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 0., 0., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([0., 1., 0., 0.])]\n",
      "[array([0., 0., 0., 0.]), array([1., 0., 1., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([0., 1., 1., 0.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 0., 0., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 0., 0., 1.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 0., 1., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([0., 1., 1., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 0., 0., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 0., 0., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([1., 0., 1., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([0., 1., 1., 0.])]\n",
      "[array([0., 0., 0., 0.]), array([0., 0., 0., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 0., 1., 1.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 0., 1., 1.])]\n",
      "[array([1., 0., 0., 0.]), array([0., 0., 1., 0.])]\n",
      "[array([0., 0., 0., 1.]), array([1., 0., 1., 0.])]\n",
      "[array([1., 0., 0., 1.]), array([0., 0., 0., 1.])]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8242dc71-3bdf-4a89-a513-a97cd446a04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_filtered_samples(\n",
    "    samples: List[SolutionSample],\n",
    "    threshold: float = 0,\n",
    "    allowed_status: Tuple[OptimizationResultStatus] = (OptimizationResultStatus.SUCCESS,),\n",
    "):\n",
    "    res = []\n",
    "    for s in samples:\n",
    "        if s.status in allowed_status and s.probability > threshold:\n",
    "            res.append(s)\n",
    "\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fa5624fd-c2c0-4d79-8a8f-c99656f71496",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'SolutionSample'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[79], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m filtered_samples \u001b[38;5;241m=\u001b[39m get_filtered_samples(\n\u001b[1;32m      2\u001b[0m     qaoa_result\u001b[38;5;241m.\u001b[39msamples, threshold\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.005\u001b[39m, allowed_status\u001b[38;5;241m=\u001b[39m(OptimizationResultStatus\u001b[38;5;241m.\u001b[39mSUCCESS,)\n\u001b[1;32m      3\u001b[0m )\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;43mset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mqaoa_result\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msamples\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m^\u001b[39m\u001b[38;5;28mset\u001b[39m(filtered_samples)\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'SolutionSample'"
     ]
    }
   ],
   "source": [
    "filtered_samples = get_filtered_samples(\n",
    "    qaoa_result.samples, threshold=0.005, allowed_status=(OptimizationResultStatus.SUCCESS,)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ba4d745e-d641-4bbb-b9c0-2ed3cdb0b52c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fvals = [s.fval for s in qaoa_result.samples]\n",
    "probabilities = [s.probability for s in qaoa_result.samples]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fe766d25-8f71-4294-a99c-6e5a0813547b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "{'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.2124240224248315,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=0': 0.1055112380534506,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0724403305393964,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=1': 0.1055112380534506,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=0': 0.0359811892881256,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=0': 0.0524075442527284,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0730856230081007,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=1': 0.0359811892881256,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=0': 0.0363017067442135,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=0': 0.0178718950196374,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=0 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0163930281204503,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0249187969889517,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=1': 0.0363017067442135,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=0 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=0': 0.0081424345170085,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=0': 0.0123771929892593,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=0': 0.0180310963812515,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=0 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0055903111240974,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=0 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=1': 0.0081424345170085,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=1': 0.0123771929892593,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=1 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=1 x_p1_l1_t1=0': 0.0061477649326848,\n",
       " 'x_p0_l0_t0=0 x_p0_l0_t1=1 x_p0_l1_t0=1 x_p0_l1_t1=0 x_p1_l0_t0=0 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.005640109152892,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=1 x_p0_l1_t0=0 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0064966765941441,\n",
       " 'x_p0_l0_t0=1 x_p0_l0_t1=0 x_p0_l1_t0=0 x_p0_l1_t1=0 x_p1_l0_t0=1 x_p1_l0_t1=1 x_p1_l1_t0=0 x_p1_l1_t1=1': 0.0070368066163875}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_for_plot = {\n",
    "    \" \".join(f\"{qaoa_result.variables[i].name}={int(v)}\" for i, v in enumerate(s.x)): s.probability\n",
    "    for s in filtered_samples\n",
    "}\n",
    "samples_for_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ea91d127-324a-4138-bed4-1179332f515e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_histogram(samples_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "edb25350-08bb-4b57-899e-e43e6dae69e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "maximum_value = max(samples_for_plot.values())\n",
    "best_choice = max(samples_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2955a531-023a-46e3-85ea-d0a9ddb5620a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x_p0_l0_t0=1',\n",
       " 'x_p0_l0_t1=1',\n",
       " 'x_p0_l1_t0=1',\n",
       " 'x_p0_l1_t1=1',\n",
       " 'x_p1_l0_t0=1',\n",
       " 'x_p1_l0_t1=1',\n",
       " 'x_p1_l1_t0=1',\n",
       " 'x_p1_l1_t1=1']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_choice.split(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9bded689-2408-4df5-8a0d-3708f1194e11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(samples_for_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e4b6bd-0f91-40c6-9363-3ab1ecb698cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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