QAOA_for_TSP copy.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjB6BhZPB1W3",
        "outputId": "43a5fd58-4aa9-4b18-b2fb-a66f30b8b501"
      },
      "outputs": [],
      "source": [
        "from qiskit import *\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "# Import from Qiskit Aer noise module\n",
        "from qiskit.providers.aer import AerSimulator\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "oBpo_3nZHliC"
      },
      "outputs": [],
      "source": [
        "# nqubits = 3**2\n",
        "\n",
        "backend = AerSimulator()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "xgqNgWGVY2B-",
        "outputId": "912f12f2-a980-4091-9faa-94e9b547fbbf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(22, 15), (29, 27), (7, 3)]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "# city_number = int(nqubits**0.5)\n",
        "map_x = 30\n",
        "map_y = 30\n",
        "\n",
        "# def create_city(map_x, map_y):\n",
        "#     x0 = random.randint(0, map_x)\n",
        "#     y0 = random.randint(0, map_y)\n",
        "#     return x0, y0\n",
        "\n",
        "\n",
        "# def create_map(city_number, map_x, map_y):\n",
        "#     city_list = []\n",
        "#     for i in range(city_number):\n",
        "#         while True: \n",
        "#             this_city = create_city(map_x, map_y)\n",
        "#             if this_city in city_list:\n",
        "#                 continue\n",
        "#             else:\n",
        "#                 city_list.append(this_city)\n",
        "#                 break\n",
        "                \n",
        "#     return city_list\n",
        "\n",
        "# my_map = create_map(city_number, map_x, map_y)\n",
        "# print(my_map)\n",
        "\n",
        "\n",
        "def plot_map(my_map, my_path=None):\n",
        "    xs = [x[0] for x in my_map]\n",
        "    ys = [x[1] for x in my_map]\n",
        "    plt.scatter(xs, ys, s=100)\n",
        "    plt.xlim(0, map_x)\n",
        "    plt.ylim(0, map_y)\n",
        "#     plt.figure(figsize=(5,5))\n",
        "    for i in range(len(my_map)):\n",
        "        plt.text(xs[i]+2, ys[i]-3, i, fontsize = 20)\n",
        "    \n",
        "    if my_path:\n",
        "        for i in range(len(my_path)-1):\n",
        "            plt.plot([my_map[my_path[i]][0], my_map[my_path[i+1]][0]], [my_map[my_path[i]][1], my_map[my_path[i+1]][1]], 'ro-')\n",
        "    plt.show()\n",
        "\n",
        "# my_path = None\n",
        "# my_path = [0, 1, 2, 3, 0]\n",
        "# my_map = [(10, 10), (10, 20), (22, 20)]\n",
        "my_map = [(10, 10), (10, 20), (20, 10)]\n",
        "plot_map(my_map)\n",
        "\n",
        "\n",
        "def dist(p, q):\n",
        "    return math.sqrt((float(p[0]) - float(q[0])) ** 2 + (float(p[1]) - float(q[1])) ** 2)\n",
        "\n",
        "def to_distance_matrix(my_map):\n",
        "    ret = []\n",
        "    for i in range(len(my_map)):\n",
        "        this_line = []\n",
        "        for j in range(len(my_map)):\n",
        "            this_line.append(dist(my_map[i], my_map[j]))\n",
        "        ret.append(this_line)\n",
        "            \n",
        "    return ret\n",
        "\n",
        "distance_matrix = to_distance_matrix(my_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "PDsjvbWwZIk6",
        "outputId": "c9379edc-e7fe-4a83-bc6d-563054c138c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0, 2, 1, 0] 34.14213562373095\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sys import maxsize\n",
        "from itertools import permutations\n",
        "def brute_tsp(graph, s=0):\n",
        "    vertex = []\n",
        "    V = len(graph)\n",
        "    for i in range(V):\n",
        "        if i != s:\n",
        "            vertex.append(i)\n",
        "\n",
        "    min_path = maxsize\n",
        "    next_permutation=permutations(vertex)\n",
        "    for i in next_permutation:\n",
        "        current_pathweight = 0\n",
        "        k = s\n",
        "        for j in i:\n",
        "            current_pathweight += graph[k][j]\n",
        "            k = j\n",
        "        current_pathweight += graph[k][s]\n",
        "        min_path = min(min_path, current_pathweight)\n",
        "        if min_path == current_pathweight:\n",
        "            ret_path = list(i)\n",
        "    ret_path = [0] + ret_path + [0]\n",
        "    return ret_path, min_path\n",
        "\n",
        "s = 0\n",
        "min_path, min_distance = brute_tsp(distance_matrix, s)\n",
        "print(min_path, min_distance)\n",
        "\n",
        "plot_map(my_map, min_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "x0I9VPzEJVDJ",
        "outputId": "269f2a6b-109f-478e-af9c-afb3aefabf05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "000000000      31768.591949959053\n",
            "000000001      -3586.7471093683243\n",
            "000000010      -3586.7471093683225\n",
            "000000011      -10657.814921233803\n",
            "000000100      -3586.747109368326\n",
            "000000101      -10657.814921233807\n",
            "000000110      -10657.814921233807\n",
            "000000111      10555.388514362625\n",
            "000001000      -3586.747109368327\n",
            "000001001      -10657.814921233807\n",
            "000001010      -38927.94403307198\n",
            "000001011      -17714.740597475553\n",
            "000001100      -38927.94403307198\n",
            "000001101      -17714.740597475553\n",
            "000001110      -45984.86970931373\n",
            "000001111      3512.6049737446\n",
            "000010000      -3586.7471093683253\n",
            "000010001      -38927.94403307198\n",
            "000010010      -10657.814921233805\n",
            "000010011      -17714.740597475553\n",
            "000010100      -38927.94403307198\n",
            "000010101      -45984.86970931373\n",
            "000010110      -17714.740597475553\n",
            "000010111      3512.604973744598\n",
            "000011000      -10657.814921233803\n",
            "000011001      -17714.740597475553\n",
            "000011010      -17714.740597475553\n",
            "000011011      3512.6049737445996\n",
            "000011100      -45984.86970931373\n",
            "000011101      -24757.524138093577\n",
            "000011110      -24757.524138093577\n",
            "000011111      24754.09268058848\n",
            "000100000      -3586.747109368329\n",
            "000100001      -38927.94403307198\n",
            "000100010      -38927.94403307198\n",
            "000100011      -45984.86970931373\n",
            "000100100      -10657.814921233808\n",
            "000100101      -17714.740597475553\n",
            "000100110      -17714.74059747556\n",
            "000100111      3512.6049737446006\n",
            "000101000      -10657.814921233803\n",
            "000101001      -17714.740597475553\n",
            "000101010      -45984.86970931373\n",
            "000101011      -24757.524138093577\n",
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            "111001101      45984.86970931373\n",
            "111001110      17714.740597475553\n",
            "111001111      95516.48652799579\n",
            "111010000      3504.3207024971516\n",
            "111010001      -3532.604973744603\n",
            "111010010      24737.524138093577\n",
            "111010011      45984.86970931373\n",
            "111010100      -3532.604973744599\n",
            "111010101      17714.740597475553\n",
            "111010110      45984.86970931373\n",
            "111010111      95516.48652799579\n",
            "111011000      24737.524138093577\n",
            "111011001      45984.86970931373\n",
            "111011010      45984.86970931373\n",
            "111011011      95516.48652799579\n",
            "111011100      17714.740597475553\n",
            "111011101      67246.35741615761\n",
            "111011110      67246.35741615761\n",
            "111011111      145062.24548230157\n",
            "111100000      3504.3207024971516\n",
            "111100001      -3532.6049737445974\n",
            "111100010      -3532.6049737446037\n",
            "111100011      17714.740597475553\n",
            "111100100      24737.524138093577\n",
            "111100101      45984.86970931373\n",
            "111100110      45984.86970931373\n",
            "111100111      95516.48652799579\n",
            "111101000      24737.524138093577\n",
            "111101001      45984.86970931373\n",
            "111101010      17714.740597475553\n",
            "111101011      67246.35741615761\n",
            "111101100      45984.86970931373\n",
            "111101101      95516.48652799579\n",
            "111101110      67246.35741615761\n",
            "111101111      145062.24548230157\n",
            "111110000      24737.524138093577\n",
            "111110001      17714.740597475553\n",
            "111110010      45984.86970931373\n",
            "111110011      67246.35741615761\n",
            "111110100      45984.86970931373\n",
            "111110101      67246.35741615761\n",
            "111110110      95516.48652799579\n",
            "111110111      145062.24548230157\n",
            "111111000      74254.99882115191\n",
            "111111001      95516.48652799579\n",
            "111111010      95516.48652799579\n",
            "111111011      145062.24548230157\n",
            "111111100      95516.48652799579\n",
            "111111101      145062.24548230157\n",
            "111111110      145062.24548230157\n",
            "111111111      222892.27568406926\n",
            "The minimum value of the Hamiltonian: -74263.28309239936 the configuration: 001010100\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import networkx as nx\n",
        "from itertools import combinations\n",
        "\n",
        "\n",
        "def tsp_matrix_to_QUBO_matrix(w, penality=1000):\n",
        "    n = len(w)\n",
        "    Q = np.zeros((n*n, n*n))\n",
        "    c = np.ones(n*n)\n",
        "    A = np.max(w) * penality # penality\n",
        "    c = c * (-4*A)\n",
        "\n",
        "    # k, l is the index of Q\n",
        "    # i, j, p, p_ is the index of w\n",
        "    for k in range(n*n):\n",
        "        for l in range(n*n):\n",
        "            i = k // n\n",
        "            j = l // n\n",
        "            p = k % n \n",
        "            p_ = l % n \n",
        "            if  (p+1)%n == p_ and i != j:\n",
        "                Q[k, l] = w[i, j] \n",
        "            elif p == p_:\n",
        "                Q[k, l] = Q[k, l] + A\n",
        "            elif i == j:\n",
        "                Q[k, l] = Q[k, l] + A\n",
        "\n",
        "    return Q, c\n",
        "\n",
        "def QUBO_to_Ising(Q, c):\n",
        "    J = Q/4\n",
        "    h = (c + np.sum(Q, axis=1))/2\n",
        "\n",
        "    for i in range(len(h)):\n",
        "        J[i][i] += h[i]\n",
        "\n",
        "    return J\n",
        "\n",
        "\n",
        "distance_matrix = np.array(distance_matrix)\n",
        "Q, c = tsp_matrix_to_QUBO_matrix(distance_matrix)\n",
        "\n",
        "Ising_coeff = QUBO_to_Ising(Q, c)\n",
        "Ising_coeff = Ising_coeff.tolist()\n",
        "\n",
        "\n",
        "# Visualize the connectivity of the Ising model\n",
        "G = nx.Graph()\n",
        "pairs = []\n",
        "for i in range(0, nqubits):\n",
        "    G.add_node(i, weight = Ising_coeff[i][i])\n",
        "    for j in range(i+1, nqubits):\n",
        "        pairs.append((i, j, Ising_coeff[i][j]))\n",
        "G.add_weighted_edges_from(pairs)\n",
        "labels = {n: str(n) + ';   ' + str(G.nodes[n]['weight']) for n in G.nodes}\n",
        "nx.draw(G, with_labels=True, labels=labels, alpha=0.8, node_size=500)\n",
        "\n",
        "\n",
        "# @jit(nopython=True, parallel=True, fastmath=True)\n",
        "def H_Ising(bitstring, Ising_coeff):\n",
        "    spins = []\n",
        "    for bit in bitstring:\n",
        "        if bit == '0':\n",
        "            spins.append(-1)\n",
        "        elif bit == '1':\n",
        "            spins.append(1)\n",
        "    H = 0\n",
        "    for i in range(0, nqubits):\n",
        "        H += Ising_coeff[i][i]*spins[i]\n",
        "        for j in range(nqubits):\n",
        "            if i != j:\n",
        "                H += Ising_coeff[i][j]*spins[i]*spins[j]\n",
        "    return H\n",
        "\n",
        "\n",
        "H_min = 10000\n",
        "for i in range(2**len(Ising_coeff)):\n",
        "    bitstring = np.binary_repr(i, nqubits)\n",
        "    print(bitstring, \"    \", H_Ising(bitstring, Ising_coeff))\n",
        "    if H_Ising(bitstring, Ising_coeff) < H_min:\n",
        "        H_min = H_Ising(bitstring, Ising_coeff)\n",
        "        config_min = bitstring\n",
        "print('The minimum value of the Hamiltonian:', H_min, 'the configuration:', config_min)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "vDfqwYSbJMQT"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "def compute_expectation(counts, Ising_coeff):   \n",
        "    \"\"\"\n",
        "    Computes expectation value based on measurement results\n",
        "    \"\"\"   \n",
        "    sum = 0\n",
        "    sum_count = 0\n",
        "    for bitstring, count in counts.items():\n",
        "        sum += count*H_Ising(bitstring, Ising_coeff)\n",
        "        sum_count += count\n",
        "    return sum/sum_count\n",
        "\n",
        "\n",
        "# We will also bring the different circuit components that\n",
        "# build the qaoa circuit under a single function\n",
        "def create_qaoa_circ(Ising_coeff, theta): \n",
        "    \"\"\"\n",
        "    Creates a parametrized qaoa circuit\n",
        "    \"\"\"   \n",
        "    p = len(theta)//2  # number of alternating unitaries\n",
        "    qc = QuantumCircuit(nqubits)\n",
        "    \n",
        "    beta = theta[:p]\n",
        "    gamma = theta[p:]\n",
        "    \n",
        "    # initial_state\n",
        "    for i in range(0, nqubits):\n",
        "        qc.h(i)\n",
        "    \n",
        "    for irep in range(0, p):\n",
        "        \n",
        "        # problem unitary\n",
        "        for i in range(nqubits):\n",
        "            for j in range(i+1, nqubits):\n",
        "                if Ising_coeff != 0:\n",
        "                    qc.rzz(2 * Ising_coeff[i][j]*gamma[irep], i, j)\n",
        "        # mixer unitary\n",
        "        for i in range(nqubits):\n",
        "            qc.rz(2 * Ising_coeff[i][i]*gamma[irep], i)\n",
        "            qc.rx(2 * beta[irep], i)\n",
        "            \n",
        "    qc.measure_all()\n",
        "        \n",
        "    return qc\n",
        "\n",
        "# Finally we write a function that executes the circuit on the chosen backend\n",
        "def get_expectation(theta):\n",
        "    \"\"\"\n",
        "    Runs parametrized circuit and get expectation value\n",
        "    \n",
        "    \"\"\"\n",
        "    qc = create_qaoa_circ(Ising_coeff, theta)       \n",
        "    qc = transpile(qc, backend=backend)\n",
        "    if seed:\n",
        "        seed_simulator = 10\n",
        "    else:\n",
        "        seed_simulator = None\n",
        "    counts = backend.run(qc, seed_simulator=seed_simulator, nshots=shots).result().get_counts()       \n",
        "\n",
        "    return compute_expectation(counts, Ising_coeff)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "lW0r_T-dWzH8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐                                                            »\n",
              "   q_0: ┤ H ├─■────────────■────────────■─────────────────────────■──────»\n",
              "        ├───┤ │ZZ(7071.1)  │            │                         │      »\n",
              "   q_1: ┤ H ├─■────────────┼────────────┼────────────■────────────┼──────»\n",
              "        ├───┤              │ZZ(7071.1)  │            │ZZ(7071.1)  │      »\n",
              "   q_2: ┤ H ├──────────────■────────────┼────────────■────────────┼──────»\n",
              "        ├───┤                           │ZZ(7071.1)               │      »\n",
              "   q_3: ┤ H ├───────────────────────────■─────────────────────────┼──────»\n",
              "        ├───┤                                                     │ZZ(5) »\n",
              "   q_4: ┤ H ├─────────────────────────────────────────────────────■──────»\n",
              "        ├───┤                                                            »\n",
              "   q_5: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_6: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_7: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_8: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        └───┘                                                            »\n",
              "meas: 9/═════════════════════════════════════════════════════════════════»\n",
              "                                                                         »\n",
              "«                                                                          »\n",
              "«   q_0: ─────────■────────────────────────────■───────────────────────────»\n",
              "«                 │                            │                           »\n",
              "«   q_1: ─■───────┼───────■────────────────────┼────────────■──────────────»\n",
              "«         │       │       │                    │            │              »\n",
              "«   q_2: ─┼───────┼───────┼────────────■───────┼────────────┼───────■──────»\n",
              "«         │ZZ(0)  │       │            │ZZ(5)  │            │       │      »\n",
              "«   q_3: ─■───────┼───────┼────────────■───────┼────────────┼───────┼──────»\n",
              "«                 │       │ZZ(7071.1)          │            │       │ZZ(0) »\n",
              "«   q_4: ─────────┼───────■────────────────────┼────────────┼───────■──────»\n",
              "«                 │ZZ(0)                       │            │ZZ(5)         »\n",
              "«   q_5: ─────────■────────────────────────────┼────────────■──────────────»\n",
              "«                                              │ZZ(7071.1)                 »\n",
              "«   q_6: ──────────────────────────────────────■───────────────────────────»\n",
              "«                                                                          »\n",
              "«   q_7: ──────────────────────────────────────────────────────────────────»\n",
              "«                                                                          »\n",
              "«   q_8: ──────────────────────────────────────────────────────────────────»\n",
              "«                                                                          »\n",
              "«meas: 9/══════════════════════════════════════════════════════════════════»\n",
              "«                                                                          »\n",
              "«                                                          ┌───────────┐»\n",
              "«   q_0: ─■─────────────────────────────────────────■──────┤ Rz(21233) ├»\n",
              "«         │                                         │      └───────────┘»\n",
              "«   q_1: ─┼───────■─────────────────────────────────┼───────■───────────»\n",
              "«         │       │                                 │       │           »\n",
              "«   q_2: ─┼───────┼───────■─────────────────────────┼───────┼───────────»\n",
              "«         │       │       │                         │       │           »\n",
              "«   q_3: ─┼───────┼───────┼────────────■────────────┼───────┼───────────»\n",
              "«         │       │       │            │ZZ(7071.1)  │       │           »\n",
              "«   q_4: ─┼───────┼───────┼────────────■────────────┼───────┼───────────»\n",
              "«         │       │       │ZZ(7071.1)               │       │           »\n",
              "«   q_5: ─┼───────┼───────■─────────────────────────┼───────┼───────────»\n",
              "«         │       │ZZ(0)                            │       │           »\n",
              "«   q_6: ─┼───────■─────────────────────────────────┼───────┼───────────»\n",
              "«         │ZZ(5)                                    │       │ZZ(7071.1) »\n",
              "«   q_7: ─■─────────────────────────────────────────┼───────■───────────»\n",
              "«                                                   │ZZ(0)              »\n",
              "«   q_8: ───────────────────────────────────────────■───────────────────»\n",
              "«                                                                       »\n",
              "«meas: 9/═══════════════════════════════════════════════════════════════»\n",
              "«                                                                       »\n",
              "«        ┌───────┐                                                            »\n",
              "«   q_0: ┤ Rx(2) ├────────────────────────────────────────────────────────────»\n",
              "«        └───────┘                     ┌───────────┐  ┌───────┐               »\n",
              "«   q_1: ───────────────────────■──────┤ Rz(21233) ├──┤ Rx(2) ├───────────────»\n",
              "«                               │      └───────────┘  └───────┘               »\n",
              "«   q_2: ──■────────────────────┼──────────■──────────────────────────────────»\n",
              "«          │                    │          │                                  »\n",
              "«   q_3: ──┼───────■────────────┼──────────┼─────────■────────────────────────»\n",
              "«          │       │            │          │         │                        »\n",
              "«   q_4: ──┼───────┼────────────┼──────────┼─────────┼────────────■───────────»\n",
              "«          │       │ZZ(7071.1)  │          │         │            │ZZ(7071.1) »\n",
              "«   q_5: ──┼───────■────────────┼──────────┼─────────┼────────────■───────────»\n",
              "«          │ZZ(5)               │          │         │ZZ(7071.1)              »\n",
              "«   q_6: ──■────────────────────┼──────────┼─────────■────────────────────────»\n",
              "«                               │          │ZZ(0)                             »\n",
              "«   q_7: ───────────────────────┼──────────■──────────────────────────────────»\n",
              "«                               │ZZ(5)                                        »\n",
              "«   q_8: ───────────────────────■─────────────────────────────────────────────»\n",
              "«                                                                             »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════════»\n",
              "«                                                                             »\n",
              "«                                                                             »\n",
              "«   q_0: ─────────────────────────────────────────────────────────────────────»\n",
              "«                                                                             »\n",
              "«   q_1: ─────────────────────────────────────────────────────────────────────»\n",
              "«                     ┌───────────┐┌───────┐                                  »\n",
              "«   q_2: ─■───────────┤ Rz(21233) ├┤ Rx(2) ├──────────────────────────────────»\n",
              "«         │           └───────────┘└───────┘        ┌───────────┐  ┌───────┐  »\n",
              "«   q_3: ─┼────────────■─────────────────────■──────┤ Rz(21237) ├──┤ Rx(2) ├──»\n",
              "«         │            │                     │      └───────────┘  └───────┘  »\n",
              "«   q_4: ─┼────────────┼─────────────■───────┼───────■────────────────────────»\n",
              "«         │            │             │       │       │                        »\n",
              "«   q_5: ─┼────────────┼─────────────┼───────┼───────┼────────────■───────────»\n",
              "«         │            │             │ZZ(0)  │       │            │ZZ(7.0711) »\n",
              "«   q_6: ─┼────────────┼─────────────■───────┼───────┼────────────■───────────»\n",
              "«         │            │ZZ(7.0711)           │       │ZZ(7071.1)              »\n",
              "«   q_7: ─┼────────────■─────────────────────┼───────■────────────────────────»\n",
              "«         │ZZ(7071.1)                        │ZZ(0)                           »\n",
              "«   q_8: ─■──────────────────────────────────■────────────────────────────────»\n",
              "«                                                                             »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════════»\n",
              "«                                                                             »\n",
              "«                                                                         »\n",
              "«   q_0: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_1: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_2: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_3: ─────────────────────────────────────────────────────────────────»\n",
              "«                     ┌───────────┐  ┌───────┐                            »\n",
              "«   q_4: ─■───────────┤ Rz(21237) ├──┤ Rx(2) ├────────────────────────────»\n",
              "«         │           └───────────┘  └───────┘  ┌───────────┐  ┌───────┐  »\n",
              "«   q_5: ─┼───────────────■─────────■───────────┤ Rz(21237) ├──┤ Rx(2) ├──»\n",
              "«         │               │         │           └───────────┘  └───────┘  »\n",
              "«   q_6: ─┼───────────────┼─────────┼────────────■────────────■───────────»\n",
              "«         │               │ZZ(0)    │            │ZZ(7071.1)  │           »\n",
              "«   q_7: ─┼───────────────■─────────┼────────────■────────────┼───────────»\n",
              "«         │ZZ(7.0711)               │ZZ(7071.1)               │ZZ(7071.1) »\n",
              "«   q_8: ─■─────────────────────────■─────────────────────────■───────────»\n",
              "«                                                                         »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════»\n",
              "«                                                                         »\n",
              "«                                            ░ ┌─┐                        \n",
              "«   q_0: ────────────────────────────────────░─┤M├────────────────────────\n",
              "«                                            ░ └╥┘┌─┐                     \n",
              "«   q_1: ────────────────────────────────────░──╫─┤M├─────────────────────\n",
              "«                                            ░  ║ └╥┘┌─┐                  \n",
              "«   q_2: ────────────────────────────────────░──╫──╫─┤M├──────────────────\n",
              "«                                            ░  ║  ║ └╥┘┌─┐               \n",
              "«   q_3: ────────────────────────────────────░──╫──╫──╫─┤M├───────────────\n",
              "«                                            ░  ║  ║  ║ └╥┘┌─┐            \n",
              "«   q_4: ────────────────────────────────────░──╫──╫──╫──╫─┤M├────────────\n",
              "«                                            ░  ║  ║  ║  ║ └╥┘┌─┐         \n",
              "«   q_5: ────────────────────────────────────░──╫──╫──╫──╫──╫─┤M├─────────\n",
              "«        ┌───────────┐  ┌───────┐            ░  ║  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "«   q_6: ┤ Rz(21237) ├──┤ Rx(2) ├────────────░──╫──╫──╫──╫──╫──╫─┤M├──────\n",
              "«        └───────────┘┌─┴───────┴─┐┌───────┐ ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "«   q_7: ─■───────────┤ Rz(21237) ├┤ Rx(2) ├─░──╫──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "«         │ZZ(7071.1) ├───────────┤├───────┤ ░  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "«   q_8: ─■───────────┤ Rz(21237) ├┤ Rx(2) ├─░──╫──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "«                     └───────────┘└───────┘ ░  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "«meas: 9/═══════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "«                                               0  1  2  3  4  5  6  7  8 </pre>"
            ],
            "text/plain": [
              "        ┌───┐                                                            »\n",
              "   q_0: ┤ H ├─■────────────■────────────■─────────────────────────■──────»\n",
              "        ├───┤ │ZZ(7071.1)  │            │                         │      »\n",
              "   q_1: ┤ H ├─■────────────┼────────────┼────────────■────────────┼──────»\n",
              "        ├───┤              │ZZ(7071.1)  │            │ZZ(7071.1)  │      »\n",
              "   q_2: ┤ H ├──────────────■────────────┼────────────■────────────┼──────»\n",
              "        ├───┤                           │ZZ(7071.1)               │      »\n",
              "   q_3: ┤ H ├───────────────────────────■─────────────────────────┼──────»\n",
              "        ├───┤                                                     │ZZ(5) »\n",
              "   q_4: ┤ H ├─────────────────────────────────────────────────────■──────»\n",
              "        ├───┤                                                            »\n",
              "   q_5: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_6: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_7: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        ├───┤                                                            »\n",
              "   q_8: ┤ H ├────────────────────────────────────────────────────────────»\n",
              "        └───┘                                                            »\n",
              "meas: 9/═════════════════════════════════════════════════════════════════»\n",
              "                                                                         »\n",
              "«                                                                          »\n",
              "«   q_0: ─────────■────────────────────────────■───────────────────────────»\n",
              "«                 │                            │                           »\n",
              "«   q_1: ─■───────┼───────■────────────────────┼────────────■──────────────»\n",
              "«         │       │       │                    │            │              »\n",
              "«   q_2: ─┼───────┼───────┼────────────■───────┼────────────┼───────■──────»\n",
              "«         │ZZ(0)  │       │            │ZZ(5)  │            │       │      »\n",
              "«   q_3: ─■───────┼───────┼────────────■───────┼────────────┼───────┼──────»\n",
              "«                 │       │ZZ(7071.1)          │            │       │ZZ(0) »\n",
              "«   q_4: ─────────┼───────■────────────────────┼────────────┼───────■──────»\n",
              "«                 │ZZ(0)                       │            │ZZ(5)         »\n",
              "«   q_5: ─────────■────────────────────────────┼────────────■──────────────»\n",
              "«                                              │ZZ(7071.1)                 »\n",
              "«   q_6: ──────────────────────────────────────■───────────────────────────»\n",
              "«                                                                          »\n",
              "«   q_7: ──────────────────────────────────────────────────────────────────»\n",
              "«                                                                          »\n",
              "«   q_8: ──────────────────────────────────────────────────────────────────»\n",
              "«                                                                          »\n",
              "«meas: 9/══════════════════════════════════════════════════════════════════»\n",
              "«                                                                          »\n",
              "«                                                          ┌───────────┐»\n",
              "«   q_0: ─■─────────────────────────────────────────■──────┤ Rz(21233) ├»\n",
              "«         │                                         │      └───────────┘»\n",
              "«   q_1: ─┼───────■─────────────────────────────────┼───────■───────────»\n",
              "«         │       │                                 │       │           »\n",
              "«   q_2: ─┼───────┼───────■─────────────────────────┼───────┼───────────»\n",
              "«         │       │       │                         │       │           »\n",
              "«   q_3: ─┼───────┼───────┼────────────■────────────┼───────┼───────────»\n",
              "«         │       │       │            │ZZ(7071.1)  │       │           »\n",
              "«   q_4: ─┼───────┼───────┼────────────■────────────┼───────┼───────────»\n",
              "«         │       │       │ZZ(7071.1)               │       │           »\n",
              "«   q_5: ─┼───────┼───────■─────────────────────────┼───────┼───────────»\n",
              "«         │       │ZZ(0)                            │       │           »\n",
              "«   q_6: ─┼───────■─────────────────────────────────┼───────┼───────────»\n",
              "«         │ZZ(5)                                    │       │ZZ(7071.1) »\n",
              "«   q_7: ─■─────────────────────────────────────────┼───────■───────────»\n",
              "«                                                   │ZZ(0)              »\n",
              "«   q_8: ───────────────────────────────────────────■───────────────────»\n",
              "«                                                                       »\n",
              "«meas: 9/═══════════════════════════════════════════════════════════════»\n",
              "«                                                                       »\n",
              "«        ┌───────┐                                                            »\n",
              "«   q_0: ┤ Rx(2) ├────────────────────────────────────────────────────────────»\n",
              "«        └───────┘                     ┌───────────┐  ┌───────┐               »\n",
              "«   q_1: ───────────────────────■──────┤ Rz(21233) ├──┤ Rx(2) ├───────────────»\n",
              "«                               │      └───────────┘  └───────┘               »\n",
              "«   q_2: ──■────────────────────┼──────────■──────────────────────────────────»\n",
              "«          │                    │          │                                  »\n",
              "«   q_3: ──┼───────■────────────┼──────────┼─────────■────────────────────────»\n",
              "«          │       │            │          │         │                        »\n",
              "«   q_4: ──┼───────┼────────────┼──────────┼─────────┼────────────■───────────»\n",
              "«          │       │ZZ(7071.1)  │          │         │            │ZZ(7071.1) »\n",
              "«   q_5: ──┼───────■────────────┼──────────┼─────────┼────────────■───────────»\n",
              "«          │ZZ(5)               │          │         │ZZ(7071.1)              »\n",
              "«   q_6: ──■────────────────────┼──────────┼─────────■────────────────────────»\n",
              "«                               │          │ZZ(0)                             »\n",
              "«   q_7: ───────────────────────┼──────────■──────────────────────────────────»\n",
              "«                               │ZZ(5)                                        »\n",
              "«   q_8: ───────────────────────■─────────────────────────────────────────────»\n",
              "«                                                                             »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════════»\n",
              "«                                                                             »\n",
              "«                                                                             »\n",
              "«   q_0: ─────────────────────────────────────────────────────────────────────»\n",
              "«                                                                             »\n",
              "«   q_1: ─────────────────────────────────────────────────────────────────────»\n",
              "«                     ┌───────────┐┌───────┐                                  »\n",
              "«   q_2: ─■───────────┤ Rz(21233) ├┤ Rx(2) ├──────────────────────────────────»\n",
              "«         │           └───────────┘└───────┘        ┌───────────┐  ┌───────┐  »\n",
              "«   q_3: ─┼────────────■─────────────────────■──────┤ Rz(21237) ├──┤ Rx(2) ├──»\n",
              "«         │            │                     │      └───────────┘  └───────┘  »\n",
              "«   q_4: ─┼────────────┼─────────────■───────┼───────■────────────────────────»\n",
              "«         │            │             │       │       │                        »\n",
              "«   q_5: ─┼────────────┼─────────────┼───────┼───────┼────────────■───────────»\n",
              "«         │            │             │ZZ(0)  │       │            │ZZ(7.0711) »\n",
              "«   q_6: ─┼────────────┼─────────────■───────┼───────┼────────────■───────────»\n",
              "«         │            │ZZ(7.0711)           │       │ZZ(7071.1)              »\n",
              "«   q_7: ─┼────────────■─────────────────────┼───────■────────────────────────»\n",
              "«         │ZZ(7071.1)                        │ZZ(0)                           »\n",
              "«   q_8: ─■──────────────────────────────────■────────────────────────────────»\n",
              "«                                                                             »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════════»\n",
              "«                                                                             »\n",
              "«                                                                         »\n",
              "«   q_0: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_1: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_2: ─────────────────────────────────────────────────────────────────»\n",
              "«                                                                         »\n",
              "«   q_3: ─────────────────────────────────────────────────────────────────»\n",
              "«                     ┌───────────┐  ┌───────┐                            »\n",
              "«   q_4: ─■───────────┤ Rz(21237) ├──┤ Rx(2) ├────────────────────────────»\n",
              "«         │           └───────────┘  └───────┘  ┌───────────┐  ┌───────┐  »\n",
              "«   q_5: ─┼───────────────■─────────■───────────┤ Rz(21237) ├──┤ Rx(2) ├──»\n",
              "«         │               │         │           └───────────┘  └───────┘  »\n",
              "«   q_6: ─┼───────────────┼─────────┼────────────■────────────■───────────»\n",
              "«         │               │ZZ(0)    │            │ZZ(7071.1)  │           »\n",
              "«   q_7: ─┼───────────────■─────────┼────────────■────────────┼───────────»\n",
              "«         │ZZ(7.0711)               │ZZ(7071.1)               │ZZ(7071.1) »\n",
              "«   q_8: ─■─────────────────────────■─────────────────────────■───────────»\n",
              "«                                                                         »\n",
              "«meas: 9/═════════════════════════════════════════════════════════════════»\n",
              "«                                                                         »\n",
              "«                                            ░ ┌─┐                        \n",
              "«   q_0: ────────────────────────────────────░─┤M├────────────────────────\n",
              "«                                            ░ └╥┘┌─┐                     \n",
              "«   q_1: ────────────────────────────────────░──╫─┤M├─────────────────────\n",
              "«                                            ░  ║ └╥┘┌─┐                  \n",
              "«   q_2: ────────────────────────────────────░──╫──╫─┤M├──────────────────\n",
              "«                                            ░  ║  ║ └╥┘┌─┐               \n",
              "«   q_3: ────────────────────────────────────░──╫──╫──╫─┤M├───────────────\n",
              "«                                            ░  ║  ║  ║ └╥┘┌─┐            \n",
              "«   q_4: ────────────────────────────────────░──╫──╫──╫──╫─┤M├────────────\n",
              "«                                            ░  ║  ║  ║  ║ └╥┘┌─┐         \n",
              "«   q_5: ────────────────────────────────────░──╫──╫──╫──╫──╫─┤M├─────────\n",
              "«        ┌───────────┐  ┌───────┐            ░  ║  ║  ║  ║  ║ └╥┘┌─┐      \n",
              "«   q_6: ┤ Rz(21237) ├──┤ Rx(2) ├────────────░──╫──╫──╫──╫──╫──╫─┤M├──────\n",
              "«        └───────────┘┌─┴───────┴─┐┌───────┐ ░  ║  ║  ║  ║  ║  ║ └╥┘┌─┐   \n",
              "«   q_7: ─■───────────┤ Rz(21237) ├┤ Rx(2) ├─░──╫──╫──╫──╫──╫──╫──╫─┤M├───\n",
              "«         │ZZ(7071.1) ├───────────┤├───────┤ ░  ║  ║  ║  ║  ║  ║  ║ └╥┘┌─┐\n",
              "«   q_8: ─■───────────┤ Rz(21237) ├┤ Rx(2) ├─░──╫──╫──╫──╫──╫──╫──╫──╫─┤M├\n",
              "«                     └───────────┘└───────┘ ░  ║  ║  ║  ║  ║  ║  ║  ║ └╥┘\n",
              "«meas: 9/═══════════════════════════════════════╩══╩══╩══╩══╩══╩══╩══╩══╩═\n",
              "«                                               0  1  2  3  4  5  6  7  8 "
            ]
          },
          "execution_count": 123,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "circuit = create_qaoa_circ(Ising_coeff, [1, 1])\n",
        "# circuit = transpile(circuit, backend=backend)\n",
        "circuit.draw()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "QaZ4s3ZuT0td"
      },
      "outputs": [],
      "source": [
        "# def optimizer(eps, theta, lr, momentum):\n",
        "#     grad = [0 for t in range(len(theta))]\n",
        "#     for i in range(len(theta)):\n",
        "#         # Uses a symmetric difference to calulate the gradient\n",
        "#         theta[i] += eps\n",
        "#         grad[i] = get_expectation(theta)\n",
        "#         theta[i] -= 2*eps\n",
        "#         grad[i] -= get_expectation(theta)\n",
        "#         grad[i] /= 2*eps\n",
        "#         theta[i] += eps      \n",
        "#         # Update the parameters by gradient descent with momentum\n",
        "#         v[i] = momentum*v[i] - lr*grad[i]\n",
        "#         theta[i] = theta[i] + v[i]\n",
        "\n",
        "#     return theta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "V0GmZqmQXff1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The expectation values at iteration = 0 is: 379.82563518748896\n",
            "The expectation values at iteration = 1 is: 2033.9174411867573\n",
            "The expectation values at iteration = 2 is: 4475.572594488419\n",
            "The expectation values at iteration = 3 is: 4475.572594488419\n",
            "The expectation values at iteration = 4 is: 4475.572594488419\n",
            "The expectation values at iteration = 5 is: 4475.572594488419\n",
            "The expectation values at iteration = 6 is: 4475.572594488419\n",
            "The expectation values at iteration = 7 is: 4475.572594488419\n",
            "The expectation values at iteration = 8 is: 4475.572594488419\n",
            "The expectation values at iteration = 9 is: 4475.572594488419\n",
            "The expectation values at iteration = 10 is: 4475.572594488419\n",
            "The expectation values at iteration = 11 is: 4475.572594488419\n",
            "The expectation values at iteration = 12 is: 4475.572594488419\n",
            "The expectation values at iteration = 13 is: 4475.572594488419\n",
            "The expectation values at iteration = 14 is: 4475.572594488419\n",
            "The expectation values at iteration = 15 is: 4475.572594488419\n",
            "The expectation values at iteration = 16 is: 4475.572594488419\n",
            "The expectation values at iteration = 17 is: 4475.572594488419\n",
            "The expectation values at iteration = 18 is: 4475.572594488419\n",
            "The expectation values at iteration = 19 is: 4475.572594488419\n",
            "The expectation values at iteration = 20 is: 4475.572594488419\n",
            "The expectation values at iteration = 21 is: 4475.572594488419\n",
            "The expectation values at iteration = 22 is: 4475.572594488419\n",
            "The expectation values at iteration = 23 is: 4475.572594488419\n",
            "The expectation values at iteration = 24 is: 4475.572594488419\n",
            "The expectation values at iteration = 25 is: 4475.572594488419\n",
            "The expectation values at iteration = 26 is: 4475.572594488419\n",
            "The expectation values at iteration = 27 is: 4475.572594488419\n",
            "The expectation values at iteration = 28 is: 4475.572594488419\n",
            "The expectation values at iteration = 29 is: 4475.572594488419\n",
            "The expectation values at iteration = 30 is: 4475.572594488419\n"
          ]
        }
      ],
      "source": [
        "# seed = True\n",
        "# shots = 1024\n",
        "# eps = 10**-6\n",
        "# p = 2\n",
        "# lr = 100\n",
        "# momentum = 0.0\n",
        "# num_steps = 30\n",
        "# schedule = [25]\n",
        "# lr_decay = 0.1\n",
        "\n",
        "\n",
        "# # Initialize the parameters\n",
        "# torch.manual_seed(0)\n",
        "# theta = torch.empty(int(2*p))\n",
        "# torch.nn.init.normal_(theta, mean=0.5, std=0.01)\n",
        "# theta = theta.tolist()\n",
        "\n",
        "\n",
        "# expectation_list = []\n",
        "# theta_list = []\n",
        "# # Perform SGD with momentum\n",
        "# v = [0 for i in range(int(2*p))]\n",
        "# for i in range(num_steps + 1):      # Update the parameters\n",
        "#     expectation = get_expectation(theta)\n",
        "#     expectation_list.append(expectation)\n",
        "#     if i in schedule:      # Learning rate scheduling (step)\n",
        "#         lr = lr*lr_decay\n",
        "#     theta = optimizer(eps, theta, lr, momentum)\n",
        "#     if (i != 0) and (i % 10 == 0):\n",
        "#         theta_list.append(theta)\n",
        "#     print('The expectation values at iteration =', i, 'is:', expectation, )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[[10616.601717798214,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  0.0],\n",
              " [3535.533905932738,\n",
              "  10616.601717798214,\n",
              "  3535.533905932738,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  2.5],\n",
              " [3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  10616.601717798214,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738],\n",
              " [3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  10618.672785610077,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  3.5355339059327378,\n",
              "  0.0],\n",
              " [0.0,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  3535.533905932738,\n",
              "  10618.672785610077,\n",
              "  3535.533905932738,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3.5355339059327378],\n",
              " [2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  10618.672785610077,\n",
              "  3.5355339059327378,\n",
              "  0.0,\n",
              "  3535.533905932738],\n",
              " [3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3.5355339059327378,\n",
              "  0.0,\n",
              "  10618.672785610077,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738],\n",
              " [0.0,\n",
              "  3535.533905932738,\n",
              "  2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3.5355339059327378,\n",
              "  3535.533905932738,\n",
              "  10618.672785610077,\n",
              "  3535.533905932738],\n",
              " [2.5,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3.5355339059327378,\n",
              "  0.0,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  3535.533905932738,\n",
              "  10618.672785610077]]"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Ising_coeff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "WhjeyIroT0wE"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\"\\nseed = True\\nshots = 1024\\neps = 10**-3\\nmomentum = 0.0\\nnum_steps = 100\\nschedule = [60]\\nlr_decay = 0.1\\n\\np_list = [int(i+1) for i in range(10)]\\nlr_list = [0.5*10**-2, 1*10**-2, 2*10**-2, 5*10**-2, 10*10**-2, 20*10**-2, 50*10**-2]\\n\\np_list = [2]\\nlr_list = [0.01]\\n\\n\\nminimum_list = []\\nlr_best_list = []\\ntheta_best_list = []\\nfor num_p in p_list:\\n    p = num_p\\n    print('\\nFor p =', p, ':')\\n    minimum = 1\\n    for lr in lr_list:\\n        # Initialize the parameters\\n        torch.manual_seed(0)\\n        theta = torch.empty(1, int(2*p))\\n        torch.nn.init.normal_(theta, mean=0.5, std=0.01)\\n        theta = theta.tolist()[0]\\n        # Perform SGD with momentum\\n        v = [0 for i in range(len(theta))]\\n        for i in range(num_steps + 1):      # Update the parameters\\n            if i in schedule:      # Learning rate scheduling (step)\\n                lr = lr*lr_decay\\n            theta = optimizer(eps, theta, lr, momentum)\\n\\n        expectation = get_expectation(theta)\\n        if expectation < minimum:\\n            minimum = expectation\\n            lr_best = lr\\n            theta_best = theta\\n\\n        print('Learning rate =', lr, 'the expectation value =', expectation)\\n\\n    minimum_list.append(minimum)\\n    lr_best_list.append(lr_best)\\n    theta_best_list.append(theta_best)\\n    print('The min. expectation value = ', minimum, 'when learning rate =', lr_best)\\n    print('-----------------Temporary results-----------------')\\n    for i in range(len(minimum_list)): \\n        print('The optimized parameters:',  theta_best_list[i])\\n    print('Min. expectaion value =', minimum_list)\\n    print('Best learning rate =', lr_best_list)\\n\""
            ]
          },
          "execution_count": 127,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\"\n",
        "seed = True\n",
        "shots = 1024\n",
        "eps = 10**-3\n",
        "momentum = 0.0\n",
        "num_steps = 100\n",
        "schedule = [60]\n",
        "lr_decay = 0.1\n",
        "\n",
        "p_list = [int(i+1) for i in range(10)]\n",
        "lr_list = [0.5*10**-2, 1*10**-2, 2*10**-2, 5*10**-2, 10*10**-2, 20*10**-2, 50*10**-2]\n",
        "\n",
        "p_list = [2]\n",
        "lr_list = [0.01]\n",
        "\n",
        "\n",
        "minimum_list = []\n",
        "lr_best_list = []\n",
        "theta_best_list = []\n",
        "for num_p in p_list:\n",
        "    p = num_p\n",
        "    print('\\nFor p =', p, ':')\n",
        "    minimum = 1\n",
        "    for lr in lr_list:\n",
        "        # Initialize the parameters\n",
        "        torch.manual_seed(0)\n",
        "        theta = torch.empty(1, int(2*p))\n",
        "        torch.nn.init.normal_(theta, mean=0.5, std=0.01)\n",
        "        theta = theta.tolist()[0]\n",
        "        # Perform SGD with momentum\n",
        "        v = [0 for i in range(len(theta))]\n",
        "        for i in range(num_steps + 1):      # Update the parameters\n",
        "            if i in schedule:      # Learning rate scheduling (step)\n",
        "                lr = lr*lr_decay\n",
        "            theta = optimizer(eps, theta, lr, momentum)\n",
        "\n",
        "        expectation = get_expectation(theta)\n",
        "        if expectation < minimum:\n",
        "            minimum = expectation\n",
        "            lr_best = lr\n",
        "            theta_best = theta\n",
        "\n",
        "        print('Learning rate =', lr, 'the expectation value =', expectation)\n",
        "\n",
        "    minimum_list.append(minimum)\n",
        "    lr_best_list.append(lr_best)\n",
        "    theta_best_list.append(theta_best)\n",
        "    print('The min. expectation value = ', minimum, 'when learning rate =', lr_best)\n",
        "    print('-----------------Temporary results-----------------')\n",
        "    for i in range(len(minimum_list)): \n",
        "        print('The optimized parameters:',  theta_best_list[i])\n",
        "    print('Min. expectaion value =', minimum_list)\n",
        "    print('Best learning rate =', lr_best_list)\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "UeKPmGBoT0yo"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "execution_count": 128,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# from qiskit.visualization import plot_histogram\n",
        "\n",
        "# qc_result = create_qaoa_circ(Ising_coeff, theta)\n",
        "# counts = backend.run(qc_result).result().get_counts()\n",
        "\n",
        "# plot_histogram(counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "ywiNwMYOT79F"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'011110000': 1, '001010011': 1, '011001101': 1, '110101111': 1, '010000010': 1, '101111111': 1, '010101110': 1, '101010110': 1, '100110011': 1, '010001111': 1, '111100000': 1, '001011001': 1, '111110111': 1, '001101000': 1, '100000110': 1, '101011000': 1, '010110101': 1, '100101000': 1, '101011001': 1, '010000011': 1, '100100011': 1, '011011011': 1, '011010001': 1, '000001100': 1, '101111100': 1, '001111111': 2, '011000111': 1, '100110101': 1, '010011010': 1, '111110100': 2, '000011011': 2, '100111111': 3, '100011101': 1, '000110100': 3, '110001011': 1, '001111100': 2, '000000001': 2, '110100011': 1, '101111001': 1, '101101101': 2, '110101101': 1, '011100101': 4, '101000111': 2, '110010001': 3, '000101001': 1, '100101001': 3, '011000011': 1, '001000011': 4, '001001100': 1, '000100100': 1, '101000001': 1, '111001100': 3, '011011000': 2, '010011000': 1, '101000100': 3, '100110111': 1, '001001111': 2, '101100111': 2, '001000110': 1, '000001011': 2, '011000001': 1, '110010010': 3, '100101010': 1, '000011100': 1, '110000100': 3, '011101110': 6, '100001111': 1, '000010110': 4, '000100010': 1, '100110110': 1, '110101000': 1, '011010111': 2, '011101001': 4, '100101101': 1, '100001101': 1, '100101111': 5, '100000000': 2, '110110111': 3, '100011110': 1, '100001001': 2, '110010100': 1, '001100111': 5, '101010001': 4, '111110110': 3, '000010100': 1, '110111011': 2, '010000100': 2, '110110101': 4, '001101111': 3, '011010101': 1, '011001111': 4, '101101000': 2, '010100101': 1, '010010010': 1, '010000111': 4, '010111001': 6, '011001100': 1, '011101011': 12, '100011000': 1, '101100001': 2, '110000010': 1, '010110110': 1, '101011110': 1, '011001011': 3, '101110011': 9, '010111100': 3, '111010100': 1, '010000110': 7, '011101010': 13, '001111110': 3, '110010000': 3, '100110000': 3, '001110011': 1, '101110001': 5, '001011100': 1, '010110001': 1, '010010111': 5, '101010011': 3, '111101010': 18, '110110110': 2, '011111010': 1, '100000111': 2, '001100011': 13, '000101101': 4, '100010100': 3, '000100111': 8, '111011101': 4, '010001110': 9, '100110100': 3, '010011011': 3, '101100101': 3, '001101001': 4, '000000111': 20, '111000011': 1, '010100110': 2, '111101110': 1, '110111101': 2, '111100111': 5, '101110110': 7, '110110011': 1, '001001011': 1, '011111000': 5, '110010101': 4, '111001010': 5, '010001011': 19, '111110011': 3, '011110111': 2, '000001111': 8, '110111110': 2, '000011010': 1, '011000110': 2, '101011111': 2, '101111101': 4, '011100010': 2, '101110010': 2, '101111000': 4, '110011010': 6, '011111110': 1, '111101011': 5, '000101111': 2, '001100010': 1, '110011101': 5, '110011111': 1, '111100010': 4, '000000010': 4, '001100110': 4, '010110111': 1, '010101100': 2, '011110001': 4, '111110101': 10, '110011000': 3, '101101011': 4, '001100101': 20, '011101101': 10, '000111010': 1, '001111101': 3, '110011100': 10, '101001111': 3, '111000111': 7, '010011100': 1, '010011111': 1, '111111000': 4, '000000000': 2, '101000010': 2, '011000010': 1, '101110000': 3, '101011010': 2, '100010101': 16, '111010011': 2, '111010001': 4, '110000111': 2, '101001110': 3, '100111010': 3, '011001000': 2, '110011110': 11, '000000100': 3, '111011100': 8, '001000111': 3, '001100000': 4, '100111011': 1, '001111010': 1, '000010010': 1, '111100011': 3, '110001111': 1, '111011011': 4, '000110111': 3, '111101000': 6, '011101100': 5, '000111100': 1, '101100000': 2, '100010110': 18, '001011110': 1, '100010000': 1, '111111010': 4, '000000101': 1, '001111000': 1, '001110010': 1, '111110000': 5, '001011011': 3, '110101010': 6, '101110101': 14, '011111001': 1, '100011111': 2, '100100111': 2, '110001100': 2, '101101001': 2, '000000110': 1, '010001001': 2, '100010111': 7, '011111101': 1, '101101111': 5, '101011101': 1, '000010111': 10, '110011001': 6, '001100001': 1, '001101101': 3, '111111101': 3, '101100011': 3, '110100110': 1, '011110101': 3, '111111100': 6, '111111011': 5, '100011011': 3, '111011000': 7, '111000100': 2, '101101110': 1, '000100101': 2, '010100011': 1, '111001111': 7, '111110010': 1, '011001010': 3, '001010111': 2, '101011100': 4, '010111010': 3, '011110100': 2, '010100001': 3, '101011011': 1, '010001000': 8, '111111001': 5, '110011011': 6, '100011010': 1, '100000101': 11, '111001110': 2, '010100111': 1, '100111100': 4, '011111100': 1, '011111011': 6, '001001101': 1, '101110100': 3, '010000101': 2, '011100110': 3, '111011110': 7, '100010011': 3, '010111000': 1, '001010100': 1, '111010111': 3, '001101110': 2, '010010110': 2, '111111111': 2, '111001001': 1, '111010101': 4, '101000101': 2, '001000010': 4, '110111001': 5, '010101010': 1, '011101000': 4, '111110001': 14, '010001010': 1, '010100100': 1, '100100100': 1, '110111000': 4, '000110110': 5, '100000100': 2, '001011000': 1, '111111110': 5, '000111001': 3, '100000001': 1, '000000011': 2, '001010010': 1, '001011010': 1, '110010110': 5, '010111111': 1, '011011110': 2, '001101011': 3, '001011111': 1, '101111110': 6, '010010011': 3, '110101001': 1, '011110011': 2, '101100010': 5, '001110110': 1, '110100100': 1, '000111111': 5, '110101011': 1, '011001110': 6, '110111100': 1, '100011001': 1, '110001101': 3, '111010110': 1, '011011111': 6, '110010111': 3, '010011110': 3, '000011111': 4, '110110000': 2}\n"
          ]
        }
      ],
      "source": [
        "# print(counts)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "QAOA_for_TSP.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 ('base')",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
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  "nbformat": 4,
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