docs/tutorials/03_experiments_on_lima_backend.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d32fe45-4b9b-412d-9e7a-f829023285c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "import itertools\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from qiskit import transpile, QuantumCircuit\n",
    "from qiskit.providers.fake_provider import FakeLima\n",
    "from qiskit.primitives import Estimator\n",
    "from qiskit.circuit.random import random_circuit\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "\n",
    "import torch\n",
    "from torch.optim import Adam\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "from torch.nn.functional import dropout\n",
    "\n",
    "from torch_geometric.nn import (\n",
    "    GCNConv, \n",
    "    TransformerConv, \n",
    "    GATv2Conv, \n",
    "    global_mean_pool, \n",
    "    Linear, \n",
    "    ChebConv, \n",
    "    SAGEConv,\n",
    "    ASAPooling,\n",
    "    dense_diff_pool,\n",
    "    avg_pool_neighbor_x\n",
    ")\n",
    "from torch_geometric.data import Data\n",
    "from torch_geometric.loader import DataLoader\n",
    "from torch_geometric.utils import to_dense_adj, to_dense_batch\n",
    "\n",
    "from tqdm.notebook import tqdm_notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from blackwater.data.loaders.exp_val import CircuitGraphExpValMitigationDataset\n",
    "from blackwater.data.generators.exp_val import exp_value_generator\n",
    "from blackwater.data.utils import generate_random_pauli_sum_op\n",
    "from blackwater.library.ngem.estimator import ngem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3460f256-4eed-479e-9372-6448f32485e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from blackwater.data.utils import (\n",
    "    generate_random_pauli_sum_op,\n",
    "    create_estimator_meas_data,\n",
    "    circuit_to_graph_data_json,\n",
    "    get_backend_properties_v1,\n",
    "    encode_pauli_sum_op,\n",
    "    create_meas_data_from_estimators\n",
    ")\n",
    "from blackwater.data.generators.exp_val import ExpValueEntry\n",
    "from blackwater.metrics.improvement_factor import improvement_factor, Trial, Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4be35871-a3d9-499c-8290-ea5481d07f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_aer.primitives import Estimator as AerEstimator\n",
    "\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbc2e0ee-cb0a-4f08-b730-d6c24b3057b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = FakeLima()\n",
    "properties = get_backend_properties_v1(backend)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac3d50d-2fa9-4b82-ab88-7f5b0582aecf",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34e2acd0-c21f-4718-802d-a9beb40c1f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(48, 16, 19)"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depths = list(range(1, 10))\n",
    "\n",
    "all_paths = {}\n",
    "for depth in depths:\n",
    "    # all_paths[depth] = glob.glob(f\"./data/uniform_distr_dataset/post_transpilation_depth/5_qubits/pyg_data/{depth}_**.json\")\n",
    "    all_paths[depth] = glob.glob(f\"./data/pyg_data/{depth}_**.json\")\n",
    "\n",
    "train_paths = []\n",
    "val_paths = []\n",
    "test_paths = []\n",
    "\n",
    "for i, (depth, paths) in enumerate(all_paths.items()):\n",
    "    train_paths += paths[:12]\n",
    "    val_paths += paths[12:16]\n",
    "    test_paths += paths[16:]\n",
    "\n",
    "len(train_paths), len(val_paths), len(test_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "['./data/pyg_data/3_-0.9.json',\n './data/pyg_data/3_-0.8.json',\n './data/pyg_data/3_-1.0.json',\n './data/pyg_data/3_0.1.json',\n './data/pyg_data/5_-0.4.json',\n './data/pyg_data/5_-0.5.json',\n './data/pyg_data/5_-0.9.json',\n './data/pyg_data/5_0.6.json',\n './data/pyg_data/7_0.3.json',\n './data/pyg_data/7_-0.1.json',\n './data/pyg_data/7_1.0.json',\n './data/pyg_data/7_0.4.json',\n './data/pyg_data/9_0.1.json',\n './data/pyg_data/9_-0.3.json',\n './data/pyg_data/9_-0.4.json',\n './data/pyg_data/9_0.6.json']"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_paths"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "import json\n",
    "f = open(all_paths[3][0])\n",
    "\n",
    "data = json.load(f)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "{1: [],\n 2: [],\n 3: ['./data/pyg_data/3_0.8.json',\n  './data/pyg_data/3_1.0.json',\n  './data/pyg_data/3_0.4.json',\n  './data/pyg_data/3_-0.1.json',\n  './data/pyg_data/3_0.5.json',\n  './data/pyg_data/3_0.9.json',\n  './data/pyg_data/3_0.2.json',\n  './data/pyg_data/3_-0.7.json',\n  './data/pyg_data/3_-0.6.json',\n  './data/pyg_data/3_0.3.json',\n  './data/pyg_data/3_-0.5.json',\n  './data/pyg_data/3_0.0.json',\n  './data/pyg_data/3_-0.9.json',\n  './data/pyg_data/3_-0.8.json',\n  './data/pyg_data/3_-1.0.json',\n  './data/pyg_data/3_0.1.json',\n  './data/pyg_data/3_-0.4.json',\n  './data/pyg_data/3_-0.3.json',\n  './data/pyg_data/3_0.6.json',\n  './data/pyg_data/3_0.7.json',\n  './data/pyg_data/3_-0.2.json'],\n 4: [],\n 5: ['./data/pyg_data/5_0.3.json',\n  './data/pyg_data/5_0.2.json',\n  './data/pyg_data/5_-0.1.json',\n  './data/pyg_data/5_0.5.json',\n  './data/pyg_data/5_-0.6.json',\n  './data/pyg_data/5_0.9.json',\n  './data/pyg_data/5_0.8.json',\n  './data/pyg_data/5_-0.7.json',\n  './data/pyg_data/5_1.0.json',\n  './data/pyg_data/5_0.7.json',\n  './data/pyg_data/5_-0.8.json',\n  './data/pyg_data/5_-1.0.json',\n  './data/pyg_data/5_-0.4.json',\n  './data/pyg_data/5_-0.5.json',\n  './data/pyg_data/5_-0.9.json',\n  './data/pyg_data/5_0.6.json',\n  './data/pyg_data/5_-0.2.json',\n  './data/pyg_data/5_0.1.json',\n  './data/pyg_data/5_0.0.json',\n  './data/pyg_data/5_-0.3.json'],\n 6: [],\n 7: ['./data/pyg_data/7_-0.8.json',\n  './data/pyg_data/7_0.6.json',\n  './data/pyg_data/7_-1.0.json',\n  './data/pyg_data/7_-0.4.json',\n  './data/pyg_data/7_-0.5.json',\n  './data/pyg_data/7_0.7.json',\n  './data/pyg_data/7_-0.9.json',\n  './data/pyg_data/7_-0.2.json',\n  './data/pyg_data/7_0.0.json',\n  './data/pyg_data/7_0.1.json',\n  './data/pyg_data/7_-0.3.json',\n  './data/pyg_data/7_0.2.json',\n  './data/pyg_data/7_0.3.json',\n  './data/pyg_data/7_-0.1.json',\n  './data/pyg_data/7_1.0.json',\n  './data/pyg_data/7_0.4.json',\n  './data/pyg_data/7_0.8.json',\n  './data/pyg_data/7_-0.6.json',\n  './data/pyg_data/7_-0.7.json',\n  './data/pyg_data/7_0.9.json',\n  './data/pyg_data/7_0.5.json'],\n 8: [],\n 9: ['./data/pyg_data/9_0.4.json',\n  './data/pyg_data/9_1.0.json',\n  './data/pyg_data/9_-0.6.json',\n  './data/pyg_data/9_0.8.json',\n  './data/pyg_data/9_0.9.json',\n  './data/pyg_data/9_-0.7.json',\n  './data/pyg_data/9_0.5.json',\n  './data/pyg_data/9_0.2.json',\n  './data/pyg_data/9_-0.1.json',\n  './data/pyg_data/9_0.3.json',\n  './data/pyg_data/9_-0.2.json',\n  './data/pyg_data/9_0.0.json',\n  './data/pyg_data/9_0.1.json',\n  './data/pyg_data/9_-0.3.json',\n  './data/pyg_data/9_-0.4.json',\n  './data/pyg_data/9_0.6.json',\n  './data/pyg_data/9_-1.0.json',\n  './data/pyg_data/9_-0.8.json',\n  './data/pyg_data/9_-0.9.json',\n  './data/pyg_data/9_0.7.json',\n  './data/pyg_data/9_-0.5.json']}"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_paths"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c5c23a05-37f8-44ef-b476-57de0fe7db27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[6251, 28], edge_index=[2, 15335], edge_attr=[9084, 3], y=[32, 1], observable=[32, 1, 21], circuit_depth=[32, 1], noisy_0=[32, 1], noisy_1=[32, 1], batch=[6251], ptr=[33])\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    CircuitGraphExpValMitigationDataset(\n",
    "        train_paths,\n",
    "    ),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    CircuitGraphExpValMitigationDataset(\n",
    "        val_paths,\n",
    "    ),\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "for data in train_loader:\n",
    "    print(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e22ec18-5837-4f8c-bd9a-01e10bc1defd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2aaed92-8f30-42fc-96eb-55709162e1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExpValCircuitGraphModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        n_qubits: int,\n",
    "        num_node_features: int,\n",
    "        hidden_channels: int\n",
    "    ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.transformer1 = TransformerConv(\n",
    "            num_node_features, hidden_channels,\n",
    "            heads=3,\n",
    "            dropout=0.1\n",
    "        )\n",
    "        self.pooling1 = ASAPooling(hidden_channels * 3, 0.5)\n",
    "\n",
    "        self.transformer2 = TransformerConv(\n",
    "            hidden_channels * 3, hidden_channels,\n",
    "            heads=2,\n",
    "            dropout=0.1\n",
    "        )\n",
    "        self.pooling2 = ASAPooling(hidden_channels * 2, 0.5)\n",
    "        \n",
    "        self.body_seq = torch.nn.Sequential(\n",
    "            Linear(hidden_channels * 2 + 2, hidden_channels),\n",
    "            torch.nn.Dropout(0.2),\n",
    "            Linear(hidden_channels, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, \n",
    "                exp_value, observable, \n",
    "                circuit_depth, nodes, \n",
    "                edge_index, batch):\n",
    "        graph = self.transformer1(nodes, edge_index)    \n",
    "        graph, edge_index, _, batch, _ = self.pooling1(\n",
    "            graph, edge_index, batch=batch\n",
    "        )\n",
    "        \n",
    "        graph = self.transformer2(graph, edge_index)\n",
    "        graph, edge_index, _, batch, _ = self.pooling2(\n",
    "            graph, edge_index, batch=batch\n",
    "        )\n",
    "        \n",
    "        graph = global_mean_pool(graph, batch)\n",
    "    \n",
    "        merge = torch.cat((\n",
    "            graph, \n",
    "            exp_value,\n",
    "            circuit_depth\n",
    "        ), dim=1)\n",
    "\n",
    "        return self.body_seq(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1409ba51-4733-4786-a082-e1376d56a8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExpValCircuitGraphModel(\n",
    "    n_qubits=5,\n",
    "    num_node_features=28, \n",
    "    hidden_channels=20\n",
    ")\n",
    "criterion = torch.nn.MSELoss()\n",
    "\n",
    "optimizer = Adam(model.parameters(), lr=0.00001)\n",
    "scheduler = ReduceLROnPlateau(optimizer, \n",
    "                              'min', \n",
    "                              factor=0.5, \n",
    "                              patience=20, \n",
    "                              verbose=True, \n",
    "                              min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64b9b3e2-6d7f-4b74-9f04-28c939ea64a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b7f39db-75a7-4785-bd76-a3a70371ffdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Model training:   0%|          | 0/50 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "39f4828f2cd74e808ffbfde4f44384f0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[0;32mIn [19]\u001B[0m, in \u001B[0;36m<cell line: 9>\u001B[0;34m()\u001B[0m\n\u001B[1;32m     13\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mzero_grad()\n\u001B[1;32m     15\u001B[0m out \u001B[38;5;241m=\u001B[39m model(\n\u001B[1;32m     16\u001B[0m     data\u001B[38;5;241m.\u001B[39mnoisy_0, \n\u001B[1;32m     17\u001B[0m     data\u001B[38;5;241m.\u001B[39mobservable, \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m     21\u001B[0m     data\u001B[38;5;241m.\u001B[39mbatch\n\u001B[1;32m     22\u001B[0m )\n\u001B[0;32m---> 23\u001B[0m loss \u001B[38;5;241m=\u001B[39m \u001B[43mcriterion\u001B[49m(out, data\u001B[38;5;241m.\u001B[39my)        \n\u001B[1;32m     25\u001B[0m train_loss \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m loss\u001B[38;5;241m.\u001B[39mitem()\n\u001B[1;32m     27\u001B[0m loss\u001B[38;5;241m.\u001B[39mbackward()\n",
      "Input \u001B[0;32mIn [19]\u001B[0m, in \u001B[0;36m<cell line: 9>\u001B[0;34m()\u001B[0m\n\u001B[1;32m     13\u001B[0m optimizer\u001B[38;5;241m.\u001B[39mzero_grad()\n\u001B[1;32m     15\u001B[0m out \u001B[38;5;241m=\u001B[39m model(\n\u001B[1;32m     16\u001B[0m     data\u001B[38;5;241m.\u001B[39mnoisy_0, \n\u001B[1;32m     17\u001B[0m     data\u001B[38;5;241m.\u001B[39mobservable, \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m     21\u001B[0m     data\u001B[38;5;241m.\u001B[39mbatch\n\u001B[1;32m     22\u001B[0m )\n\u001B[0;32m---> 23\u001B[0m loss \u001B[38;5;241m=\u001B[39m \u001B[43mcriterion\u001B[49m(out, data\u001B[38;5;241m.\u001B[39my)        \n\u001B[1;32m     25\u001B[0m train_loss \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m loss\u001B[38;5;241m.\u001B[39mitem()\n\u001B[1;32m     27\u001B[0m loss\u001B[38;5;241m.\u001B[39mbackward()\n",
      "File \u001B[0;32m_pydevd_bundle/pydevd_cython_darwin_310_64.pyx:1179\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_darwin_310_64.SafeCallWrapper.__call__\u001B[0;34m()\u001B[0m\n",
      "File \u001B[0;32m_pydevd_bundle/pydevd_cython_darwin_310_64.pyx:620\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_darwin_310_64.PyDBFrame.trace_dispatch\u001B[0;34m()\u001B[0m\n",
      "File \u001B[0;32m_pydevd_bundle/pydevd_cython_darwin_310_64.pyx:929\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_darwin_310_64.PyDBFrame.trace_dispatch\u001B[0;34m()\u001B[0m\n",
      "File \u001B[0;32m_pydevd_bundle/pydevd_cython_darwin_310_64.pyx:920\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_darwin_310_64.PyDBFrame.trace_dispatch\u001B[0;34m()\u001B[0m\n",
      "File \u001B[0;32m_pydevd_bundle/pydevd_cython_darwin_310_64.pyx:317\u001B[0m, in \u001B[0;36m_pydevd_bundle.pydevd_cython_darwin_310_64.PyDBFrame.do_wait_suspend\u001B[0;34m()\u001B[0m\n",
      "File \u001B[0;32m/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevd.py:1160\u001B[0m, in \u001B[0;36mPyDB.do_wait_suspend\u001B[0;34m(self, thread, frame, event, arg, send_suspend_message, is_unhandled_exception)\u001B[0m\n\u001B[1;32m   1157\u001B[0m         from_this_thread\u001B[38;5;241m.\u001B[39mappend(frame_id)\n\u001B[1;32m   1159\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_threads_suspended_single_notification\u001B[38;5;241m.\u001B[39mnotify_thread_suspended(thread_id, stop_reason):\n\u001B[0;32m-> 1160\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_do_wait_suspend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mthread\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mframe\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mevent\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43marg\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msuspend_type\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfrom_this_thread\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/Applications/PyCharm.app/Contents/plugins/python/helpers/pydev/pydevd.py:1175\u001B[0m, in \u001B[0;36mPyDB._do_wait_suspend\u001B[0;34m(self, thread, frame, event, arg, suspend_type, from_this_thread)\u001B[0m\n\u001B[1;32m   1172\u001B[0m             \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_mpl_hook()\n\u001B[1;32m   1174\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprocess_internal_commands()\n\u001B[0;32m-> 1175\u001B[0m         \u001B[43mtime\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msleep\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0.01\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1177\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcancel_async_evaluation(get_current_thread_id(thread), \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mid\u001B[39m(frame)))\n\u001B[1;32m   1179\u001B[0m \u001B[38;5;66;03m# process any stepping instructions\u001B[39;00m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "min_valid_loss = np.inf\n",
    "\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "N_EPOCHS = 50\n",
    "\n",
    "progress = tqdm_notebook(range(N_EPOCHS), desc='Model training', leave=True)\n",
    "for epoch in progress:\n",
    "    train_loss = 0.0\n",
    "    model.train()\n",
    "    for i, data in enumerate(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        out = model(\n",
    "            data.noisy_0, \n",
    "            data.observable, \n",
    "            data.circuit_depth,\n",
    "            data.x, \n",
    "            data.edge_index, \n",
    "            data.batch\n",
    "        )\n",
    "        loss = criterion(out, data.y)        \n",
    "        \n",
    "        train_loss += loss.item()\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "                \n",
    "    valid_loss = 0.0\n",
    "    model.eval()\n",
    "    for i, data in enumerate(val_loader):\n",
    "        out = model(\n",
    "            data.noisy_0, \n",
    "            data.observable, \n",
    "            data.circuit_depth, \n",
    "            data.x, \n",
    "            data.edge_index, \n",
    "            data.batch)\n",
    "        loss = criterion(out, data.y)        \n",
    "\n",
    "        valid_loss += loss.item()\n",
    "\n",
    "    scheduler.step(valid_loss)\n",
    "\n",
    "    if epoch >= 1:    \n",
    "        train_losses.append(train_loss / len(train_loader))\n",
    "        val_losses.append(valid_loss / len(val_loader))\n",
    "\n",
    "        progress.set_description(f\"{round(train_losses[-1], 5)}, {round(val_losses[-1], 5)}\")\n",
    "        progress.refresh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "model_path = './model/finetuning/iskandar.pth'"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "# torch.save(model.state_dict(), model_path)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "# import pickle\n",
    "#\n",
    "# to_save = {'train_losses': train_losses, 'val_losses': val_losses}\n",
    "# with open('.'+model_path.split('.')[1]+'.pk', 'wb') as handle:\n",
    "#     pickle.dump(to_save, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "\n",
    "plt.plot(train_losses, label=\"train_loss\")\n",
    "plt.plot(val_losses, label=\"val_loss\")\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ab70cb2e-acf8-40aa-897b-a7b0210af0f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_losses, label=\"train_loss\")\n",
    "plt.plot(val_losses, label=\"val_loss\")\n",
    "plt.yscale('log')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "a8f7afcb-6ce5-43c5-9121-bc93f9bc0859",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(train_losses, label=\"train_loss\")\n",
    "# plt.plot(val_losses, label=\"val_loss\")\n",
    "# plt.yscale('log')\n",
    "\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# distances = []\n",
    "# for i, data in enumerate(val_loader):\n",
    "#     out = model(data.noisy_0, data.observable, data.circuit_depth, data.x, data.edge_index, data.batch)\n",
    "    \n",
    "#     for ideal, noisy, zne_mitigated, ngm_mitigated in zip(\n",
    "#         data.y.tolist(),\n",
    "#         data.noisy_0.tolist(),\n",
    "#         data.noisy_1.tolist(),\n",
    "#         out.tolist()\n",
    "#     ):\n",
    "#         ideal = np.mean(ideal)\n",
    "#         noisy = np.mean(noisy)\n",
    "#         ngm_mitigated = np.mean(ngm_mitigated)\n",
    "#         zne_mitigated = np.mean(zne_mitigated)\n",
    "#         distances.append({\n",
    "#             \"ideal\": ideal,\n",
    "#             \"noisy\": noisy,\n",
    "#             \"ngm_mitigated\": ngm_mitigated,\n",
    "#             \"zne_mitigated\": zne_mitigated,\n",
    "#             \"dist_noisy\": np.abs(ideal - noisy),\n",
    "#             \"dist_ngm\": np.abs(ideal - ngm_mitigated),\n",
    "#             \"dist_zne\": np.abs(ideal - zne_mitigated),\n",
    "#         })\n",
    "        \n",
    "# df = pd.DataFrame(distances)\n",
    "# sns.boxplot(data=df[[\"dist_noisy\", \"dist_zne\", \"dist_ngm\"]], orient=\"h\", showfliers = False)\n",
    "# plt.title(\"Dist to ideal exp value (dataset)\")\n",
    "# plt.show()\n",
    "\n",
    "# sns.histplot([df['ideal'], df['noisy'], df[\"ngm_mitigated\"], df[\"zne_mitigated\"]], kde=True, bins=40)\n",
    "# plt.title(\"Exp values distribution\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b468c979-38f3-47d5-a18e-b3e1c7d46fbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f5569e6-50b0-46cd-ade2-b45fc0ffbec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/9 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ae7393fed1c6430f9f5489a18a797cfd"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_report_data = []\n",
    "\n",
    "ngem_improvement_factors = {}\n",
    "zne_improvement_factors = {}\n",
    "\n",
    "ideal_means = []\n",
    "noisy_means = []\n",
    "ngem_mitigated_means = []\n",
    "zne_mitigated_means = []\n",
    "\n",
    "for depth, paths in tqdm_notebook(list(all_paths.items())):\n",
    "    test_paths = paths[16:]\n",
    "        \n",
    "    if len(test_paths) == 0: continue\n",
    "    \n",
    "    test_loader = DataLoader(\n",
    "        CircuitGraphExpValMitigationDataset(\n",
    "            test_paths,\n",
    "        ),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        shuffle=False\n",
    "    )\n",
    "    \n",
    "    ngem_problems = []\n",
    "    zne_problems = []\n",
    "\n",
    "    ideal_values = []\n",
    "    ngem_values = []\n",
    "    zne_values = []\n",
    "    noisy_valuses = []\n",
    "    \n",
    "    for i, data in enumerate(test_loader):\n",
    "        out = model(\n",
    "            data.noisy_0, \n",
    "            data.observable, \n",
    "            data.circuit_depth, \n",
    "            data.x, \n",
    "            data.edge_index, \n",
    "            data.batch)\n",
    "\n",
    "        for ideal, noisy, zne_mitigated, ngm_mitigated in zip(\n",
    "            data.y.tolist(),\n",
    "            data.noisy_0.tolist(),\n",
    "            data.noisy_1.tolist(),\n",
    "            out.tolist()\n",
    "        ):\n",
    "            ngem_problem = Problem(\n",
    "                trials=[Trial(noisy=noisy[0], mitigated=ngm_mitigated[0])],\n",
    "                ideal_exp_value=ideal[0]\n",
    "            )\n",
    "            ngem_problems.append(ngem_problem)\n",
    "            \n",
    "            zne_problem = Problem(\n",
    "                trials=[Trial(noisy=noisy[0], mitigated=zne_mitigated[0])],\n",
    "                ideal_exp_value=ideal[0]\n",
    "            )\n",
    "            zne_problems.append(zne_problem)\n",
    "            \n",
    "            ideal_values.append(ideal[0])\n",
    "            ngem_values.append(np.abs(ideal[0] - ngm_mitigated[0]))\n",
    "            noisy_valuses.append(np.abs(ideal[0] - noisy[0]))\n",
    "            zne_values.append(np.abs(ideal[0] - zne_mitigated[0]))\n",
    "\n",
    "            all_report_data.append({\n",
    "                \"ideal\": ideal[0],\n",
    "                \"ngem\": ngm_mitigated[0],\n",
    "                \"zne\": zne_mitigated[0],\n",
    "                \"noisy\": noisy[0],\n",
    "                \"depth\": depth\n",
    "            })\n",
    "\n",
    "    ideal_means.append(np.average(ideal_values))\n",
    "    noisy_means.append(np.average(noisy_valuses))\n",
    "    ngem_mitigated_means.append(np.average(ngem_values))\n",
    "    zne_mitigated_means.append(np.average(zne_values))    \n",
    "\n",
    "    ngem_improvement_factors[depth] = improvement_factor(ngem_problems, 1, 1)\n",
    "    zne_improvement_factors[depth] = improvement_factor(zne_problems, 1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dee2c25a-1677-4c61-bf4a-0a03704d1b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "      ideal      ngem     zne   noisy  depth\n0 -0.443359 -0.011574  0.0100  0.0140      3\n1 -0.406250  0.009010  0.0240  0.0095      3\n2 -0.431641  0.610551 -0.0585 -0.1665      3\n3 -0.365234 -2.139157 -0.2850 -0.0595      3\n4 -0.367188 -0.013134 -0.3650 -0.1290      3",
      "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>ideal</th>\n      <th>ngem</th>\n      <th>zne</th>\n      <th>noisy</th>\n      <th>depth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.443359</td>\n      <td>-0.011574</td>\n      <td>0.0100</td>\n      <td>0.0140</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.406250</td>\n      <td>0.009010</td>\n      <td>0.0240</td>\n      <td>0.0095</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.431641</td>\n      <td>0.610551</td>\n      <td>-0.0585</td>\n      <td>-0.1665</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.365234</td>\n      <td>-2.139157</td>\n      <td>-0.2850</td>\n      <td>-0.0595</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.367188</td>\n      <td>-0.013134</td>\n      <td>-0.3650</td>\n      <td>-0.1290</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(all_report_data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef464e95-519f-4a59-a59c-b7304949cb4c",
   "metadata": {},
   "source": [
    "#### Plot improvement factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = {\"capsize\": 10, \"lw\": 2}\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "fig.suptitle('NGEM Lima random circuits')\n",
    "\n",
    "dapth_data = [k for k, v in list(all_paths.items())[:10] if len(v) > 0]\n",
    "\n",
    "ax1.plot(dapth_data, zne_improvement_factors.values(), label=\"zne\")\n",
    "ax1.plot(dapth_data, ngem_improvement_factors.values(), label=\"ngem\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(dapth_data, noisy_means, label=\"noisy\")\n",
    "ax2.plot(dapth_data, ngem_mitigated_means, label=\"ngem\")\n",
    "ax2.plot(dapth_data, zne_mitigated_means, label=\"zne\")\n",
    "\n",
    "plt.xlabel(\"Depth of circuit\")\n",
    "plt.ylabel(\"Distance to ideal exp value\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "378c29e3-22c1-4e9c-99e7-1d73f577efd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = {\"capsize\": 10, \"lw\": 2}\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "fig.suptitle('NGEM Lima random circuits')\n",
    "\n",
    "dapth_data = [k for k, v in list(all_paths.items())[:10] if len(v) > 0]\n",
    "\n",
    "ax1.plot(dapth_data, zne_improvement_factors.values(), label=\"zne\")\n",
    "ax1.plot(dapth_data, ngem_improvement_factors.values(), label=\"ngem\")\n",
    "\n",
    "ax2.plot(dapth_data, noisy_means, label=\"noisy\")\n",
    "ax2.plot(dapth_data, ngem_mitigated_means, label=\"ngem\")\n",
    "ax2.plot(dapth_data, zne_mitigated_means, label=\"zne\")\n",
    "\n",
    "plt.xlabel(\"Depth of circuit\")\n",
    "plt.ylabel(\"Distance to ideal exp value\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a41aaf-9050-4ecd-8ef6-9a3ec9c01280",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "58be6d98-45de-49c7-8486-233b6f3cb90a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"dist_noisy\"] = np.abs(df[\"ideal\"] - df[\"noisy\"])\n",
    "df[\"dist_zne\"] = np.abs(df[\"ideal\"] - df[\"zne\"])\n",
    "df[\"dist_ngem\"] = np.abs(df[\"ideal\"] - df[\"ngem\"])\n",
    "\n",
    "df[\"bias_noisy\"] = df[\"ideal\"] - df[\"noisy\"]\n",
    "df[\"bias_zne\"] = df[\"ideal\"] - df[\"zne\"]\n",
    "df[\"bias_ngem\"] = df[\"ideal\"] - df[\"ngem\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "4184e235-698e-4aef-8492-5e2a4321c7f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dapth_data = []\n",
    "zne_bias = []\n",
    "zne_dist = []\n",
    "zne_variance = []\n",
    "ngem_bias = []\n",
    "ngem_dist = []\n",
    "ngem_variance = []\n",
    "noisy_bias = []\n",
    "noisy_dist = []\n",
    "for depth in df['depth'].unique():\n",
    "    dapth_data.append(depth)\n",
    "    \n",
    "    noisy_bias.append(np.mean(df[df['depth'] == depth]['bias_noisy']))\n",
    "    noisy_dist.append(np.mean(df[df['depth'] == depth]['dist_noisy']))\n",
    "\n",
    "    ngem_bias.append(np.mean(df[df['depth'] == depth]['bias_ngem']))\n",
    "    ngem_dist.append(np.mean(df[df['depth'] == depth]['dist_ngem']))\n",
    "    ngem_variance.append(np.var(df[df['depth'] == depth]['dist_ngem']))\n",
    "    \n",
    "    zne_bias.append(np.mean(df[df['depth'] == depth]['bias_zne']))\n",
    "    zne_dist.append(np.mean(df[df['depth'] == depth]['dist_zne']))\n",
    "    zne_variance.append(np.var(df[df['depth'] == depth]['dist_zne']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804cff0a-7ac9-4e88-80ef-c9dc004e375a",
   "metadata": {},
   "source": [
    "#### Distance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "26f4bae3-fe82-42c2-9942-a3631e0c586e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = {\"capsize\": 1, \"lw\": 2}\n",
    "\n",
    "plt.errorbar(dapth_data, \n",
    "             zne_dist, \n",
    "             yerr=zne_variance, \n",
    "             label=\"zne\", \n",
    "             fmt='o',\n",
    "             **kwargs)\n",
    "plt.errorbar(dapth_data, \n",
    "             ngem_dist, \n",
    "             yerr=ngem_variance, \n",
    "             label=\"ngem\", \n",
    "             fmt='s',\n",
    "             **kwargs)\n",
    "plt.errorbar(dapth_data, \n",
    "             noisy_dist, \n",
    "             label=\"noisy\", \n",
    "             fmt='o',\n",
    "             color='black',\n",
    "             **kwargs)\n",
    "plt.axhline(y=0, color='r', linestyle='-')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a98b5c6-10ed-4989-bb79-069a79b4e69f",
   "metadata": {},
   "source": [
    "#### Bias plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1d6508bb-cccc-4b29-a55c-2913e0718275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = {\"capsize\": 1, \"lw\": 2}\n",
    "\n",
    "plt.errorbar(dapth_data, \n",
    "             zne_bias, \n",
    "             yerr=zne_variance, \n",
    "             label=\"zne\", \n",
    "             fmt='o',\n",
    "             **kwargs)\n",
    "plt.errorbar(dapth_data, \n",
    "             ngem_bias, \n",
    "             yerr=ngem_variance, \n",
    "             label=\"ngem\", \n",
    "             fmt='s',\n",
    "             **kwargs)\n",
    "plt.errorbar(dapth_data, \n",
    "             noisy_bias, \n",
    "             label=\"noisy\", \n",
    "             fmt='o',\n",
    "             color='black',\n",
    "             **kwargs)\n",
    "plt.axhline(y=0, color='r', linestyle='-')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d960c48e-0f80-47d1-bc31-14f5885e79f1",
   "metadata": {},
   "source": [
    "#### Distribution plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "72ee01e2-6a99-4a3d-94ea-fa19fca4ced5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df[[\"dist_noisy\", \"dist_zne\", \"dist_ngem\"]], orient=\"h\", showfliers = False)\n",
    "plt.title(\"Dist to ideal exp value (dataset)\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot([df['ideal'], df['noisy'], df[\"ngem\"], df[\"zne\"]], kde=True, bins=40)\n",
    "plt.title(\"Exp values distribution\")\n",
    "plt.show()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}