Dinner.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from blueqat import opt\n",
    "import numpy as np\n",
    "from sympy import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 6 #料理の数\n",
    "\n",
    "P = 80 #たんぱく質の推奨値\n",
    "\n",
    "L = 240 #脂質の推奨値\n",
    "\n",
    "C = 450 #炭水化物の推奨値\n",
    "\n",
    "E = 780 #エネルギーの推奨値"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "protain = [46, 25.6, 28.4, 47.2, 9.6, 14.8]\n",
    "lipid = [246.6, 144, 120.6, 169.2, 73.8, 148.5]\n",
    "carbon = [29.6, 149.2, 55.2, 59.6, 107.6, 74.8]\n",
    "\n",
    "energy = []\n",
    "for i in range (N):\n",
    "    energy.append(round(protain[i]+lipid[i]+carbon[i], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "q0, q1, q2, q3, q4, q5 = symbols(\"q0 q1 q2 q3 q4 q5\")\n",
    "q = [q0, q1, q2, q3, q4, q5]\n",
    "\n",
    "#各コスト関数を計算\n",
    "cost_p = 0\n",
    "cost_l = 0\n",
    "cost_c = 0\n",
    "cost_e = 0\n",
    "\n",
    "sigma = 0\n",
    "\n",
    "for i in range (N):\n",
    "    sigma += protain[i]*q[i]\n",
    "cost_p = (P - sigma)**2\n",
    "\n",
    "for i in range (N):\n",
    "    sigma += lipid[i]*q[i]\n",
    "cost_l = (L - sigma)**2\n",
    "\n",
    "for i in range (N):\n",
    "    sigma += carbon[i]*q[i]\n",
    "cost_c = (C - sigma)**2\n",
    "\n",
    "for i in range (N):\n",
    "    cost_e += energy[i]*q[i]\n",
    "cost_e = (E - sigma)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#展開\n",
    "cost_p = expand(cost_p)\n",
    "cost_c = expand(cost_c)\n",
    "cost_l = expand(cost_l)\n",
    "cost_e = expand(cost_e)\n",
    "\n",
    "#qi**2 = qi\n",
    "cost_p = cost_p.subs([(q0**2, q0), (q1**2, q1), (q2**2, q2), (q3**2, q3), (q4**2, q4), (q5**2, q5)])\n",
    "cost_c = cost_c.subs([(q0**2, q0), (q1**2, q1), (q2**2, q2), (q3**2, q3), (q4**2, q4), (q5**2, q5)])\n",
    "cost_l = cost_l.subs([(q0**2, q0), (q1**2, q1), (q2**2, q2), (q3**2, q3), (q4**2, q4), (q5**2, q5)])\n",
    "cost_e = cost_e.subs([(q0**2, q0), (q1**2, q1), (q2**2, q2), (q3**2, q3), (q4**2, q4), (q5**2, q5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#6*6の行列を作成\n",
    "qubo_p = np.zeros((6,6))\n",
    "qubo_l = np.zeros((6,6))\n",
    "qubo_c = np.zeros((6,6))\n",
    "qubo_e = np.zeros((6,6))\n",
    "\n",
    "flag = 0\n",
    "\n",
    "for i in range (N):\n",
    "    for j in range(N):\n",
    "        if i < j:\n",
    "            qubo_p[i][j] = cost_p.coeff(q[i]*q[j], 1)\n",
    "        elif j == i:\n",
    "                flag = cost_p.coeff(q[i], 1) \n",
    "                qubo_p[i][i] = flag.subs([(q0, 0), (q1, 0), (q2, 0), (q3, 0), (q4, 0), (q5, 0)])\n",
    "                \n",
    "for i in range (N):\n",
    "    for j in range(N):\n",
    "        if i < j:\n",
    "            qubo_l[i][j] = cost_l.coeff(q[i]*q[j], 1)\n",
    "        elif j == i:\n",
    "                flag = cost_l.coeff(q[i], 1) \n",
    "                qubo_l[i][i] = flag.subs([(q0, 0), (q1, 0), (q2, 0), (q3, 0), (q4, 0), (q5, 0)])\n",
    "                \n",
    "for i in range (N):\n",
    "    for j in range(N):\n",
    "        if i < j:\n",
    "            qubo_c[i][j] = cost_c.coeff(q[i]*q[j], 1)\n",
    "        elif j == i:\n",
    "                flag = cost_c.coeff(q[i], 1) \n",
    "                qubo_c[i][i] = flag.subs([(q0, 0), (q1, 0), (q2, 0), (q3, 0), (q4, 0), (q5, 0)])\n",
    "                \n",
    "for i in range (N):\n",
    "    for j in range(N):\n",
    "        if i < j:\n",
    "            qubo_e[i][j] = cost_e.coeff(q[i]*q[j], 1)\n",
    "        elif j == i:\n",
    "                flag = cost_e.coeff(q[i], 1) \n",
    "                qubo_e[i][i] = flag.subs([(q0, 0), (q1, 0), (q2, 0), (q3, 0), (q4, 0), (q5, 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-645063.56  512474.56  352980.56  486688.48  295849.68  403788.04]\n",
      " [      0.   -637065.6   312390.72  427774.72  272344.    359774.24]\n",
      " [      0.         0.   -471993.16  292604.96  181407.28  243984.12]\n",
      " [      0.         0.         0.   -588975.2   247865.76  334935.76]\n",
      " [      0.         0.         0.         0.   -431418.28  209431.  ]\n",
      " [      0.         0.         0.         0.         0.   -526208.85]]\n"
     ]
    }
   ],
   "source": [
    "qubo = qubo_p + qubo_l + qubo_c + qubo_e\n",
    "print(qubo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'000101': 9,\n",
       "         '010100': 4,\n",
       "         '101000': 9,\n",
       "         '010001': 8,\n",
       "         '010010': 7,\n",
       "         '100001': 8,\n",
       "         '000110': 7,\n",
       "         '100100': 7,\n",
       "         '011000': 10,\n",
       "         '001011': 14,\n",
       "         '110000': 9,\n",
       "         '100010': 8})"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = opt.opt() \n",
    "a.qubo = qubo\n",
    "result = a.sa(shots = 100, sampler = \"fast\")\n",
    "opt.counter(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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