docs/tutorials/h07_finetuning_parse_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import json\n",
    "import glob, pickle\n",
    "from collections import defaultdict\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from qiskit import transpile\n",
    "from qiskit import execute\n",
    "from qiskit.providers.fake_provider import FakeLima\n",
    "from qiskit.primitives import Estimator\n",
    "from qiskit.circuit.random import random_circuit\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 GCNConv, global_mean_pool, Linear, ChebConv, SAGEConv\n",
    "from torch_geometric.data import Data\n",
    "from torch_geometric.loader import DataLoader\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 qiskit.quantum_info import random_clifford\n",
    "\n",
    "import random\n",
    "from qiskit.circuit.library import HGate, SdgGate\n",
    "from qiskit.circuit import ClassicalRegister\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 qiskit_aer import AerSimulator, QasmSimulator\n",
    "from qiskit.providers.fake_provider import FakeMontreal, FakeLima, FakeBelem\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 qiskit import QuantumCircuit\n",
    "from qiskit.circuit.library import U3Gate, CZGate, PhaseGate, CXGate\n",
    "from mbd_utils import construct_random_clifford, cal_z_exp, calc_imbalance, cal_all_z_exp, construct_mbl_circuit, generate_disorder, random_clifford_circuit\n",
    "from gnn import ExpValCircuitGraphModel\n",
    "from qiskit.quantum_info import Clifford"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "{'shots': 10000,\n 'backend': AerSimulator('aer_simulator(fake_montreal)'\n              noise_model=<NoiseModel on ['reset', 'cx', 'id', 'x', 'sx', 'measure']>),\n 'name': 'noisy_montreal'}"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend_name = 'fakemontreal'\n",
    "\n",
    "if backend_name == 'fakelima':\n",
    "    backend = FakeLima()\n",
    "    properties = get_backend_properties_v1(backend)\n",
    "\n",
    "    ## Local\n",
    "    backend_ideal = QasmSimulator() # Noiseless\n",
    "    backend_noisy = AerSimulator.from_backend(FakeLima()) # Noisy\n",
    "\n",
    "    run_config_ideal = {'shots': 10000, 'backend': backend_ideal, 'name': 'ideal'}\n",
    "    run_config_noisy = {'shots': 10000, 'backend': backend_noisy, 'name': 'noisy_lima'}\n",
    "\n",
    "elif backend_name == 'fakebelem':\n",
    "    backend = FakeBelem()\n",
    "    properties = get_backend_properties_v1(backend)\n",
    "\n",
    "    ## Local\n",
    "    backend_ideal = QasmSimulator() # Noiseless\n",
    "    backend_noisy = AerSimulator.from_backend(FakeBelem()) # Noisy\n",
    "\n",
    "    run_config_ideal = {'shots': 10000, 'backend': backend_ideal, 'name': 'ideal'}\n",
    "    run_config_noisy = {'shots': 10000, 'backend': backend_noisy, 'name': 'noisy_belem'}\n",
    "\n",
    "elif backend_name == 'fakemontreal':\n",
    "    backend = FakeMontreal()\n",
    "    properties = get_backend_properties_v1(backend)\n",
    "\n",
    "    ## Local\n",
    "    backend_ideal = QasmSimulator() # Noiseless\n",
    "    backend_noisy = AerSimulator.from_backend(FakeMontreal()) # Noisy\n",
    "\n",
    "    run_config_ideal = {'shots': 10000, 'backend': backend_ideal, 'name': 'ideal'}\n",
    "    run_config_noisy = {'shots': 10000, 'backend': backend_noisy, 'name': 'noisy_montreal'}\n",
    "\n",
    "else:\n",
    "    raise NotImplementedError\n",
    "\n",
    "run_config_noisy"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
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'./data/circ_linked_to_pyg_data/qasm_5_-0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_5_-1.0.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.0.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_5_0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_6_-1.0.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.0.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_6_0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_7_-1.0.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.0.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_7_0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_8_-1.0.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.0.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_8_0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-0.9.txt',\n './data/circ_linked_to_pyg_data/qasm_9_-1.0.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.0.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.1.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.2.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.3.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.4.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.5.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.6.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.7.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.8.txt',\n './data/circ_linked_to_pyg_data/qasm_9_0.9.txt']"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = sorted(glob.glob('./data/circ_linked_to_pyg_data/*.txt'))\n",
    "files"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "circs = defaultdict(list)\n",
    "\n",
    "#for file in tqdm_notebook(files, leave=False):\n",
    "for file in ['./data/circ_linked_to_pyg_data/qasm_11_-0.3.txt']:\n",
    "    print(file)\n",
    "    depth = int(file.split('_')[-2])\n",
    "    if depth not in [3, 5, 7, 9, 11]:\n",
    "        continue\n",
    "    # exp_val_bin = float(file.split('_')[-1][:-4])\n",
    "\n",
    "    # f_name = '_'.join([depth, round(exp_val_bin, 1)]) + '.json'\n",
    "    f_name = file.split('/')[-1][5:-4] + '.json'\n",
    "    with open('./data/pyg_data/'+f_name, 'r') as in_f:\n",
    "        pyg_dicts = json.load(in_f)\n",
    "        print('len(pyg_dicts)\\t', len(pyg_dicts))\n",
    "\n",
    "    qc_list = []\n",
    "    with open(file, \"r\") as f:\n",
    "        lines = f.read().split('-----')\n",
    "        print('len(lines)\\t', len(lines) - 1)\n",
    "        print('exp_val, \\t ideal_exp_value, \\t noisy_exp_values')\n",
    "        for i, line in enumerate(lines):\n",
    "            splitted = line.split('\\n')\n",
    "            valid = splitted[3:]\n",
    "            if len(valid) and valid[0].startswith('qasm'):\n",
    "                valid = valid[1:]\n",
    "            elif not len(valid):\n",
    "                continue\n",
    "            try:\n",
    "                exp_val = float(splitted[1])\n",
    "            except:\n",
    "                exp_val = float(splitted[2])\n",
    "            qasm_str = '\\n'.join(valid)\n",
    "            qc = QuantumCircuit.from_qasm_str(qasm_str)\n",
    "            pyg_dict = pyg_dicts[i]\n",
    "            print(exp_val, pyg_dict['ideal_exp_value'], pyg_dict['noisy_exp_values'])\n",
    "            qc_list.append((exp_val, qc, pyg_dict))\n",
    "    print('len(qc_list)', len(qc_list))\n",
    "    circs[depth] += qc_list"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "# with open('./data/circ_parsed_pyg_data/parsed.pk', 'wb') as out:\n",
    "#     pickle.dump(circs, out)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990\n",
      "1990\n",
      "1990\n",
      "1990\n",
      "1976\n",
      "1976\n",
      "1984\n",
      "1984\n",
      "1990\n",
      "1990\n",
      "1982\n",
      "1982\n",
      "1982\n",
      "1982\n",
      "1978\n",
      "1978\n",
      "1971\n",
      "1971\n",
      "1953\n",
      "1953\n",
      "1984\n",
      "1984\n",
      "1990\n",
      "1990\n",
      "1960\n",
      "1960\n",
      "1984\n",
      "1984\n",
      "1953\n",
      "1953\n"
     ]
    }
   ],
   "source": [
    "from random import shuffle\n",
    "import copy\n",
    "\n",
    "new_parsed = {}\n",
    "for d in range(1, 19):\n",
    "    if not len(parsed[float(d)]): continue\n",
    "    print(len(parsed[float(d)]))\n",
    "    tmp = copy.deepcopy(parsed[float(d)])\n",
    "    print(len(tmp))\n",
    "    shuffle(tmp)\n",
    "    new_parsed[int(d)] = tmp"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [],
   "source": [
    "# with open('./data/circ_parsed_pyg_data/new_parsed.pk', 'wb') as out:\n",
    "#     pickle.dump(new_parsed, out)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "with open('./data/circ_parsed_pyg_data/new_parsed.pk', 'rb') as in_f:\n",
    "    new_parsed = pickle.load(in_f)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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",
    "for d in [4]:\n",
    "    sns.histplot(list(zip(*new_parsed[d]))[0][:1000])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# with open('./data/pyg_data/3_0.0.json', 'r') as in_f:\n",
    "#     pyg_d = json.load(in_f)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fakemontreal\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/15 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "29a117ccb8e747ddb9e0bfaeb3344bc6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1990\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "639f147b57454d3eb68ff6ba4b4ae894"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "2 1990\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1622fe75bbfc477c9d86896f24f5208a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "3 1976\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "41899697db704d5ab87c7549e320261e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "4 1984\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9357953efc254b9384d44325927213d5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "5 1990\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "28e8e442595544de9ca079015fcd8e2d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n",
      "6 1982\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a326af87b6814c8aaafc1b09d4957a41"
      }
     },
     "metadata": {},
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     "name": "stdout",
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     "name": "stdout",
     "output_type": "stream",
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      "400\n",
      "9 1971\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n"
     ]
    }
   ],
   "source": [
    "print(backend_name)\n",
    "for depth, qc_list in tqdm_notebook(new_parsed.items()):\n",
    "    circuits = list(list(zip(*qc_list))[1])\n",
    "    if depth not in list(range(1, 10)): continue\n",
    "    print(int(depth), len(circuits))\n",
    "    entries = []\n",
    "    # trian circuits[:1000]\n",
    "    # val circuits[1000:1400]\n",
    "    # finetune circuits[1400:1800]\n",
    "    for circuit in tqdm_notebook(circuits[1000:1400]):\n",
    "        job_ideal = execute(circuit, **run_config_ideal)\n",
    "        job_noisy = execute(circuit, **run_config_noisy)\n",
    "\n",
    "        counts_ideal = job_ideal.result().get_counts()\n",
    "        counts_noisy = job_noisy.result().get_counts()\n",
    "\n",
    "        ideal_exp_val = cal_all_z_exp(counts_ideal)\n",
    "        noisy_exp_val = cal_all_z_exp(counts_noisy)\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=[],\n",
    "            ideal_exp_value=[ideal_exp_val],\n",
    "            noisy_exp_values=[[noisy_exp_val]],\n",
    "            circuit_depth=circuit.depth()\n",
    "        )\n",
    "        to_append = entry.to_dict()\n",
    "        to_append['circuit'] = circuit\n",
    "        entries.append(to_append)\n",
    "\n",
    "    print(len(entries))\n",
    "    with open(f'./data/circ_parsed_pyg_data/val/{backend_name}_depth{depth}.pk', 'wb') as out_file:\n",
    "        pickle.dump(entries, out_file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "with open(f'./data/circ_parsed_pyg_data/train/fakemontreal_depth1.json', 'r') as in_file:\n",
    "    d = json.load(in_file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[[-0.0688]] [0.0074]\n",
      "[[0.0104]] [-0.0014]\n",
      "[[0.0804]] [0.0048]\n",
      "[[-0.0288]] [-0.044]\n",
      "[[-0.0098]] [-0.0142]\n",
      "[[-0.0774]] [0.017]\n",
      "[[0.0596]] [0.0038]\n",
      "[[-0.0274]] [-0.1124]\n",
      "[[0.075]] [-0.0128]\n",
      "[[0.0368]] [0.0118]\n",
      "[[-0.098]] [-0.0916]\n",
      "[[-0.033]] [0.003]\n",
      "[[0.0266]] [0.0086]\n",
      "[[-0.058]] [-0.0666]\n",
      "[[-0.0036]] [-0.0096]\n",
      "[[-0.0184]] [-0.0302]\n",
      "[[0.0244]] [-0.0066]\n",
      "[[0.0094]] [0.0068]\n",
      "[[0.0068]] [-0.0144]\n",
      "[[0.0218]] [-0.0066]\n",
      "[[-0.1854]] [-0.1318]\n",
      "[[0.0222]] [0.0072]\n",
      "[[0.0366]] [-0.0028]\n",
      "[[0.0234]] [-0.0178]\n",
      "[[0.0348]] [-0.014]\n",
      "[[0.0358]] [-0.0138]\n",
      "[[0.0248]] [-0.0016]\n",
      "[[-0.0282]] [0.0036]\n",
      "[[-0.0258]] [-0.0464]\n",
      "[[-0.136]] [-0.1264]\n",
      "[[-0.003]] [0.0014]\n",
      "[[-0.1114]] [-0.2304]\n",
      "[[-0.085]] [-0.0942]\n",
      "[[0.0422]] [-0.0184]\n",
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      "[[-0.0496]] [-0.1508]\n",
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      "[[-0.1206]] [-0.1416]\n",
      "[[-0.1066]] [-0.1128]\n",
      "[[-0.0076]] [0.001]\n",
      "[[0.0012]] [-0.0056]\n",
      "[[-0.0952]] [-0.115]\n",
      "[[0.0036]] [-0.0072]\n",
      "[[0.0184]] [-0.0242]\n",
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      "[[0.0102]] [0.0096]\n",
      "[[0.0028]] [0.0204]\n",
      "[[0.0006]] [0.0272]\n",
      "[[-0.013]] [-0.009]\n",
      "[[0.0004]] [-0.0102]\n",
      "[[0.0394]] [-0.0104]\n",
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      "[[0.013]] [-0.003]\n",
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      "[[-0.1066]] [-0.2036]\n",
      "[[-0.0738]] [-0.1408]\n",
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      "[[-0.0886]] [-0.104]\n",
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      "[[-0.0394]] [-0.1546]\n",
      "[[-0.197]] [-0.2006]\n",
      "[[-0.126]] [-0.1768]\n",
      "[[-0.174]] [-0.2324]\n",
      "[[-0.1442]] [-0.185]\n",
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      "[[-0.2066]] [-0.255]\n",
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      "[[-0.1016]] [-0.2006]\n",
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      "[[-0.1378]] [-0.2518]\n",
      "[[-0.1216]] [-0.1546]\n",
      "[[-0.1762]] [-0.204]\n",
      "[[-0.2282]] [-0.225]\n",
      "[[-0.135]] [-0.182]\n",
      "[[-0.137]] [-0.1592]\n",
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      "[[-0.1416]] [-0.2034]\n",
      "[[-0.1272]] [-0.1946]\n",
      "[[-0.1334]] [-0.1698]\n",
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      "[[-0.1364]] [-0.1902]\n",
      "[[-0.191]] [-0.2078]\n",
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      "[[-0.088]] [-0.169]\n",
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      "[[-0.1772]] [-0.235]\n",
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      "[[-0.3308]] [-0.3832]\n",
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      "[[-0.173]] [-0.232]\n",
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      "[[-0.2746]] [-0.352]\n",
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      "[[-0.2838]] [-0.3316]\n",
      "[[-0.2206]] [-0.32]\n",
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      "[[-0.2682]] [-0.3316]\n",
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      "[[-0.2308]] [-0.2734]\n",
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      "[[-0.2144]] [-0.282]\n",
      "[[-0.168]] [-0.301]\n",
      "[[-0.2314]] [-0.3626]\n",
      "[[-0.1962]] [-0.3134]\n",
      "[[-0.1834]] [-0.26]\n",
      "[[-0.2314]] [-0.237]\n",
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      "[[-0.1634]] [-0.308]\n",
      "[[-0.204]] [-0.2556]\n",
      "[[-0.243]] [-0.3178]\n",
      "[[-0.195]] [-0.2584]\n",
      "[[-0.2284]] [-0.3252]\n",
      "[[-0.2532]] [-0.342]\n",
      "[[-0.2312]] [-0.297]\n",
      "[[-0.24]] [-0.2742]\n",
      "[[-0.242]] [-0.3182]\n",
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      "[[-0.2128]] [-0.2204]\n",
      "[[-0.2414]] [-0.2962]\n",
      "[[-0.1984]] [-0.2584]\n",
      "[[-0.3288]] [-0.3406]\n",
      "[[-0.2094]] [-0.2332]\n",
      "[[-0.193]] [-0.2524]\n",
      "[[-0.2756]] [-0.294]\n",
      "[[-0.24]] [-0.2864]\n",
      "[[-0.2176]] [-0.31]\n",
      "[[-0.2236]] [-0.2472]\n",
      "[[-0.231]] [-0.3126]\n",
      "[[-0.214]] [-0.3732]\n",
      "[[-0.3472]] [-0.4528]\n",
      "[[-0.2178]] [-0.3266]\n",
      "[[-0.3302]] [-0.353]\n",
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      "[[-0.3092]] [-0.376]\n",
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      "[[-0.2848]] [-0.3882]\n",
      "[[-0.319]] [-0.4718]\n",
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      "[[-0.2914]] [-0.4024]\n",
      "[[-0.3148]] [-0.392]\n",
      "[[-0.2272]] [-0.3168]\n",
      "[[-0.2114]] [-0.3674]\n",
      "[[-0.3006]] [-0.3944]\n",
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      "[[-0.2978]] [-0.3758]\n",
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      "[[-0.6874]] [-0.8488]\n",
      "[[-0.6386]] [-0.8112]\n",
      "[[-0.68]] [-0.8108]\n",
      "[[-0.679]] [-0.8306]\n",
      "[[-0.6612]] [-0.828]\n",
      "[[-0.7572]] [-0.9228]\n",
      "[[-0.7606]] [-0.9268]\n",
      "[[-0.7498]] [-0.9054]\n",
      "[[-0.7528]] [-0.9326]\n",
      "[[-0.698]] [-0.843]\n",
      "[[-0.6974]] [-0.845]\n",
      "[[-0.675]] [-0.8548]\n",
      "[[-0.66]] [-0.9022]\n",
      "[[-0.681]] [-0.846]\n",
      "[[-0.701]] [-0.9404]\n",
      "[[-0.775]] [-0.9486]\n",
      "[[-0.656]] [-0.8888]\n",
      "[[-0.692]] [-0.8446]\n",
      "[[-0.7]] [-0.914]\n",
      "[[-0.698]] [-0.8634]\n",
      "[[-0.6262]] [-0.9016]\n",
      "[[-0.7274]] [-0.9332]\n",
      "[[-0.7122]] [-0.897]\n",
      "[[-0.6876]] [-0.8418]\n",
      "[[-0.696]] [-0.8634]\n",
      "[[-0.6828]] [-0.912]\n",
      "[[-0.7438]] [-0.939]\n",
      "[[-0.7274]] [-0.8994]\n",
      "[[-0.7362]] [-0.9208]\n",
      "[[-0.7088]] [-0.8812]\n",
      "[[-0.6848]] [-0.8832]\n",
      "[[-0.7528]] [-0.9304]\n",
      "[[-0.7376]] [-0.9002]\n",
      "[[-0.7198]] [-0.9012]\n",
      "[[-0.7272]] [-0.8874]\n",
      "[[-0.6572]] [-0.9642]\n",
      "[[-0.7548]] [-0.9254]\n",
      "[[-0.753]] [-0.938]\n",
      "[[-0.6704]] [-0.9526]\n",
      "[[-0.739]] [-0.8972]\n",
      "[[-0.7462]] [-0.9144]\n",
      "[[-0.7214]] [-0.8982]\n",
      "[[-0.7384]] [-0.9236]\n",
      "[[-0.7792]] [-0.9604]\n",
      "[[-0.7546]] [-0.93]\n",
      "[[-0.7506]] [-0.9178]\n",
      "[[-0.7264]] [-0.9056]\n",
      "[[-0.6628]] [-0.9028]\n",
      "[[-0.713]] [-0.9544]\n",
      "[[-0.6996]] [-0.9238]\n",
      "[[-0.5634]] [-0.919]\n",
      "[[-0.775]] [-0.9414]\n",
      "[[-0.6858]] [-0.8568]\n",
      "[[-0.7156]] [-0.9574]\n",
      "[[-0.7424]] [-0.9036]\n",
      "[[-0.7482]] [-0.9184]\n",
      "[[-0.6598]] [-0.9298]\n",
      "[[-0.6874]] [-0.842]\n",
      "[[-0.6728]] [-0.885]\n",
      "[[-0.752]] [-0.9104]\n",
      "[[-0.8064]] [-0.955]\n",
      "[[-0.7754]] [-0.9314]\n",
      "[[-0.6556]] [-0.8888]\n",
      "[[-0.7584]] [-0.9068]\n",
      "[[-0.6866]] [-0.8726]\n",
      "[[-0.6344]] [-0.8518]\n",
      "[[-0.7288]] [-0.9474]\n",
      "[[-0.6472]] [-0.835]\n",
      "[[-0.6822]] [-0.8986]\n",
      "[[-0.7646]] [-0.9296]\n",
      "[[-0.6894]] [-0.8632]\n",
      "[[-0.7174]] [-0.8712]\n",
      "[[-0.6638]] [-0.814]\n",
      "[[-0.5334]] [-0.8938]\n",
      "[[-0.7396]] [-0.9212]\n",
      "[[-0.7244]] [-0.8896]\n",
      "[[-0.4748]] [-0.8592]\n",
      "[[-0.7348]] [-0.8984]\n",
      "[[-0.7322]] [-0.9382]\n",
      "[[-0.723]] [-0.8926]\n",
      "[[-0.7258]] [-0.8752]\n",
      "[[-0.79]] [-0.9388]\n",
      "[[-0.78]] [-0.9472]\n",
      "[[-0.6786]] [-0.9054]\n",
      "[[-0.7328]] [-0.9034]\n",
      "[[-0.7544]] [-0.9436]\n",
      "[[-0.7674]] [-0.9556]\n",
      "[[-0.7608]] [-0.9226]\n",
      "[[-0.689]] [-0.9096]\n",
      "[[-0.763]] [-0.954]\n",
      "[[-0.6244]] [-0.8372]\n",
      "[[-0.7456]] [-0.9398]\n",
      "[[-0.578]] [-0.8342]\n",
      "[[-0.7112]] [-0.868]\n",
      "[[-0.6702]] [-0.8484]\n",
      "[[-0.7226]] [-0.885]\n",
      "[[-0.6724]] [-0.8534]\n",
      "[[-0.7018]] [-0.9056]\n",
      "[[-0.7496]] [-0.931]\n",
      "[[-0.7148]] [-0.8748]\n",
      "[[-0.6594]] [-0.8238]\n",
      "[[-0.5642]] [-0.9502]\n",
      "[[-0.7174]] [-0.9494]\n",
      "[[-0.7424]] [-0.926]\n",
      "[[-0.7672]] [-0.9474]\n",
      "[[-0.7704]] [-0.945]\n",
      "[[-0.788]] [-0.9462]\n",
      "[[-0.7954]] [-1.0]\n",
      "[[-0.8156]] [-1.0]\n",
      "[[-0.8072]] [-1.0]\n",
      "[[-0.8272]] [-1.0]\n",
      "[[-0.7118]] [-1.0]\n",
      "[[-0.82]] [-1.0]\n",
      "[[-0.7592]] [-0.9958]\n",
      "[[-0.8106]] [-1.0]\n",
      "[[-0.7518]] [-1.0]\n",
      "[[-0.8154]] [-1.0]\n",
      "[[-0.808]] [-1.0]\n",
      "[[-0.8082]] [-0.9894]\n",
      "[[-0.8032]] [-1.0]\n",
      "[[-0.83]] [-1.0]\n",
      "[[-0.8108]] [-1.0]\n",
      "[[-0.8186]] [-1.0]\n",
      "[[-0.796]] [-1.0]\n",
      "[[-0.7936]] [-1.0]\n",
      "[[-0.8156]] [-1.0]\n",
      "[[-0.718]] [-1.0]\n",
      "[[-0.8018]] [-1.0]\n",
      "[[-0.8132]] [-1.0]\n",
      "[[-0.7996]] [-1.0]\n",
      "[[-0.7106]] [-1.0]\n",
      "[[-0.8226]] [-1.0]\n",
      "[[-0.823]] [-1.0]\n",
      "[[-0.8018]] [-1.0]\n",
      "[[-0.813]] [-1.0]\n",
      "[[-0.8238]] [-1.0]\n",
      "[[-0.7624]] [-0.944]\n",
      "[[-0.792]] [-0.9752]\n",
      "[[-0.8056]] [-1.0]\n",
      "[[-0.726]] [-0.998]\n",
      "[[-0.8054]] [-1.0]\n",
      "[[-0.8038]] [-1.0]\n",
      "[[-0.8226]] [-1.0]\n",
      "[[-0.7558]] [-1.0]\n",
      "[[-0.7764]] [-0.978]\n",
      "[[-0.759]] [-1.0]\n",
      "[[-0.7508]] [-1.0]\n",
      "[[-0.8146]] [-1.0]\n",
      "[[-0.792]] [-1.0]\n",
      "[[-0.712]] [-1.0]\n",
      "[[-0.8028]] [-1.0]\n",
      "[[-0.825]] [-1.0]\n",
      "[[-0.7566]] [-1.0]\n",
      "[[-0.8076]] [-1.0]\n",
      "[[-0.8008]] [-1.0]\n",
      "[[-0.807]] [-1.0]\n",
      "[[-0.7468]] [-1.0]\n",
      "[[-0.7122]] [-1.0]\n",
      "[[-0.7042]] [-1.0]\n",
      "[[-0.716]] [-1.0]\n",
      "[[-0.7842]] [-1.0]\n",
      "[[-0.8288]] [-1.0]\n",
      "[[-0.8016]] [-1.0]\n",
      "[[-0.7914]] [-1.0]\n",
      "[[-0.8044]] [-1.0]\n",
      "[[-0.813]] [-1.0]\n",
      "[[-0.818]] [-1.0]\n",
      "[[-0.7192]] [-1.0]\n",
      "[[-0.8118]] [-0.997]\n",
      "[[-0.8164]] [-1.0]\n",
      "[[-0.7114]] [-1.0]\n",
      "[[-0.8142]] [-1.0]\n",
      "[[-0.8252]] [-1.0]\n",
      "[[-0.7996]] [-0.989]\n",
      "[[-0.7522]] [-0.9998]\n",
      "[[-0.7964]] [-1.0]\n",
      "[[-0.7184]] [-1.0]\n",
      "[[-0.8258]] [-1.0]\n",
      "[[-0.791]] [-1.0]\n",
      "[[-0.8188]] [-1.0]\n",
      "[[-0.795]] [-1.0]\n",
      "[[-0.7828]] [-0.9858]\n",
      "[[-0.8162]] [-1.0]\n",
      "[[-0.837]] [-0.9956]\n",
      "[[-0.8158]] [-1.0]\n",
      "[[-0.8044]] [-1.0]\n",
      "[[-0.7354]] [-1.0]\n",
      "[[-0.8124]] [-1.0]\n",
      "[[-0.8182]] [-1.0]\n",
      "[[-0.8214]] [-1.0]\n",
      "[[-0.81]] [-1.0]\n",
      "[[-0.7864]] [-1.0]\n",
      "[[-0.7974]] [-1.0]\n",
      "[[-0.8072]] [-1.0]\n",
      "[[-0.7842]] [-1.0]\n",
      "[[-0.7898]] [-1.0]\n",
      "[[-0.7182]] [-1.0]\n",
      "[[-0.803]] [-1.0]\n",
      "[[-0.8196]] [-1.0]\n",
      "[[-0.835]] [-1.0]\n",
      "[[-0.802]] [-1.0]\n",
      "[[-0.787]] [-0.9688]\n",
      "[[-0.7962]] [-1.0]\n",
      "[[-0.7242]] [-1.0]\n",
      "[[-0.7296]] [-1.0]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(d)):\n",
    "    print(d[i]['noisy_exp_values'], d[i]['ideal_exp_value'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
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