docs/tutorials/04_ngem_vqe.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "46d95310-4a81-427e-8295-dde05b79dccd",
   "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, ConfigurableFakeBackend\n",
    "from qiskit.primitives import Estimator, BackendEstimator\n",
    "from qiskit.providers import Options\n",
    "from qiskit.circuit.random import random_circuit\n",
    "from qiskit.quantum_info import random_clifford\n",
    "from qiskit.quantum_info import SparsePauliOp\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\n",
    "\n",
    "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\n",
    "\n",
    "from pprint import pprint\n",
    "\n",
    "from qiskit.opflow import I, X, Z\n",
    "from qiskit.primitives import Estimator\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit.algorithms.minimum_eigensolvers import VQE, VQEResult\n",
    "from qiskit.algorithms.optimizers import SLSQP, SPSA, COBYLA\n",
    "\n",
    "from qiskit_aer.primitives import Estimator as AerEstimator\n",
    "from qiskit_aer import AerSimulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68529e6e-f4d6-4a22-86d0-5b4e6ac81b8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b61b7d9c-d745-4129-a226-7da8af2da375",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = ConfigurableFakeBackend(\"Tashkent\", n_qubits=2, version=1,\n",
    "                                 # qubit_t1=0.00011377694598809795,\n",
    "                                 # qubit_t2=0.00010553074510239035\n",
    "                                 ) #FakeLima()\n",
    "properties = get_backend_properties_v1(backend)\n",
    "\n",
    "ideal_estimator = AerEstimator(skip_transpilation=True) #Estimator()\n",
    "aer_esimator = AerEstimator(skip_transpilation=True)\n",
    "backend_estimator = BackendEstimator(backend, skip_transpilation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee13b511-8702-4420-82d4-a9a8b07ad858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(TwoLocal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86919e37-ce65-42d7-bbcc-b5e4c617553a",
   "metadata": {},
   "outputs": [],
   "source": [
    "operator = (-1.052373245772859 * I ^ I) + \\\n",
    "            (0.39793742484318045 * I ^ Z) + \\\n",
    "            (-0.39793742484318045 * Z ^ I) + \\\n",
    "            (-0.01128010425623538 * Z ^ Z) + \\\n",
    "            (0.18093119978423156 * X ^ X)\n",
    "\n",
    "ansatz = TwoLocal(rotation_blocks='rx', entanglement_blocks='cx', reps=3)\n",
    "ansatz.num_qubits = operator.num_qubits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "     ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐\nq_0: ┤ Rx(θ[0]) ├──■──┤ Rx(θ[2]) ├──■──┤ Rx(θ[4]) ├──■──┤ Rx(θ[6]) ├\n     ├──────────┤┌─┴─┐├──────────┤┌─┴─┐├──────────┤┌─┴─┐├──────────┤\nq_1: ┤ Rx(θ[1]) ├┤ X ├┤ Rx(θ[3]) ├┤ X ├┤ Rx(θ[5]) ├┤ X ├┤ Rx(θ[7]) ├\n     └──────────┘└───┘└──────────┘└───┘└──────────┘└───┘└──────────┘",
      "text/html": "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐\nq_0: ┤ Rx(θ[0]) ├──■──┤ Rx(θ[2]) ├──■──┤ Rx(θ[4]) ├──■──┤ Rx(θ[6]) ├\n     ├──────────┤┌─┴─┐├──────────┤┌─┴─┐├──────────┤┌─┴─┐├──────────┤\nq_1: ┤ Rx(θ[1]) ├┤ X ├┤ Rx(θ[3]) ├┤ X ├┤ Rx(θ[5]) ├┤ X ├┤ Rx(θ[7]) ├\n     └──────────┘└───┘└──────────┘└───┘└──────────┘└───┘└──────────┘</pre>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz.decompose().draw()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "071b5b46-309a-4dad-bf32-39e768eeaf93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "         ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐\nq_0 -> 0 ┤ U3(θ[0],-π/2,π/2) ├──■──┤ U3(θ[2],-π/2,π/2) ├──■──┤ U3(θ[4],-π/2,π/2) ├──■──┤ U3(θ[6],-π/2,π/2) ├\n         ├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤\nq_1 -> 1 ┤ U3(θ[1],-π/2,π/2) ├┤ X ├┤ U3(θ[3],-π/2,π/2) ├┤ X ├┤ U3(θ[5],-π/2,π/2) ├┤ X ├┤ U3(θ[7],-π/2,π/2) ├\n         └───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘",
      "text/html": "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐\nq_0 -> 0 ┤ U3(θ[0],-π/2,π/2) ├──■──┤ U3(θ[2],-π/2,π/2) ├──■──┤ U3(θ[4],-π/2,π/2) ├──■──┤ U3(θ[6],-π/2,π/2) ├\n         ├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤\nq_1 -> 1 ┤ U3(θ[1],-π/2,π/2) ├┤ X ├┤ U3(θ[3],-π/2,π/2) ├┤ X ├┤ U3(θ[5],-π/2,π/2) ├┤ X ├┤ U3(θ[7],-π/2,π/2) ├\n         └───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘</pre>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled_ansatz = transpile(ansatz, backend, optimization_level=0)\n",
    "transpiled_ansatz.draw(fold=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "636ea8b8-972d-48b8-97a8-2802c40808c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4717a7f4-d8ae-48c7-b2f5-488ef326309c",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_params = len(list(ansatz.parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4c3b691f-f757-4ad3-82fd-f102f6710e3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_FILES = 20\n",
    "N_ENTRIES_PER_FILE = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "762a9556-40e4-4a1c-9137-8df12da1bd77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e8bd77ec39d7497db42859eca4b63a62"
      }
     },
     "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 [41]\u001B[0m, in \u001B[0;36m<cell line: 3>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m _ \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(N_ENTRIES_PER_FILE):\n\u001B[1;32m      6\u001B[0m     \u001B[38;5;66;03m# transpiled_circuit = transpile(ansatz, backend, optimization_level=0)\u001B[39;00m\n\u001B[1;32m      7\u001B[0m     circuit \u001B[38;5;241m=\u001B[39m transpiled_ansatz\u001B[38;5;241m.\u001B[39mbind_parameters(np\u001B[38;5;241m.\u001B[39mrandom\u001B[38;5;241m.\u001B[39muniform(\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m5\u001B[39m, \u001B[38;5;241m5\u001B[39m, (num_params)))\n\u001B[0;32m----> 9\u001B[0m     exp_vals \u001B[38;5;241m=\u001B[39m \u001B[43mcreate_meas_data_from_estimators\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m     10\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcircuits\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcircuit\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[1;32m     11\u001B[0m \u001B[43m        \u001B[49m\u001B[43mobservables\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moperator\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\n\u001B[1;32m     12\u001B[0m \u001B[43m        \u001B[49m\u001B[43mestimators\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mideal_estimator\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbackend_estimator\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m     13\u001B[0m \u001B[43m        \u001B[49m\u001B[43mshots\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m10000\u001B[39;49m\n\u001B[1;32m     14\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     15\u001B[0m     ideal_exp_val, noisy_exp_val \u001B[38;5;241m=\u001B[39m exp_vals\n\u001B[1;32m     17\u001B[0m     graph_data \u001B[38;5;241m=\u001B[39m circuit_to_graph_data_json(\n\u001B[1;32m     18\u001B[0m         circuit\u001B[38;5;241m=\u001B[39mcircuit,\n\u001B[1;32m     19\u001B[0m         properties\u001B[38;5;241m=\u001B[39mproperties,\n\u001B[1;32m     20\u001B[0m         use_qubit_features\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m     21\u001B[0m         use_gate_features\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[1;32m     22\u001B[0m     )\n",
      "File \u001B[0;32m~/GitHub/blackwater/blackwater/data/utils.py:442\u001B[0m, in \u001B[0;36mcreate_meas_data_from_estimators\u001B[0;34m(circuits, observables, estimators, **run_params)\u001B[0m\n\u001B[1;32m    440\u001B[0m results \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m    441\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m estimator \u001B[38;5;129;01min\u001B[39;00m estimators:\n\u001B[0;32m--> 442\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[43mestimator\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcircuits\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mobservables\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mrun_params\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mresult()\n\u001B[1;32m    443\u001B[0m     results\u001B[38;5;241m.\u001B[39mappend(result\u001B[38;5;241m.\u001B[39mvalues[\u001B[38;5;241m0\u001B[39m])\n\u001B[1;32m    444\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m results\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/site-packages/qiskit/primitives/base/base_estimator.py:235\u001B[0m, in \u001B[0;36mBaseEstimator.run\u001B[0;34m(self, circuits, observables, parameter_values, **run_options)\u001B[0m\n\u001B[1;32m    232\u001B[0m run_opts \u001B[38;5;241m=\u001B[39m copy(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moptions)\n\u001B[1;32m    233\u001B[0m run_opts\u001B[38;5;241m.\u001B[39mupdate_options(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mrun_options)\n\u001B[0;32m--> 235\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_run\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    236\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcircuits\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    237\u001B[0m \u001B[43m    \u001B[49m\u001B[43mobservables\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    238\u001B[0m \u001B[43m    \u001B[49m\u001B[43mparameter_values\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    239\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mrun_opts\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;18;43m__dict__\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    240\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/site-packages/qiskit_aer/primitives/estimator.py:176\u001B[0m, in \u001B[0;36mEstimator._run\u001B[0;34m(self, circuits, observables, parameter_values, **run_options)\u001B[0m\n\u001B[1;32m    172\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_observables\u001B[38;5;241m.\u001B[39mappend(observable)\n\u001B[1;32m    173\u001B[0m job \u001B[38;5;241m=\u001B[39m PrimitiveJob(\n\u001B[1;32m    174\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call, circuit_indices, observable_indices, parameter_values, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mrun_options\n\u001B[1;32m    175\u001B[0m )\n\u001B[0;32m--> 176\u001B[0m \u001B[43mjob\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubmit\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m job\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/site-packages/qiskit/primitives/primitive_job.py:43\u001B[0m, in \u001B[0;36mPrimitiveJob.submit\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     40\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_future \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m     41\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m JobError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPrimitive job has already been submitted.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m---> 43\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ThreadPoolExecutor(max_workers\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m executor:\n\u001B[1;32m     44\u001B[0m     future \u001B[38;5;241m=\u001B[39m executor\u001B[38;5;241m.\u001B[39msubmit(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_function, \u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_kwargs)\n\u001B[1;32m     45\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_future \u001B[38;5;241m=\u001B[39m future\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/concurrent/futures/_base.py:649\u001B[0m, in \u001B[0;36mExecutor.__exit__\u001B[0;34m(self, exc_type, exc_val, exc_tb)\u001B[0m\n\u001B[1;32m    648\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__exit__\u001B[39m(\u001B[38;5;28mself\u001B[39m, exc_type, exc_val, exc_tb):\n\u001B[0;32m--> 649\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mshutdown\u001B[49m\u001B[43m(\u001B[49m\u001B[43mwait\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    650\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/concurrent/futures/thread.py:235\u001B[0m, in \u001B[0;36mThreadPoolExecutor.shutdown\u001B[0;34m(self, wait, cancel_futures)\u001B[0m\n\u001B[1;32m    233\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m wait:\n\u001B[1;32m    234\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m t \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_threads:\n\u001B[0;32m--> 235\u001B[0m         \u001B[43mt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mjoin\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/threading.py:1096\u001B[0m, in \u001B[0;36mThread.join\u001B[0;34m(self, timeout)\u001B[0m\n\u001B[1;32m   1093\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcannot join current thread\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m   1095\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m timeout \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1096\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_wait_for_tstate_lock\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1097\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   1098\u001B[0m     \u001B[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001B[39;00m\n\u001B[1;32m   1099\u001B[0m     \u001B[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001B[39;00m\n\u001B[1;32m   1100\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_wait_for_tstate_lock(timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mmax\u001B[39m(timeout, \u001B[38;5;241m0\u001B[39m))\n",
      "File \u001B[0;32m/opt/anaconda3/envs/ngem/lib/python3.10/threading.py:1116\u001B[0m, in \u001B[0;36mThread._wait_for_tstate_lock\u001B[0;34m(self, block, timeout)\u001B[0m\n\u001B[1;32m   1113\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m\n\u001B[1;32m   1115\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1116\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[43mlock\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43macquire\u001B[49m\u001B[43m(\u001B[49m\u001B[43mblock\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m:\n\u001B[1;32m   1117\u001B[0m         lock\u001B[38;5;241m.\u001B[39mrelease()\n\u001B[1;32m   1118\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_stop()\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "obs = encode_pauli_sum_op(operator)\n",
    "\n",
    "for i in tqdm_notebook(range(N_FILES)):\n",
    "    entries = []\n",
    "    for _ in range(N_ENTRIES_PER_FILE):\n",
    "        # transpiled_circuit = transpile(ansatz, backend, optimization_level=0)\n",
    "        circuit = transpiled_ansatz.bind_parameters(np.random.uniform(-5, 5, (num_params)))\n",
    "\n",
    "        exp_vals = create_meas_data_from_estimators(\n",
    "            circuits=circuit, \n",
    "            observables=operator, \n",
    "            estimators=[ideal_estimator, backend_estimator],\n",
    "            shots=10000\n",
    "        )\n",
    "        ideal_exp_val, noisy_exp_val = exp_vals\n",
    "\n",
    "        graph_data = circuit_to_graph_data_json(\n",
    "            circuit=circuit,\n",
    "            properties=properties,\n",
    "            use_qubit_features=True,\n",
    "            use_gate_features=True,\n",
    "        )\n",
    "\n",
    "        entry = ExpValueEntry(\n",
    "            circuit_graph=graph_data,\n",
    "            observable=obs,\n",
    "            ideal_exp_value=ideal_exp_val,\n",
    "            noisy_exp_values=[\n",
    "                noisy_exp_val\n",
    "            ],\n",
    "            circuit_depth=circuit.depth()\n",
    "        )\n",
    "        entries.append(entry.to_dict())\n",
    "\n",
    "    with open(f\"./data/demo_vqe_dataset/vqe_dataset_{i}.json\", \"w\") as json_file:\n",
    "        json.dump(entries, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe39307b-a42b-44e9-b267-6578cde348ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "### load data\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "becef77f-39cc-4b34-9449-7c17e4e20abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = glob.glob(\"./data/demo_vqe_dataset/vqe_dataset_**.json\")\n",
    "# random.shuffle(paths)\n",
    "train_paths = paths[:13]\n",
    "val_paths = paths[13:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fdbfdd5-85a2-48a1-89e3-e6c11739dd37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b3406622-be7c-40a8-86ae-fed50c179957",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[352, 21], edge_index=[2, 736], edge_attr=[384, 3], y=[32, 1], observable=[32, 5, 9], circuit_depth=[32, 1], noisy_0=[32, 1], batch=[352], 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": 28,
   "id": "6ba05ae2-3dae-4ea2-9aa8-a207fd1dcc09",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08d707a6-874e-448a-b2c1-a2d83d8a9de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExpValCircuitGraphModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self,\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": 30,
   "id": "d9302402-357b-408f-ae02-516aa7d9c8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExpValCircuitGraphModel(\n",
    "    num_node_features=21, \n",
    "    hidden_channels=10\n",
    ")\n",
    "criterion = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f23edc03-c690-4241-b1e7-bd91d96d97e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(model.parameters(), lr=0.001)\n",
    "scheduler = ReduceLROnPlateau(optimizer, \n",
    "                              'min', \n",
    "                              factor=0.1, \n",
    "                              patience=15, \n",
    "                              verbose=True, \n",
    "                              min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95853920-82c3-4c43-bc8f-a2e4aa651fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f17b5a5cb8d64f4796a6d5e8c4787c31",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Model training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00082: reducing learning rate of group 0 to 1.0000e-04.\n",
      "Epoch 00098: reducing learning rate of group 0 to 1.0000e-05.\n"
     ]
    }
   ],
   "source": [
    "min_valid_loss = np.inf\n",
    "\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "N_EPOCHS = 100\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": 33,
   "id": "aaf88308-6c65-4602-a321-5a8603eb602f",
   "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"
    },
    {
     "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()\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, ngm_mitigated in zip(\n",
    "        data.y.tolist(),\n",
    "        data.noisy_0.tolist(),\n",
    "        out.tolist()\n",
    "    ):\n",
    "        ideal = np.mean(ideal)\n",
    "        noisy = np.mean(noisy)\n",
    "        ngm_mitigated = np.mean(ngm_mitigated)\n",
    "        distances.append({\n",
    "            \"ideal\": ideal,\n",
    "            \"noisy\": noisy,\n",
    "            \"ngm_mitigated\": ngm_mitigated,\n",
    "            \"dist_noisy\": np.abs(ideal - noisy),\n",
    "            \"dist_ngm\": np.abs(ideal - ngm_mitigated),\n",
    "        })\n",
    "        \n",
    "df = pd.DataFrame(distances)\n",
    "sns.boxplot(data=df[[\"dist_noisy\", \"dist_ngm\"]], orient=\"h\", showfliers = False)\n",
    "plt.title(\"Dist to ideal exp value\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot([df['ideal'], df['noisy'], df[\"ngm_mitigated\"]], kde=True, bins=40)\n",
    "plt.title(\"Exp values distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e7d715c-d74e-4770-bfed-d62df8aba4b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8e73a5eb-4aea-4fe0-92f6-6da4f02a8e52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<qiskit_aer.primitives.estimator.Estimator object at 0x7fe8a80cb790> VQE execution...\n",
      "<abc.NGEMBackendEstimator object at 0x7fe8c92daa90> VQE execution...\n",
      "<qiskit.primitives.backend_estimator.BackendEstimator object at 0x7fe8df43e850> VQE execution...\n"
     ]
    }
   ],
   "source": [
    "ideal_vals = []\n",
    "ideal_callback = lambda count, params, mean, std: ideal_vals.append(mean)\n",
    "\n",
    "noisy_vals = []\n",
    "noisy_callback = lambda count, params, mean, std: noisy_vals.append(mean)\n",
    "\n",
    "mitigated_vals = []\n",
    "mitigated_callback = lambda count, params, mean, std: mitigated_vals.append(mean)\n",
    "\n",
    "default_callback = lambda count, params, mean, std: print(mean, params)\n",
    "\n",
    "initial_point = np.ones(num_params) #np.random.random(num_params)# \n",
    "\n",
    "options = Options(optimization_level=0)\n",
    "NgemEstimator = ngem(BackendEstimator, model=model, backend=backend, options=options)\n",
    "ngem_estimator = NgemEstimator(backend, skip_transpilation=True)\n",
    "\n",
    "for estimator, callback in zip(\n",
    "    [\n",
    "        ideal_estimator, \n",
    "        ngem_estimator, \n",
    "        backend_estimator\n",
    "    ],\n",
    "    [\n",
    "        ideal_callback, \n",
    "        mitigated_callback, \n",
    "        noisy_callback\n",
    "    ]\n",
    "):\n",
    "    print(f\"{estimator} VQE execution...\")\n",
    "    # vqe_optimizer = SPSA(maxiter=100) \n",
    "    vqe_optimizer = COBYLA(maxiter=500, tol=0.0001)\n",
    "    vqe = VQE(estimator, transpiled_ansatz, vqe_optimizer, callback=callback, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b540530b-8712-4ff1-9bc7-a0d3045a59a0",
   "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(ideal_vals, label=\"ideal\")\n",
    "plt.plot(mitigated_vals, label=\"mitigated\")\n",
    "plt.plot(noisy_vals, label=\"noisy\")\n",
    "plt.legend()\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"VQE H2 sim\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6df82f4b-76a6-479a-ab13-412ef8ce31a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92fcc3dc-564f-4826-b343-ecc141024ce1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8775fb38-dee4-41e9-a089-7148b66d3ef3",
   "metadata": {},
   "source": [
    "## H2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['XX', 'ZZ']\n",
      "SparsePauliOp(['XX', 'ZZ'],\n",
      "              coeffs=[0.5+0.j, 0.5+0.j])\n"
     ]
    }
   ],
   "source": [
    "from qiskit.opflow import I, X, Y, Z\n",
    "str2opflow = {'I': I, 'X': X, 'Y': Y, 'Z': Z}\n",
    "\n",
    "coefficient = [1] * 2 #[0.2, 0.4, 0.4]\n",
    "coefficient /= np.sum(coefficient)\n",
    "operator_components = ['XX', 'ZZ']\n",
    "# num_ops_total, num_ops_from_train = 2, 2\n",
    "# coefficient = np.random.normal(0, 1, num_ops_total)\n",
    "# operator_components = np.random.choice(train_meas_bases, size=num_ops_from_train).tolist() + np.random.choice(test_meas_bases, size=num_ops_total-num_ops_from_train).tolist()\n",
    "print(operator_components)\n",
    "\n",
    "operator_components_opflow = []\n",
    "for op_component in operator_components:\n",
    "    op_f = 1\n",
    "    for op_str in list(op_component):\n",
    "        # op_f = str2opflow[op_str] ^ op_f\n",
    "        op_f = op_f ^ str2opflow[op_str]\n",
    "    operator_components_opflow.append(op_f)\n",
    "\n",
    "operator = np.dot(coefficient, operator_components_opflow)\n",
    "operator = SparsePauliOp.from_operator(operator)\n",
    "print(operator)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83113e70-aac5-4b47-ba2e-cb0ac49f834f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "16"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond_operators = []\n",
    "\n",
    "with open(\"./h2-hamiltonian-qubit-params.txt\", \"r\") as f:\n",
    "    entries = f.read().split(\"\\n\\n\")\n",
    "    for entry in entries:\n",
    "        if entry:\n",
    "            length, fci, c1, c2, c3, c4, c5 = entry.strip().split(\"\\n\")\n",
    "            length = float(length.split(\" \")[0])\n",
    "            fci = float(fci.split(\" \")[-1])\n",
    "\n",
    "            c1, c2, c3, c4, c5 = [\n",
    "                float(x.split(\" \")[0]) for x in\n",
    "                [c1, c2, c3, c4, c5]\n",
    "            ]\n",
    "\n",
    "            # operator = (c1 * I ^ I) + \\\n",
    "            #             (c2 * X ^ X) + \\\n",
    "            #             (c3 * Z ^ I) + \\\n",
    "            #             (c4 * Z ^ Z) + \\\n",
    "            #             (c5 * I ^ Z)\n",
    "            operator_components_opflow = [I^I, X^X, Z^I, Z^Z, I^Z]\n",
    "            coefficient = [c1, c2, c3, c4, c5]\n",
    "            operator = np.dot(coefficient, operator_components_opflow)\n",
    "            operator = SparsePauliOp.from_operator(operator)\n",
    "\n",
    "            bond_operators.append((length, operator))\n",
    "\n",
    "len(bond_operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "[(0.2,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[2.0115282 +0.j, 0.93048853+0.j, 0.15797271+0.j, 0.93048853+0.j,\n   0.01362387+0.j])),\n (0.4,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[0.46036349+0.j, 0.68881943+0.j, 0.16451542+0.j, 0.68881943+0.j,\n   0.01291397+0.j])),\n (0.5,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[0.11064654+0.j, 0.58307963+0.j, 0.16887023+0.j, 0.58307963+0.j,\n   0.01251643+0.j])),\n (0.67,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.24514794+0.j,  0.44102801+0.j,  0.17738032+0.j,  0.44102801+0.j,\n    0.01168821+0.j])),\n (0.7,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.28794508+0.j,  0.42045568+0.j,  0.17900058+0.j,  0.42045568+0.j,\n    0.0115074 +0.j])),\n (0.72,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.31402572+0.j,  0.40739954+0.j,  0.18009857+0.j,  0.40739954+0.j,\n    0.01137972+0.j])),\n (0.73,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.32638631+0.j,  0.40106074+0.j,  0.18065279+0.j,  0.40106074+0.j,\n    0.01131368+0.j])),\n (0.74,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.33831674+0.j,  0.39484436+0.j,  0.18121046+0.j,  0.39484436+0.j,\n    0.01124616+0.j])),\n (1.0,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.54006628+0.j,  0.26752865+0.j,  0.19679058+0.j,  0.26752865+0.j,\n    0.00901493+0.j])),\n (1.2,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.61168972+0.j,  0.20018958+0.j,  0.20979147+0.j,  0.20018958+0.j,\n    0.00696216+0.j])),\n (1.4,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.64737531+0.j,  0.14956794+0.j,  0.22302209+0.j,  0.14956794+0.j,\n    0.00503054+0.j])),\n (1.6,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.66256274+0.j,  0.11130875+0.j,  0.23590129+0.j,  0.11130875+0.j,\n    0.00344779+0.j])),\n (1.8,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.66629324+0.j,  0.08240979+0.j,  0.24801699+0.j,  0.08240979+0.j,\n    0.00226467+0.j])),\n (2.0,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-0.66396774+0.j,  0.06062801+0.j,  0.25913847+0.j,  0.06062801+0.j,\n    0.0014311 +0.j])),\n (3.0,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-6.33651340e-01+0.j,  1.12352608e-02+0.j,  2.99211543e-01+0.j,\n    1.12352608e-02+0.j,  7.36102715e-05+0.j])),\n (4.0,\n  SparsePauliOp(['II', 'IZ', 'XX', 'ZI', 'ZZ'],\n                coeffs=[-6.12002832e-01+0.j,  1.43413915e-03+0.j,  3.21156858e-01+0.j,\n    1.43413915e-03+0.j,  1.13653883e-06+0.j]))]"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond_operators"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ad8f49ee-c0fa-4b47-a3bd-9f29a7ad1ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ansatz = TwoLocal(rotation_blocks='rx', entanglement_blocks='cx', reps=3)\n",
    "ansatz.num_qubits = operator.num_qubits\n",
    "\n",
    "num_params = len(list(ansatz.parameters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d4c0ac3c-b8ec-4148-b120-ef9ca3ad8ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/16 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "96c66fe50098409fa41ad1c8acb60549"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    entries = []\n",
    "    obs = encode_pauli_sum_op(operator)\n",
    "\n",
    "    for _ in range(200):\n",
    "        circuit = transpiled_ansatz.bind_parameters(np.random.uniform(-5, 5, (num_params)))\n",
    "\n",
    "        exp_vals = create_meas_data_from_estimators(\n",
    "            circuits=circuit,\n",
    "            observables=operator,\n",
    "            estimators=[ideal_estimator, backend_estimator],\n",
    "            shots=10000\n",
    "        )\n",
    "        ideal_exp_val, noisy_exp_val = exp_vals\n",
    "\n",
    "        graph_data = circuit_to_graph_data_json(\n",
    "            circuit=circuit,\n",
    "            properties=properties,\n",
    "            use_qubit_features=True,\n",
    "            use_gate_features=True,\n",
    "        )\n",
    "\n",
    "        entry = ExpValueEntry(\n",
    "            circuit_graph=graph_data,\n",
    "            observable=obs,\n",
    "            ideal_exp_value=ideal_exp_val,\n",
    "            noisy_exp_values=[\n",
    "                noisy_exp_val\n",
    "            ],\n",
    "            circuit_depth=circuit.depth()\n",
    "        )\n",
    "        # to_append = entry.to_dict()\n",
    "        # to_append['circuit'] = circuit\n",
    "        # to_append['operator'] = operator\n",
    "        # entries.append(to_append)\n",
    "        entries.append(entry.to_dict())\n",
    "\n",
    "    with open(f\"./data/vqe/h2/train/bond_{bond_length}.json\", \"w\") as json_file:\n",
    "        json.dump(entries, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "50048089-121f-4107-b590-4808d25456ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "##  load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "176538f4-1233-4849-bb5e-94883354fdda",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths = glob.glob(\"./data/vqe/h2/train/**.json\")\n",
    "val_paths = glob.glob(\"./data/vqe/h2/val/**.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6a6d9698-ad10-4645-a39c-2089d5763e82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[352, 21], edge_index=[2, 736], edge_attr=[384, 3], y=[32, 1], observable=[32, 5, 9], circuit_depth=[32, 1], noisy_0=[32, 1], batch=[352], 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": 50,
   "id": "9a6043de-4f87-4973-8631-7b1821dc97dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExpValCircuitGraphModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self,\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 + 7, 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",
    "        obs_vec = observable[:, :, 0]\n",
    "\n",
    "        merge = torch.cat((\n",
    "            graph, \n",
    "            exp_value,\n",
    "            circuit_depth,\n",
    "            obs_vec\n",
    "        ), dim=1)\n",
    "\n",
    "        return self.body_seq(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f55670f3-3bed-4aa4-b7d7-c24fa47d5ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExpValCircuitGraphModel(\n",
    "    num_node_features=21, \n",
    "    hidden_channels=10\n",
    ")\n",
    "criterion = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a5e5e163-4692-42ee-8bfc-95be031301fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(model.parameters(), lr=0.001)\n",
    "scheduler = ReduceLROnPlateau(optimizer, \n",
    "                              'min', \n",
    "                              factor=0.1, \n",
    "                              patience=15, \n",
    "                              verbose=True, \n",
    "                              min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9b3dfa2a-6443-4352-9049-d65d6ba34849",
   "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": "a41e515d5ab44d09bcda109baa3893b3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00043: reducing learning rate of group 0 to 1.0000e-04.\n"
     ]
    }
   ],
   "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": 68,
   "id": "54854d4d-bc26-4570-9d63-a5e72bdfeb9b",
   "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"
    },
    {
     "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()\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, ngm_mitigated in zip(\n",
    "        data.y.tolist(),\n",
    "        data.noisy_0.tolist(),\n",
    "        out.tolist()\n",
    "    ):\n",
    "        ideal = np.mean(ideal)\n",
    "        noisy = np.mean(noisy)\n",
    "        ngm_mitigated = np.mean(ngm_mitigated)\n",
    "        distances.append({\n",
    "            \"ideal\": ideal,\n",
    "            \"noisy\": noisy,\n",
    "            \"ngm_mitigated\": ngm_mitigated,\n",
    "            \"dist_noisy\": np.abs(ideal - noisy),\n",
    "            \"dist_ngm\": np.abs(ideal - ngm_mitigated),\n",
    "        })\n",
    "        \n",
    "df = pd.DataFrame(distances)\n",
    "sns.boxplot(data=df[[\"dist_noisy\", \"dist_ngm\"]], orient=\"h\", showfliers = False)\n",
    "plt.title(\"Dist to ideal exp value\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot([df['ideal'], df['noisy'], df[\"ngm_mitigated\"]], kde=True, bins=40)\n",
    "plt.title(\"Exp values distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "1dd79562-ab44-4fbe-96a7-10381e18cd53",
   "metadata": {},
   "outputs": [],
   "source": [
    "## test estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0aa4f9c6-f950-4950-a98c-cbaae57833a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.ones(num_params) #np.random.random(num_params)# \n",
    "\n",
    "optimal_values = []\n",
    "\n",
    "options = Options(optimization_level=0)\n",
    "NgemEstimator = ngem(BackendEstimator, model=model, backend=backend, options=options)\n",
    "ngem_estimator = NgemEstimator(backend, skip_transpilation=True)\n",
    "\n",
    "\n",
    "#ideal_estimator,  ngem_estimator, backend_estimator\n",
    "vqe_optimizer = COBYLA(maxiter=100, tol=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d78280b2-4e1b-4c67-8a90-c23017e70659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd71b8f566034e2a82e7468bea2e075a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(ideal_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    ideal_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "3bd012ef-8255-48f0-bae5-a91b683abaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ad3f49a85964e69b820a8680ebf139e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noisy_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(backend_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    noisy_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "680bb93e-e4b1-4dc3-b8ff-7d4f2d5461d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61105967afd7492086216e84379630a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mitigated_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(ngem_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    mitigated_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37b85053-b12e-4910-9a98-f1da5196e367",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "9e074fb6-3df8-4b5c-8b3d-69d645cd6c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blenghts = [l for l, _ in bond_operators]\n",
    "\n",
    "plt.plot(blenghts, ideal_values, label=\"ideal\")\n",
    "plt.plot(blenghts, noisy_values, 'ro--', linewidth=1, markersize=4, label=\"noisy\")\n",
    "plt.plot(blenghts, mitigated_values, 'go--', linewidth=1, markersize=4, label=\"mitigated\")\n",
    "\n",
    "plt.title(\"VQE on H2 molecule (sim)\")\n",
    "plt.xlabel(\"Bond length\")\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "1e3c82a1-da63-407c-93cf-0324a0045295",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./data/results/vqe_h2.json\", \"w\") as f:\n",
    "    json.dump({\n",
    "        \"bond_lengths\": blenghts,\n",
    "        \"ideal_values\": ideal_values,\n",
    "        \"noisy_values\": noisy_values,\n",
    "        \"mitigated_values\": mitigated_values\n",
    "    }, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e7a615c-957a-4a75-a865-167e200ea16d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8be16b6b-cb9d-48e4-9997-b9ba8ed10141",
   "metadata": {},
   "source": [
    "## H2 with random clifford ansatz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d40b546-40f0-4cae-a0b4-9bbdab2bc73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond_operators = []\n",
    "\n",
    "with open(\"./data/molecule_data/h2-hamiltonian-qubit-params.txt\", \"r\") as f:\n",
    "    entries = f.read().split(\"\\n\\n\")\n",
    "    for entry in entries:\n",
    "        length, fci, c1, c2, c3, c4, c5 = entry.split(\"\\n\")\n",
    "        length = float(length.split(\" \")[0])\n",
    "        fci = float(fci.split(\" \")[-1])\n",
    "        \n",
    "        c1, c2, c3, c4, c5 = [\n",
    "            float(x.split(\" \")[0]) for x in\n",
    "            [c1, c2, c3, c4, c5]\n",
    "        ]\n",
    "                \n",
    "        operator = (c1 * I ^ I) + \\\n",
    "                    (c2 * X ^ X) + \\\n",
    "                    (c3 * Z ^ I) + \\\n",
    "                    (c4 * Z ^ Z) + \\\n",
    "                    (c5 * I ^ Z)\n",
    "        \n",
    "        bond_operators.append((length, operator))\n",
    "        \n",
    "len(bond_operators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "70295a57-885d-4689-9c99-906f9d7b2b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_qubits = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d66baf84-0ca5-4c85-b1bd-ba0d111a6c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = ConfigurableFakeBackend(\"Tashkent_4q\", n_qubits=num_qubits, version=1)\n",
    "properties = get_backend_properties_v1(backend)\n",
    "\n",
    "ideal_estimator = AerEstimator(skip_transpilation=True)\n",
    "aer_esimator = AerEstimator(skip_transpilation=True)\n",
    "backend_estimator = BackendEstimator(backend, skip_transpilation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "45564e02-0723-4668-931b-73a6e0b2a558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([-0.00390625]), metadata=[{'shots': 1024, 'variance': 0.9999847412109375, 'simulator_metadata': [{'parallel_state_update': 8, 'parallel_shots': 1, 'sample_measure_time': 0.000359458, 'noise': 'ideal', 'batched_shots_optimization': False, 'remapped_qubits': False, 'device': 'CPU', 'active_input_qubits': [0, 1], 'measure_sampling': True, 'num_clbits': 2, 'input_qubit_map': [[1, 1], [0, 0]], 'num_qubits': 2, 'method': 'statevector', 'fusion': {'applied': False, 'max_fused_qubits': 5, 'threshold': 14, 'enabled': True}}]}])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc = transpile(random_clifford(num_qubits).to_circuit(), backend, optimization_level=3, seed_transpiler=42)\n",
    "tc.draw(fold=-1)\n",
    "\n",
    "ideal_estimator.run([tc], [SparsePauliOp(\"ZZ\")]).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "20ae4cf8-b600-45ee-8225-8c31b971aca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">         ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐\n",
       "q_0 -> 0 ┤ U3(θ[0],-π/2,π/2) ├──■──┤ U3(θ[2],-π/2,π/2) ├──■──┤ U3(θ[4],-π/2,π/2) ├\n",
       "         ├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤\n",
       "q_1 -> 1 ┤ U3(θ[1],-π/2,π/2) ├┤ X ├┤ U3(θ[3],-π/2,π/2) ├┤ X ├┤ U3(θ[5],-π/2,π/2) ├\n",
       "         └───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘</pre>"
      ],
      "text/plain": [
       "         ┌───────────────────┐     ┌───────────────────┐     ┌───────────────────┐\n",
       "q_0 -> 0 ┤ U3(θ[0],-π/2,π/2) ├──■──┤ U3(θ[2],-π/2,π/2) ├──■──┤ U3(θ[4],-π/2,π/2) ├\n",
       "         ├───────────────────┤┌─┴─┐├───────────────────┤┌─┴─┐├───────────────────┤\n",
       "q_1 -> 1 ┤ U3(θ[1],-π/2,π/2) ├┤ X ├┤ U3(θ[3],-π/2,π/2) ├┤ X ├┤ U3(θ[5],-π/2,π/2) ├\n",
       "         └───────────────────┘└───┘└───────────────────┘└───┘└───────────────────┘"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz = TwoLocal(rotation_blocks='rx', entanglement_blocks='cx', reps=2)\n",
    "ansatz.num_qubits = num_qubits\n",
    "\n",
    "num_params = len(list(ansatz.parameters))\n",
    "\n",
    "transpiled_ansatz = transpile(ansatz, backend, optimization_level=0, seed_transpiler=42)\n",
    "transpiled_ansatz.draw(fold=-1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 109,
   "id": "77333f3f-04f2-4124-a4a1-1407e294be50",
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    {
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     },
     "metadata": {},
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   ],
   "source": [
    "# for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "#     entries = []\n",
    "#     obs = encode_pauli_sum_op(operator)\n",
    "    \n",
    "#     for _ in tqdm_notebook(range(50)):\n",
    "#         circuit = transpile(random_clifford(num_qubits).to_circuit(), \n",
    "#                   backend, \n",
    "#                   optimization_level=3, \n",
    "#                   seed_transpiler=42\n",
    "#                  )\n",
    "\n",
    "#         exp_vals = create_meas_data_from_estimators(\n",
    "#             circuits=circuit, \n",
    "#             observables=operator, \n",
    "#             estimators=[ideal_estimator, backend_estimator],\n",
    "#             shots=10000\n",
    "#         )\n",
    "#         ideal_exp_val, noisy_exp_val = exp_vals\n",
    "\n",
    "#         graph_data = circuit_to_graph_data_json(\n",
    "#             circuit=circuit,\n",
    "#             properties=properties,\n",
    "#             use_qubit_features=True,\n",
    "#             use_gate_features=True,\n",
    "#         )\n",
    "\n",
    "#         entry = ExpValueEntry(\n",
    "#             circuit_graph=graph_data,\n",
    "#             observable=obs,\n",
    "#             ideal_exp_value=ideal_exp_val,\n",
    "#             noisy_exp_values=[\n",
    "#                 noisy_exp_val\n",
    "#             ],\n",
    "#             circuit_depth=circuit.depth()\n",
    "#         )\n",
    "#         entries.append(entry.to_dict())\n",
    "\n",
    "#     with open(f\"./data/vqe/h2_clifford/val/bond_{bond_length}.json\", \"w\") as json_file:\n",
    "#         json.dump(entries, json_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3c89db27-ac30-4b7f-82d5-b15f4f64dc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths = glob.glob(\"./data/vqe/h2_clifford/train/**.json\")\n",
    "val_paths = glob.glob(\"./data/vqe/h2_clifford/val/**.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "55907ea2-a0cf-4992-a7f2-6bad0bc726a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataBatch(x=[193, 21], edge_index=[2, 368], edge_attr=[175, 3], y=[32, 1], observable=[32, 5, 9], circuit_depth=[32, 1], noisy_0=[32, 1], batch=[193], 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": 120,
   "id": "e24fef68-dc1b-4db4-866c-cffeffb85e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ExpValCircuitGraphModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self,\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 + 7, 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",
    "        obs_vec = observable[:, :, 0]\n",
    "\n",
    "        merge = torch.cat((\n",
    "            graph, \n",
    "            exp_value,\n",
    "            circuit_depth,\n",
    "            obs_vec\n",
    "        ), dim=1)\n",
    "\n",
    "        return self.body_seq(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "098b23ac-5616-4a4b-b657-5be495bcc756",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExpValCircuitGraphModel(\n",
    "    num_node_features=21, \n",
    "    hidden_channels=20\n",
    ")\n",
    "criterion = torch.nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0801d124-8fd0-4a18-b240-4bfb8f61d4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(model.parameters(), lr=0.001)\n",
    "scheduler = ReduceLROnPlateau(optimizer, \n",
    "                              'min', \n",
    "                              factor=0.1, \n",
    "                              patience=15, \n",
    "                              verbose=True, \n",
    "                              min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "41ce2dd3-a581-4779-b78c-3eca13388039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04ff3b7b1fbc49d8aece2ff841bde3fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Model training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 00077: reducing learning rate of group 0 to 1.0000e-04.\n",
      "Epoch 00093: reducing learning rate of group 0 to 1.0000e-05.\n"
     ]
    }
   ],
   "source": [
    "min_valid_loss = np.inf\n",
    "\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "\n",
    "N_EPOCHS = 100\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": 124,
   "id": "213aaf3c-8b01-4d0c-9258-375efc4e91b6",
   "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"
    },
    {
     "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()\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, ngm_mitigated in zip(\n",
    "        data.y.tolist(),\n",
    "        data.noisy_0.tolist(),\n",
    "        out.tolist()\n",
    "    ):\n",
    "        ideal = np.mean(ideal)\n",
    "        noisy = np.mean(noisy)\n",
    "        ngm_mitigated = np.mean(ngm_mitigated)\n",
    "        distances.append({\n",
    "            \"ideal\": ideal,\n",
    "            \"noisy\": noisy,\n",
    "            \"ngm_mitigated\": ngm_mitigated,\n",
    "            \"dist_noisy\": np.abs(ideal - noisy),\n",
    "            \"dist_ngm\": np.abs(ideal - ngm_mitigated),\n",
    "        })\n",
    "        \n",
    "df = pd.DataFrame(distances)\n",
    "sns.boxplot(data=df[[\"dist_noisy\", \"dist_ngm\"]], orient=\"h\", showfliers = False)\n",
    "plt.title(\"Dist to ideal exp value\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot([df['ideal'], df['noisy'], df[\"ngm_mitigated\"]], kde=True, bins=40)\n",
    "plt.title(\"Exp values distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d4fb82-7403-4a96-860a-65ce00935cda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b6e543bf-9be9-44c3-bda6-fb8a2a3be295",
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_point = np.ones(num_params) #np.random.random(num_params)# \n",
    "\n",
    "optimal_values = []\n",
    "\n",
    "options = Options(optimization_level=0)\n",
    "NgemEstimator = ngem(BackendEstimator, model=model, backend=backend, options=options)\n",
    "ngem_estimator = NgemEstimator(backend, skip_transpilation=True)\n",
    "\n",
    "\n",
    "#ideal_estimator,  ngem_estimator, backend_estimator\n",
    "vqe_optimizer = COBYLA(maxiter=100, tol=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d947c64f-bcdc-49d6-a796-702c587e0a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2ac9b36b881c493295f7ee859d085a91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(ideal_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    ideal_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7cc72ab9-1f5d-431b-89ab-5f0400658da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bd3f5b36da6439289072df9b3f86504",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noisy_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(backend_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    noisy_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "b46e726a-d00b-40cc-b981-e586c0e9da63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b981bfbd39ae4c0cb1bab6fccb9adb1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mitigated_values = []\n",
    "for bond_length, operator in tqdm_notebook(bond_operators):\n",
    "    vqe = VQE(ngem_estimator, transpiled_ansatz, vqe_optimizer, callback=None, initial_point=initial_point)\n",
    "    result = vqe.compute_minimum_eigenvalue(operator)\n",
    "    mitigated_values.append(result.optimal_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "98df32fc-3bfe-4b27-9672-4be69d123244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blenghts = [l for l, _ in bond_operators]\n",
    "\n",
    "plt.plot(blenghts, ideal_values, label=\"ideal\")\n",
    "plt.plot(blenghts, noisy_values, 'ro--', linewidth=1, markersize=4, label=\"noisy\")\n",
    "plt.plot(blenghts, mitigated_values, 'go--', linewidth=1, markersize=4, label=\"mitigated\")\n",
    "\n",
    "plt.title(\"VQE on H2 molecule (sim)\")\n",
    "plt.xlabel(\"Bond length\")\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "052c3092-8cf1-4b21-8867-196dc0ce7dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./results/vqe_h2_on_random_cliffords.json\", \"w\") as f:\n",
    "    json.dump({\n",
    "        \"bond_lengths\": blenghts,\n",
    "        \"ideal_values\": ideal_values,\n",
    "        \"noisy_values\": noisy_values,\n",
    "        \"mitigated_values\": mitigated_values\n",
    "    }, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c41d62c-4abe-44a8-bf5d-d56b1cce63eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faeb74f2-4443-4707-ac7d-bf87c4b89584",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}