AWS_circuit_cut_SPSA_CE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:19.320003Z",
     "start_time": "2024-02-15T18:09:19.308526Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_32520/2279893312.py:5: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n",
      "/tmp/ipykernel_32520/2279893312.py:9: DeprecationWarning: ``qiskit.algorithms`` has been migrated to an independent package: https://github.com/qiskit-community/qiskit-algorithms. The ``qiskit.algorithms`` import path is deprecated as of qiskit-terra 0.25.0 and will be removed in Qiskit 1.0. Please run ``pip install qiskit_algorithms`` and use ``import qiskit_algorithms`` instead.\n",
      "  from qiskit.algorithms.optimizers import ADAM, SPSA\n"
     ]
    }
   ],
   "source": [
    "from circuit_knitting.cutting import partition_problem\n",
    "from qiskit.circuit import QuantumCircuit, ParameterVector\n",
    "from qiskit.quantum_info import SparsePauliOp, PauliList\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from qiskit_algorithms.optimizers.cobyla import COBYLA\n",
    "from qiskit.algorithms.optimizers import ADAM, SPSA\n",
    "from qiskit_algorithms.utils import algorithm_globals\n",
    "from IPython.display import clear_output\n",
    "from loss_optimization.accuracy_score import encode_y\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b94d0bd-445c-48c7-949c-11d240fce1a6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "seed = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25a998ef-cc41-407e-80d9-4ef13cc6e64d",
   "metadata": {},
   "source": [
    "## AWS imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd53ac89-0774-4ce2-80af-0bc17aac5710",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_braket_provider import AWSBraketProvider, BraketLocalBackend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc3b1f6e-2890-4b2f-92e5-033473d480d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "aws_provider = AWSBraketProvider()\n",
    "# backends = aws_provider.backends()\n",
    "\n",
    "online_sim = aws_provider.get_backend(\"TN1\")\n",
    "local_sim = BraketLocalBackend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c00f7c-def5-4106-bd98-522927f57892",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84e0ff14-514d-440f-851a-63c136d80243",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.482075Z",
     "start_time": "2024-02-15T18:09:20.472994Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.923998</td>\n",
       "      <td>1.531329</td>\n",
       "      <td>0.566517</td>\n",
       "      <td>-0.920265</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>0.004682</td>\n",
       "      <td>-1.66872</td>\n",
       "      <td>-0.104720</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.771994</td>\n",
       "      <td>-0.457820</td>\n",
       "      <td>0.257508</td>\n",
       "      <td>-1.301064</td>\n",
       "      <td>-3.141593</td>\n",
       "      <td>-0.650792</td>\n",
       "      <td>-2.40918</td>\n",
       "      <td>-2.094395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "0    -0.923998  1.531329       0.566517      -0.920265 -3.141593  0.004682   \n",
       "1    -2.771994 -0.457820       0.257508      -1.301064 -3.141593 -0.650792   \n",
       "\n",
       "   DiabetesPedigreeFunction       Age  Outcome  \n",
       "0                  -1.66872 -0.104720        1  \n",
       "1                  -2.40918 -2.094395        0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"diabetes_normalized.csv\")\n",
    "data = data.drop([\"Unnamed: 0\"], axis=1)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92a101c1-41f3-4fbe-b1c4-78eee1e9d524",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:20.926480Z",
     "start_time": "2024-02-15T18:09:20.920864Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = data[\"Outcome\"]\n",
    "x = data.drop([\"Outcome\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8ef2ba7-dda4-41ea-944f-c710c9fc9528",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.304727Z",
     "start_time": "2024-02-15T18:09:21.299733Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(537, 8) (537,) (231, 8) (231,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
    "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "505092d4-3e8d-4853-9f59-3391739bae30",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:21.800966Z",
     "start_time": "2024-02-15T18:09:21.796485Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train_A = x_train.iloc[:, :4]\n",
    "x_train_B = x_train.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea2b1465-28bb-4db5-9541-4ba60e770021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:22.250366Z",
     "start_time": "2024-02-15T18:09:22.235445Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_test_A = x_test.iloc[:, :4]\n",
    "x_test_B = x_test.iloc[:, 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "878cdeed-18a8-4f22-bea4-2031a7be0a08",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_y_train = encode_y(y_train)\n",
    "new_y_test = encode_y(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202a0b22-ad7d-468f-878a-e4569cc6d0fa",
   "metadata": {},
   "source": [
    "## Data Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23a08b2f-5a0b-4aa5-96e7-5cd2a4655aaa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:23.793347Z",
     "start_time": "2024-02-15T18:09:23.786288Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Data Embedding - Angle Encoding\n",
    "def angle_encoding(feature_dims: int):\n",
    "    embedding = QuantumCircuit(feature_dims)\n",
    "    feature_param = ParameterVector(\"Theta\", feature_dims)\n",
    "    for qubit in range(feature_dims):\n",
    "        embedding.ry(feature_param[qubit], qubit)\n",
    "    return embedding, feature_param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3cbfc86a-2a27-4381-84ce-2a469fb57661",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:24.329495Z",
     "start_time": "2024-02-15T18:09:24.321044Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "embedding, feature_params = angle_encoding(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09640b63-6b10-40b0-837f-7acbc1f88c47",
   "metadata": {},
   "source": [
    "## Tensor Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "06ed2511-a0bb-4f5b-85b9-85940a954de3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T18:09:25.102164Z",
     "start_time": "2024-02-15T18:09:25.070178Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensor_network.ttn import TTN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c2d9089-9292-4580-98e4-6f6287a59473",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 705.552x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "ttn.draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ebafa996-adaf-4060-9f32-21cdb706a932",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# ttn.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f81b56c-c727-47cb-94a6-e2b5359d8a19",
   "metadata": {},
   "source": [
    "## Circuit Cutting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cda3f8fc3c6b2d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.371027Z",
     "start_time": "2024-02-15T03:34:50.309487Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "observables = PauliList([\"ZIIIIIII\"])\n",
    "partitioned_problem = partition_problem(circuit=ttn, partition_labels=\"AAAABBBB\", observables=observables)\n",
    "sub_circuits = partitioned_problem.subcircuits\n",
    "sub_observables = partitioned_problem.subobservables\n",
    "bases = partitioned_problem.bases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a43241fdcd1decfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:34:50.869863Z",
     "start_time": "2024-02-15T03:34:50.865500Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': PauliList(['IIII']), 'B': PauliList(['ZIII'])}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_observables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81331ea7-8123-4e9b-867d-54fd786e3fe2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 723.581x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"A\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2af67465-17d0-4791-8e1c-279835483dc5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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il2HWLG2fV4T9gYjIeHg5mIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIgPhhCVQ/WgGH9dykby1VzkFRQBAAr+f1qrJi6wsuLfUEREVHsxBBI94k7uA/ywIwUb9/6Nsyk50Gh0pebn3CtEu2H/g52tAsGtPPDa4AC81LcpHOx5KBERUe3C31xEAG5nqzBraTw2RF2GulBT6fIP1BrE/nkTsX/exL+/PI0JI1pi9ptBDINERFRr8DcWwatTa4Rvn1tqWlG+CveuZOLy1mNIXrMXOo1WpOqM75cDV/H2Z8eRlfOgWuvn3i/EvDVnsPW3q/jhk27o3L6ewBUan9T7ABGRFDEEkt6V7TFIj04AZDLYe7rAf3h3hM0di7oBDXBixiqxyxOcRqPFO/NOYOXPFwRpL+XaPXQduwdf/rsDpr7SRpA2TU1qfYCISMoYAknvztmruLItRv/9xXW/YmjMUjR7+VkkzN8E9Z17IlYnLK1Wh399FIP1u/8WtF2dDpi28BTUhRq8N66doG2bgpT6ABGR1PHxxsekpqZizZo1GD9+PIKDg2FjYwOZTIaxY8eKXZrJFavUuJ2QAplcDmff2neJsyLvL4sXPAA+atbSeKzflWK09k3FkvsAEZHU8UzgY5YsWYKlS5eKXYbZcGpc8otfnZsnciXCiflDiS9++MugdeI2DYKXhwOUWQUIfWlXldaZNO8EeoR6o5F3neqUaTYssQ8QERFD4BM8PDzQr18/hIaGIiQkBPv27cOKFSvELsskrOxtYOvmpL8frPmYPnBv2wS3E1Jw70qm2OUJokBVjH/NiYFOV/myj/LycIBPPUeD1rmfX4TXP47Br9+GQyaTGbZBkUihDxARUQmGwMfMnj271PcnT54UqRLTax85Cu0jR5Walhp1EqdmfS9SRcL79pdk/H3ddPe1HTxxAweOZ+D5zj4m22ZNSKEPGKKoSItdR65h15HryLlXCHs7Bdq3cMe/hjTDU+72YpdHRFQjFn9PYFZWFiIjI+Hv7w87Ozs0bNgQU6ZMQX5+PsaNGweZTIbly5eLXaZZuPjTAfw6Yi4ORnyG+E9/woPs+3D0dodGXahfpvvKqei+alqp9Wxc6mBE4mo0eaGrqUs2iFarE+xJYEOs2JJs8m1Wl6X3garS6XT49udkNO67BcOmR2P97r+x++h1/PzrVcxaGg+f5zbj1Q+O4u79wsobIyIyUxYdAhMTE9G2bVssXLgQSqUSrVq1QlFREZYtW4aRI0ciObnkl3NQUJC4hZqJe1eUyIw5i4zoP3FuxU4cenU+PIKaotOCt/TLnJi1Gk+FNoffkM76aR0/fx23Tl/Ale0xZTVrNn47mWHSs4AP7TmWhms37pt8u9Vh6X2gqmYtjceE/xzHjVsFZc4vKtZi/e6/0XXsHmTfVZu4OiIiYVhsCMzKysLAgQOhVCoxffp0ZGZmIiEhAUqlEgsWLEBUVBTi4uIgk8kQGBgodrlm6Xb8RVzeegx+QzrDM6Q5AKAwNw/Hp69Eh89eh309V/j27wivZ1rjxEzzH0Nuz7E0Ubar1eqwPzZDlG3XlKX1gapY+79LWLC2ag8OnU3JwbBph6Az9CZTIiIzYLEhcPLkyUhPT8ekSZOwaNEiODk56edFRkaiXbt2KC4uRuPGjeHs7CxipebtzOKt0BZr0H7GSP20jMOJSN19HN2WT0bH+W/g+PSVUOeY/5Ojf5zPkuS2a8qS+kBltFodPludaNA6h+MyceLMLeMURERkRBYZApOTk7FlyxZ4eHhg3rx5ZS4THBwMAGjXruYD+j4Mm2FhYbC1ta01T4JWxf1UJa7ujEX9boF4qkNL/fT4uevh5OeFjOg/kX4oQcQKq0aj0SLxQrZo26/NIdBS+kBV/Bqbjivphl+6r033fRIRPWSRIXDTpk3QarWIiIhAnTplj9Fmb1/yZJ8QIfDvv//Gtm3b4OXlhdDQ0Bq3Z27+WroNWk3pM0HFKjXyrt1CTvJ1ESuruuy7ahQ8KBZt+9eV+aJtWwiW0AeqYn9serXW2/d79dYjIhKTRQ4REx0dDQDo2bNnucukp5f80BYiBHbr1g2ZmSVjqH388ceIjY2tcZsAEBISAqVSWeEy1jo55iCsRttRnkjCOu9h5c6/m5KB9T4jy50vpGYBzVAk0wrerkbmDLhOL3f+w8Ggy+PlYa//b9rBUeUuV95g0neyc+HjI8wwMULs88eZUx94lLH6Q3lyHIcAtu0NXi87t0Cw/UtEZAgvLy/Ex8dXa12LDIHXrl0DAPj6+pY5v7i4WB/UhAiBcrlxTqgqlUpkZFT8QIGNTAFY0Nu8bmTeQKFOI3zDVvcB1/JnV3UwaCuF3OBBowFApymqdF9WlaXt84oYrT+Up34uYFuN9bRqwfYvEZGpWGQIzM8vufSmUqnKnL9lyxZkZWXByckJfn5+pizNIF5eXpUuY62TA6Y7UWJ09b3rG+XMjw4K3NBpAJmizPnKrLKHAnnIy8MeVgo5ijVaKLPK7lcVtWMle4B6DRpUveAKWNo+r4ix+kN58m1ykVuN9Wy1mfAQaP8SERmiKlmhPBYZAr28vJCTk4OEhAR06tSp1LzMzEzMmDEDABAYGGjWD3FU5fRuUcEDbGw62gTVPGn/i3MEb/NSyiVYO9gJ3i4ABA3/H85cLPvhkMreB5x2cBR86jlCmaVCw+c2G7ztEQM7YuP89wxeryxi7vPHGaMPPMqY/aEsqgfFaNB7E3LuGTYI9Kblb2Los58bqSoiIuOwyAdDevfuDQBYsGABLl26pJ8eFxeHnj17Iiur5ElNDhItLcGtPETctrto26aqs7ezwvjhLStf8BFNfJwwsHsjI1VERGQ8FhkCIyMj4e7ujrS0NLRu3Rpt27ZFQEAAwsLC0KRJE/Tq1QtA2fcDxsbGwsPDQ//11VdfAQA2b95carpQD3+Q6fQM9ZbktskwH7/dHs91ql+lZV2cbLBzaW9YWVnkj1IisnAW+ZPLx8cHMTEx6N+/P+zs7JCamgo3NzesWrUKUVFR+rODZYXAoqIi3LlzR//18L5CtVpdanpRUZFJPxPV3LDnGsPdpTp3/ddMWBtPtG8p3llIMoyNtQK7lj2HMQP9K1yueeO6+P3HAWgT4GaiyoiIhGWR9wQCQMuWLbFnz54npufl5SE1NRVyuRxt2rR5Yn6PHj34CigLZWdrhXFDm+GLH86adLsTRxl2eZHEZ2drhR8/646PxrfHtz9fwK4j1/H39bvQ6gA7GwV2LuuN3h0bQC4333uKiYgqY5FnAiuSlJQEnU6HgIAAODiUPy6cobZu3YqtW7fi/Pnzpb6v7tg9ZBxTIlqjrpONybbXvHFdjHjefJ9Ap4o1beiMhdPDcHH3MHh7lvy8cHexRZ9nfBgAiajWk1wIPHu25CyQEOMDPmr48OEYPnw4fvnll1LfL1++XNDtVEXQ9BFQ2FoDALosmYhWb/Q3uI1G4aHwfDqgSsv6j+iBly/+iAH7F+inhW+bixdPfYNBBxdi0MGFaPXmAP28rt9MwcgzqxH2yViD66qp+k85YklkB5NsSy6XYd2n3WBna/wT7uawz9tOfgFDY5bi1Yyf0Si89Jtz7Nyd8dx/P8ALsV9j8OGvUK/jP2dHxewPRERSxhAoEJ1OV+bXunXrBN1OVQT9+59AUF2NwsPgGdysyssrjydhT/jMUtPi5qzDrudmYNdzM3D+u38uzcdMXIqL6w/WqL6aeHVQAAZ0a2jQOsqsAqTfzK90PMFH/fvVNujY7ilDy6sWc9jnmcf+wsGIz3Dz5JPv0Q3+YDRu/5GC7Z3fwe/vfoNu37wLmVXJmI1i9wciIqmy2HsCy2OsEGguOi14EwDQd8en0Gm0KLiZg7oBDdDn5zlwrO+O3ItpODp+MbRFxZBZKfB05Ch4dWkDhbUV7l7JxInIVfAMboaGfULg3S0Q/iN6IPmH/Ug/+Ae6rXwX1k72UNjaQBl7DqdmrwVq4f2TMpkMG+f3wLNv7EN8UlaV1qlsHMHHDXuuMT6fHFKd8gxmLvs8K/HvcmtsPKgTtnd6BwBw58xlFNzMhlenVsiMMe39mURE9A/JhcCH7xW2VCdmfofmY/pg35APUXivAF2WTIRbaz/sHzYHWnUxwv/3CXz7d8DVHbFo8/ZgFKnUiOo3CwAQOHUY2s98Cafe/x5pB+KRnZSK86ujAAAKW2scGjMfxQUPIJPL0WvdTPgNegZXd5Y/VE7wB6PRPnIUci+l44/PNyLv+i2T/BtUhXMdGxxYFY4Bkw7geKKwdb3Utwl+/E93KBSmOdFuTvu8LLaudSC3soLqdq5+Wl7abTg28BTs34CIiAwnuRAoRdf3nYJGVfIGhKzEv+HUuOQVM43CQ2Hj5IDG/UrukZPbWCEv7XbZjchlCJ49GvXCWgAyGew9nJF74Xq5geDYO8tQcOMOAKDFa+Ho/dMs7Og+VeBPVjOuzrb47bu++HD5H/jqp3M1Pqlpb6fA5++EYHJEa9EfGhBjnxMRUe3CECgBGvU/YxrqNFrIFSX3YslkMpyavRY3jp6ptI3Wbw2EvUddRPWfBY26CKEfvwqFXfn3oD0MgABw4Yf9CP1oDGxd60Cdk1eDTyI8ezsrLPp3B7zQuzHenPs7ki7nVqudHqHe+O6jzgjwrStsgdUkxj4vjzonD1qNBvaeLvqzgXUaeiI/o5zwSUREJiG5B0OkoPB+AaydKx/+5vr+02j15gAo7EuGTFHY28Clmc//t6GCtdM/bdjUdYTqVg406iLYe7qg8YBOZbYJADKFHHYe/4Qh3/4doMq6a3YB8FHPBNXD2e0v4NDqvnixd2MoFJWfyXOws8IbLzZHwpbBOLymn6gBUOx9Xplru0+g+Zg+AAD3dk3h4OUG5Ynz1W6PiIhqjmcCLVDSt7vRZ/NH0KjUKLiZU+5yZ5fvQLtp1hgQNU9/KfTcNzuQeykdl7ceRZelk9AoPAwX1u1H8vd70WP1dAw+shgqZTZuxPxVbrsKG2v03vA+FDZW0Gl1eJB9H4denS/0xxScTCZDrw710atDfeQXFCHxYjbik24j+epd5KuKIJfJ4ORojcBmbghu5YE2/q6wtVGIXTYA8fc5AAS++yKav9IHdu7OeKbFBHT47HXs6jMD6jv3EP+fDei6fDJeiP0amqJixExaBl2xRsh/AiIiMpBMx9dj1GpFBQ+wseloUWvwH9EDjfqGIfq1L6q8TtD0EbCp64DTH60rNT3i8gZYO9gJXKFlqa37vCK1oT/49N6EjFsFaPCUA9J/e0nscoiIaoyXg6nGih8Uwq1141IDB1ek6zdT0OTFrii8rzJyZWQshu7zirA/EBGJg5eDqcZSdx1H6q7jVV4+ZuJSI1ZDpmDoPq8I+wMRkTh4JpCIiIhIghgCiYiIiCSIIZCIiIhIgnhPYC1nZW+LiMsbxC5DMFb2tmKXYPYsbZ9XhP2BiMh4GAJrOZlMZjZDaJBpcJ8TEZEQeDmYiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIIYAomIiIgkiCGQiIiISIKsxC6Aakan06FYpRa7DMFY2dtCJpOJXQYREZHFYwis5YpVamxsOlrsMgQTcXkDrB3sxC6DiIjI4vFyMBEREZEEMQQSERERSRBDIBEREZEEMQQSERERSRBDIBEREZEEMQQSERERSRBDIBEREZEEcZxAglen1gjfPrfUtKJ8Fe5dycTlrceQvGYvdBqtSNURERGRMTAEkt6V7TFIj04AZDLYe7rAf3h3hM0di7oBDXBixiqxyyMiIiIBMQSS3p2zV3FlW4z++4vrfsXQmKVo9vKzSJi/Ceo790SsjoiIiITEewLLkJqaijVr1mD8+PEIDg6GjY0NZDIZxo4dK3ZpJlWsUuN2QgpkcjmcfeuJXQ4REREJiGcCy7BkyRIsXbpU7DLMglPjkvCnzs0TuRIiIiISEkNgGTw8PNCvXz+EhoYiJCQE+/btw4oVK8Quy+is7G1g6+akvyew+Zg+cG/bBLcTUnDvSqbY5REREZGAGALLMHv27FLfnzx5UqRKTKt95Ci0jxxValpq1EmcmvW9SBWJT6fT4Y/zWbhxqwAKhQx+DZzQqqmr2GURERHVmCTuCczKykJkZCT8/f1hZ2eHhg0bYsqUKcjPz8e4ceMgk8mwfPlyscsU3cWfDuDXEXNxMOIzxH/6Ex5k34ejtzs06kL9Mt1XTkX3VdNKrWfjUgcjElejyQtdTV2y0eQXFGHZxiS0HLwNoS/twuApv2HApINoPXQ7Oo3ehZ92p6C4mMPmEBFR7WXxZwITExPRt29fKJVKODo6olWrVrhx4waWLVuGy5cvIzs7GwAQFBQkbqFm4N4VJTJjzgIAMqL/xM3TF9Bv56fotOAtHJ2wGABwYtZqDI7+En5DOuPqjlgAQMfPX8et0xdwZXtMuW3XJsqsAvR7+wD+vHCnzPkn/7qNk3/dxi8HUrH5i55wsLf4w4iIiCyQRZ8JzMrKwsCBA6FUKjF9+nRkZmYiISEBSqUSCxYsQFRUFOLi4iCTyRAYGCh2uWbndvxFXN56DH5DOsMzpDkAoDA3D8enr0SHz16HfT1X+PbvCK9nWuPETMsYR/B+fiHCJ/xabgB81O6j1/Hye4eh4UDaRERUC1l0CJw8eTLS09MxadIkLFq0CE5OTvp5kZGRaNeuHYqLi9G4cWM4OzuLWKn5OrN4K7TFGrSfMVI/LeNwIlJ3H0e35ZPRcf4bOD59JdQ5lvH08PJNyThzMbvKy+88fB17jqUZsSIiIiLjsNgQmJycjC1btsDDwwPz5s0rc5ng4GAAQLt27Wq8va1bt+LFF1+Er68vHBwc0KJFC3zwwQfIy6vd4eh+qhJXd8aifrdAPNWhpX56/Nz1cPLzQkb0n0g/lCBihcLRaLT49pdkg9dbscXwdYiIiMRmsSFw06ZN0Gq1iIiIQJ06dcpcxt7eHoAwIXDRokVQKBT4/PPPsW/fPkyYMAErV65EeHg4tNrafbnwr6XboNWUPhtYrFIj79ot5CRfF7EyYR2JU+J6Zr7B6x04noEbtwxfj4iISEwWe0d7dHQ0AKBnz57lLpOeng5AmBC4e/dueHp66r/v3r07PD09ERERgd9//x3dunUzuM2QkBAolcoKl7HWyTEHYQa3/SjliSSs8x5W7vy7KRlY7zOy3PlCahbQDEUycUJzvk0QUGdotdYNCu0JG80NYQsis5LpMg2Q10WmMhM+Pj5il0NEBADw8vJCfHx8tda12BB47do1AICvr2+Z84uLixEbW/J0qxAh8NEA+FBISAgAICMjo1ptKpXKSte1kSkAC3qj243MGyjUacTZuIsvUPZJ40rdvn0LUFVvP1Mt4aQB5IBWo6n2MU1EZE4sNgTm55dcnlOpVGXO37JlC7KysuDk5AQ/Pz+j1HD48GEAQMuWLStZsmxeXl6VLmOtkwO1+2pzKfW964t2JlBtJUeWoSvpdIBMBi93Oyh0DYxRFpmJTIUCWgByhQLeDbivicg8VCUrlMdiQ6CXlxdycnKQkJCATp06lZqXmZmJGTNmAAACAwMhk8kE335GRgY+/PBDhIeHV3sMwqqc3i0qeICNTUdXq/2a2v/iHMHbvJRyCdYOdoK3WxUajRb+/X9B6g0DHuaRyRDe2Qf7Vl4wXmFkFnx6b0LGrQJ4e3kj/Vy62OUQEdWYxT4Y0rt3bwDAggULcOnSJf30uLg49OzZE1lZJed8jDFIdF5eHgYPHgwbGxusXbtW8PbJOBQKOcaPaGHwem+PrN6ZXiIiIjFZbAiMjIyEu7s70tLS0Lp1a7Rt2xYBAQEICwtDkyZN0KtXLwBl3w8YGxsLDw8P/ddXX30FANi8eXOp6Q/vKXyUSqXCwIEDcfXqVRw4cADe3t7G/aAkqImjWuHplu5VXv7F3o3Rv1tDI1ZERERkHBYbAn18fBATE4P+/fvDzs4OqampcHNzw6pVqxAVFaU/O1hWCCwqKsKdO3f0Xw/vK1Sr1aWmFxUVPbHesGHDEB8fj3379qFVq1bG/6AkqDoO1ti34nmEtPaodNmhz/rip8+7Qy4X/nYCIiIiY7PYewKBkgcy9uzZ88T0vLw8pKamQi6Xo02bNk/M79GjB3Q6nUHbejgm4aFDh7B3716EhdVs2BYSz1Pu9ji6tj9+3JWCbzafR9Ll3FLzuwV7YcKIFhjexw8KhcX+HUVERBbOokNgeZKSkqDT6dCsWTM4ODgI0ubEiRPxyy+/4L333oODgwNOnjypn9e0adMyh5Ah8+Vgb4UJI1ti/IgWOHMxG73f2Ic7d9V4ys0OR3/oL3Z5RERENSbJ0xhnz54FIMz4gA/t27cPADB//nx06tSp1FdUVJRg2yHTkslkCGrhDjtbBQDA2kqShwwREVkgSZ4JNEYITE1NFaytmgqaPgJnl/8PGnURuiyZiOykVJxfbVgQbRQeCtWtXNxOSKl0Wf8RPRD26Wu4d1WJPeEzS9XhN7QLtIVFeJB9H78O+xgA0PWbKajfpQ2u7ozF6Y/WGVQXERERCYMh0AIF/XsEzq/eA426qPKFy9EoPAzZSalVCoEAoDyehOjXvtB/3/L1fnBt5YudPadBW1QMe08X/byYiUsRNH0EbOoKcymeiIiIDCfJEPjwvcKWqNOCNwEAfXd8Cp1Gi4KbOagb0AB9fp4Dx/ruyL2YhqPjF0NbVAyZlQJPR46CV5c2UFhb4e6VTJyIXAXP4GZo2CcE3t0C4T+iB5J/2I/0g3+g28p3Ye1kD4WtDZSx53Bq9tqSN2aUoc2Ewfh1+MfQFhUDAFS3c031T0BERERVIMkQaMlOzPwOzcf0wb4hH6LwXgG6LJkIt9Z+2D9sDrTqYoT/7xP49u+Aqzti0ebtwShSqRHVbxYAIHDqMLSf+RJOvf890g7El7qMrLC1xqEx81Fc8AAyuRy91s2E36BncHXnk2MlWtexh71nXTR8PhSNB5S8rSVp1W6k7jpuun8IIiIiqhBDoARc33cKGlUhACAr8W84NS55z2Cj8FDYODmgcb8OAAC5jRXy0m6X3YhchuDZo1EvrAUgk8Hewxm5F66XGQJlVgrIra1gZWeDqP6zUMfHE/12f4a7f2cg5/w143xIIiIiMghDoAQ8em+gTqOFXFHypKtMJsOp2Wtx4+iZStto/dZA2HvURVT/WdCoixD68atQ2FmXuWxhbh6K8lS4vO0YACAv/TZuxV2ER5A/QyAREZGZ4HgXFqjwfgGsnSt/6OL6/tNo9eYAKOxtAAAKexu4NPP5/zZUsHb6pw2buo5Q3cqBRl0Ee08X/WXe8lzZ8Tsa9Gxfsq5LHXi0ZwAkIiIyJzwTaIGSvt2NPps/gkalRsHNnHKXO7t8B9pNs8aAqHn65zvOfbMDuZfScXnrUXRZOgmNwsNwYd1+JH+/Fz1WT8fgI4uhUmbjRsxfFdaQ8PlGdF48ES3GPq/fVlbi34J9RiIiIqoZhkALdOarX3Dmq1/KnBf/yXr9/+s0WiQu3ILEhVueWO7OmcvY2WNqqWkPHyCpCnVOHqLHLqjy8kRERGRavBxMNVb8oBBurRtjwP6qhb6u30xBkxe7ovC+ysiVERERUXl4JpBqLHXXcYOGf4mZuNSI1RAREVFV8EwgERERkQQxBBIRERFJEC8H13JW9raIuLxB7DIEY2VvK3YJREREksAQWMvJZDJYO9iJXQYRERHVMrwcTERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSERERCRBDIFEREREEsQQSEREZAK5ubn4+OOPceTIEbFLMZhWq8XixYvRokUL2NnZoWHDhpg+fTry8/PFLo1qgCGQiIjIBHJzczF37txaGQKnTp2KadOmoVWrVvj6668xfPhwLFu2DAMHDoRWqxW7PKomK7ELICIiIvOVlJSEr7/+Gi+88AK2bdumn+7n54fJkydj8+bNePnll0WskKqLIbCW0+l0KFapxS5DMFb2tpDJZGKXYdYsbZ9XhP2hbOwDplFYWIglS5bgv//9Ly5dugRra2sEBARg7NixmDRpEgBg7Nix+PHHH6HT6Z5YXyaT4dVXX8W6detw5MgR9OzZEwAwd+5czJ07FwDg6+uL1NRUg+o6fPgwFi1ahJMnTyI/Px/169dHz549sWDBAnh4eCAyMhILFy7E+vXr8corr+jX++uvv9ChQwd07NgRhw4dglxetYuBmzZtgk6nw7vvvltq+htvvIH33nsPGzZsMHkI5DEgUNtGaZVMplilxsamo8UuQzARlzfA2sFO7DLMmqXt84qwP5SNfcD4CgsL8fzzz+PIkSPo06cPRo8eDTs7O5w9exbbt2/Xh8CqatmyJRYvXoypU6di6NCheOGFFwAAderUMaidVatWYcKECWjQoAEmTJgAX19fXL9+Hbt370Z6ejo8PDzw2Wef4dixY3j77bfRsWNHBAQEoKCgACNHjoSjoyM2bNhQ5QAIAHFxcZDL5QgLCys13c7ODkFBQYiLizPoMwiBx4AwGAKJiIges2TJEhw5cgSzZs3C559/Xmpede6Bq1evHoYMGYKpU6ciMDAQo0cbHmDS09MxefJktGjRAsePH4eLi4t+3qeffqqvy9raGps2bUL79u0xatQonDhxAu+88w4uXLiAXbt2oUGDBgZt98aNG/Dw8ICtre0T8xo0aIDjx4+jsLAQNjY2Bn8mEhcfDCEiInrMxo0b4erqio8++uiJeYacRRPSL7/8gsLCQsyZM6dUAHzo0br8/Pzw3XffISEhAb169cLatWsxefJkDBw40ODtFhQUlBkAgZKzgQ+XodqHIZCIiOgxKSkp+uFQzEVKSgoAoH379lVafsSIEYiIiEBsbCzatGmDL774olrbdXBwgFpd9v13Dx480C9DtQ9DIBERUTWVd8N+cXGxiSt5Um5uLn7//XcAJZd0b926Va126tevj6ysrDKDYEZGBjw8PHgpuJZiCCQiInpMs2bNcOHChXLPgD3k5uYGAMjOzi41/cqVK08sW9MnPJs1awYASExMrNLy48aNQ3p6Or7++msUFBRg9OjR0Gg0Bm83NDQUWq0Wp0+fLjX9wYMHSExMREhIiMFtknlgCCQqw937hTh8+gYWrTuLcXNikH235BdBzr1CLN90HifO3ESBSvy/9Ml4btzKx+4j1/HxigS8+sFRfR/IvV+IH3em4FxKNoqLOUiupYqIiEBOTg7+85//PDHv0eFgHgaz3377rdQyX3755RPrPXwS+PHAWFXDhg2DjY0N5s6di3v37lVY17fffovt27dj9uzZmDRpEhYtWoRjx46V+XkqM3LkSMhkMixZsqTU9NWrV6OgoAAREREGt0nmQaYra3AjqjWKCh5Y1GPyYg4JotXqcPBEBlZsScaeY2nQais+NGxtFBgV3gRvj2yJsLaeJqrS8vZ5RUzdH1QPirF5/xWs2JKM+KSsSpf3dLXDuBeaYfzwFvCt72SCCkuwDxhfYWEhevfujZiYGDz//PPo06cP7OzskJSUhIsXL+pD37179+Dn5weNRoNp06bBzc0N+/fvR1ZWFk6dOqUfJ/ChgIAA3L17Fx988AHq1asHR0dHgx7WWLlyJSZOnIiGDRtizJgx8PX1RUZGBnbu3Im1a9ciKCgI586dQ1hYGEJDQxEdHQ2FQgEAGDJkCPbs2YMjR46gS5cuBv17vPPOO1i+fDmGDh2Kfv36ITk5GcuWLUPnzp0RHR1t8odleAwIgyGwlhPiQPDq1Brh2+eWbjdfhXtXMnF56zEkr9kLncY0ZzzE+oF/LD4Tb34Si4upd6u1fpf29bD64y5o4ecibGFlMMYPP3PqA48yVX/Q6XT4fttFvLc0Xn/GzxAyGTBmYAC+mtEBbnXLfopSSOwDpvHgwQN8+eWX+O9//4vLly/Dzs4OAQEBeO211/D222/rlzt16hSmTZuGP/74A3Xq1MELL7yAL774Aq6urk+EwNOnT2Pq1KlITExEQUFBtQaLPnDgABYuXIjTp09DrVajfv36ePbZZzF//nw4ODggNDQUmZmZOHPmDHx8fPTrZWdno127dpDL5UhMTISrq2uVt6nRaLBkyRJ89913SE1NhYeHB0aOHIlPPvnE4LEOhcBjQBgMgbWckCHwyvYYpEcnADIZ7D1d4D+8O1xb+uLihoM4MWOVQBVXzNQ/8PMLivD+sngs++/5Grdla6PAJ28/jemvtoFCYby/io35w88c+sCjTNEfrmfm4fWPY3DwxI0at+Xt6YBVH3bGwB6NBKisfOwDJHU8BoTBewJJ787Zq7iyLQZXth5D0spdiOr/PvIzstDs5Wdh6+4sdnmCu5P7AL1e3ydIAAQAdaEGM5fEYVTkYagLDb/52hxIrQ8kXriD0Jd2ChIAASDzdgEGTT6IhT/8JUh7YpBaHyB6nJSOAb4xhMpVrFLjdkIKGg/sBGfferh958kbkWuru/cL8dyb+/HnhTuCt731YCqKNYfxy6JesLKq3X9nWXIfOJeSjV6v70XOvULB245cXPIarRmvBQretqlZch8wF7dv3670qd06deoIetm1sLCwSg+oeHp66u8plCpLPgZq928oI0hNTcWaNWswfvx4BAcHw8bGBjKZDGPHjhW7NFE4Na4HAFDn5olciXB0Oh1emnnYKAHwoR3R1/DeEtO/T9MYLLEP5NxTI3zCr0YJgA9FLo7DjuhUo7VvSpbYB8xJaGgovL29K/xatGiRoNs8fvx4pdv09vZGWlqaoNutrSz1GOCZwMcsWbIES5cuFbsMUVjZ28DWzUl/H0TzMX3g3rYJbiek4N6VTLHLE8za/13Cvt/TDVonbtMgeHk4QJlVgNCXdlVpna9+OoehzzZG5/b1qlOmKKTSB6Z+cQoZtwx7zVV1+sD4T4+j69NecHepPfe0SaUPmIO4uJI/FGfPnl3peIQNGjTAvn37sH37drzwwgvw8PCo0jZCQ0PLnN6uXTscPHiw0vW9vLyqtB1LIqVjgCHwMR4eHujXrx9CQ0MREhKCffv2YcWKFWKXZRLtI0ehfeSoUtNSo07i1KzvRapIeOnKfExbdMrg9bw8HOBTz9GgdXQ64LWPjuGvrUNhZ1s7DjUp9IG9MWn4cVeKwetVpw/cvKPC5PknsXF+D4O3JxYp9AFz065duyotd+HCBXz//ffo1q1blUNgeVxdXdG7d+8atWGppHQM1I7fTCY0e/bsUt+fPHlSpEpM7+JPB5C6+wTk1lZwbdEIbSYOgaO3OzTqfy6ZdV85FZDLcPStr/TTbFzqYMiRxYj/ZD2ubI8Ro/QqW7oxCffyiky2vZRr97B5/xWMHdzMZNusCSn0gU++/dOk2/vv3sv4eEJ7BPjWNel2q0sKfYCoIlI6Biz+nsCsrCxERkbC398fdnZ2aNiwIaZMmYL8/HyMGzcOMpkMy5cvF7tMs3DvihKZMWeREf0nzq3YiUOvzodHUFN0WvCWfpkTs1bjqdDm8BvSWT+t4+ev49bpC2bf6VUPirF2xyWTb3fFlmSTb7O6LL0P/HE+C6fO3jb5dr/95YLJt1ldlt4HiCojpWPAokNgYmIi2rZti4ULF0KpVKJVq1YoKirCsmXLMHLkSCQnl/xyDgoKErdQM3U7/iIubz0GvyGd4RnSHABQmJuH49NXosNnr8O+nit8+3eE1zOtcWKm6cdOMtTWg1erNRBwTcWdy8If5yt/+4Q5srQ+sEqkMLb2f5dq7bBBltYHiAxlyceAxYbArKwsDBw4EEqlEtOnT0dmZiYSEhKgVCqxYMECREVFIS4uDjKZDIGBtX8YB2M5s3grtMUatJ8xUj8t43AiUncfR7flk9Fx/hs4Pn0l1Dnm/8TU4Tjxbug9IuK2a4p9oOZy7xfizMXqvS/WHFhSH6jNnJycEB4eDicn072ikEpY6jFgsSFw8uTJSE9P1784+9GDJjIyEu3atUNxcTEaN24MZ2fLGvxRSPdTlbi6Mxb1uwXiqQ4t9dPj566Hk58XMqL/RPqhBBErrLo/zhtvSJjKt107zwQCltMHcu+p8fd18cb3Yh+gmmrQoAE++eQTNGjQQOxSJMdSjwGLDIHJycnYsmULPDw8MG/evDKXCQ4OBlD1p7IqEhMTg969e8Pb2xu2trbw8fEpdbm5tvtr6TZoNaX/AipWqZF37RZykq+LWFnVqQs1SLqcI9r2xQygQrCEPpAo8pm42hwCAcvoA7WdWq1GWlpapcPJkHFY4jFgkU8Hb9q0CVqtFhEREeWOsG5vbw9AmBCYk5ODtm3b4q233sJTTz2F9PR0zJs3D506dcK5c+dKvcDbECEhIVAqlRUuY62TYw7CqtX+Q8oTSVjnPazc+XdTMrDeZ2S584XULKAZimTCv6BbI3OExjWy3PkPx4Arj5eHvf6/aQdHlbtceWPIpVzNrHY/eJwQ+/xx5tQHHiVkf1BZtwKcyv4Mle1/oOZ9YOOWndi/5iUDKi4f+4BlGDas/H/vsty6dQubNm3CSy+9hKeeeqpK6wwdOrQ6pZk9HgP/8PLyQnx8fLXatsgQGB0dDQDo2bNnucukp5cMFixECBw0aBAGDRpUalpoaCiaN2+Obdu2YcqUKdVqV6lUIiMjo8JlbGQKoPaMRVypG5k3UKgzwg30Vq6Aa/mzqzoGnJVCbvBYcQCg08kq3ZdVZWn7vCKC9oe6DYBybqUyZAzA6vaBBw+K2AeqwWg/E8xAfn6+QcurVCr9f6u6rlB9ztzwGBCGRYbAa9euAQB8fX3LnF9cXIzY2FgAwoTAsri7uwMArKyq/09clZHarXVywIL+SK7vXd9IZwIdUNE5VWVWxW+P8PKwh5VCjmKNFsoslcHtyGVaeAt0H4+l7fOKCNkfVNbOKO+CcGX7H6h5H7Czs4I7+4DBjPUzwRw4Ohr2x8TD4Gdvb1/ldS31/kEeA/+oyVtdLDIEPjxQHv7V9LgtW7YgKysLTk5O8PPzE2y7Go0GWq0W165dw6xZs+Dl5YURI0ZUu72qnN4tKniAjU1HV3sbNbH/xTmCt3kp5RKsHYR/xZZGo0XdZ35Cvqq4zPmVvQYs7eAo+NRzhDJLhYbPbTZ4++3b+CJ+s2GvqiuPmPv8ccboA48Ssj/EJ90udz9X5TVwNe0D4/81HIsjvzR4vbKwD1iGh6+Nq6oLFy5g06ZN6Nu3L1q0aFGldZYsWVKNyswfjwFhWOSDIQ9TcULCk0/qZGZmYsaMGQCAwMBAyGQywbbbvXt32NjYICAgAImJiYiOjoanp6dg7VP1KRRyBLVwF237wa3E2zaVaBvgBmsr8X7kBbeq2Wu+iIiEZpEh8OH7EBcsWIBLl/55Q0RcXBx69uyJrKySp/SEHiR6zZo1OHnyJDZt2gRnZ2f06dMH16/XzieGLFFwSzFDIAOA2GxtFGjjX8GNoUbGPkA11aJFC5w+fbrKZwGJKmORITAyMhLu7u5IS0tD69at0bZtWwQEBCAsLAxNmjRBr169AJR9P2BsbCw8PDz0X199VfJewM2bN5ea/vCewkc1b94cHTp0wKhRo3Do0CHcv38fX3zxhXE/LFXZwB6NRNmuXC5D3y7CPBlMNTOwuzh9wL+RM5o3rh3vDiYi6bDIEOjj44OYmBj0798fdnZ2SE1NhZubG1atWoWoqCj92cGyQmBRURHu3Lmj/3p4X6FarS41vaioqMIaXFxc4O/vj7///lv4D0jV0iusPpr5mv4X8aAejdDQq+yhisi03nixORQK4W4BqaoJI1pALjf9dsmyXLt2Df/617/0Dz8S1ZRFPhgCAC1btsSePXuemJ6Xl4fU1FTI5XK0adPmifk9evSATqer8fZv3bqFixcvokOHDjVui4Qhl8swYUQLTF14yqTbnTCCl27MhY+XIwb38MX2Q6km26adrQJjBzcz2fbIcqlUKpw7d67chx6JDGWxIbA8SUlJ0Ol0aNasGRwcKh4ctqpGjx4Nf39/BAUFwcXFBSkpKVi8eDGsrKwwdepUQbZBwnhreAus/PkCLl27a5Lt9e3ig+c6WeYQDbXVZ5ODERWTBnWhacae+/DNILjVtTXJtoiIDGGRl4MrcvbsWQDCjg/YsWNH7N27F6+99hr69u2LhQsXomvXrkhMTIS/v79g26mqoOkjoLC1BgB0WTIRrd7ob3AbjcJD4fl0QJWW9R/RAy9f/BED9i/QT+sfNQ+DDi7EoIMLMfjwVxibuRWuLUvGbQz58BUMi1+JXj+U/wYPY7G3s8IPn3aFgA+Fl8u5jjW++6iLoE+gl8cc9nnbyS9gaMxSvJrxMxqFh5ZavqJ5Xb+ZgpFnViPsk7EG11wdLfxc8J9JwSbZVnArD0S+FmiSbZl7H+i8+G0MOvQlBh1ciAH75sO7S1v9PFP3AbJM5n4MPOTVuQ3GpG8pVZ9YvxcZAgUwadIknD59Gjk5OVCpVLh06RJWrVpV7mDVxhb0738OhOpqFB4Gz+CqX8JSHk/CnvCZ+u+j+s/CrudmYNdzM5C46GfkJF9DTnLJfSzxn/6ExC+21Ki+mngmqB5mjTNs/yuzCpB+M79Kgwo/9M37z8DHy/A3S1SHOezzzGN/4WDEZ7h58sl3Zlc0L2biUlxcf7B6RVfT1Fdao3uIYQOsGtoH6jhY48f/dIOViYalMfc+EDdnHXY9Ox27npuB4zNWocd30/DwrzEx+gBZHnM/BgDA2skBwR9EIOPQn6Wmi/V7UXKXg40RAs1JpwVvAgD67vgUOo0WBTdzUDegAfr8PAeO9d2RezENR8cvhraoGDIrBZ6OHAWvLm2gsLbC3SuZOBG5Cp7BzdCwTwi8uwXCf0QPJP+wH+kH/0C3le/C2skeClsbKGPP4dTstUAV7p8MePlZpGyKNvZHN8ink4KhvKPC2v9dqnxhVG0w4UctnBaG0QNMcxbYXPZ5VmL5D0FVNE8MCoUcO5c+h2ff2Ic/zmdVaR1D+oC9nQK7lvVGaxMNSVMb+kDhvX/Cs42TMLfiSI23tzfmzp0Lb29vsUsxO7XhGACAjp+Pw19LtsG3n3k8LyC5EPjwvcKW6sTM79B8TB/sG/IhCu8VoMuSiXBr7Yf9w+ZAqy5G+P8+gW//Dri6IxZt3h6MIpUaUf1mAQACpw5D+5kv4dT73yPtQDyyk1JxfnUUAEBha41DY+ajuOABZHI5eq2bCb9Bz+DqzieHynmUQ313eHVqhZh3lhn9sxtCLpdh9ZwuqGNvhWX/PS9ou0tndsSkl1oJ1mZlzG2f1xZ1nWxwaHVfDJ5yEEfjK3qpoGFcnGyw++vn0OXp6r/KyVC1pQ8Evx8B34GdYFvXEYdfX1SlPyLpH3Xr1kXfvn3FLsMs1YZjwLd/R+i0OqQdiGcIJNO5vu8UNKpCACV/pTg1Lvnl1Cg8FDZODmj8/51RbmOFvLTbZTcilyF49mjUC2sByGSw93BG7oXrlR4I/iN7Iu3gH1Bn3xfuAwlELpdh6Xud0KtDfbz1SSxu3qnZE3ctm7jgh0+6okPgUwJVWH1i7vPapK6TDX77ri8W/XgWc1YkoLCoZi8j7dfVB9991AUN6pnmNoCKmGMf+OPzjfjj843w7toWIR++gr2DZkNbVParHOlJOTk5+O2339C7d2+4uoo38HltYU7HgL2nC9q9+6LRXzFnKIZACdCo/xnTUKfRQq5QAABkMhlOzV6LG0fPVNpG67cGwt6jLqL6z4JGXYTQj1+Fwq7yey8CRvbEife+q37xJjC4py+6Pu2FD5bFY/3uv1HwwLBfSu4utpg4shVmvR4IO1vzOKTE3Oe1jZWVHO+Na4dBPRohcnEc9sakGXyCqplvXXzwRju8MtDfJA8CVYU594HMmLOwrmMP15aNcOevKzVuTypu3ryJhQsXom3btgyBVWBOx4B7YBPY13PFoN8WAQBs3ZzQsE8IbN2d8ef8TQa3JxTJPRgiBYX3C2DtXPk9N9f3n0arNwdAYW8DAFDY28Clmc//t6GC9SP37djUdYTqVg406iLYe7qg8YBOlbbv3aUtZFZy3Dj6VzU/iem41bXFyg87I+O3UVg6syNC23jAxrr8w8PR3go9Q72x/rNuSD84CnMnPi1qADSXfV6btWrqij3L++By1AjM/FcgWvjVrfApck9XOwx7rjEOfheOC7texJhBAaIGQHPuAzIrhf4sDAB4BPnDzt0Z96/drFZ7RGUx52Mg/VACtgS+jq1hb2Nr2Nu4tuckzizeKmoABHgm0CIlfbsbfTZ/BI1KjYKbOeUud3b5DrSbZo0BUfP0Zz7OfbMDuZfScXnrUXRZOgmNwsNwYd1+JH+/Fz1WT8fgI4uhUmbjRkzlwS7gpV74e/PhWnXfj4uzLSZHtMbkiNYoLNLgXEoOkq/mokBVDLlchjoO1ghs5oZmvs5QKMznbyhz2OeB776I5q/0gZ27M55pMQEdPnsdu/rMgPrOvQrnmRs/HyfMfzcU898Nxf38QvyZfAdXM/KgLtLA2koOt7q2eLqlO3zqOZrNWT/AvPtAccEDdFk6CTbODtAVa1BUoMbhN75E4d18If8JSOLM+Rgwx591ACDTCfF6DBJNUcEDbGw6WtQa/Ef0QKO+YYh+rervSS5vnYjLG2DtYCd0iRaltu7zigRNHwGbug44/dG6UtPZH8rGPmAZ4uLiDFr+woULGDNmDNavX48WLar2JqLQ0LLHqqvtLPEYEOP3ovmcyqBaq/hBIdxaNy41YGZFQj58BW3fGQp1bp6RKyNjMXSfV6TrN1PQ5MWuKLzPV2HVJuwDpufg4IAOHToI9rYrqhkhjwGxfi/yTGAtZw5/DQnJkv/qF4ql7fOKsD+UjX3AMhh6JrA6eCaw9uOZQCIiIonTaDTIy8uDRmOa916T5WMIJCIiqgVSUlLQq1cvpKSkiF0KWQg+HVzLWdnbIuLyBrHLEIyVva3YJZg9S9vnFWF/KBv7AEkdjwGB2jZay2QSMpnMYu+XobJxnxP7AEkdjwFh8HIwERERkQQxBBIRERFJEC8HExER1QL+/v749ddf4eTkJHYpZCEYAomIiGoBKysruLq6il0GWRBeDiYiIqoF0tPTMX36dKSnp4tdClkIhkAiIqJaIC8vDzExMcjL4ys3SRgMgUREREQSxBBIREREJEEMgUREREQSJNPpdDqxiyAiIqKK3bx5Exs3bkRERATq1asndjlkARgCiYiIiCSIl4OJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiCGAKJiIiIJIghkIiIiEiC/g8koDmyNMhL8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 807.394x367.889 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_circuits[\"B\"].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77655d0a6a0d1c81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:00.944952Z",
     "start_time": "2024-02-15T03:35:00.935773Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling overhead: 9.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Sampling overhead: {np.prod([basis.overhead for basis in bases])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df0c32d-bfd9-407f-b867-d08632f53764",
   "metadata": {},
   "source": [
    "### Sub Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3327e421fea3182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:05.890185Z",
     "start_time": "2024-02-15T03:35:05.693811Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import generate_cutting_experiments\n",
    "\n",
    "subexperiments, coefficients = generate_cutting_experiments(\n",
    "    circuits=sub_circuits, observables=sub_observables, num_samples=np.inf\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "560e4327-900d-4758-b747-7ae311fe2902",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# subexperiments\n",
    "len(subexperiments[\"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5cb2b2aa32d6d16b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-15T03:35:14.153045Z",
     "start_time": "2024-02-15T03:35:14.062981Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1060.9x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"A\"][0].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a0e854a-f742-4b19-8367-8155a54ab2b5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1311.74x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subexperiments[\"B\"][3].draw(\"mpl\", style=\"iqp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cce6334-c721-40fc-b46e-f720eae969c0",
   "metadata": {},
   "source": [
    "## Neural Network Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2504dedb-a5f4-4d4e-b75d-1b0bed7615e4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import SamplerQNN, NeuralNetwork\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from typing import Callable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ca6e83e-5a98-4f06-9089-c20523852013",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# sampler = Sampler(backend_options={\"simulator\": local_sim}, transpile_options={\"optimization_level\": 3})\n",
    "sampler = Sampler(backend_options={\"simulator\": online_sim})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c252b4-15d2-44e7-a577-329c684c5c78",
   "metadata": {},
   "source": [
    "### Sampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e55d30c-e279-4f58-bfe6-e9a6d9c7d446",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from primitives.custom_sampler_qnn import CustomSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5926c303-49fe-4052-b910-9e0763014904",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5aefce4e-f3fb-4694-91a3-a5161d74aee1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]\n",
    "# final_circuits[0].draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8853b9e4-a866-43f5-a908-e1f0d968c765",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sampler_qcnn = CustomSampler(\n",
    "    circuits=final_circuits, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"A\"].parameters,\n",
    "    input_gradients=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f60076a5-2776-4eb9-b316-5e0b71ace948",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 6.814657211303711\n"
     ]
    }
   ],
   "source": [
    "weights_A = algorithm_globals.random.random(7)\n",
    "forward_output = sampler_qcnn.forward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f534f8b7-9104-4101-9646-8e0760fea432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16)\n",
      "537\n",
      "Output of the forward pass for first sample: \n",
      "[[0.3046875  0.6953125  0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.31054688 0.68945312 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.10351562 0.06835938 0.20117188 0.62695312 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.10351562 0.06738281 0.20605469 0.62304688 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.29785156 0.70214844 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.328125   0.671875   0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# forward output is a dictionary of 6 subex_circ items.\n",
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output[0].shape}\")\n",
    "print(len(forward_output[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d59acee2-9e90-4105-92bd-ee4801a2db86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Backward Pass: %s seconds 153.4615454673767\n"
     ]
    }
   ],
   "source": [
    "input_grad, weights_grad = sampler_qcnn.backward(\n",
    "    input_data=x_train_A,\n",
    "    weights=weights_A\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9b081ccf-6d7b-4d8b-bece-61518575b4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16, 7)\n",
      "Output of the backward pass for first sample for first subexperiment circuit: \n",
      "[[[ 0.953125    0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.953125    0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.92919922  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.92919922  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[-0.25        0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.01953125  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 1.19677734  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.96630859  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[-0.26953125  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.05664062  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 1.18505859  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.97216797  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.87353516  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.87353516  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]\n",
      "\n",
      " [[ 0.89453125  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [-0.89453125  0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.        ]]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {weights_grad[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7a3765-0f3f-434b-bbad-adb88c863fc7",
   "metadata": {},
   "source": [
    "#### For subexperiments[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "476fd0f1-1156-4a1a-96a8-17a2c1812c49",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4cb1766-d548-4c35-b0c5-c612372e0d7c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sampler_qcnn2 = CustomSampler(\n",
    "    circuits=final_circuits2, \n",
    "    sampler=sampler,\n",
    "    input_params=feature_params.params,\n",
    "    weight_params=sub_circuits[\"B\"].parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "35b3ceb5-5e16-4f30-b89d-56a5651c7f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 24.65860366821289\n"
     ]
    }
   ],
   "source": [
    "weights_B = algorithm_globals.random.random(8)\n",
    "forward_output2 = sampler_qcnn2._forward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc2aef81-e8c6-4b1b-a563-2dd41b3e27d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16)\n",
      "537\n",
      "Output of the forward pass for first sample: \n",
      "[[0.60839844 0.39160156 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.6015625  0.3984375  0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.84667969 0.15332031 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.33691406 0.66308594 0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.05859375 0.08300781 0.53027344 0.328125   0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.05273438 0.08691406 0.5390625  0.32128906 0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_A)} samples: {forward_output2[0].shape}\")\n",
    "print(len(forward_output2[0]))\n",
    "print(f\"Output of the forward pass for first sample: \\n{np.array([forward_output2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "734f3787-0de1-44f5-8c66-c7f1425d9f11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Backward Pass: %s seconds 587.3339047431946\n"
     ]
    }
   ],
   "source": [
    "input_grad2, weights_grad2 = sampler_qcnn2._backward(\n",
    "    input_data=x_train_B,\n",
    "    weights=weights_B\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e91322c9-7a6e-4b7c-8c5d-b298dba19b07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output shape for 537 samples: (537, 16, 8)\n",
      "Output of the backward pass for first sample for first subexperiment circuit: \n",
      "[[[ 0.26416016  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [-0.26416016  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
      "    0.          0.          0.        ]]\n",
      "\n",
      " [[ 0.29003906  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [-0.29003906  0.          0.          0.          0.\n",
      "    0.          0.          0.        ]\n",
      "  [ 0.          0.          0.          0.          0.\n",
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      "\n",
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     ]
    }
   ],
   "source": [
    "print(f\"Output shape for {len(x_train_B)} samples: {weights_grad2[0].shape}\")\n",
    "print(f\"Output of the backward pass for first sample for first subexperiment circuit: \\n{np.array([weights_grad2[i][0] for i in range(6)])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49fdb89d-3094-44a5-8aae-078836e29602",
   "metadata": {},
   "source": [
    "## Loss and Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "73d6684f-d62d-452e-a503-5676541f9a4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.utils.loss_functions import L2Loss\n",
    "from qiskit_algorithms.optimizers import COBYLA, SPSA, GradientDescent\n",
    "from loss_optimization.objective_func import CustomMultiClassObjectiveFunction\n",
    "from loss_optimization.custom_cross_entropy_loss import CustomCrossEntropyLoss\n",
    "# from loss_optimization.callback import callback\n",
    "from loss_optimization.optimization import create_objective, minimizer, print_optimizer_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a795c0c0-88f0-44f3-9de9-dc333d2d6466",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def callback_spsa(nfev=None, params=None, fval=None, stepsize=None, accepted=None):\n",
    "    \"\"\"\n",
    "    nfev: the number of function evals\n",
    "    params: the current parameters\n",
    "    fval: the current function value\n",
    "    stepsize: size of the update step\n",
    "    accepted: whether the step was accepted (not used for )\n",
    "    \"\"\"\n",
    "    # objective_func_vals = loss\n",
    "    global objective_func_vals\n",
    "\n",
    "    if (nfev % 3) == 0:\n",
    "        objective_func_vals.append(fval)\n",
    "        print(f\"SPSA Epoch {len(objective_func_vals)}: {fval:.5f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "00fcbea5-d74a-4227-aa21-ae9fe3892fea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "objective_func_vals = []\n",
    "# loss = L2Loss()\n",
    "loss = CustomCrossEntropyLoss()\n",
    "# optimizer = COBYLA(maxiter=10)\n",
    "optimizer = SPSA(maxiter=5, callback=callback_spsa)\n",
    "# optimizer = GradientDescent(maxiter=2) # This doesn't work yet. The gradient shape doesn't match."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba6ba3e-791d-4f6a-92d8-116d7e03d4d6",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc3ab8f7-1902-4c28-bcd3-13035cd4c0d5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "initial_point = np.random.random((7,))\n",
    "function = CustomMultiClassObjectiveFunction(x_train_A, new_y_train, sampler_qcnn, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8269a9e8-beae-4ee3-b9da-2f727af3b1f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 6.043295860290527\n",
      "Running Time for Forward Pass: %s seconds 6.067039728164673\n",
      "Running Time for Forward Pass: %s seconds 6.251935243606567\n",
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      "Running Time for Forward Pass: %s seconds 6.226389169692993\n",
      "Running Time for Forward Pass: %s seconds 6.722661972045898\n",
      "SPSA Epoch 1: -6.82677\n",
      "Running Time for Forward Pass: %s seconds 6.36667275428772\n",
      "Running Time for Forward Pass: %s seconds 6.766683340072632\n",
      "Running Time for Forward Pass: %s seconds 6.361257076263428\n",
      "SPSA Epoch 2: -7.02339\n",
      "Running Time for Forward Pass: %s seconds 6.476662874221802\n",
      "Running Time for Forward Pass: %s seconds 6.777965545654297\n",
      "Running Time for Forward Pass: %s seconds 6.016891956329346\n",
      "SPSA Epoch 3: -7.08505\n",
      "Running Time for Forward Pass: %s seconds 6.251931667327881\n",
      "Running Time for Forward Pass: %s seconds 6.060129642486572\n",
      "Running Time for Forward Pass: %s seconds 6.691417694091797\n",
      "SPSA Epoch 4: -7.09596\n",
      "Running Time for Forward Pass: %s seconds 6.504234313964844\n",
      "Running Time for Forward Pass: %s seconds 6.637292385101318\n",
      "Running Time for Forward Pass: %s seconds 6.903577089309692\n",
      "SPSA Epoch 5: -7.10871\n",
      "{   'fun': -7.108707970295499,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 1.92460819,  1.08123869,  0.68230983,  1.54001489, -0.04515279,\n",
      "        0.23760917,  0.78371237])}\n",
      "Running Time for optimizing Subex-A1: %s seconds 418.5108804702759\n",
      "Running Time for Forward Pass: %s seconds 6.102735996246338\n",
      "Running Time for Forward Pass: %s seconds 6.799691200256348\n",
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      "Running Time for Forward Pass: %s seconds 6.384112119674683\n",
      "Running Time for Forward Pass: %s seconds 6.490422487258911\n",
      "SPSA Epoch 6: -6.82441\n",
      "Running Time for Forward Pass: %s seconds 5.9721839427948\n",
      "Running Time for Forward Pass: %s seconds 6.238009929656982\n",
      "Running Time for Forward Pass: %s seconds 6.741623878479004\n",
      "SPSA Epoch 7: -7.02456\n",
      "Running Time for Forward Pass: %s seconds 6.216174364089966\n",
      "Running Time for Forward Pass: %s seconds 6.436516046524048\n",
      "Running Time for Forward Pass: %s seconds 6.320688486099243\n",
      "SPSA Epoch 8: -7.08060\n",
      "Running Time for Forward Pass: %s seconds 6.580101013183594\n",
      "Running Time for Forward Pass: %s seconds 6.000468969345093\n",
      "Running Time for Forward Pass: %s seconds 7.359799861907959\n",
      "SPSA Epoch 9: -7.10374\n",
      "Running Time for Forward Pass: %s seconds 7.4613189697265625\n",
      "Running Time for Forward Pass: %s seconds 6.437783718109131\n",
      "Running Time for Forward Pass: %s seconds 6.276983976364136\n",
      "SPSA Epoch 10: -7.10238\n",
      "{   'fun': -7.102378315535572,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 1.92129285, -0.05937963,  1.06766458,  0.8896559 ,  1.1701145 ,\n",
      "        0.62219548,  1.17857829])}\n",
      "Running Time for optimizing Subex-A2: %s seconds 419.42446088790894\n",
      "Running Time for Forward Pass: %s seconds 6.69395112991333\n",
      "Running Time for Forward Pass: %s seconds 6.273415565490723\n",
      "Running Time for Forward Pass: %s seconds 6.297377824783325\n",
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      "Running Time for Forward Pass: %s seconds 5.844584941864014\n",
      "Running Time for Forward Pass: %s seconds 6.47004508972168\n",
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      "Running Time for Forward Pass: %s seconds 5.600985527038574\n",
      "Running Time for Forward Pass: %s seconds 6.32520055770874\n",
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      "Running Time for Forward Pass: %s seconds 6.599343776702881\n",
      "Running Time for Forward Pass: %s seconds 6.409280300140381\n",
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      "Running Time for Forward Pass: %s seconds 8.381065130233765\n",
      "Running Time for Forward Pass: %s seconds 6.812044858932495\n",
      "Running Time for Forward Pass: %s seconds 6.57581901550293\n",
      "Running Time for Forward Pass: %s seconds 6.321197032928467\n",
      "Running Time for Forward Pass: %s seconds 6.99496603012085\n",
      "Running Time for Forward Pass: %s seconds 6.690901041030884\n",
      "Running Time for Forward Pass: %s seconds 6.25628137588501\n",
      "Running Time for Forward Pass: %s seconds 6.473351240158081\n",
      "Running Time for Forward Pass: %s seconds 6.687673807144165\n",
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      "Running Time for Forward Pass: %s seconds 6.375327825546265\n",
      "Running Time for Forward Pass: %s seconds 5.930993556976318\n",
      "Running Time for Forward Pass: %s seconds 6.538464307785034\n",
      "Running Time for Forward Pass: %s seconds 6.352737188339233\n",
      "Running Time for Forward Pass: %s seconds 6.785691499710083\n",
      "Running Time for Forward Pass: %s seconds 5.751980304718018\n",
      "Running Time for Forward Pass: %s seconds 6.2655298709869385\n",
      "Running Time for Forward Pass: %s seconds 6.349684715270996\n",
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      "Running Time for Forward Pass: %s seconds 6.388166666030884\n",
      "Running Time for Forward Pass: %s seconds 6.33011269569397\n",
      "Running Time for Forward Pass: %s seconds 6.196053981781006\n",
      "Running Time for Forward Pass: %s seconds 6.3081042766571045\n",
      "Running Time for Forward Pass: %s seconds 6.693129062652588\n",
      "Running Time for Forward Pass: %s seconds 5.955363750457764\n",
      "Running Time for Forward Pass: %s seconds 6.4122302532196045\n",
      "Running Time for Forward Pass: %s seconds 7.249831438064575\n",
      "Running Time for Forward Pass: %s seconds 6.450284481048584\n",
      "SPSA Epoch 11: -3.59083\n",
      "Running Time for Forward Pass: %s seconds 8.338863849639893\n",
      "Running Time for Forward Pass: %s seconds 6.2194836139678955\n",
      "Running Time for Forward Pass: %s seconds 6.752431869506836\n",
      "SPSA Epoch 12: -3.97992\n",
      "Running Time for Forward Pass: %s seconds 6.233137130737305\n",
      "Running Time for Forward Pass: %s seconds 6.520797252655029\n",
      "Running Time for Forward Pass: %s seconds 6.360280752182007\n",
      "SPSA Epoch 13: -4.57923\n",
      "Running Time for Forward Pass: %s seconds 6.239372491836548\n",
      "Running Time for Forward Pass: %s seconds 6.770793199539185\n",
      "Running Time for Forward Pass: %s seconds 6.677255868911743\n",
      "SPSA Epoch 14: -4.58439\n",
      "Running Time for Forward Pass: %s seconds 6.1736860275268555\n",
      "Running Time for Forward Pass: %s seconds 6.216546535491943\n",
      "Running Time for Forward Pass: %s seconds 6.573373317718506\n",
      "SPSA Epoch 15: -5.32209\n",
      "{   'fun': -5.322093173787746,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 2.27158872,  0.70752877,  0.24770143,  0.67590738,  0.75952765,\n",
      "       -0.5494493 ,  1.30616798])}\n",
      "Running Time for optimizing Subex-A3: %s seconds 421.83006739616394\n",
      "Running Time for Forward Pass: %s seconds 6.523826360702515\n",
      "Running Time for Forward Pass: %s seconds 6.77744722366333\n",
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      "Running Time for Forward Pass: %s seconds 6.56418514251709\n",
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      "Running Time for Forward Pass: %s seconds 5.849802017211914\n",
      "Running Time for Forward Pass: %s seconds 6.368067264556885\n",
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      "Running Time for Forward Pass: %s seconds 6.514101505279541\n",
      "Running Time for Forward Pass: %s seconds 6.347651720046997\n",
      "Running Time for Forward Pass: %s seconds 6.428186893463135\n",
      "Running Time for Forward Pass: %s seconds 6.415517091751099\n",
      "Running Time for Forward Pass: %s seconds 6.368345022201538\n",
      "Running Time for Forward Pass: %s seconds 8.137380599975586\n",
      "Running Time for Forward Pass: %s seconds 6.14415168762207\n",
      "Running Time for Forward Pass: %s seconds 6.306982517242432\n",
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      "Running Time for Forward Pass: %s seconds 6.444845199584961\n",
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      "Running Time for Forward Pass: %s seconds 7.3151023387908936\n",
      "Running Time for Forward Pass: %s seconds 6.360072612762451\n",
      "Running Time for Forward Pass: %s seconds 6.5509631633758545\n",
      "Running Time for Forward Pass: %s seconds 6.528831243515015\n",
      "Running Time for Forward Pass: %s seconds 6.422200918197632\n",
      "Running Time for Forward Pass: %s seconds 6.195278167724609\n",
      "Running Time for Forward Pass: %s seconds 6.47956919670105\n",
      "Running Time for Forward Pass: %s seconds 6.197718381881714\n",
      "Running Time for Forward Pass: %s seconds 6.064504623413086\n",
      "Running Time for Forward Pass: %s seconds 6.487624645233154\n",
      "Running Time for Forward Pass: %s seconds 7.5721375942230225\n",
      "Running Time for Forward Pass: %s seconds 6.357293128967285\n",
      "SPSA Epoch 21: -6.84025\n",
      "Running Time for Forward Pass: %s seconds 6.394328594207764\n",
      "Running Time for Forward Pass: %s seconds 6.0866076946258545\n",
      "Running Time for Forward Pass: %s seconds 6.498124837875366\n",
      "SPSA Epoch 22: -7.03094\n",
      "Running Time for Forward Pass: %s seconds 6.355143070220947\n",
      "Running Time for Forward Pass: %s seconds 6.625967979431152\n",
      "Running Time for Forward Pass: %s seconds 6.321792840957642\n",
      "SPSA Epoch 23: -7.08372\n",
      "Running Time for Forward Pass: %s seconds 6.595130205154419\n",
      "Running Time for Forward Pass: %s seconds 7.145542860031128\n",
      "Running Time for Forward Pass: %s seconds 7.323233366012573\n",
      "SPSA Epoch 24: -7.09910\n",
      "Running Time for Forward Pass: %s seconds 6.9267027378082275\n",
      "Running Time for Forward Pass: %s seconds 5.91550612449646\n",
      "Running Time for Forward Pass: %s seconds 6.476846694946289\n",
      "SPSA Epoch 25: -7.11232\n",
      "{   'fun': -7.112317442844547,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 1.92134987,  1.09128987,  0.45845359,  1.60620219, -0.36150858,\n",
      "        1.37055503,  0.98929593])}\n",
      "Running Time for optimizing Subex-A5: %s seconds 420.52907156944275\n",
      "Running Time for Forward Pass: %s seconds 6.403785943984985\n",
      "Running Time for Forward Pass: %s seconds 6.5364861488342285\n",
      "Running Time for Forward Pass: %s seconds 6.341782331466675\n",
      "Running Time for Forward Pass: %s seconds 5.975269079208374\n",
      "Running Time for Forward Pass: %s seconds 6.424525260925293\n",
      "Running Time for Forward Pass: %s seconds 6.072812795639038\n",
      "Running Time for Forward Pass: %s seconds 6.237454891204834\n",
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      "Running Time for Forward Pass: %s seconds 6.735600709915161\n",
      "Running Time for Forward Pass: %s seconds 6.258676767349243\n",
      "Running Time for Forward Pass: %s seconds 6.67939829826355\n",
      "Running Time for Forward Pass: %s seconds 6.448937177658081\n",
      "Running Time for Forward Pass: %s seconds 6.428893089294434\n",
      "Running Time for Forward Pass: %s seconds 7.462273120880127\n",
      "Running Time for Forward Pass: %s seconds 6.578388452529907\n",
      "Running Time for Forward Pass: %s seconds 6.69843316078186\n",
      "Running Time for Forward Pass: %s seconds 6.886230230331421\n",
      "Running Time for Forward Pass: %s seconds 6.05733585357666\n",
      "Running Time for Forward Pass: %s seconds 6.4706597328186035\n",
      "Running Time for Forward Pass: %s seconds 6.407244682312012\n",
      "Running Time for Forward Pass: %s seconds 6.5351293087005615\n",
      "Running Time for Forward Pass: %s seconds 6.364525318145752\n",
      "Running Time for Forward Pass: %s seconds 7.374361515045166\n",
      "Running Time for Forward Pass: %s seconds 6.3491480350494385\n",
      "Running Time for Forward Pass: %s seconds 8.408715724945068\n",
      "Running Time for Forward Pass: %s seconds 7.115978717803955\n",
      "Running Time for Forward Pass: %s seconds 6.3657238483428955\n",
      "Running Time for Forward Pass: %s seconds 6.540362358093262\n",
      "Running Time for Forward Pass: %s seconds 6.760977506637573\n",
      "Running Time for Forward Pass: %s seconds 7.047267913818359\n",
      "Running Time for Forward Pass: %s seconds 6.585832357406616\n",
      "Running Time for Forward Pass: %s seconds 7.429126024246216\n",
      "Running Time for Forward Pass: %s seconds 6.862270355224609\n",
      "Running Time for Forward Pass: %s seconds 6.0817835330963135\n",
      "Running Time for Forward Pass: %s seconds 6.5673301219940186\n",
      "Running Time for Forward Pass: %s seconds 6.559921741485596\n",
      "Running Time for Forward Pass: %s seconds 6.507153034210205\n",
      "Running Time for Forward Pass: %s seconds 6.525354623794556\n",
      "Running Time for Forward Pass: %s seconds 6.560556650161743\n",
      "Running Time for Forward Pass: %s seconds 6.775709390640259\n",
      "Running Time for Forward Pass: %s seconds 7.091947317123413\n",
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      "Running Time for Forward Pass: %s seconds 6.974715709686279\n",
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      "Running Time for Forward Pass: %s seconds 6.235037565231323\n",
      "Running Time for Forward Pass: %s seconds 6.480703592300415\n",
      "Running Time for Forward Pass: %s seconds 6.941289663314819\n",
      "Running Time for Forward Pass: %s seconds 6.5524516105651855\n",
      "Running Time for Forward Pass: %s seconds 6.752203464508057\n",
      "Running Time for Forward Pass: %s seconds 6.930330991744995\n",
      "Running Time for Forward Pass: %s seconds 8.335107564926147\n",
      "Running Time for Forward Pass: %s seconds 6.429394006729126\n",
      "Running Time for Forward Pass: %s seconds 6.291286945343018\n",
      "SPSA Epoch 26: -6.83460\n",
      "Running Time for Forward Pass: %s seconds 6.614579439163208\n",
      "Running Time for Forward Pass: %s seconds 6.169931888580322\n",
      "Running Time for Forward Pass: %s seconds 6.5888512134552\n",
      "SPSA Epoch 27: -7.02253\n",
      "Running Time for Forward Pass: %s seconds 6.2120139598846436\n",
      "Running Time for Forward Pass: %s seconds 6.576163291931152\n",
      "Running Time for Forward Pass: %s seconds 7.482536315917969\n",
      "SPSA Epoch 28: -7.08852\n",
      "Running Time for Forward Pass: %s seconds 6.693866491317749\n",
      "Running Time for Forward Pass: %s seconds 6.421365976333618\n",
      "Running Time for Forward Pass: %s seconds 6.21250057220459\n",
      "SPSA Epoch 29: -7.09933\n",
      "Running Time for Forward Pass: %s seconds 6.888769626617432\n",
      "Running Time for Forward Pass: %s seconds 6.7224507331848145\n",
      "Running Time for Forward Pass: %s seconds 6.2391533851623535\n",
      "SPSA Epoch 30: -7.10227\n",
      "{   'fun': -7.102273693142846,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 1.92371456, -0.61643006, -0.94073727,  0.23853382, -0.62170845,\n",
      "        0.18157099, -0.3815412 ])}\n",
      "Running Time for optimizing Subex-A6: %s seconds 433.2862458229065\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-A\n",
    "start_time = time.time()\n",
    "optimizer_result_A0 = minimizer(function, function.objective0, function.gradient0, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A1 = minimizer(function, function.objective1, function.gradient1, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A2: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A2 = minimizer(function, function.objective2, function.gradient2, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A3: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A3 = minimizer(function, function.objective3, function.gradient3, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A4: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A4 = minimizer(function, function.objective4, function.gradient4, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A5: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_A5 = minimizer(function, function.objective5, function.gradient5, initial_point, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-A6: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d06fd73-6d5f-4251-b91f-7803a4eb8098",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_A = [optimizer_result_A0, optimizer_result_A1, optimizer_result_A2, optimizer_result_A3, optimizer_result_A4, optimizer_result_A5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62002702-1c92-4bec-942b-7a6cfa2ee79d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New params (The final point of the minimization): [ 1.92460819  1.08123869  0.68230983  1.54001489 -0.04515279  0.23760917\n",
      "  0.78371237]\n",
      "The final value of the minimization: -7.108707970295499\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n",
      "New params (The final point of the minimization): [ 1.92129285 -0.05937963  1.06766458  0.8896559   1.1701145   0.62219548\n",
      "  1.17857829]\n",
      "The final value of the minimization: -7.102378315535572\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n",
      "New params (The final point of the minimization): [ 2.27158872  0.70752877  0.24770143  0.67590738  0.75952765 -0.5494493\n",
      "  1.30616798]\n",
      "The final value of the minimization: -5.322093173787746\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n",
      "New params (The final point of the minimization): [ 1.42734849 -0.46839693  0.8090601   0.0393028   0.78102977  0.26555437\n",
      "  2.01559168]\n",
      "The final value of the minimization: -4.79703674039397\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n",
      "New params (The final point of the minimization): [ 1.92134987  1.09128987  0.45845359  1.60620219 -0.36150858  1.37055503\n",
      "  0.98929593]\n",
      "The final value of the minimization: -7.112317442844547\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n",
      "New params (The final point of the minimization): [ 1.92371456 -0.61643006 -0.94073727  0.23853382 -0.62170845  0.18157099\n",
      " -0.3815412 ]\n",
      "The final value of the minimization: -7.102273693142846\n",
      "The final gradient of the minimization: None\n",
      "The total number of function evaluations: 15\n",
      "The total number of gradient evaluations: None\n",
      "The total number of iterations: 5\n"
     ]
    }
   ],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-A\n",
    "for opt_result in optimizer_results_A:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8be438f3-e234-4217-bd1c-7e313b393e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training loss')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(objective_func_vals[:5], label=\"B Subex1\")\n",
    "plt.plot(objective_func_vals[5:10], label=\"B Subex2\")\n",
    "plt.plot(objective_func_vals[10:15], label=\"B Subex3\")\n",
    "plt.plot(objective_func_vals[15:20], label=\"B Subex4\")\n",
    "plt.plot(objective_func_vals[20:25], label=\"B Subex5\")\n",
    "plt.plot(objective_func_vals[25:30], label=\"B Subex6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15b7a7f5-8003-43cd-af0f-90535b6f61bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-6.826768058964233,\n",
       " -7.0233884090274,\n",
       " -7.085045872473956,\n",
       " -7.095961475448374,\n",
       " -7.108707970295499,\n",
       " -6.824414055127897,\n",
       " -7.02455669241284,\n",
       " -7.080599420783098,\n",
       " -7.103738406641011,\n",
       " -7.102378315535572,\n",
       " -3.5908315902344703,\n",
       " -3.9799184373924605,\n",
       " -4.579233589691054,\n",
       " -4.584387370704388,\n",
       " -5.322093173787746,\n",
       " -3.0913816361982716,\n",
       " -3.1512275900645044,\n",
       " -3.9243560472602854,\n",
       " -3.970127734038934,\n",
       " -4.79703674039397,\n",
       " -6.840246910560439,\n",
       " -7.030938658369131,\n",
       " -7.083720655499427,\n",
       " -7.099100147230156,\n",
       " -7.112317442844547,\n",
       " -6.834597301353233,\n",
       " -7.022533992820137,\n",
       " -7.08851584849937,\n",
       " -7.099326829081063,\n",
       " -7.102273693142846]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "621adb10-e0d3-4016-ad2b-615a998ada42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8310166a-4dc2-4ffd-b1cf-2d8f83aa3548",
   "metadata": {},
   "source": [
    "#### Optimizer Result for sub-circuits[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d4424fc-7ce6-41f0-b177-543f3708c0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point2 = np.random.random((8,))\n",
    "function2 = CustomMultiClassObjectiveFunction(x_train_B, new_y_train, sampler_qcnn2, loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60edb6f0-937d-4faf-b4f2-24fc1cd5c731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Time for Forward Pass: %s seconds 22.825103282928467\n",
      "Running Time for Forward Pass: %s seconds 24.66137981414795\n",
      "Running Time for Forward Pass: %s seconds 25.896605491638184\n",
      "Running Time for Forward Pass: %s seconds 28.102799654006958\n",
      "Running Time for Forward Pass: %s seconds 25.2848379611969\n",
      "Running Time for Forward Pass: %s seconds 25.260991096496582\n",
      "Running Time for Forward Pass: %s seconds 25.00179100036621\n",
      "Running Time for Forward Pass: %s seconds 24.78080940246582\n",
      "Running Time for Forward Pass: %s seconds 25.821868181228638\n",
      "Running Time for Forward Pass: %s seconds 24.273216724395752\n",
      "Running Time for Forward Pass: %s seconds 28.617063760757446\n",
      "Running Time for Forward Pass: %s seconds 25.58833408355713\n",
      "Running Time for Forward Pass: %s seconds 24.99327850341797\n",
      "Running Time for Forward Pass: %s seconds 25.47034764289856\n",
      "Running Time for Forward Pass: %s seconds 24.03127360343933\n",
      "Running Time for Forward Pass: %s seconds 25.136029720306396\n",
      "Running Time for Forward Pass: %s seconds 25.44098687171936\n",
      "Running Time for Forward Pass: %s seconds 25.731511116027832\n",
      "Running Time for Forward Pass: %s seconds 25.703096628189087\n",
      "Running Time for Forward Pass: %s seconds 26.04001522064209\n",
      "Running Time for Forward Pass: %s seconds 25.478816032409668\n",
      "Running Time for Forward Pass: %s seconds 25.582245349884033\n",
      "Running Time for Forward Pass: %s seconds 22.971097946166992\n",
      "Running Time for Forward Pass: %s seconds 25.225401878356934\n",
      "Running Time for Forward Pass: %s seconds 28.654420137405396\n",
      "Running Time for Forward Pass: %s seconds 25.704617977142334\n",
      "Running Time for Forward Pass: %s seconds 26.56728482246399\n",
      "Running Time for Forward Pass: %s seconds 25.200394868850708\n",
      "Running Time for Forward Pass: %s seconds 26.323575973510742\n",
      "Running Time for Forward Pass: %s seconds 24.482831716537476\n",
      "Running Time for Forward Pass: %s seconds 26.376904487609863\n",
      "Running Time for Forward Pass: %s seconds 24.685376405715942\n",
      "Running Time for Forward Pass: %s seconds 25.551836490631104\n",
      "Running Time for Forward Pass: %s seconds 24.838248252868652\n",
      "Running Time for Forward Pass: %s seconds 25.87968349456787\n",
      "Running Time for Forward Pass: %s seconds 25.971911191940308\n",
      "Running Time for Forward Pass: %s seconds 22.337077856063843\n",
      "Running Time for Forward Pass: %s seconds 23.832658290863037\n",
      "Running Time for Forward Pass: %s seconds 29.24165964126587\n",
      "Running Time for Forward Pass: %s seconds 25.780790328979492\n",
      "Running Time for Forward Pass: %s seconds 25.920878171920776\n",
      "Running Time for Forward Pass: %s seconds 25.391479969024658\n",
      "Running Time for Forward Pass: %s seconds 25.115037441253662\n",
      "Running Time for Forward Pass: %s seconds 26.080657958984375\n",
      "Running Time for Forward Pass: %s seconds 24.575494527816772\n",
      "Running Time for Forward Pass: %s seconds 28.482722759246826\n",
      "Running Time for Forward Pass: %s seconds 24.794257164001465\n",
      "Running Time for Forward Pass: %s seconds 25.59365963935852\n",
      "Running Time for Forward Pass: %s seconds 25.898110389709473\n",
      "Running Time for Forward Pass: %s seconds 23.396623373031616\n",
      "Running Time for Forward Pass: %s seconds 25.043187618255615\n",
      "Running Time for Forward Pass: %s seconds 23.818976640701294\n",
      "Running Time for Forward Pass: %s seconds 28.054988145828247\n",
      "SPSA Epoch 1: -5.34488\n",
      "Running Time for Forward Pass: %s seconds 24.52505850791931\n",
      "Running Time for Forward Pass: %s seconds 23.68948483467102\n",
      "Running Time for Forward Pass: %s seconds 22.991256952285767\n",
      "SPSA Epoch 2: -6.53063\n",
      "Running Time for Forward Pass: %s seconds 25.879059314727783\n",
      "Running Time for Forward Pass: %s seconds 26.593304872512817\n",
      "Running Time for Forward Pass: %s seconds 26.0562264919281\n",
      "SPSA Epoch 3: -6.49447\n",
      "Running Time for Forward Pass: %s seconds 27.063310861587524\n",
      "Running Time for Forward Pass: %s seconds 25.659598112106323\n",
      "Running Time for Forward Pass: %s seconds 26.898149013519287\n",
      "SPSA Epoch 4: -6.50753\n",
      "Running Time for Forward Pass: %s seconds 25.016974925994873\n",
      "Running Time for Forward Pass: %s seconds 23.37017321586609\n",
      "Running Time for Forward Pass: %s seconds 24.013744115829468\n",
      "SPSA Epoch 5: -6.50683\n",
      "{   'fun': -6.506832782007954,\n",
      "    'jac': None,\n",
      "    'nfev': 15,\n",
      "    'nit': 5,\n",
      "    'njev': None,\n",
      "    'x': array([ 0.92127705,  1.44861498,  1.12481966,  1.31080125,  1.4835467 ,\n",
      "       -0.14597746,  0.08105209,  1.0249137 ])}\n",
      "Running Time for optimizing Subex-B1: %s seconds 1654.113070487976\n",
      "Running Time for Forward Pass: %s seconds 25.204562664031982\n",
      "Running Time for Forward Pass: %s seconds 24.386186361312866\n",
      "Running Time for Forward Pass: %s seconds 26.290399074554443\n",
      "Running Time for Forward Pass: %s seconds 24.397077798843384\n",
      "Running Time for Forward Pass: %s seconds 24.945475816726685\n",
      "Running Time for Forward Pass: %s seconds 22.460588693618774\n",
      "Running Time for Forward Pass: %s seconds 26.230518341064453\n",
      "Running Time for Forward Pass: %s seconds 25.537954807281494\n",
      "Running Time for Forward Pass: %s seconds 25.774527072906494\n",
      "Running Time for Forward Pass: %s seconds 27.612169981002808\n",
      "Running Time for Forward Pass: %s seconds 23.564829349517822\n",
      "Running Time for Forward Pass: %s seconds 23.72862935066223\n",
      "Running Time for Forward Pass: %s seconds 25.15703058242798\n",
      "Running Time for Forward Pass: %s seconds 23.111730337142944\n",
      "Running Time for Forward Pass: %s seconds 25.61606526374817\n",
      "Running Time for Forward Pass: %s seconds 24.768046617507935\n",
      "Running Time for Forward Pass: %s seconds 28.834397077560425\n",
      "Running Time for Forward Pass: %s seconds 26.11172127723694\n",
      "Running Time for Forward Pass: %s seconds 23.443572282791138\n",
      "Running Time for Forward Pass: %s seconds 25.978129625320435\n",
      "Running Time for Forward Pass: %s seconds 26.15309476852417\n",
      "Running Time for Forward Pass: %s seconds 25.558246612548828\n",
      "Running Time for Forward Pass: %s seconds 24.126087427139282\n",
      "Running Time for Forward Pass: %s seconds 25.923846006393433\n",
      "Running Time for Forward Pass: %s seconds 22.43262481689453\n",
      "Running Time for Forward Pass: %s seconds 23.968286752700806\n",
      "Running Time for Forward Pass: %s seconds 25.160929918289185\n",
      "Running Time for Forward Pass: %s seconds 26.434104681015015\n",
      "Running Time for Forward Pass: %s seconds 26.580779790878296\n",
      "Running Time for Forward Pass: %s seconds 25.92988109588623\n",
      "Running Time for Forward Pass: %s seconds 28.60514760017395\n",
      "Running Time for Forward Pass: %s seconds 25.442639350891113\n",
      "Running Time for Forward Pass: %s seconds 26.163167238235474\n",
      "Running Time for Forward Pass: %s seconds 24.86333155632019\n",
      "Running Time for Forward Pass: %s seconds 25.91948890686035\n",
      "Running Time for Forward Pass: %s seconds 25.885522842407227\n",
      "Running Time for Forward Pass: %s seconds 24.40369176864624\n",
      "Running Time for Forward Pass: %s seconds 28.89370632171631\n",
      "Running Time for Forward Pass: %s seconds 25.132203817367554\n",
      "Running Time for Forward Pass: %s seconds 23.981979846954346\n",
      "Running Time for Forward Pass: %s seconds 23.498249769210815\n",
      "Running Time for Forward Pass: %s seconds 25.625789642333984\n",
      "Running Time for Forward Pass: %s seconds 23.50435161590576\n",
      "Running Time for Forward Pass: %s seconds 24.897073984146118\n",
      "Running Time for Forward Pass: %s seconds 28.36266803741455\n"
     ]
    }
   ],
   "source": [
    "# Optimizer result for 0th circuit for sub-circuit-B\n",
    "start_time = time.time()\n",
    "optimizer_result_B0 = minimizer(function2, function2.objective0, function2.gradient0, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B1: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B1 = minimizer(function2, function2.objective1, function2.gradient1, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B2: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B2 = minimizer(function2, function2.objective2, function2.gradient2, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B3: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B3 = minimizer(function2, function2.objective3, function2.gradient3, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B4: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B4 = minimizer(function2, function2.objective4, function2.gradient4, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B5: %s seconds\", running_time)\n",
    "\n",
    "start_time = time.time()\n",
    "optimizer_result_B5 = minimizer(function2, function2.objective5, function2.gradient5, initial_point2, optimizer)\n",
    "end_time = time.time()\n",
    "running_time = end_time - start_time\n",
    "print(\"Running Time for optimizing Subex-B6: %s seconds\", running_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6607356-2e2a-4e70-85ff-f258c2f5cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_results_B = [optimizer_result_B0, optimizer_result_B1, optimizer_result_B2, optimizer_result_B3, optimizer_result_B4, optimizer_result_B5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0896372c-0930-4451-b75b-5edb6733efc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print results from 6 sub-experiments of sub-circuit-B\n",
    "for opt_result in optimizer_results_B:\n",
    "    print_optimizer_results(opt_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38f5b9e-b92e-429d-b715-f734eae3a76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(objective_func_vals[:5], label=\"B Subex1\")\n",
    "plt.plot(objective_func_vals[5:10], label=\"B Subex2\")\n",
    "plt.plot(objective_func_vals[10:15], label=\"B Subex3\")\n",
    "plt.plot(objective_func_vals[15:20], label=\"B Subex4\")\n",
    "plt.plot(objective_func_vals[20:25], label=\"B Subex5\")\n",
    "plt.plot(objective_func_vals[25:30], label=\"B Subex6\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99e6a464-84b8-497f-9e09-d57d746b6346",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed7eacba-18a0-476e-97ba-53acde85b048",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_func_vals.clear()\n",
    "objective_func_vals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8068f82d-6639-4886-a96f-f605d23f34bb",
   "metadata": {},
   "source": [
    "### At this point, the training is complete. \n",
    "We have 12 lists of 7 or 8 parameter values that will now be used to make predictions on the test cases. \n",
    "New subcircuits will be built using these parameter values and then their results will be used to reconstruct expectation values. This process will occur for all the inputs in the test set.\n",
    "\n",
    "\n",
    "Let us think of the training process as a box that we can tune/edit while training on the train set. However, once the training process is complete, the box is locked and provided to the user. The user can now send test data to the box and get its desired output.\n",
    "\n",
    "However, in the case of circuit cutting, instead of having one box, we have multiple boxes. In our case, we have a total of 12 boxes (6 boxes per cut). These 12 boxes are trained independently of each other and locked after the training process is complete. The user, now, instead of sending the test data to one box, splits it into 2 parts that are sent to these 12 boxes. The 12 boxes produce quasi-probability distributions respective to the circuits they implement which are then reconstructed to produce the expectation value according to the test input. This process is repeated for every data point in the test dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1fc3ef-8cef-4326-8ff9-bbe594b32a23",
   "metadata": {},
   "source": [
    "## Reconstruct Expectation Values and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8824ed8a-0db9-4133-adf5-41a31171b8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from circuit_knitting.cutting import reconstruct_expectation_values\n",
    "from qiskit_aer.primitives import Sampler\n",
    "from qiskit.primitives import SamplerResult\n",
    "from loss_optimization.accuracy_score import get_accuracy_score\n",
    "from circuit_cut.reconstruct_exp_val import get_subcircuit_results, get_dict_sampler_results, get_reconstructed_expvals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be3f4e61-4039-40d7-8604-bfbad8adbc6a",
   "metadata": {},
   "source": [
    "### Reconstructing for train accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331db8c-82ab-43a1-b9c7-2f401fc78493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_circuits = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"A\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed0d1504-3faa-483c-b78e-54ef28d13de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_circuits2 = [embedding.compose(subex_circuit, inplace=False) for subex_circuit in subexperiments[\"B\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22d2c629-840b-4d59-af33-90cdeb74649b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_results_A = get_subcircuit_results(x_train_A, final_circuits, optimizer_results_A)\n",
    "train_results_B = get_subcircuit_results(x_train_B, final_circuits2, optimizer_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d812aa1-58ec-4db9-98c5-102d5a7a2611",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 537 train data inputs\n",
    "A_dict = get_dict_sampler_results(x_train_A, subexperiments[\"A\"], train_results_A)\n",
    "B_dict = get_dict_sampler_results(x_train_B, subexperiments[\"B\"], train_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17f6e961-c44f-472c-bbd9-a8989db338ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a7d686f-8dbb-4f07-9ba5-b7dabd1f8eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = np.array([np.sign(expval) for expval in reconstructed_expvals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "307e1bed-0b68-4578-a52c-78974b35a50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# accuracy_score function encodes y_train inside the function. Therefore, pass original y_train here.\n",
    "train_score = get_accuracy_score(y_train, y_predicted)\n",
    "print(f\"The Train Accuracy of the model is: {train_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4897db-5983-4c81-8fdc-7d2fddb947fa",
   "metadata": {},
   "source": [
    "### Reconstructing for test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3c0935-3633-4ce9-af6a-99b4e7715944",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results_A = get_subcircuit_results(x_test_A, final_circuits, optimizer_results_A)\n",
    "test_results_B = get_subcircuit_results(x_test_B, final_circuits2, optimizer_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea59d60c-3e63-4210-b67e-3078362bd26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dict of SamplerResults of 6 sub-circuits A and B for each 231 test data inputs\n",
    "A_dict = get_dict_sampler_results(x_test_A, subexperiments[\"A\"], test_results_A)\n",
    "B_dict = get_dict_sampler_results(x_test_B, subexperiments[\"B\"], test_results_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7c9624b-ab21-4aee-99b7-4749885197dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_expvals = get_reconstructed_expvals(A_dict, B_dict, coefficients, sub_observables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69dded00-c876-44e4-a143-5f5bb4032dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = np.array([np.sign(expval) for expval in reconstructed_expvals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5fd5b7d-5afc-45bc-831b-8e715f7f99af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pass original y_test here.\n",
    "test_score = get_accuracy_score(y_test, y_predicted)\n",
    "print(f\"The Test Accuracy of the model is: {test_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cc8922-c2a7-416e-8039-cac825d48183",
   "metadata": {},
   "source": [
    "## Finishes here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24782bae-1bd5-4f80-94b5-c3145aa5ecf1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "40b0e231-939a-489f-8567-6b973a87fdb2",
   "metadata": {},
   "source": [
    "## Test original circuit for x_test[0] (No need to run rn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aec16e6c-9909-4fbb-84bb-19daa47e7354",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_machine_learning.neural_networks import EstimatorQNN\n",
    "from qiskit_machine_learning.algorithms import NeuralNetworkClassifier\n",
    "from qiskit.primitives import BackendEstimator\n",
    "from qiskit_aer import AerSimulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfb7a3be-ddf5-4d96-9bd0-76d02eac5dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding, features = angle_encoding(8)\n",
    "new_ttn = TTN(num_qubits=8).ttn_simple(complex_structure=False)\n",
    "org_circuit = embedding.compose(new_ttn)\n",
    "# final_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfed65af-ebb2-495b-8258-cb0b5af6c914",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpu_simulator = AerSimulator(device='GPU')\n",
    "simulator = AerSimulator()\n",
    "backend = BackendEstimator(backend=simulator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67c702c-de14-48ee-a04a-e5ae82092c82",
   "metadata": {},
   "outputs": [],
   "source": [
    "observable = SparsePauliOp([\"ZIIIIIII\"])\n",
    "estimator_qnn = EstimatorQNN(\n",
    "    estimator=backend,\n",
    "    circuit=org_circuit,\n",
    "    observables=observable,\n",
    "    input_params=features.params,\n",
    "    weight_params=new_ttn.parameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0b6b000-2d96-4d31-8b4e-525712575671",
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = np.random.random(len(new_ttn.parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "268bba60-b00f-4cda-8217-f5ced8f8d27a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forward pass\n",
    "output = estimator_qnn.forward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {output.shape}\")\n",
    "print(f\"Output of the forward pass for first sample: {output[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "723e885f-c12c-44a6-8224-f1e80f457434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backward pass\n",
    "_, weights_grad = estimator_qnn.backward(x_train, weights)\n",
    "print(f\"Output shape for {len(x)} samples: {weights_grad.shape}\")\n",
    "print(f\"Output of the backward pass for first sample: {weights_grad[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c36d86e1-a036-4137-b27d-90ab4849f897",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.random.random((len(new_ttn.parameters),))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fb2319e-c998-487e-af51-26002fd16e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_y(y):\n",
    "    y_encoded = y.replace({0: -1, 1: 1})\n",
    "    return y_encoded\n",
    "\n",
    "new_y = encode_y(y_test)\n",
    "new_y_train = encode_y(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77f89c6-a938-4068-bfff-08398099255f",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = NeuralNetworkClassifier(\n",
    "    estimator,\n",
    "    optimizer=SPSA(maxiter=10, callback=callback),\n",
    "    initial_point=initial_point,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "370ae6ca-1f60-417a-a7b7-5514280c2bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(x_test.values), len(new_y.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be3c666f-dbde-4091-871b-a0afdf836e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.fit(x_train, new_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "065c196d-eab9-4924-85d1-2ca43c50ea58",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(objective_func_vals)\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.title(\"Training loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b24d698-69c9-4243-8386-c6414619981d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict(x_test.values[17])\n",
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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