docs/tutorials/h36_ising_4q_hardware_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json, pickle\n",
    "import glob\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\n",
    "from qiskit_aer.noise import depolarizing_error, coherent_unitary_error, NoiseModel\n",
    "from noise_utils import AddNoise, RemoveReadoutErrors\n",
    "\n",
    "from functools import partial\n",
    "from qiskit_ibm_runtime import Estimator as RuntimeEstimator\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader, TensorDataset\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "import torch.nn as nn\n",
    "\n",
    "D = {'output': 'mpl', 'fold': -1, 'idle_wires': False}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "from qiskit_ibm_provider import IBMProvider\n",
    "provider = IBMProvider()\n",
    "backend_ideal = QasmSimulator() # Noiseless\n",
    "real_backend = provider.get_backend('ibm_algiers')\n",
    "estimator_ideal = Estimator()\n",
    "\n",
    "run_config_ideal = {'shots': 10000, 'backend': backend_ideal, 'name': 'ideal'}\n",
    "run_config_real_backend = {'shots': 10000, 'backend': real_backend, 'name': 'real_backend'}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService, Session, Estimator, Options\n",
    "service = QiskitRuntimeService()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "def check_f(f, f_ext, batch_indices):\n",
    "    return f.endswith(f_ext) and any([f\"batch_%02d\"%batch_index in f for batch_index in batch_indices])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "def load_results(data_dir, step_indices, f_ext='.json'):\n",
    "    noisy_exp_vals = []\n",
    "    zne_mitigated_vals = []\n",
    "    data_files = sorted([os.path.join(data_dir, f) for f in os.listdir(data_dir) if check_f(f, f_ext, step_indices)])\n",
    "    for data_file in tqdm(data_files, leave=True):\n",
    "        loaded = json.load(open(data_file, 'r'))\n",
    "        ret_job_result = service.job(loaded['job_id']).result()\n",
    "        noisy_exp_vals.append([x['zne']['noise_amplification']['values'][0] for x in ret_job_result.metadata])\n",
    "        zne_mitigated_vals.append(ret_job_result.values)\n",
    "    return noisy_exp_vals, zne_mitigated_vals\n",
    "\n",
    "noisy_exp_vals, zne_mitigated_vals = load_results('./data/ising_init_from_qasm_hardware/shuffled/', list(range(60)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/60 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6141ef0e74b449bd9c81c85dec3b65b5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_circuits(data_dir, step_indices, f_ext='.pk'):\n",
    "    circuit_batches = []\n",
    "    data_files = sorted([os.path.join(data_dir, f) for f in os.listdir(data_dir) if check_f(f, f_ext, step_indices)])\n",
    "    for data_file in tqdm(data_files, leave=True):\n",
    "        loaded = pickle.load(open(data_file, 'rb'))\n",
    "        circuit_batches.append(loaded['circuit_batch'])\n",
    "    return circuit_batches\n",
    "\n",
    "circuit_batches = load_circuits('./data/ising_init_from_qasm_hardware/shuffled/', list(range(60)))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "obs_list = ['ZIII', 'IZII', 'IIZI', 'IIIZ']\n",
    "\n",
    "def form_all_qubit_observable(observable, measurement_qubits, total_num_qubits):\n",
    "    \"\"\"Input observable in non-endian, output observable in endian\"\"\"\n",
    "    assert len(observable) == len(measurement_qubits)\n",
    "    converted_obs = list('I' * total_num_qubits)\n",
    "    for qubit, basis in zip(measurement_qubits, list(observable)):\n",
    "        converted_obs[qubit] = basis\n",
    "    return ''.join(converted_obs)[::-1]\n",
    "\n",
    "def get_measurement_qubits(qc, num_measured_qubit):\n",
    "    measurement_qubits = []\n",
    "    for measurement in range(num_measured_qubit - 1, -1, -1):\n",
    "        measurement_qubits.append(qc.data[-1 - measurement][1][0].index)\n",
    "    return measurement_qubits"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "import qiskit\n",
    "def get_active_qubits_dynamics(circuit):\n",
    "    operations = circuit.data\n",
    "    active_qubits = set()\n",
    "\n",
    "    for op in operations:\n",
    "        if isinstance(op[0], qiskit.circuit.Barrier) or isinstance(op[0], qiskit.circuit.Delay):\n",
    "            continue\n",
    "        for qubit in op[1]:\n",
    "            active_qubits.add(qubit.index)\n",
    "\n",
    "    return active_qubits\n",
    "\n",
    "def remove_idle_qubits(circuit, layout):\n",
    "    # Get active qubits, which excludes 'Delay' and 'Barrier' operations\n",
    "    active_qubits = get_active_qubits_dynamics(circuit)\n",
    "\n",
    "    # Create a new list ordered_qubits, preserving the order in layout\n",
    "    ordered_qubits = [qubit for qubit in layout if qubit in active_qubits]\n",
    "\n",
    "    # Keep the original ClassicalRegisters intact\n",
    "    new_qc = QuantumCircuit(len(ordered_qubits))\n",
    "    for cr in circuit.cregs:\n",
    "        new_qc.add_register(cr)\n",
    "\n",
    "    # Go through each instruction and replace the qubit indices with the indices of ordered qubits\n",
    "    for instr, qargs, cargs in circuit.data:\n",
    "        ordered_qarg_indices = [ordered_qubits.index(qubit.index) for qubit in qargs if qubit.index in ordered_qubits]\n",
    "        # Since we're keeping the classical bits as they were, there's no need to change the indices\n",
    "        if ordered_qarg_indices:  # If list is not empty, means qubits are involved in operation\n",
    "            new_qc.append(instr, qargs=ordered_qarg_indices, cargs=cargs)\n",
    "\n",
    "    return new_qc"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1268.33x325.08 with 1 Axes>",
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\n"
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k, l = 29, 2\n",
    "circuit_batches[k][l].draw(**D)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1260.68x325.08 with 1 Axes>",
      "image/png": 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\n"
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = remove_idle_qubits(circuit_batches[k][l])\n",
    "qc.draw(**D)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/60 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f76496938d0847349bdc908a4ae1430e"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideal_exp_vals_estimator = []\n",
    "for circuits in tqdm(circuit_batches):\n",
    "    for trans_circuit in circuits:\n",
    "        trans_circuit = remove_idle_qubits(trans_circuit, [0, 1, 2, 3])\n",
    "        padded_obs = [SparsePauliOp(\n",
    "            form_all_qubit_observable(obs, get_measurement_qubits(trans_circuit, 4), 4)\n",
    "        ) for obs in obs_list]\n",
    "        trans_circuit.remove_final_measurements()\n",
    "        ideal_exp_vals_estimator.append(estimator_ideal.run([trans_circuit]*4, padded_obs).result().values.tolist())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "outputs": [],
   "source": [
    "to_save = {\n",
    "    'ideal': ideal_exp_vals_estimator,\n",
    "    'noisy': noisy_exp_vals,\n",
    "    'zne_mitigated': zne_mitigated_vals,\n",
    "}\n",
    "with open('./data/ising_init_from_qasm_hardware/shuffled/results.pk', 'wb') as file:\n",
    "    pickle.dump(to_save, file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# import os\n",
    "# from tqdm.notebook import tqdm\n",
    "#\n",
    "# def load_circuits(data_dir, filename=None, f_ext='.pk'):\n",
    "#     circuits = []\n",
    "#     ideal_exp_vals = []\n",
    "#     if filename is None:\n",
    "#         data_files = sorted([os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(f_ext)])\n",
    "#     else:\n",
    "#         data_files = sorted([os.path.join(data_dir, f) for f in [filename]])\n",
    "#     # print(data_files)\n",
    "#     for data_file in tqdm(data_files):\n",
    "#         for entry in pickle.load(open(data_file, 'rb')):\n",
    "#             # circuits.append(entry['circuit'])\n",
    "#             ideal_exp_vals.append(entry['ideal_exp_val'])\n",
    "#     return ideal_exp_vals\n",
    "#\n",
    "# root1 = './data/ising_init_from_qasm_hardware/train'\n",
    "# root2 = './data/ising_init_from_qasm_hardware/val'\n",
    "# ideal_exp_vals = load_circuits(root1)\n",
    "# ideal_exp_vals.extend(load_circuits(root2))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "outputs": [],
   "source": [
    "# ideal_exp_vals = [x.tolist() for x in ideal_exp_vals]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# for ideal_1, ideal_2 in zip(ideal_exp_vals, ideal_exp_vals_estimator):\n",
    "#     print([round(x, 4) for x in ideal_2])\n",
    "#     print(ideal_1)\n",
    "#     print('-'*5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Unshuffle"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "with open('./data/ising_init_from_qasm_hardware/shuffled/results.pk', 'rb') as file:\n",
    "    loaded = pickle.load(file)\n",
    "noisy_exp_vals = loaded['noisy']\n",
    "zne_mitigated_vals = loaded['zne_mitigated']\n",
    "ideal_exp_vals_estimator = loaded['ideal']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "noisy_exp_vals = np.array(noisy_exp_vals)\n",
    "zne_mitigated_vals = np.array(zne_mitigated_vals)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "noisy_exp_vals = noisy_exp_vals.reshape(-1, 4).tolist()\n",
    "zne_mitigated_vals = zne_mitigated_vals.reshape(-1, 4).tolist()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "with open('./data/ising_init_from_qasm_hardware/shuffled/index_order.json', 'r') as file:\n",
    "    index_order = json.load(file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def unshuffle_list(shuffled_list, index_order):\n",
    "    unshuffled_list = [None] * len(shuffled_list)\n",
    "    for i, index in enumerate(index_order):\n",
    "        unshuffled_list[index] = shuffled_list[i]\n",
    "    return unshuffled_list"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "noisy_exp_vals = unshuffle_list(noisy_exp_vals, index_order)\n",
    "zne_mitigated_vals = unshuffle_list(zne_mitigated_vals, index_order)\n",
    "ideal_exp_vals_estimator = unshuffle_list(ideal_exp_vals_estimator, index_order)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
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-0.018206300372629686,\n   -0.05588267970914715,\n   -0.05088164453262402],\n  [0.0724, 0.0276, -0.0022, 0.1152],\n  [0.09689999999999999,\n   -0.02240000000000003,\n   -0.045400000000000024,\n   0.11589999999999996]),\n ([-0.03042136589836858,\n   0.012902362412465757,\n   -0.01219879583220343,\n   -0.04232998312512315],\n  [0.024, 0.276, 0.0426, 0.0894],\n  [-0.030000000000000027,\n   0.2361999999999999,\n   0.03759999999999998,\n   0.08959999999999996]),\n ([-0.014192885644133117,\n   -0.41462303208211704,\n   0.2880979169876243,\n   0.4112268041718173],\n  [0.002, -0.3082, 0.0636, 0.2608],\n  [-0.018800000000000004, -0.44759999999999994, 0.0713, 0.3685999999999999]),\n ([0.060490816463578775,\n   -0.0013820415304069272,\n   -0.04641599666189432,\n   -0.024612288000187027],\n  [0.1048, -0.0892, -0.0026, 0.0994],\n  [0.14529999999999998,\n   -0.11069999999999999,\n   -0.011600000000000003,\n   0.060899999999999954]),\n ([0.0659010629972556,\n   0.00884836450632126,\n   -0.03918130021343402,\n   -0.013084407741362082],\n  [0.1008, -0.01, -0.0232, 0.061],\n  [0.148, -0.015500000000000003, 0.003200000000000036, 0.007299999999999945]),\n ([-0.18440882762658758,\n   -0.653561666377983,\n   0.30774962008093976,\n   0.07040718626477699],\n  [-0.1312, -0.5674, 0.142, 0.0774],\n  [-0.15859999999999996,\n   -0.6581999999999998,\n   0.17449999999999993,\n   0.09969999999999997]),\n ([-0.09837436636215918,\n   -0.3766044980992929,\n   0.011597394914724168,\n   -0.2891486030308705],\n  [-0.1328, -0.1822, -0.1134, -0.291],\n  [-0.14350000000000002,\n   -0.31079999999999997,\n   -0.1260999999999999,\n   -0.35119999999999996]),\n ([0.16415786030703133,\n   0.6751634606555358,\n   -0.13071612751339373,\n   0.3639871928168742],\n  [0.1592, 0.5462, -0.1458, 0.3266],\n  [0.1706, 0.6519999999999999, -0.21220000000000003, 0.38880000000000003]),\n ([0.05894577532289405,\n   -0.003199266507264986,\n   -0.047580206286251286,\n   -0.026811370135595286],\n  [0.1568, -0.0914, 0.057, 0.0796],\n  [0.15839999999999996,\n   -0.09480000000000002,\n   0.015399999999999955,\n   0.06619999999999995]),\n ([-0.08126702998219579,\n   -0.29154580936141616,\n   -0.01893128029970964,\n   -0.29238727069285714],\n  [0.133, -0.216, 0.0592, 0.2778],\n  [0.1172, -0.3227, 0.12550000000000006, 0.3794]),\n ([-0.014242000007341216,\n   -0.01112081912226931,\n   -0.047782097487362785,\n   -0.06610055141210296],\n  [0.0066, 0.0358, 0.0162, 0.0586],\n  [0.008799999999999999,\n   -0.00930000000000003,\n   -0.02370000000000002,\n   0.022199999999999956]),\n ([-0.18059497534752703,\n   -0.649794198567646,\n   0.2703560838995399,\n   -0.018683967212634763],\n  [-0.0904, -0.162, 0.0812, 0.067],\n  [-0.10649999999999996,\n   -0.27590000000000003,\n   0.13540000000000002,\n   0.09999999999999999]),\n ([-0.10613517374868009,\n   -0.41324925710622284,\n   0.02693913325925018,\n   -0.28437467883210976],\n  [-0.1046, -0.3144, -0.1058, -0.1584],\n  [-0.10839999999999998, -0.4031, -0.0907, -0.14929999999999996]),\n ([-0.08152917862222048,\n   -0.29288416423322167,\n   -0.01849510341161853,\n   -0.2924074122367224],\n  [-0.0848, -0.231, -0.1506, -0.1948],\n  [-0.09889999999999999,\n   -0.29479999999999995,\n   -0.15649999999999997,\n   -0.19349999999999998]),\n ([0.010906081045517213,\n   0.1649870394853704,\n   -0.11769784300129922,\n   -0.18105288536672737],\n  [-0.0388, 0.1462, -0.1274, -0.0958],\n  [0.01810000000000002,\n   0.18349999999999997,\n   -0.14209999999999998,\n   -0.06679999999999994]),\n ([-0.004424123559926885,\n   -0.015615862356541083,\n   -0.0519631342619046,\n   -0.06526823600504933],\n  [0.0246, 0.058, -0.0036, 0.0986],\n  [0.0366, -0.00550000000000006, -0.05140000000000002, 0.08539999999999999]),\n ([-0.014133964998894383,\n   -0.4133639933316873,\n   0.28898262157344357,\n   0.4185044078782175],\n  [-0.0028, -0.206, 0.1554, 0.3414],\n  [0.02240000000000001, -0.28959999999999997, 0.24180000000000004, 0.4657]),\n ([0.012481632780410923,\n   0.1713776792470343,\n   -0.1184317665035982,\n   -0.17767205515602258],\n  [-0.0598, 0.298, -0.1276, -0.0026],\n  [-0.005099999999999966, 0.3442999999999999, -0.1604, -0.013100000000000007]),\n ([-0.07643223612623676,\n   -0.26671515590020206,\n   -0.026808394413797065,\n   -0.29165085701615634],\n  [-0.1206, -0.1146, -0.1446, -0.274],\n  [-0.2592000000000001, -0.1289, -0.18979999999999997, -0.4035000000000001]),\n ([0.08984270744360376,\n   0.446320937565999,\n   0.014011441229846294,\n   0.5756335411884779],\n  [0.1622, 0.3778, -0.086, 0.401],\n  [0.14919999999999994, 0.4533999999999999, -0.1357, 0.5436000000000001]),\n ([-0.02564156128180045,\n   -0.002521563445134628,\n   -0.038881884355249835,\n   -0.06344958214758638],\n  [0.0046, 0.102, 0.0654, 0.0754],\n  [0.03280000000000001,\n   0.10869999999999994,\n   0.04649999999999999,\n   0.054799999999999974]),\n ([-0.007519188211000727,\n   0.08456549864294555,\n   -0.10668351505609464,\n   -0.21748951531896882],\n  [-0.0186, -0.0658, -0.1214, 0.128],\n  [0.007800000000000015,\n   -0.054999999999999966,\n   -0.11629999999999992,\n   0.05919999999999995]),\n ([0.21314693719027428,\n   0.7560346605860873,\n   -0.3478550960990362,\n   -0.05525333401882185],\n  [0.1726, 0.5722, -0.2386, 0.079],\n  [0.29019999999999996, 0.7503, -0.3283, 0.09059999999999999]),\n ([-0.013798895305389053,\n   0.009064952700915313,\n   0.0036844926040519865,\n   -0.01522216346232208],\n  [0.0084, 0.3738, 0.088, 0.1168],\n  [0.024100000000000003,\n   0.40149999999999986,\n   0.09359999999999999,\n   0.10069999999999996]),\n ([-0.012397300703226033,\n   0.008252526369429858,\n   0.0038739430723458423,\n   -0.013527225773314402],\n  [0.0766, 0.2796, 0.0946, 0.0614],\n  [0.07589999999999997, 0.2941, 0.09179999999999999, 0.093]),\n ([0.03995409305336564,\n   -0.015718442889670114,\n   -0.05474798111598547,\n   -0.04516172680066838],\n  [0.0546, -0.0032, -0.0088, 0.0692],\n  [0.092, -0.006900000000000002, -0.025400000000000006, 0.015599999999999933]),\n ([-0.03210058873708946,\n   0.010255130121172185,\n   -0.019770388724561827,\n   -0.04980258428054972],\n  [0.0336, 0.1876, 0.0284, 0.0228],\n  [0.01759999999999999,\n   0.18259999999999987,\n   0.02219999999999999,\n   0.004400000000000001]),\n ([0.00863976002340816,\n   -0.01888491007386911,\n   -0.05506955533065827,\n   -0.061801106132337746],\n  [0.1034, -0.0062, -0.002, 0.0412],\n  [0.1309,\n   -0.017800000000000003,\n   -0.05020000000000003,\n   -0.0032999999999999974]),\n ([-0.08925690760515931,\n   -0.33189766442841456,\n   -0.0052059215827616745,\n   -0.2920528761062265],\n  [0.0818, -0.2506, 0.084, 0.1726],\n  [0.06340000000000001, -0.3476, 0.18220000000000003, 0.18719999999999992]),\n ([-0.14229539722536236,\n   -0.5595934119269618,\n   0.1147066145574756,\n   -0.22658316436340925],\n  [-0.092, -0.4612, 0.0014, -0.1818],\n  [-0.13090000000000002,\n   -0.5726999999999999,\n   0.05770000000000003,\n   -0.21079999999999993]),\n ([-0.015625969538136913,\n   -0.010312310545907671,\n   -0.047006585461080636,\n   -0.06604734456765439],\n  [0.06, 0.094, 0.0316, 0.071],\n  [0.0878, 0.0493999999999999, -0.025200000000000042, 0.052399999999999974]),\n ...]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(ideal_exp_vals_estimator, noisy_exp_vals, zne_mitigated_vals))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "outputs": [],
   "source": [
    "# to_save = {\n",
    "#     'ideal': ideal_exp_vals_estimator,\n",
    "#     'noisy': noisy_exp_vals,\n",
    "#     'zne_mitigated': zne_mitigated_vals,\n",
    "# }\n",
    "# with open('./data/ising_init_from_qasm_hardware/shuffled/results_unshuffled.pk', 'wb') as file:\n",
    "#     pickle.dump(to_save, file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train-test split"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/60 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e6828f7c821a4c8887ff3cf0c32f5e32"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "all_circuits = []\n",
    "for circuits in tqdm(circuit_batches):\n",
    "    for trans_circuit in circuits:\n",
    "        # trans_circuit = remove_idle_qubits(trans_circuit)\n",
    "        all_circuits.append(trans_circuit)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "all_circuits_unshuffled = unshuffle_list(all_circuits, index_order)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1087.73x325.08 with 1 Axes>",
      "image/png": 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Y3c4zO5cvX2bv3r0sXrzY6kQIoGvXrpw/f56ff/45bVlSUhLffPMNbdq0wc/P745jy08iq2efCAG0qIpLJUIAZYrYp97SLvZgslZpCPTNeuhskwmKFYJqJR0bl4iIo40fP97oEPKsm/eVGzduzLFsdonQTZs2bUpXr7gOhz0ZCg0NpXbt2kycOJGgoCBKlSrFwoUL0/rQ2OLiCgkJyXakj8DAQDZt2pTrZkpdunTh7rvvpk+fPkyaNIkSJUrwwQcfcOzYMebNm3enYec7lYtDt4aw8C8w8W9iZDJZWkc1qGCZS8bVhJWCHdYPYmiVAp6uNyeThzsMbAUfrIYrt3SzunmtBPhA/0hwUxNtEXFxPXr0MDqEPKts2bIA7N2b/fCkNWvWzDERurWecuXK2T5YMZTDngy5ubmxYMECwsLCGDRoEH369CE4OJghQ4bg7u6eNnhC2bJlOXPmDImJiWnbHj582GYX3+301zCZTCxZsoR7772X5557ji5dunD27FlWrlypbwhuU/MqMKITNKn077LqJSw3ub3Cwc3hg77b313lwMe6bm9WaxQK3i44dXLxwjCyM9x717/LSgTA/fXhxXsg2N+w0EREHKZ69epGh5BndevWjbCwMLZvz35iuvLly+Pj48OMGTOynUfo8OHDhIWF0aZNG3uEKwZy6G1UlSpVMnQ66927NzVq1MDHxwewNKdr1qwZn376adoACjExMbRs2dKRoWYQEBDARx99xEcffWRoHK6kVCA81Bg2HrC872/sKbY7Lw9oXxN+sNEcSgU8oVUN29TljPy8Lb/fkm2W9y/eY2w8IiKSd9zsq56Tn376iYCAAC5dupRtuRs3bvD333/bIjRxMoZ/p7x582aaNGmSbtnMmTN54oknmDZtGl5eXsybN8/qkeREnFmLqrD9OBw+l3WZ3THW1fVAAwjwtU1cIiIi+VVOiZC4NkOTofj4ePbt28fgwYPTLQ8NDWX9+vUGRSViP25u0OdueG8VnL+SeZmsRpm7Vcvq0LCCTUMTEREnk9nIuCJiW4YmQwULFiQlJcXIEEQcrpAPDG0Ln6+HI+dzt62bCTrUtgwwoTneRERc24wZM4wOQcTluWA3dRHnV9gHnmlrGSCggKd125QJguHtoV1NJUIiIvnBoEGDjA5BxOUZ3mdIJL9yc7MMENCsCmw9ArtOwIlYuJTw/+tNEFIYygVbRo0rH6wkSEQkP9FkniL2p2RIxGDeHhBeyfIDMOwry79vPwzuenYrIiIiYje61RJxUkqEREREROxLt1siIiIiTmjPnj1GhyDi8tRMTkRERMQJzZ8/nx49ehgdRp5Qt27dXG9z6NgpAELLlkj32t77FeeiZEhERETECY0dO1bJkJWmTZuW621GvvUxAG++1D/da8lf1ExORERERETyJSVDIiIiIiKSLykZEhEREXFC06dPNzoEEZenZEhERETECYWFhRkdgojLUzIkIiIi4oQiIiKMDkHE5Wk0OREREREH8/DwICgoKMdyWZXx8NAtnIgt6JMkIiIi4mAFChQgJSUl2zLNmjXDx8cn03Xu7u72CEsk31EyJCIiIuJg8fHxxMbGZlvm999/JyYmJtN1QUFB+Pr62iM0kXxFfYZERERERCRfUjIkIiIiIiL5kpIhERERESe0cOFCo0MQcXlKhkREREREJF9SMiQiIiLihLp162Z0CCIuT6PJGWzvL3DlrNFRZM+/GFRtZXQUIiIiYm/Dhg0jOjrakH3XrVuXadOmGbJvyb+UDBnsylm4eMLoKEREREQgOjqaqKgoo8MQcRg1kxMRERFxQkOGDDE6BBGXp2RIRERExAk9/fTTRocg4vKUDImhEpP/fX010bg4xDmlpv77OjYeUs3GxSKSn5nNcCkBzlyCc1cgOcXoiPKHFi1aGB2CiMtTnyFxuDOX4Pf9sPcUnL387/LRCyHAF0KLQnglqFQcTCbj4hRjXE+Cvw7BtqNwIu7f5a8uBm8PKB0E9ctbfrw9jYpSxPWlpMLuGPjjIBw9D/G3fGHl7gYlCkNYacvf6wBf4+J0ZefOnTM6BBGXp2Qoj3p+RiR7jm7E3d0TNzd3QgIr8Ejr0UTU6W50aFm6ch2++wuij2Vd5uI12HrU8lM6CHo2gVKBjotRjJNqhl/3wrLt6Z8Y3ioxGQ6etfws2Qb33mW5EVPSLGJbe0/B/D/hQnzm61NSLV9WnIiDVbugWRXoXNfyhYWISF6iP1t5WK82r9Crzf9ISUlm8YYPeOPrR6hU6i5KBVcyOrQM9p+B2b/mrinciVh4Zzl0rQcR1ewXmxjvaiJ8/iscOGP9NteTLDdrO47DE3dDAT0lErljqWZYvBWi/sndNr/uhT0x8GQkhBS2W3j5To0aNYwOQcTlqc+QC3B396Bj46dISU3m4Mloo8PJYP9p+OiX2+sTlGqGRVtgzW7bxyXOIeEGzFiTu0ToVv+cgpm/ZP00SUSsYzbD/D9ylwjd6nw8vL/K0hRabOO7774zOgQRl6dkyAUkJd/gpw0zACgdXMXgaNK7nGD5xj85NesyT0VafrLzY7Sl2Ya4nm//SN836L+suT6OnIfvN9syKpH85/f9sOlg9mVy+jxeTYRP10OSBliwiTFjxhgdQo4qV67MwIEDmTVrFsuXL2fFihXMnTuXYcOGUa9evUy3ad68ObNnz6ZEiRIOjlYkIzWTs8KJEyeYNGkSmzdvJjo6moSEBHbu3EnNmjUNjevrNa+zIGoyCYlXcHf35LnunxBasjYAy//8lNVb5qaVPRV7iFoV7mbUI185NMaFf8G1G9mXCStlXV3fbIKRndVp3pVEH8u+DxlYf338cRDqloXqJe88LpH85kK8pR9eTqz5PJ69DCt2QJe77jyu/G7BggW8+uqrRoeRqZYtW/Lyyy/Tpk2bTNc/+uijAGzZsoXJkyfzzTffANCqVSt+/PFHfH19WbhwIT/99JPDYhbJjJIhKxw4cIBvv/2W+vXrExERwYoVK4wOCYBHWo+mV5v/ceVaHO8s6Mf2A2vp2KgfAB0b9Ut7HXv5NC981JI+HV53aHwxcZb+HLYSdw3+PAR3V7Vdnc7kRGz65ikTfoCmlS0/Pl6GhWU3ZrPlhsmWVuxUMiRyO9btgRs2bGoatRda1QA/b9vVKc7B19eXKVOmMGDAAACuXr3K4sWL+eOPP9i/fz9ms5kyZcrQsGFD7r//furXr8+8efPo1asXX3zxBV988QW+vr7MmjWLpUuXGvzbiCgZskqLFi04c8bSoWH27NlOkwzd5O8byHPdP+HxNyuyYddimtbsmrYuNTWVN+b1ol/HNwgJKu/QuH7fZ/s6f9sHzau43uhhW4/AlxssCcJNF65amgf+cQiGtgF/H6Ois49DZ+G0jfsWHD0Px2OhTJBt6xVxZYlJli+abCk5xVJny+q2rVeM5e/vz88//0x4eDg3btzgtdde47333uPSpYx/zGfNmsXQoUN57LHHeOutt+jcuTOdOnXCzc2NWbNmMWDAAMxmTR4nxnN4n6HU1FQmT55M5cqVKVCgAHXq1CEqKoqqVavSv3//O6o7Li6ODh06sGNH5l83m81mxo4dS0xMTK7qdXNz/q5VhXyDePDu5/hsxcuk3jJT5dxV46kQUotmNe9zeEx77NDH58xly/DbruTcZUsilGqGzP5buLne1djj+gD456R96hVxVUfO22cAEn0W71xUVJTRIaQxmUwsWrSI8PBwjh49Sv369ZkwYUKmidBNiYmJaYlPSkoKbm5uXLp0iRdeeEGJkDgNh9/l9+vXjwkTJjBgwACWL19Ojx496NmzJ4cOHaJ+/fp3VHdiYiI///wzsbGxma6Pi4tj4cKFREREcPy4DdtvOYn7736W2MunWLVlDgBb969hy76VPHXPJIfHEn8d4q7ap+7jmZ/ePOu3/ZZEKCtmYO9p2z9FMZq9zuMJF7s+ROzNXp/F47Hpn3ZL7u3e7TxDqT7zzDO0bt2a06dPExERwa5du6zarlWrVsyePRt3d3cuXLhA4cKFefvtt+0crYj1TGYHpubz5s3jkUceYd26dURERKQtf/DBB/n+++/5888/adiw4W3Xf/r0aUqUKMHPP/9MixYtMi1z7tw52rdvz/Xr1/nll18oX758rvYxe/Zs+vTpc1sDKNxzzz0sW7Ys3bLJA9dSp2Jkruqxxs1+QhP7Lb/j5nHbD67jhZktc7VNsfL16PnalnTLnoq0vjP8TbtjYNa69MvWfzmcbSum5a4iJ/bY2/8QWCLnjlBRc4cR/fO7DojIMXpP2kNQyfQTSOX2Gsns+jhz6C++GdPojuMTyS9aPjGd2m0GpVtmq7/X05/0J+l6FjO35mPPPfccLVq0yPHJz9SpUxk+fHim6yIiIli/fj1TpkyxR4jpBAUFcfz4cXx9fbn33nv58ccfrdru1sESZs2axbvvvsuWLVvw9vamQYMGbNmyJedK7OilNz8C4K2RA9K9lryvU6dOVvdJc+iToYkTJ9KhQ4d0iRBApUqV8PT0pHZty0hoY8aMoUqVKri5ubFw4cJc76d9+/b4+Phk+lO2bFn27NnD4cOHefzxx23yezmjL1dP4Or1S7z97RM8PyOS52dEMm2h4z7gJjf3PFm3Edw8rBsdwd3KcnmFm53Oo6tdHyL2Zte/1ybnb2YuOevTpw++vr6sWLHithOhAQMGsHv3bj744AMABg8ebM+QRazmsCdDJ06coEyZMnz66af07ds33bqePXvyzz//sG2bZVzPDRs2EBISQt++fXn66afp1q2bVfu4+WRo4sSJ1KpVK9MyqampjBw5koMHD/Ldd9/RuXPnXP0ed/JkKDObv4GLJ+64GrsKKA0NHs7dNuevwGtLrCs7rZfl32FWjvr9cBNoUjF38TizWevg75M5Nye5nW9qndmUFXDsQs7lcnt9VAmBwa1vPy6R/ObHbbDmb+vK5ubz6O4Gkx6y/CsZnT17Nstm/TdVr16dPXv2ZLouKCiIYsWK2TyuyMjIDE+soqOjqVOnDl26dLFqKOzMEqGbt5uhoaEcPHiQa9euERAQQFJSUtp2ERERrFu3zqa/T3ZGvvUxAG++1D/da8lfHDaa3IkTljv+kJCQdMsTEhKIioqiY8eOacuaNm16R/sKDw8nMjIyw/KUlBT69OnDoUOH+OGHH9LtU2wrqCAU8ITrSTmXza3Sgbav00jNKlual2TFBBT2heouNjdd6UDrkqHcKuVi14eIvdnrM1MyQInQnRo/frzRIeDr60vNmjVJSkpi9erVOZbPLhECOHToEHv37qVq1aqEhYURHR1tx+hFcuawP1PBwcEA7NuXfrzlSZMmcerUqTsePMEaly9fZu/evSxevFiJkJ25maB8sO3rLeAJJQJsX6+RqpWEu8pmvs6EZRjxhxpDHhjUMFcqFM1b9Yq4Knt9Zsrrs3jHevToYXQIVK9eHXd3d/bs2cP169ezLduyZctsE6GbbrYEutk9QsRIDnsyFBoaSu3atZk4cSJBQUGUKlWKhQsXpg0oYItkKCQkJNuhGgMDA9m0aROm25ik5mbfpc2bNwOwatUq/vnnH/z8/JRYZSG8Evxj4+GTG4W63jeNbiZ4tBkU8Ydf96Yf4jYkAO6rB1Vd7KkQQJ2y8N1m2z49LFQAamjSVZFcCfSDaiVs//c63IWaMxslu2ZyjnLp0iU++ugjjhw5kmPZd955J8dECGD58uVcuXKFw4cP2zhakdxzWDLk5ubGggULGDBgAIMGDaJIkSI8/vjjDBkyhJdfftlh3w7cTiIE0L1793Tvn3vuOQDKlStn1R+I/KhmaShSEC7YaCAhN5NlwlVX5O4GnetC25pw4LQlIQr2t0we6moTzN7k5WFpImhtXwVrNK8CHho/QSTXIqrZNhmqXBxKqsmqSzhw4AADBw60quwrr7xChQoV+PDDD7P9cnrOnDnMmTPHViGK3BGHJUMAVapUYe3atemW9e7dmxo1auDj4+PIUHJNk4PlnrubpXnX9DXZl8uuv8yt2tWEYoXuPC5n5u0BYaWNjsJx2tWC7cfgfDYJs7XXR4nC0KqGbeISyW+ql4R65WDr0ezLWfN59HSHHhrdPl+ydihjEWfi0GQoM5s3b6ZJkybplr3yyit8/vnnnDt3jp07dzJs2DCioqKoWNH1n7mfvxTDRz8+j79vEOWKh9G12RAAtu5bzaotczBjpkv4IKqXbcLU7/rj6+1PUnIizzwwPUOZxKQEVm+Za9l+/yre6r+acsWrs3LzF+w9/hdD7/+AmPMH+HTZSIoULkXp4Cpp+7OVKiHQukb23/7/d16KzIQWtTw1Edfi7WFpIvjhakhKybyMNdfHzXr0VEjk9j3Y0DJR6rkrWZex5vP4YAMo6uJfXDlKZoNBiYhtGdr7Ij4+nn379lGvXr10yydMmMCJEydITEzkwoULnDhxIl8kQgA/bfqI+5o/wzMPTOePf5aSnGLpULHw1yk81/0Thnf7mG/Wvsn2Q+soERTKoHunUtivKLuPbMhQpl7l1rz48Gya1byPh1uNolzx6kRtn0/sldOkpFo6pmz6+0fa1OvNkK7vcurCQU6eP2jz36lzXWiR85yiWQotCv1bul5fIbEoHwxPRli+Tb4dBTxhQEuNIidyp/y8LcPSF/W//Trurw9NKtkupvxuxowZRocg4vIMvb0sWLAgKSkpDB061MgwnErcldMUDSgDgL9PIFevX7KsMJvx9PDC29OHpOTEdOWKBpTmwuWTGcoApKQks2rzF3RtanniE1GnB5F1HkrbX9sGj/Pn3uVMXzyMuPgzxMWfsfnvZDJZ/oPs3RR8czFvqJvJ0jRucGvLDa+4rqol4IVOuR+BsFJxeKEjhNp+qg2RfCnQD57rAI1z+f1joJ/lb3VENfvElV8NGjTI6BBEXJ7hzeQkvWIBZTl/8QTFAspw5VosBQsEAODlUYCk5BukmlPw8ihAsYCy7Dz8KwDnLp6gXPGwDGUANuxeTHhY1ywHjrhw+SQdGz1J1TINePvbPhQLyGKM5ztkMkH9ClClBPy2DzYegMsJmZf1creUbVHV9YbRlqwVLwTPtIVtR+G3/XD4XNZlKxWH5pWhdllL0iwituPjBT2bQONQWL8XdhyH1Cy6zRb1h2ZVLBNh60sr23PkBKQi+ZWSISfTsdGTzPzxOVZunk3zWg8w48fhDOj8DvffPYwpC54kOTWJXm1eoUrp+qzeMpcPFz8LQFj5piT/pwzAjsPr6dT4qSz3F1yoFFMXPkUh3yKEBJanaIB9e+/7F4COtS1PfE5fsrRPv3gVzFieGpUJglJBlj4gkv+4uVkS4foVIO6q5fo4c8nSn8jT3ZIclwmyTEIrIvYVWszyc+0GnIiFmDhYvNWy7tGmUKaIJRnSFxIikpfpltPJBBUK4eVeX2dYXqdiBHUqRqRbNqzbRzmWGdL13Qx1hQSVZ9iDMwHw9w1kzGML7zTsXHN3s/TxUD8PyUqgn+WHMkZHIpK/+XpZBsOpEvJvMtSggrExiYjYirqki4iIiDghoydcFckPlAyJiIiIOKH58+cbHYKIy1MzOYP554FRsPJCjCIiIq5m7Nix9OjRw6H7rFu37m1td+jYKQBCy5ZI99oR+xa5E0qGDFa1ldERiIiIiFhMmzbttrYb+dbHALz5Uv90r0WcnZrJiYiIiIhIvqRkSERERMQJTZ8+3egQRFyekiERERERJxQWFmZ0CCIuT8mQiIiIiBOKiIjIuZCI3BElQyIiIiIiki9pNDkRERERBytYsCA+Pj45litVqlSmy93d3W0dkki+pGRIRERExMGuX79OcnJytmXCw8NJSEjIdJ2Hhwe+vr72CE0kX1EyJCIiIuJgycnJxMbGZltm48aNWZYJCgqyR1gi+Y76DImIiIiISL6kZEhERERERPIlJUMiIiIiTmjhwoVGhyDi8pQMiYiIiIhIvqRkSERERMQJdevWzegQRFyeRpNzQnt/gStnHb9f/2JQtdXtbfv9ZoiJs2081ioVCA80sK6sUcfWVeXmmjHqGsnN9fFfRl4vd/J5FBERsZdhw4YRHR3t8P3WrVuXadOm2bxeJUNO6MpZuHjC6ChyJyYODuaBJCMvHltXkVeukVvpehEREUkvOjqaqKgoo8OwGTWTExEREXFCQ4YMMToEEZenZEhERETECT399NNGhyDi8tRMTkQkH0u4AXtPw/ELcPYypKSClweUDICywVC5OLjrazObiL8Oe0/B8Vg4Hw+pqVDAE0oGQoVgqFAM3ExGR+kaLl6Dfactxzo2Hsxm8PW29CGsUBTKFQFTHjjWLVq0YP369UaHIeLSlAyJiORDF6/Byp2w+QjcSM64PvqY5d/CPtCsMkRWtyRJkntnL8PKXRB9FJJTM67fetTyb3BBuLsqNK+iBPR2nYiFVbtg5wlINWdcv/mw5d8ShSGiOjQKde4E9Ny5c0aHIOLy9F9bHvX8jEj2HN2Iu7snbm7uhARW4JHWo4mo093o0CQXdB7zvrx4Dv84CIu2wPWknMteSoBlO+Cvw9CrKZQPtn98rsJshnX/wNLtkJySc/nz8Zbz8tdh6BUOJQLsHqLLSEmFn3fC6t2ZJ0H/deoSfLPJkhz1bAJFCto/RhFxTvruKQ/r1eYVfnw9nu/HXaB9wyd44+tHiDl/wOiwJJd0HvO+vHIOzWZYGg3zNlmXCN3q3BV4fxXs0uh6Vkk1w7d/wOKt1iVCtzoRC9N+hkN5bPRFoySnwOe/Wp6+WZMI3erAGcuxPnXRLqHdsRo1ahgdgojLUzLkAtzdPejY+ClSUpM5eDLa6HDkNuk85n3Ofg6j9sKq3be/fUqq5abzyHnbxeSqfoqGTQdvf/vEZPh4HZy5bKuIXNc3f9xZkn7lOkxfA5cTbBeTrXz33XdGhyBiE0WLFqVFixa0a9eOZs2aERAQkG35ypUr4+fn55DY1EzOSmvWrGHOnDls2LCBmJgYihUrRosWLRg/fjwVKlQwNLak5Bv8tGEGAKWDqxgai7WSrl9l2fs9OLJ92W1t/+yXufz6Lw/Ii+fRXvLq9eHM5/D0JfhxW/Zlnoq0/DtrXdZlUlLhqw0wopP6EGXl4FlY+3f2Zaw51teTYN5GeKYtuOmry0xFH/u3H1BWrDnWV67D/D+hXwvnGlhhzJgxvPrqq0aHIXJbKleuzMCBA+nevTtlypTJsH7//v189dVXzJo1i5MnT6YtHzJkCB988AHffPMNPXv2tHuc+q/MSjNnzuTixYu8+OKLVKlShePHjzNhwgTq16/P1q1bKV++vMNj+nrN6yyImkxC4hXc3T15rvsnhJasDcDyPz9l9Za5aWVPxR6iVoW7GfXIVw6PMzMH/vqOyo2703XEUqNDMVxePo/2kteuj7xwDr/fbElkshNWyrq6zl2BdXugXa07j+tO3EiG7ccs/T883KBaCctIYUbezJrNsPBPyCkdt/ZYHzkPfx6CJpXuOLQ7cj0Jth399/2JWCgdZFw8YGke991fOZez9ljvOgF7TkINK8s7woIFC5QMSZ7j5+fHm2++mW5o+CtXrrBz507i4+MJDAykZs2aVK5cmXHjxvHyyy/z2muv8cYbbzBgwAA++OADAJYudcw9gJIhK02fPp2iRYumW9a8eXNCQ0OZMWMGb731lsNjeqT1aHq1+R9XrsXxzoJ+bD+wlo6N+gHQsVG/tNexl0/zwkct6dPhdYfHmJUDm7+n/aAvAUhJvsE3YxpRJqw1LXq9k1Zm24p32bZ8Cr0mbsfbL8CgSO0vL59He8lr14ezn8PTlyzDDNvS7/uhdZhxo55tPQIL/oSEW/o+rdxlGTq5bwvjOsQfOGtJzmzp133QuKJxSd6ve2HJNki6pe/T5OVQsRg8cTf4FzAmru3HLU90bOnXfc6VDInkNRUrVmT58uVUrlyZpKQk5s6dy8yZM9m8eTNm879fE3l4eHD33XczePBgunXrxquvvkrfvn3THi4MGTKEL7/80iExG/LfWGpqKpMnT6Zy5coUKFCAOnXqEBUVRdWqVenfv/8d1R0XF0eHDh3YsWNHpuvNZjNjx44lJiYmV/X+NxECKF++PMHBwZw4YWyPYn/fQJ7r/gl//LOUDbsWp1uXmprKG/N60a/jG4QElTcmwP+Ij43B2zcQrwKWuxV3Dy86DP6Knb/M5PjuXwA4f3wnGxa8TLuBcwy/0XWUvHYe7SUvXx/Oeg635NCM6HZcSoD9Z2xfrzV2nYA5v6dPhG46GWcZ6CHexjfJ1vrrkO3rjIkzroP/xgPw3eb0idBNh87Ch6st/ZuMYI9j/c9J2ydYIvlFuXLlWLduHZUrV2b79u00bNiQfv368ddff6VLhACSk5NZu3Yt3bt3p2XLlly4cCEtERoxYgTTp093WNyGJEP9+vVjwoQJDBgwgOXLl9OjRw969uzJoUOHqF+//h3VnZiYyM8//0xsbGym6+Pi4li4cCEREREcP378jva1a9cuzp07R1hY2B3VYwuFfIN48O7n+GzFy6Sm/tsWZu6q8VQIqUWzmvcZFtuN6/HcSLiS9n7vhq+p1uzRdGWKlA6jafeJrPz4Ca5ePM2K6b2o0/ZpSlePcHS4hnLm82gvrnZ9OOM5PHrBPvUeM2AgBbPZMkJbVg9JzFjmUPptnyOj+tcxex1rO9WbneSU7PuZmbE8dbRHsp0Ts9k+x8SMZQJiZxEVFWV0CCJWcXNz4+uvv6Z06dKsX7+eZs2asX37dqu2DQsLo0iRImnv69WrZ68wM+XwZGjevHnMnj2bJUuW8MILL9CyZUtGjx5NeHg4ycnJNjsAN27c4Pr16xl+fH19WbFiBV5eXkRERHDkyJHbqj8pKYkBAwYQHBzMgAEDbBLznbr/7meJvXyKVVvmALB1/xq27FvJU/dMMiymw9uWsuDV5uxcMzNt2Yk96yhTo2WGsnXbP0NQyep89XJt3Nw8CO82wZGhOg1nPI/24qrXh7Odw5MX81a92Tly3tJnKac+ORsNGNk8JdV+o78Zcax3x8C1G9mXMQGbDDjWlxJyju12GXGss7J79x0M/yjiQEOHDqVp06bExMTQtWtXrl69atV2NwdLABg7dizx8fH07NmTrl272jPcdBzeZ2jixIl06NCBiIj03+hWqlQJT09PateuTVxcHL1792bfvn34+PhQvHhxpk+fTqVK1vcgbd++vVXlHn/88Vx/82I2m3nyySf566+/+Omnn9Jls9m55557WLYs59GxJg9cS52KkdmWeWfQugzL/AoU4vtXLU/EYi+f5oMfnmZiv+V4enhZFV9U1Doa9sx4E2qNB0evpXT1yAzLK9x1D8k3rvHn4teo33kE545GU7RcXUyZDI1kMpkoXT2SYztX0qDLKNxzEfcz7ayL25pj60j2OI+OlJtrJrNrxNmuj/8y6rMId/Z5/K/Bn8TjWeDfIUqfisy+U/m0XhmX7Y7JOBrXD0uW0afFPTaJ0VqVG/eg09Bvcyx38VoqJpO7AyL6l5ePP4Nmpc+GbHWsZ3z0KQ82fPKOY8yNuu2fJaL3tGzLmIF/Dp/BZApxSEw3BZSowuNv7023zFbHesy412i78JU7jjE7zz33HC1atMjx/mPq1KkMHz4803URERGsX7+eKVOm2CPEXHvpzY8Ay9/qW187s7wYszPy8PDgxRdfBGDw4MFcvHjRqu1uTYSGDBnC9OnTuXjxIu+++y6jRo1i8eL0zc2joqKsPj+dOnWyegAGhyZDJ06cYNeuXZl+sI8dO0ZYWBje3t4kJCQwbNgw2rRpA8B7771Hnz59+PXXX63e18SJE6lVK/OhjlJTUxk5ciQHDx5kxIgRuf49nn76ab788ku+/vpr2rVrl+vtHeHL1RO4ev0Sb3/7RNqyMkWrMqzbRw6PpcJdXVj96VOcO7aDPb/NpVarzJ+knT++8/9vil/ij0XjqdTwQQoFl3VwtM7Fmc6jvbj69eEM5zAl5Qae2H6+huTkRJvXmZMbCdaNTpB0Pd7OkWSUkmx5VGE2m21+Q5XipMfabE4l8ZqNR4ywQmqynR4LYcyxFsnL7r33XkqWLMnff//NkiVLrNoms0QIYNasWYwdO5bGjRtTr149tm7dare4bzKZ/9ujyY42bdpEeHg4S5cupVOnTmnLExISqFixIh07duTTTz/NsN3mzZu57777rBqo4PTp05QoUYK1a9cSGRmZYX1KSgp9+vRh/vz5LFq0iI4dO+bqd3j++eeZOnUqn3zyCX379s3Vttba/A1cNGBMhoDS0ODh29v2/VWWuTWysmpWP7x9A7h89hCdhy/KsD45KZFvxjQk9K4uNO3xOis/6sOV80d4YNSaTJ8S3KpiMRja1ro4jTq2rio310x214izXB//ZeT1ciefx/+ausK6fkM3vzkfZuWo361qwL133X5ctyMpBcZ+n30TKROWoagfauywsNKMXwRx13Iul9tjfV99iKx2+3HdjvjrMHZRzkOyt68FHWs7JqabUlLhpfmWfk05ye2xfrw53FXu9mOz1tmzZ7Ps33xT9erV2bNnT6brgoKCKFasmD1Cuy0j3/oYgDdf6p/utTPLizE7g8jIyHRPNT/88EMGDx7MyJEjrRpdOatE6KaPPvqI/v37M2LECCZPnpy2PCIignXr1tnml7iFQ/sMBQcHA7BvX/qerZMmTeLUqVNZDp4wbdo07rvvPpvEcPnyZfbu3cvixYtznQiNHj2aKVOm8N5779ktEXJV1Zo9yvaV71O2VuZP0jZ8a2n21PiBcQBEPvYel88fYety53j8L/al68O+ythpPhh71ZsdT3doWT3r9SYsE5S2qOqwkNIpY12r6dzXa8CxLlgAmlbOer3JBAU8oakBcyC5u1mGUbcHI451VsaPH290CCI5unn//ueff+ZYNqdECOCPP/5IV6+9ObSZXGhoKLVr12bixIkEBQVRqlQpFi5cmNaPJrNfevz48Rw4cIBffvnFqn2EhIRkGL7vVoGBgWzatCnXTRjefvttJk6cyEMPPUSDBg3YtGlT2rpChQpRo0aNXNWX35SuHol/cDmqhGf8qvvYrjXsWvsxPSdswd3DE7C0vW83cC4/TGpPudrtCS5j8OyOYle6Puyrdln4bb9t6/Ryt0xyaoTWYXDpmuV3MpF+MAUPd8vcNyUCjImtdhnYcWcDlWZQyAfKB9u2Tmt1vQsuX7PM6fPfY13AA/q3hMK+xsRWpwwctfGIhiUDjJujKjM9evQwOgSRHJUsWRKAgwcPZluuc+fOOSZCAAcOHEhXr705NBlyc3NjwYIFDBgwgEGDBlGkSBEef/xxhgwZwssvv0zt2umfs7/22mv89NNPrFq1Cl9f2/21vZ223Dc7YX377bd8+236zrv2emznSkwmE93/t54Cfhm/yitbszWDP83Yvr9U1eYM+dS60Ugkb9P1YV+Vi0OxQnDWhiOd1a8APgaN6eFmgm6NoGGoZfLXP/9/vplOtS3N4wr5GBMXQN2y8MMWiLdht5PwSsZNbnszuTxwFjbut4zk5+luSfoahYKvtzFxgWUi2mXbITmHZny50ayycZPbZia7ZnIizqJq1ar4+PjkOHCCn58fly9f5qWXXmLmzJlZltu4cSNBQUFcu2ZFm2MbcPhoclWqVGHt2rXplvXu3ZsaNWrg4/Pv/2Djx49n2bJlrFq1ioCAAAdHmZGSnTvnF2jQ18iSJ+j6sB+TydK35xMbTVlSwNPST8Ro5YItPzeToXZOEJOHO3SqA/Nzbi1ilcI+EOHgvkL/ZTJZEurKxY2N47/8vKFNGKzYaZv6QgpbEiwRyZ2EhAQSEhJyLPftt9+yYMGCdHPwZSYpKYm4uDhbhZcjhydDmdm8eTNNmjRJe797927GjRtHxYoV0w2CEB0d7fjgJF9Z/PuH7D3+FympSew6/BtfjT4KwJIN0zlyehcJifE80WECgf4hDHm3AZVL1cfD3ZPnus8C4EBMNFMWPsn0Zzez+8gGfto4Ex9vfwL9i9O77RhmLX0JszmV2CunGXzvNAr5FeFqwiWe+SCctwf8gru7B9MXP0ugfwiXr55neLdZrI2ex/4TW7h6/RKdwwdhNqdmqBfgr39WsHjDB7zW9ycOxETz06aZeHkUINC/OD1bjcpQ5tDJHcxb+wYFPH1pXL0zzWvdz42k63z00wvUrNCclnVt1HtfnELN0tCwAvyVzQSZu2Osq+v++hBgUNOovCC8kqVZ2d5TWZex9lg/1Bh8nW9UfafRtibsOgEnsrlvsuZYu5ngkXBLMisi9pNTImQEw5Oh+Ph49u3bx+DBg9OWhYWFZdvvRyy2H1zHuC/uJ7REHU7HHqZiybpUKd2Ae5sN4fjZfzK9YZbsdW02BIBZS19i3OP/jmpW2K8ozzwwnajtC9i2fw2lgivj6+2Pu5s71ctZEvm4K2dY8denFPK19KCOT4hj6P0f4lvAn5c+bsfe45s5d/E4hf2CCQmqQCG/IqSmpvLp8pcpWaRiWh0PRb5EaMnafPDDUE7HHmbd9m8pV6wGnh7elC1Wjd1Hfk9XL8Cxs/+w++gGUlKSAdh3YjN/H9lA0YAyVC5VL9MyC6ImM6DzOwQXLslLH7ejea37Wbh+Cm4mg9rj5EFn4o4y5vOuVCxZlyKFStCgavsMn8lX+yzmy1UTuKtya8M/k90bwcVrsP9M5uv/O99KZlrX0LfnOTGZLCOSTV+d9U26Ncf6/vpQI5t5c8TSfPDJSMtolReyGE09p2NtAno1hbJ2GvziTmQ2Kq6I2Jbhdz0FCxYkJSWFoUOHGh1KnlOrQguqlmnEO4PWUTs0gmcemM6VhFgK+Qal3Yg/88CH7Dr8m9Gh5inHzv5DSmoylUvXS1sWUac7v+78ni9Xv0r1ck0oHlSeFx+ew/M9PmXLvpWciTvK5yv+R98OEzFhaXDeuPo9+HgX5Ks1r9O6Xi9OXThI4YJFGXLfewBsO/ALc1eNp3OTAfj/fwJVPiSM0JK1+WPPMkwmNwr6BBB35TRP3vMWkXUfZuH6KRnqvXItju/WT6F3m39vriuUqMU7g6IY+/j3rNw8O9Myl66eI7iwpXPizX50j7R+mUql/v29JXs7Dq0nyN8y2WSN8k0z/UwCXEmIdYrPpJeHZWLKhhVyv627G9xXDzrXtXVUrsnXCwa3yX4S0Kx4e1ieUhjdPC6vCPCFZ9pCaNHcb+vnDf0ioH55m4dlEzNmzDA6BBGXZ3gyJLfvVOwhSgSFAnDu0nGuJMRRPqQmkPFGXKy3ZMOH3NcsfXK+dd9q7q71ANMG/8bny0dz/Ow/XL1umWiwoE8gf+5ZxpVrsXy8dARHz/7Nys1fcO36FaYseJLqZZvQrsHjBPqH4OddCIAAv6JcvnqBPcc2sWTjdPYc28j8dZMA+HLVBGLO72dI13cp6BOIv08Q7m7uBPgVxWxOzVDv77t/ICExnvd/eJqjZ/9m098/8fWa1zFjxsvDGzc3j0zLFClciguXLe149CT29lQr04gXHvqcF3p8xvfrp3LywsF0n8ngwqU4fHoX5UNqOs1n0svD8i34kxGWQRWsUSUERnSCyOrO1bnc2fl6WY5zr3AItGLOWxNQqzS8dI9lcAKxXmFfeLotPNAA/AvkXN7NBA3Kw8jOliakzmrQoEFGhyDi8gxvJie37+jp3ZQLCSMlNQWTyY0Nu36gS7jlD+e161eYsWQYLe96hHqVWxscad5y9MzfhASVB2DawgEM6/YRfx/dyPodC0hKucE94QMpFVyZj396geDCpSniX4IuTQfRpanl2I+a1YF2DR5n8vy+xJzfz8q/Pmf1ljmMeGg2v2z7ig9/eIak5ES6hA8iok53ACZ98wQ9Il9k1eY5rN46l2plGvHm14/yaNsxNA3rypQFT5GQeIUBXaYwfcmz6ep98eEv6NCwT9q+m9TojLu7J+/M70tAwWK0qd+btvV7ZyhTokgoH/34PB7uXtzf/BnHH2gXcODkNmqUC8fNzY0C3gU5dubvdJ9JIO1z6WyfyZqlLU8t9p+BHcfgeOy/E7MW9IaSgZZmQw0qWDqWy+0xmSyj3tUvD3+ftPRfOR4LJ/5/rs1CPpbhnMsHW8o507DOeY2byTK/VNNKluHN/zllOdanLlrWB/ha5iaqUNRyrAsbOOqgtTR4k4j9KRnKw46c2U2NcuEkJSdyMf4s5y4ep5CfpblVZjfMYp23B6xJez2s20cAPNr2lQzlxjy2MNPt33hqBQAv9Pgsw7rh3T7OdJsXH54NQNsGj9G2wWPp1pUuWiXd+8zq/e++G1ZtT8Oq7bMtU654DV7u9XWG9e0bPpFl/ZKeJSkeQWG/ojSq1inDZ/LC5VNcunqeQn5FMiTHzvCZNJksT32qWFr6Mewry7+vdTMuJlfl5mZJQG8+hbh5rF99wLiYXJWHO9Qrb/mBf4/1uPuNikhEnJmSoTzskdYvp72e9Xz6sUWzu2EWEduoUro+r/Sen+m6m5/JwV2nAfpMioiIOCP1GRIRERFxQppwVcT+9GTICfkXy3v7LRVouzjsuW+jjq2rys3xNOoauZP9Gnm96FoVkfnz59OjRw+jwxBJp27durne5tAxy4BNoWVLpHtt7/1aQ8mQE6rayugIcu+BBkZHYJ28eGxdRV65Rm6l60VEjDR27FglQ+J0pk2bluttRr5l6TP95kv90712BmomJyIiIiIi+ZKSIRERERERyZeUDImIiIg4oenTpxsdgojLUzIkIiIi4oTCwsKMDkHE5SkZEhEREXFCERERRocg4vI0mpyIiIiIg3l4eBAUFJRjuazKeHjoFk7EFvRJEhEREXGwAgUKkJKSkm2ZZs2a4ePjk+k6d3d3e4Qlku8oGRIRERFxsPj4eGJjY7Mt8/vvvxMTE5PpuqCgIHx9fe0Rmki+oj5DIiIiIiKSLykZEhERERGRfEnJkIiIiIgTWrhwodEhiLg8JUMiIiIiIpIvKRkSERERcULdunUzOgQRl6fR5JzQ3l/gylnH79e/GFRtdXvbfr8ZYuJsG4+1SgXCAw2M2Xd28uJ5tCejrpE7uT6MOofgvOdRRERsY9iwYURHRxuy77p16zJt2jRD9u1slAw5oStn4eIJo6PInZg4OGjQTaOzyovn0Z7y4jWicygiIvYSHR1NVFSU0WHke2omJyIiIuKEhgwZYnQIIi5PyZCIiIiIE3r66aeNDkHE5SkZEhGRPCk5BU7e0g8tNh7MZuPiEbG1Fi1aGB2CiMtTnyEREckzbiRD9DHYdACOXoCU1H/XvboY/LyhWgloVhkqFAWTybhYRe7UuXPnjA5BxOUpGcqjnp8RyZ6jG3F398TNzZ2QwAo80no0EXW6Gx2a5ILOY96nc+g4u2Ng/h9wKSHrMlcTYcsRy0+VEHi4MQQVdFSEIiKS16iZXB7Wq80r/Ph6PN+Pu0D7hk/wxtePEHP+gNFhSS7pPOZ9Oof2lWqG7zbDrHXZJ0L/te80vLUU/o6xW2gidlWjRg2jQxBxeUqGXIC7uwcdGz9FSmoyB09GGx2O3Cadx7xP59D2zGZY8Cf8uvf2tk9Mhk+ilBBJ3vTdd98ZHYKIy1MyZIXVq1fTunVrSpQogbe3NyVKlKBz585s3LjR6NAASEq+wU8bZgBQOriKwdHI7dJ5zPt0Dm3vz0OwMYeHbE9FWn6ykmqGub/DpWu2jEzE/saMGWN0COIkTCYT7dq1Y+LEiaxYsYLt27cTHR3N8uXLee2112jdujWmTDpJ9unTh+XLlxMcHGxA1HmD+gxZ4cKFC9SpU4eBAwdSrFgxTp8+zdSpU2nRogVRUVE0bdrUkLi+XvM6C6Imk5B4BXd3T57r/gmhJWsDsPzPT1m9ZW5a2VOxh6hV4W5GPfKVIbH+V9L1qyx7vwdHti+7re2f/dJ1hozKy+fRXvLa9eFq5zA5BTzcjY7C0iRu0Zacy4WVyrlMQhIs+AuejLjzuGwpJRXc9bWkQ6SmglseO9YLFizg1VdfNToMMZDJZKJ///68+OKLhIaGZlhfp04dOnTowOjRo9m/fz9vvfUWn376KWCZp+qDDz4AoESJEpw/f96hsecVSoas8NBDD/HQQw+lW9axY0eKFi3K559/blgy9Ejr0fRq8z+uXIvjnQX92H5gLR0b9bPE16hf2uvYy6d54aOW9OnwuiFxZubAX99RuXF3uo5YanQohsvL59Fe8tr1kdfP4aGzsObvf9+PWgANQ6F1DShi4OADv+2F60m2q2/XCTh1EUoE2K7O3Po7Btbu+ff96IXQpCK0qgGFfIyLy9WYzbDtKKz7599lY76HppUhsjr4ehkXm4i1ypQpw5w5c4iMjATg8OHDzJs3j7/++ovDhw9jMpmoUKECjRo14pFHHqFy5cp88sknPPzww6xdu5bXX7f8XzNkyBB27txp4G/i3Bz+HUlqaiqTJ0+mcuXKFChQgDp16hAVFUXVqlXp37//HdUdFxdHhw4d2LFjR6brzWYzY8eOJSbmzhuPFyxYEG9vbzw9Pe+4rjvl7xvIc90/4Y9/lrJh1+J061JTU3ljXi/6dXyDkKDyxgSYiQObv6dSo24ApCTf4KuX67L+q+fTldm24l0+e7YciVcvGhCh4+XF82gvefX6yIvncPNheH8V/H3y32VJKbBxP0xenn4eH0dKSYWNB21f7+/7bF+ntdbugY/XwYGz/y67nmS5YX9nuWWeJLGNH7fBnN/h+IV/l8Unwspd8O7PEH/duNhErFGxYkV+//13IiMjOXPmDA899BCVKlVi9OjR/PDDD2nN5BYtWsSoUaMIDQ2lV69enDt3jjZt2qRLhKZPn27wb+PcHJ4M9evXjwkTJjBgwACWL19Ojx496NmzJ4cOHaJ+/fp3VHdiYiI///wzsbGxma6Pi4tj4cKFREREcPz48VzXn5KSQlJSEkePHmXIkCGYzWYGDhx4RzHbSiHfIB68+zk+W/Eyqan/Trwxd9V4KoTUolnN+4wL7j/iY2Pw9g3Eq4DlK2d3Dy86DP6Knb/M5PjuXwA4f3wnGxa8TLuBc/D2CzAwWsfKS+fRXvL69ZGXzmHcVfh6I5jJOFmpGcuN+me/WvrcONqpi/a5Yd13xvZ1WuP4BVi81fI6s4lhLyfAlxscG5Or2h0Dv/z/07fMLt2zly2jE+YFUVFRRocgBvD392flypWUKVOGX3/9lbCwMObPn5/u/5T/SklJ4euvv+add95JW3bmzBlmz57tgIjzNocmQ/PmzWP27NksWbKEF154gZYtWzJ69GjCw8NJTk6mXr16NtnPjRs3uH79eoYfX19fVqxYgZeXFxERERw5ciRX9UZERODl5UX58uVZtGgRy5Yto3bt2jaJ2Rbuv/tZYi+fYtWWOQBs3b+GLftW8tQ9kwyN68b1eG4kXEl7v3fD11Rr9mi6MkVKh9G0+0RWfvwEVy+eZsX0XtRp+zSlqztZA38HcNbzaC+ueH3klXO4YX/2iY7ZDOevwP7TjovpphOZf6d1x85dtm3TO2v9ug+ym//VDBw6Z9yTOFfy697sJ9s1A9uP5W6YdqPs3r3b6BDEAG+//TahoaFs27aNjh07cuHChZw3wvIU6M033wQgJiaG4sWLM3HiRHuG6hJMZnNm31HZR61atShdujTLly9Pt/yll15i6tSpXLlyBW9vb+677z4OHTqEu7s7np6eTJw4kTZt2uRY/+nTpylRooTV8dwcAMFae/fu5dKlS8TExPDJJ5+wfv16fvzxx7S2nDm55557WLYs5w7hkweupU5F6+rMys2+CRP7Lbe6Sc72g+t4YWbL29rfg6PXUrp6ZIblh7ctZcOC0VRr2ov6nUcAsPjte7j3+R8x/acnq9ls5odJHTh3dBsFA0vz0PhNuHvk3LD7xJ51fPf67cVtT3nxPNpTZteIs18fRp1DcMx57D7md0pUDs90BKKbzOZUNv/4Jhvmj7ZrLP/V+P4xNHlwfLplT0VaN1jCrXbHWOYnutUXI6py8ZRj28v1mXaEQsHlciy37ouhbF/1gQMicl1DPk/Aw7NAjuWWvtuNA38ZM3T1c889Z9U9yNSpUxk+fHim6yIiIli/fj1TpkyxR4i59tKbHwHw1sgB6V47M2eMuVatWuzYsYPExETq169vdUJ862AJQ4YMYcOGDfz55594enpSrVo19u69zfkJbMTRx7pTp04sXWpdv2OHDaBw4sQJdu3alemH+tixY4SFheHt7Q3A7NmzCQgIAGDbtm1ERkYSGxuLu7t1wxtNnDiRWrVqZbouNTWVkSNHcvDgQUaMGJGr36Fq1appr7t27Up4eDjPPvss27dvz1U9jvDl6glcvX6Jt799Im1ZmaJVGdbtI4fGUeGue0i+cY0/F79G/c4jOHc0mqLl6ma40QXLiCmlq0dybOdKGnQZZdWNrqtzlvNoL/nh+nDmc+jm5p5tIgSAGUwmA4aWyymuO6k622c09uHmZt0xNFlZTrJm7fWqYy3OaPDgwQDMmjXrthOhm32EZs+ezVNPPcXAgQOzTKrFgU+GNm3aRHh4OEuXLqVTp05pyxMSEqhYsSIdO3ZMGwrwVuvWreO+++7j/PnzeHhkn7vdfDK0du3aTJ/WpKSk0KdPH+bPn8+iRYvo2LHjHf1OQ4YM4bPPPiMhwbbP2jd/AxdP2LRKqwSUhgYP396276+Cg2czX5d84zqzng6h2//Ws+fXL6jVagCBJTLOwXL++E6+HdeEOm2HsvOXmfSauINCwWVz3HfFYjC07e3FbU958TzaU1bXiDNfH0adQ3DMeVz4l2VAgZz+E3i8OdyV80MNm/ptnyU+a0zrZfl3mJWjlb/eDfy8by+u2/XZeth5POdjPbSt5ZqV2zd1BRy7kPOxHt0FihZySEiZOnv2bJZ9nG+qXr06e/bsyXRdUFAQxYo5z8Uy8q2PAXjzpf7pXjszo2OOjIxM93TQZDJx4cIFAgMDqVGjRpbn/lZZJUIAd911F1u3buXMmTOEhISk2y4iIoJ169bZ5hexgtHHOjsO6zN0c7KnffvSN02YNGkSp06dyjB4wpAhQwgNDeXBBx/ku+++yzERssbly5fZu3cvixcvvuNEKCkpid9++41KlSrdcVyuzsOrAJUaPsieX7/g8tlDmd7oJiclsmJ6L+5qP4zmD79Jxfr3s+qjxzFn01lQXIOuD+M0q5z9DaMJKOgNtUo7KqJ/lQ6yT72Bfo5PhMC6Y128MIQWdVRErqt5lZyPdZUQYxMha40fPz7nQuIyKlasSGBgIDExMXecCIGlddWFCxcoXrw4pUsb8Ic8j3BYMhQaGkrt2rWZOHEic+bMYc2aNQwaNIjPPvsMIEMy9OGHH3Lo0CG+//57RowYQXx8zmOOhoSEYDabs+zDExgYyKZNm2jfvn2uYr/vvvsYN24cixYtIioqirlz59KqVSt27drFa6+9lqu68qtqzR5l+8r3KVurXabrN3xrafbU+IFxAEQ+9h6Xzx9h63LnaAst9qXrwxglAqDD/48B89+GYyYsLdUebWbMBKwlA8DbDg25KxqUbFQJscxxkxmTyXKMe4XbtXVgvlG/fNYJvAnw9YbujRwZ0e3r0aOH0SGIA4WFhQFY1f1i0KBB2SZCN0VHR6erWzJyWDLk5ubGggULCAsLY9CgQfTp04fg4GCGDBmCu7t7lqOyRURE4Obmxu+//26TOHJsH5+Jpk2bsnTpUvr160ebNm144YUXKFKkCFFRUXTt2tUmcbm60tUj8Q8uR5XwjO1+ju1aw661H9Nh0Fe4e1jmbfLy8afdwLls+n4s549rojBXp+vDOB1qQc8mUMQ//fLQYvB0G6hm/Zg0NuXlAQ0r2L7erBISezOZoHtDuK8+FP7P5KrVS8Dw9lC2iDGxuRo3N3jibkuif+tTQDcT1ClrOdZF/bPe3plUr17d6BDEgfbv38/48eOZO3dujmXHjh0L5DyP0OzZsxk/fjyHDx+2WZyuxmEDKABUqVKFtWvXplvWu3dvatSogY+P5X+H+Ph4Lly4QLlylgbq27Zt4+DBg4b+QXjxxRd58cUXDdu/KzCZTHT/33oK+AVmWFe2ZmsGf5rxyV+pqs0Z8ulVR4QnBtP1YazGFaFRKJy8CAk3IMAXgp3gZvHuapaJV1Ns1BqyXBGoYGAzNJMJIqtBiypwIg6SkiGooKXpntiWu5sl0W9Tw3KsU1KhmD/4++S8rYhR/v77b8aNG2dV2cceewyAlStXZlvuyy+/vNOwXJ5Dk6HMbN68mSZNmqS9v3r1Kg899BDx8fF4eHhQoEABvvzyS8qWzbmjtDg3v0CDvmKWPEHXh7FMJiiVMRc1VPFC0L4mLNuRfbndMTnX5e4GDzdxjmZobm56CuQoHu5QPtjoKERsL6ckSKxnaDIUHx/Pvn370oYRBChevDibNm0yMKq8J+b8ASbM7c7M4dvSLT9xbj9f/PwKhfyCqVK6AWHlm6V7377hE8YELBlkdQ7PXTzB3FXj8S1g6el7X7OhfLV6Ar4FCuHvE8SjbV8xIlz5j+0H1zHui/sJLVGH07GHqViyLlVKN+DeZkM4fvYffto4Ex9vfwL9i9O77Rijw81TWodZRiHcm83Er/+dRygzDzSw9JESyUusncdQRG6fw/oMZaZgwYKkpKQwdOhQI8PI02Ivn2b5n59QwCtjO4ur1y/Rt9MbDLp3Kr/t+j7De3EO2Z3D42f/YduBNZy7eBx/3yAWrn+HkCKhXLkWR80KzQ2IVjJTq0ILqpZpxDuD1lE7NIJnHpjOlYRYCvkGEZ8Qx9D7P+SZBz5k1+HfjA41z3F3g74Rt993yYQlEWpmUF8hkTsxY8YMo0MQcXmGN5MT68RePs3rX6XvXB7kH8LoR7/hyU5vMmpWhwzbVC3TgPOXYhj96T3UqRiZ4b041u2cw6IBZZjUfzUhQRUY9UkHklNu0KFhX8pH1mTkrHbUrdTSUeFLNk7FHqJEUCgA5y4d50pCHOVDagLQuPo9mM1mvlrzOq3r9TIyzDzL2wP6R0LUXli6HZJTrNuuqL9lcIhQ55mKRSRXBg0apIRIxM6UDOURQYVCeGfQulxtcyAmmmIBZXir/0rGf/EgB2K2USygbNr7K9fi8Pd1sk4CLux2zuHiDR/QoWFfTCYTvt7++Hj74+tdCA93T3y8naCHuwBw9PRuyoWEkZKagsnkxoZdP9AlfBAA165fYcaSYbS86xHqVW5tcKR5l5sbtKwOdcvC7/th00GIv5552TJB0KwK1CtnGZVOJK9y5KSYIvmV/ptwQftObGH/iS1ULFmXad8NoEjhUpQoEkpySlK690qEnNfNc9i+QR+++HkMRQPKUq1sYxpX78yny0fhW6AQkXUeMjpM+X9HzuymRrlwkpITuRh/lnMXj1PIz9JDfvqSZ4k5v5+Vf33O6i1zePHhLwyONm8L9IPOdaFTHbhwxTJS2LVEy8AIgX6WRKhgAaOjFBGRvELJkIt446kVaa+rlK5PldKWSWzHPLYwXbn/vhfnkdU5nND3x3TlXuk936FxSc4eaf1y2utZz6ef9+iFHp85Opx8wc0ERQtZfkRERG6XoQMoiIiIiEjm9uzZY3QIIi5PyZCIiIiIE5o/Xy0BROxNzeSckL9BIx/dyX6NnKzR2SaKvCkvnkd7Muo83cl+jTyWznoeRcRxxo4dS48ePYwOQ+ykbt26t7XdoWOn0r0PLZv7uQdud9+uSMmQE6rayugIcu+BBkZH4Hzy4nm0p7x4jegcioiIvUybNu22thv51sfp3r/5Un8bRJN/qZmciIiIiIjkS0qGRERERJzQ9OnTjQ5BxOUpGRIRERFxQmFhYUaHIOLylAyJiIiIOKGIiAijQxBxeUqGREREREQkX9JociIiIiIO5uHhQVBQUI7lsirj4aFbOBFb0CdJRERExMGsSYTGjh1LsWKadEzEntRMTkRERMQJjRs3zugQRFyekiEREREREcmXlAyJiIiIiEi+pGRIRERERETyJSVDIiIiIiKSLykZEhERERGRfEnJkIiIiIiI5EtKhkREREREJF9SMiQiIiIiIvmSkiEnEh8fT506dfjhhx+MDkVERETE7sxmM8OHD6dx48Y0adKEX375xeiQrLZ//34qVapkdBhWS05Opk+fPjRv3pyGDRsyc+ZMo0PKUWpqKkOGDCE8PJwmTZqwdu1am+/Dw+Y1ym0xm808+eSTuLu7Gx2KiIiIiEP8+OOPHDt2jD/++IOTJ0/SqlUrdu3ahYeHc9+iTp8+nc8++4zz588bHYrV5s6di6enJ7/99hvXr1+nRo0a3H///RQvXtzo0LK0dOlSzp8/z8aNGzl8+DAdO3bkn3/+sek+nPtKy0defvllunTpwqpVq4wORURERCRbZrOZk2cucD3xBgBlShQD4ODRk+leu7u7Ub50SJb1REVF0blzZwBKlixJSEgIe/bsoVatWjaP+UZSEknJKZw+G5ttzADFigTg7eWJl5dnpnUVL16cX3/9lRIlStg8zswcO3mWpKRk4N+4b7oZs5eXR4Z1t+revTsPPvggACaTieTkZLy8vOwSb+KNJBITb3Au9lK6mDM71iWKFcHDwx0vz4xpSZcuXejYsSMAR44coXDhwjaPVcmQE/jyyy9JTU2lV69eSoZERETE6aWkpHIt4Tqfzl+Wbvmsb35K97pTyyaULF4EL8/Mk4pLly6lu8EtVKgQly5dskvMlhhMLFn9O2fOx2UZc1Bhf4b17ZZlIgSkJRWOkJSUzNkLcSxcFpXp+pvx97y3NSHBQXhmklQAFCxYEIDr16/zyCOP8PjjjxMYGGiXmL3//9jNX7qWS1euZoj15utSxYMZ0OveTBOhmzw8PBg6dCiff/45b775ps1jVZ8hJ/Dxxx+zceNGIiMjWbFiBf/73//47bffjA5LREREJFMeHu6UK1WchrWrZlmmXKniNLmrRpaJEFiSn8uXL6e9v3z5st1u0AE83N3ofX873N0zvwU2mUz0uq+tU3Vb8PT0oHa1UKqGlsmyTFiV8lSvWDbLROimkydP0rJlSxo1asSECRNsHWo6Hu5uPNK1Daas1nu48+j9bfHwyPlYv//++5w6dYpZs2YRHR1t0ziVDDmB9evXs379etatW0eHDh147bXXaN68udFhiYiIiGTJy8uTLq2bElTYP9N1j97XFs8cbnRbtGjBjz/+aGl2d/IkMTExVKtWzV4h4+bmRiF/XzpENMp0fUSTOgQHFc4yWTKKl6cnD3Vpha+Pd4Z1Bf186N4pMtsnWQCnT5+mZcuWjBo1ipEjR9or1DTu7u6EFA2iWYOama7v3CocP98CuJmySpcs/Zz+97//AeDj44OXlxembMrfDuc60yIiIiKSZ7i7W77d/+8N6n3tmuPtnfONa9euXSlTpgzh4eF06dKFDz/80O5PZbw8PWlctwblSqUfOKBE0SBahddLa+LlbLw8PHioc8sMy3t2aWXV05UJEyYQFxfHlClTiIyMJDIykr1799oj1DTeXp60a9GQYkUC0i2vWK4k9WpWzvapIViaIx48eJDmzZvTvHlznnjiCerUqWPTGE1ms9ls0xrFplLNZn5as4G6NSpTtmTWneJEREREjJB4I4m1m7axbmM0ANUrlaNnl1Y5PqkwktlsJv5aAm9//C03biTh4e7O8Ce7E1ioIG5uzvusIPFGEotX/sbW3fsBaFS3Ove0bOK0CRxYhsc+H3eZdz9bSEpqKgW8vRjR/2F8fbxt/pTndjjv2RYAdv1ziA1bdnM5/mrOhUVEREQczNvLk1bh9ShRrAh+vgXocU/OTbaMZjKZ8Pby4v52lm4JHSMb4e/n49SJEFiOddd2zQksVJAiAYXo3Mq5EyGwNE0M8Pej7d0NAHiwYwReXh5OkQhBHkyGypcvzxtvvMGQIUMIDAwkMDCQ4cOHc/XqVYYOHUpgYCDFixdP1xby/Pnz9OnTh+DgYHx9fWnTpg07duxIV+/WrVu59957KVKkCF5eXlSoUIEJEyZw64OzL7/8klq1auHj40OJEiUYMGCA3UY8ActTodW/b6VYkQBqVCpnt/2IiIiI3AkPD3d639+Wh7u0wtPJ5wi6ycvTg7DK5ekQ0YiGdarl2GTLWXi4u/HIfW149H7nGughO15enjStX5OOkY2pUqG0U10jea6ZXPny5YmNjaVr16488cQTrFixgsmTJ1OlShXCw8N59NFHWbx4MR988AE//PAD7du3p2HDhly6dInXXnuNgIAApk6dyrZt29i2bRsVKlTg+PHjVK9enVatWjF48GDc3NyYN28es2fP5rvvvuOBBx5g/fr1tGzZknHjxtGiRQsOHTrE8OHD6dKlC3PnzrUq9s8XLGfvoeN2PkIiIiIiIvlX1dAy9One0aqyzpOW5UJISAhffPEFbm5uREZGMnPmTNzd3fn8888xmUy0bt2aOXPmsHHjRs6cOcPu3buJjo6mdu3aALRv354qVarw+uuv88knn7Br1y4aNmzIt99+i4+PDwBt2rThhx9+ICoqigceeIANGzbg5+fHiy++iLe3NxEREfj5+XHq1CkjD4WIiIiIiNymPJkMNWzYMK1Np7u7O0WKFKFBgwZpbQ9NJhOBgYFcvHiRX375hXLlylGjRg2Sk5PTtmnXrl3aBKcdO3akY8eO3Lhxg127drF//362bdtGUlISN25YZlaOiIjg5Zdfpnbt2nTr1o1OnTrRrVu3XLUttTZDBdix5yBfL1lDzy6tqFOjktXbiYiIiIiIdfJkMuTvn3E8e19f30zLXrhwgSNHjuCZSTvQm8tSUlJ48cUXmTlzJgkJCZQvX56mTZvi6emZ1mcoPDycpUuXMmXKFN5++20mTpxIyZIlmTRpEr169bIq7ttpJjfvx1+Y9+MvudpGRERERCS/cvlmcrlRuHBhatasyeeff55lmTfffJMPP/yQzz77jC5duqQlW8WKpR/K+uYTpPj4eFauXMmkSZN4/PHHiYyMpFSpUnb9PURERERExLZcPhlq0aIFP//8M6VLlyYkJCRt+dNPP42npycNGjTg999/56677uKRRx5JW79161bOnTtHamoqAK+88go///wzf/75JwULFuSBBx7Az8+PDh06cOrUKauSIWsy1FSzmWmfLgTMDOubu2Z4IiIiIiJiPZe/0+7Tpw/Fixenbdu2zJs3j19++YX+/fvz4YcfUrNmTcDSB+mvv/5i6tSpREVF8eGHH9KpUydMJhNXr1rm92nVqhWbN2+mb9++rF69mh9++IFRo0ZRqVIlm86E+/f+I5y9EEfrpvWUCImIiIiI2JHLPxny9/dn/fr1jBw5kqFDh3Lt2jWqVq3Kl19+mdbXZ9SoUZw5c4Y333yTq1evUqFCBUaOHMnu3btZvXo1AC1btmTevHm89dZbzJ8/H09PT1q3bs3bb7+daX+k2+VTwJv6NatQq1qozeoUEREREZGM8tw8QyIiIiIiIragdlgiIiIiIpIvKRkSEREREZF8ScmQiIiIiIjkS0qGREREREQkX1IyJCIiIiIi+ZKSIRERERERyZeUDImIiIiISL6kZEhERERERPIlJUMiIiIiIpIvKRkSEREREZF8ScmQiIiIiIjkS0qGREREREQkX1IyJCIiIiIi+ZKSIRERERERyZeUDImIiIiISL6kZEhERERERPIlJUMiIiIiIpIvKRkSEREREZF8ScmQiIiIiIjkS0qGREREREQkX1IyJCIiIiIi+ZKSIRERERERyZeUDImIiIiISL6kZEhERERERPIlJUMiIiIiIpIvKRkSEREREZF8ScmQiIiIiIjkS0qGREREREQkX/o/nMd+YBxMUM0AAAAASUVORK5CYII=\n"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_circuits_unshuffled[199].draw(**D)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "# train_circuits, test_circuits = all_circuits_unshuffled[:2000], all_circuits_unshuffled[2000:]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "all_ideal, all_noisy, all_zne, all_circuits, all_steps = [], [], [], [], []\n",
    "train_ideal, train_noisy, train_zne, train_circuits, train_steps = [], [], [], [], []\n",
    "test_ideal, test_noisy, test_zne, test_circuits, test_steps = [], [], [], [], []\n",
    "for step, i in enumerate(range(0, 2000, 200)):\n",
    "    all_ideal.extend(ideal_exp_vals_estimator[i:i+200])\n",
    "    all_noisy.extend(noisy_exp_vals[i:i+200])\n",
    "    all_zne.extend(zne_mitigated_vals[i:i+200])\n",
    "    all_circuits.extend(all_circuits_unshuffled[i:i+200])\n",
    "    all_steps.extend([step]*200)\n",
    "\n",
    "for step, i in enumerate(range(2000, 3000, 100)):\n",
    "    all_ideal.extend(ideal_exp_vals_estimator[i:i+100])\n",
    "    all_noisy.extend(noisy_exp_vals[i:i+100])\n",
    "    all_zne.extend(zne_mitigated_vals[i:i+100])\n",
    "    all_circuits.extend(all_circuits_unshuffled[i:i+100])\n",
    "    all_steps.extend([step]*100)\n",
    "\n",
    "rows = []\n",
    "for ideal, noisy, zne, circ, s in zip(all_ideal, all_noisy, all_zne, all_circuits, all_steps):\n",
    "    rows.append([ideal, noisy, zne, circ, s])\n",
    "\n",
    "rows = sorted(rows, key=lambda x: (x[-1]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                  ideal  \\\n0     [2.7755575615628914e-17, 5.551115123125783e-17...   \n1     [0.2822944097650154, 1.0000000000000004, -0.47...   \n2     [2.7755575615628914e-17, 5.551115123125783e-17...   \n3     [0.2822944097650154, 1.0000000000000004, -0.47...   \n4     [5.551115123125783e-17, 0.0, -2.77555756156289...   \n...                                                 ...   \n2995  [-0.08828510312411605, -0.33965348162590575, 0...   \n2996  [0.026355128084189855, -0.0033706674882890697,...   \n2997  [0.03128533930903135, -0.06153497460552665, -0...   \n2998  [0.05365543668507869, -0.052795221433028616, -...   \n2999  [-0.03825623771048567, -0.24376979099895582, 0...   \n\n                                    noisy  \\\n0        [0.072, -0.0206, 0.0904, 0.0052]   \n1      [0.2362, 0.9788, -0.3122, -0.1264]   \n2       [0.2762, -0.0022, 0.089, -0.0178]   \n3       [0.231, 0.9826, -0.2886, -0.1048]   \n4     [-0.0104, -0.0252, 0.1144, -0.0408]   \n...                                   ...   \n2995      [-0.001, -0.1324, 0.13, 0.1472]   \n2996      [0.1384, 0.125, 0.0562, 0.0674]   \n2997    [0.0986, -0.0774, 0.0484, 0.1782]   \n2998      [0.1726, 0.0268, 0.087, 0.2026]   \n2999   [-0.0464, -0.2074, 0.0162, 0.1728]   \n\n                                                    zne  \\\n0     [0.047599999999999976, -0.06840000000000002, 0...   \n1     [0.2403999999999999, 0.9955999999999996, -0.33...   \n2     [0.26849999999999996, -0.046800000000000015, 0...   \n3     [0.23120000000000002, 1.0025999999999997, -0.3...   \n4     [-0.011399999999999995, -0.08460000000000004, ...   \n...                                                 ...   \n2995  [-0.042300000000000025, -0.1814, 0.19019999999...   \n2996  [0.14669999999999994, 0.15119999999999997, 0.0...   \n2997  [0.09299999999999999, -0.1106, 0.0626999999999...   \n2998  [0.18169999999999992, -0.007100000000000058, 0...   \n2999  [-0.04359999999999999, -0.3016, 0.053000000000...   \n\n                                                   circ  step  \n0     ((Instruction(name='rz', num_qubits=1, num_clb...     0  \n1     ((Instruction(name='rz', num_qubits=1, num_clb...     0  \n2     ((Instruction(name='rz', num_qubits=1, num_clb...     0  \n3     ((Instruction(name='rz', num_qubits=1, num_clb...     0  \n4     ((Instruction(name='rz', num_qubits=1, num_clb...     0  \n...                                                 ...   ...  \n2995  ((Instruction(name='rz', num_qubits=1, num_clb...     9  \n2996  ((Instruction(name='rz', num_qubits=1, num_clb...     9  \n2997  ((Instruction(name='rz', num_qubits=1, num_clb...     9  \n2998  ((Instruction(name='rz', num_qubits=1, num_clb...     9  \n2999  ((Instruction(name='rz', num_qubits=1, num_clb...     9  \n\n[3000 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ideal</th>\n      <th>noisy</th>\n      <th>zne</th>\n      <th>circ</th>\n      <th>step</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[2.7755575615628914e-17, 5.551115123125783e-17...</td>\n      <td>[0.072, -0.0206, 0.0904, 0.0052]</td>\n      <td>[0.047599999999999976, -0.06840000000000002, 0...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[0.2822944097650154, 1.0000000000000004, -0.47...</td>\n      <td>[0.2362, 0.9788, -0.3122, -0.1264]</td>\n      <td>[0.2403999999999999, 0.9955999999999996, -0.33...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[2.7755575615628914e-17, 5.551115123125783e-17...</td>\n      <td>[0.2762, -0.0022, 0.089, -0.0178]</td>\n      <td>[0.26849999999999996, -0.046800000000000015, 0...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[0.2822944097650154, 1.0000000000000004, -0.47...</td>\n      <td>[0.231, 0.9826, -0.2886, -0.1048]</td>\n      <td>[0.23120000000000002, 1.0025999999999997, -0.3...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>[5.551115123125783e-17, 0.0, -2.77555756156289...</td>\n      <td>[-0.0104, -0.0252, 0.1144, -0.0408]</td>\n      <td>[-0.011399999999999995, -0.08460000000000004, ...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2995</th>\n      <td>[-0.08828510312411605, -0.33965348162590575, 0...</td>\n      <td>[-0.001, -0.1324, 0.13, 0.1472]</td>\n      <td>[-0.042300000000000025, -0.1814, 0.19019999999...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2996</th>\n      <td>[0.026355128084189855, -0.0033706674882890697,...</td>\n      <td>[0.1384, 0.125, 0.0562, 0.0674]</td>\n      <td>[0.14669999999999994, 0.15119999999999997, 0.0...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2997</th>\n      <td>[0.03128533930903135, -0.06153497460552665, -0...</td>\n      <td>[0.0986, -0.0774, 0.0484, 0.1782]</td>\n      <td>[0.09299999999999999, -0.1106, 0.0626999999999...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2998</th>\n      <td>[0.05365543668507869, -0.052795221433028616, -...</td>\n      <td>[0.1726, 0.0268, 0.087, 0.2026]</td>\n      <td>[0.18169999999999992, -0.007100000000000058, 0...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2999</th>\n      <td>[-0.03825623771048567, -0.24376979099895582, 0...</td>\n      <td>[-0.0464, -0.2074, 0.0162, 0.1728]</td>\n      <td>[-0.04359999999999999, -0.3016, 0.053000000000...</td>\n      <td>((Instruction(name='rz', num_qubits=1, num_clb...</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>3000 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(rows)\n",
    "df.columns = ['ideal', 'noisy', 'zne', 'circ', 'step']\n",
    "df"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "train_size_per_step, test_size_per_step = 50, 250\n",
    "train_rows = df.groupby('step').head(train_size_per_step).reset_index(drop=True)\n",
    "test_rows = df.groupby('step').tail(test_size_per_step).reset_index(drop=True)\n",
    "\n",
    "train_ideal, train_noisy, train_zne, train_circuits, train_steps = list(train_rows['ideal']), list(train_rows['noisy']), list(train_rows['zne']), list(train_rows['circ']), list(train_rows['step'])\n",
    "test_ideal, test_noisy, test_zne, test_circuits, test_steps = list(test_rows['ideal']), list(test_rows['noisy']), list(test_rows['zne']), list(test_rows['circ']), list(test_rows['step'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Inspect Train and Test"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "                                                 ideal  \\\n0    [2.7755575615628914e-17, 5.551115123125783e-17...   \n1    [0.2822944097650154, 1.0000000000000004, -0.47...   \n2    [2.7755575615628914e-17, 5.551115123125783e-17...   \n3    [0.2822944097650154, 1.0000000000000004, -0.47...   \n4    [5.551115123125783e-17, 0.0, -2.77555756156289...   \n..                                                 ...   \n495  [-0.1195223205337127, -0.422936662137479, -0.0...   \n496  [0.03468435284046983, 0.5033074694656495, -0.2...   \n497  [-0.038374547811965115, -0.015929757330378075,...   \n498  [-0.042503203687466296, -0.04886391037068477, ...   \n499  [-0.21480844120549727, -0.8331609637035501, 0....   \n\n                                   noisy  \\\n0       [0.072, -0.0206, 0.0904, 0.0052]   \n1     [0.2362, 0.9788, -0.3122, -0.1264]   \n2      [0.2762, -0.0022, 0.089, -0.0178]   \n3      [0.231, 0.9826, -0.2886, -0.1048]   \n4    [-0.0104, -0.0252, 0.1144, -0.0408]   \n..                                   ...   \n495   [-0.1414, -0.334, -0.0956, -0.105]   \n496    [-0.0138, 0.2812, -0.098, 0.0674]   \n497  [0.0086, -0.1234, -0.0582, -0.1892]   \n498  [0.0142, -0.1112, -0.0958, -0.1924]   \n499  [-0.2032, -0.553, -0.0554, -0.1706]   \n\n                                                   zne  step  \n0    [0.047599999999999976, -0.06840000000000002, 0...     0  \n1    [0.2403999999999999, 0.9955999999999996, -0.33...     0  \n2    [0.26849999999999996, -0.046800000000000015, 0...     0  \n3    [0.23120000000000002, 1.0025999999999997, -0.3...     0  \n4    [-0.011399999999999995, -0.08460000000000004, ...     0  \n..                                                 ...   ...  \n495  [-0.15399999999999994, -0.46950000000000003, -...     9  \n496  [0.03960000000000001, 0.3844999999999999, -0.1...     9  \n497  [-0.02970000000000004, -0.15299999999999994, -...     9  \n498  [-0.0028000000000000178, -0.1591, -0.126199999...     9  \n499  [-0.25049999999999994, -0.7534000000000001, -0...     9  \n\n[500 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ideal</th>\n      <th>noisy</th>\n      <th>zne</th>\n      <th>step</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>[2.7755575615628914e-17, 5.551115123125783e-17...</td>\n      <td>[0.072, -0.0206, 0.0904, 0.0052]</td>\n      <td>[0.047599999999999976, -0.06840000000000002, 0...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>[0.2822944097650154, 1.0000000000000004, -0.47...</td>\n      <td>[0.2362, 0.9788, -0.3122, -0.1264]</td>\n      <td>[0.2403999999999999, 0.9955999999999996, -0.33...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>[2.7755575615628914e-17, 5.551115123125783e-17...</td>\n      <td>[0.2762, -0.0022, 0.089, -0.0178]</td>\n      <td>[0.26849999999999996, -0.046800000000000015, 0...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>[0.2822944097650154, 1.0000000000000004, -0.47...</td>\n      <td>[0.231, 0.9826, -0.2886, -0.1048]</td>\n      <td>[0.23120000000000002, 1.0025999999999997, -0.3...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>[5.551115123125783e-17, 0.0, -2.77555756156289...</td>\n      <td>[-0.0104, -0.0252, 0.1144, -0.0408]</td>\n      <td>[-0.011399999999999995, -0.08460000000000004, ...</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>495</th>\n      <td>[-0.1195223205337127, -0.422936662137479, -0.0...</td>\n      <td>[-0.1414, -0.334, -0.0956, -0.105]</td>\n      <td>[-0.15399999999999994, -0.46950000000000003, -...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>496</th>\n      <td>[0.03468435284046983, 0.5033074694656495, -0.2...</td>\n      <td>[-0.0138, 0.2812, -0.098, 0.0674]</td>\n      <td>[0.03960000000000001, 0.3844999999999999, -0.1...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>497</th>\n      <td>[-0.038374547811965115, -0.015929757330378075,...</td>\n      <td>[0.0086, -0.1234, -0.0582, -0.1892]</td>\n      <td>[-0.02970000000000004, -0.15299999999999994, -...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>498</th>\n      <td>[-0.042503203687466296, -0.04886391037068477, ...</td>\n      <td>[0.0142, -0.1112, -0.0958, -0.1924]</td>\n      <td>[-0.0028000000000000178, -0.1591, -0.126199999...</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>499</th>\n      <td>[-0.21480844120549727, -0.8331609637035501, 0....</td>\n      <td>[-0.2032, -0.553, -0.0554, -0.1706]</td>\n      <td>[-0.25049999999999994, -0.7534000000000001, -0...</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>500 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_df_train = []\n",
    "for ideal, noisy, zne, s in zip(train_ideal, train_noisy, train_zne, train_steps):\n",
    "    row = dict()\n",
    "    row['ideal'] = ideal\n",
    "    row['noisy'] = noisy\n",
    "    row['zne'] = zne\n",
    "    row['step'] = s\n",
    "    to_df_train.append(row)\n",
    "\n",
    "to_df_test = []\n",
    "for ideal, noisy, zne, s in zip(test_ideal, test_noisy, test_zne, test_steps):\n",
    "    row = dict()\n",
    "    row['ideal'] = ideal\n",
    "    row['noisy'] = noisy\n",
    "    row['zne'] = zne\n",
    "    row['step'] = s\n",
    "    to_df_test.append(row)\n",
    "\n",
    "df_train = pd.DataFrame(to_df_train)\n",
    "df_train"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kj/mfqyg_tj77nf4gt67lvscdkw0000gn/T/ipykernel_65830/3049952033.py:10: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_mean = df.groupby('step').agg('mean')\n",
      "/var/folders/kj/mfqyg_tj77nf4gt67lvscdkw0000gn/T/ipykernel_65830/3049952033.py:11: FutureWarning: The default value of numeric_only in DataFrameGroupBy.std is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_ste = df.groupby('step').agg('std') / np.sqrt(len(df[df['step']==0]))\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(to_df_test)\n",
    "\n",
    "def l2_dist(list1, list2):\n",
    "    return np.sqrt(np.sum([(a - b)**2 for a, b in zip(list1, list2)]))\n",
    "\n",
    "for col in df.columns:\n",
    "    if col not in ['step', 'ideal']:\n",
    "        df[f'L2_{col}'] = df.apply(lambda row: l2_dist(row['ideal'], row[col]), axis=1)\n",
    "\n",
    "df_mean = df.groupby('step').agg('mean')\n",
    "df_ste = df.groupby('step').agg('std') / np.sqrt(len(df[df['step']==0]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "      L2_noisy    L2_zne\nstep                    \n0     0.143743  0.143702\n1     0.175321  0.169557\n2     0.222085  0.183910\n3     0.219112  0.177614\n4     0.225672  0.184633\n5     0.193042  0.169360\n6     0.217714  0.183535\n7     0.232615  0.204955\n8     0.230549  0.222320\n9     0.237217  0.228603",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>L2_noisy</th>\n      <th>L2_zne</th>\n    </tr>\n    <tr>\n      <th>step</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.143743</td>\n      <td>0.143702</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.175321</td>\n      <td>0.169557</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.222085</td>\n      <td>0.183910</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.219112</td>\n      <td>0.177614</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.225672</td>\n      <td>0.184633</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.193042</td>\n      <td>0.169360</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.217714</td>\n      <td>0.183535</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.232615</td>\n      <td>0.204955</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.230549</td>\n      <td>0.222320</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.237217</td>\n      <td>0.228603</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "plt.figure(figsize=(8, 5))\n",
    "xs = sorted(set(df.step))\n",
    "legend_labels = ['Unmitigated', 'ZNE']\n",
    "for i, col in enumerate(['L2_noisy', 'L2_zne']):\n",
    "# legend_labels = ['Unmitigated', 'RF Mitigated']\n",
    "# for i, col in enumerate(['L2_noisy', 'L2_rfr_list']):\n",
    "    plt.plot(xs, df_mean[col], label=legend_labels[i])\n",
    "    plt.fill_between(x=xs, y1=df_mean[col]-df_ste[col], y2=df_mean[col]+df_ste[col], alpha=0.2)\n",
    "plt.xlabel('Trotter Step')\n",
    "plt.ylabel(r'Mean $L_2$-distance from the Ideal')\n",
    "plt.ylim([0, 0.4])\n",
    "plt.xlim([0, 9])\n",
    "ymin, ymax = plt.gca().get_ylim()\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.legend()\n",
    "plt.grid(None)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# RF"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [],
   "source": [
    "import mlp\n",
    "import importlib\n",
    "importlib.reload(mlp)\n",
    "from mlp import MLP1, MLP2, MLP3, encode_data, encode_data_v2_ecr\n",
    "X_train, y_train = encode_data_v2_ecr(train_circuits, train_ideal, train_noisy, obs_size=4, two_q_gate='cx')\n",
    "X_test, y_test = encode_data_v2_ecr(test_circuits, test_ideal, test_noisy, obs_size=4, two_q_gate='cx')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "train_dataset = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train))\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_dataset = TensorDataset(torch.Tensor(X_test), torch.Tensor(y_test))\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE*1000, shuffle=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [],
   "source": [
    "X_train = pd.DataFrame(X_train)\n",
    "y_train = pd.DataFrame(y_train)\n",
    "X_test = pd.DataFrame(X_test)\n",
    "y_test = pd.DataFrame(y_test)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "rfr_tree_list = []\n",
    "for q in range(4):\n",
    "    rfr = RandomForestRegressor(n_estimators=100)\n",
    "    rfr.fit(X_train, y_train.iloc[:, q])\n",
    "    rfr_tree_list.append(rfr)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "# with open('./model/ising_init_from_qasm_hardware/rfr_list.pk', 'wb') as file:\n",
    "#     pickle.dump(rfr_tree_list, file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE_noisy_0: 0.06371962731138575\n",
      "RMSE_mitigated_0: 0.02992620879253221\n",
      "RMSE_noisy_1: 0.14266696586500827\n",
      "RMSE_mitigated_1: 0.10060497345139847\n",
      "RMSE_noisy_2: 0.09719701642269073\n",
      "RMSE_mitigated_2: 0.054086110062517394\n",
      "RMSE_noisy_3: 0.1449852102185559\n",
      "RMSE_mitigated_3: 0.09008654822804739\n",
      "RMSE_noisy: 0.11713456496421219\n",
      "RMSE_mitigated: 0.07425939692926055\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distances = []\n",
    "\n",
    "num_spins = 4\n",
    "\n",
    "for batch_X, batch_y in test_loader:\n",
    "    out = []\n",
    "    for q, model in enumerate(rfr_tree_list):\n",
    "        out.append(model.predict(batch_X[:, :]))\n",
    "    out = np.array(out).transpose()\n",
    "\n",
    "    for ideal, noisy, ngm_mitigated in zip(\n",
    "        batch_y.tolist(),\n",
    "        batch_X[:, -num_spins:].tolist(),\n",
    "        out.tolist()\n",
    "    ):\n",
    "        for q in range(num_spins):\n",
    "            ideal_q = ideal[q]\n",
    "            noisy_q = noisy[q]\n",
    "            ngm_mitigated_q = ngm_mitigated[q]\n",
    "            distances.append({\n",
    "                f\"ideal_{q}\": ideal_q,\n",
    "                f\"noisy_{q}\": noisy_q,\n",
    "                f\"ngm_mitigated_{q}\": ngm_mitigated_q,\n",
    "                f\"dist_noisy_{q}\": np.abs(ideal_q - noisy_q),\n",
    "                f\"dist_mitigated_{q}\": np.abs(ideal_q - ngm_mitigated_q),\n",
    "                f\"dist_sq_noisy_{q}\": np.square(ideal_q - noisy_q),\n",
    "                f\"dist_sq_mitigated_{q}\": np.square(ideal_q - ngm_mitigated_q),\n",
    "            })\n",
    "\n",
    "plt.style.use({'figure.facecolor':'white'})\n",
    "\n",
    "df = pd.DataFrame(distances)\n",
    "\n",
    "for q in range(num_spins):\n",
    "    print(f'RMSE_noisy_{q}:', np.sqrt(df[f\"dist_sq_noisy_{q}\"].mean()))\n",
    "    print(f'RMSE_mitigated_{q}:', np.sqrt(df[f\"dist_sq_mitigated_{q}\"].mean()))\n",
    "\n",
    "print(f'RMSE_noisy:', np.sqrt(np.mean([df[f\"dist_sq_noisy_{q}\"].mean() for q in range(num_spins)])))\n",
    "print(f'RMSE_mitigated:', np.sqrt(np.mean([df[f\"dist_sq_mitigated_{q}\"].mean() for q in range(num_spins)])))\n",
    "\n",
    "sns.boxplot(data=df[[\"dist_noisy_0\", \"dist_mitigated_0\", \"dist_noisy_1\", \"dist_mitigated_1\", \"dist_noisy_2\", \"dist_mitigated_2\", \"dist_noisy_3\", \"dist_mitigated_3\"]], orient=\"h\", showfliers = False)\n",
    "plt.title(\"Dist to ideal exp value\")\n",
    "plt.show()\n",
    "\n",
    "sns.histplot([df['ideal_0'], df['noisy_0'], df[\"ngm_mitigated_0\"]], kde=True, bins=40)\n",
    "plt.title(\"Exp values distribution\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/2500 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f1c29672202a4e9ab46fd25360ce092b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_df = []\n",
    "\n",
    "start = 0 #300*9\n",
    "end = 100000000 #300*10\n",
    "\n",
    "models = [(rfr_tree_list, 'rfr_list')]\n",
    "models.append((None, 'zne'))\n",
    "\n",
    "for circ, ideal, noisy, zne, s in tqdm(zip(test_circuits, test_ideal, test_noisy, test_zne, test_steps), total=len(test_circuits)):\n",
    "    row = dict()\n",
    "    row['ideal'] = ideal\n",
    "    row['noisy'] = noisy\n",
    "    row['step'] = s\n",
    "\n",
    "    X, _ = encode_data_v2_ecr([circ], ideal, [noisy], obs_size=4, two_q_gate='cx')\n",
    "\n",
    "    for j, (model, name) in enumerate(models):\n",
    "        if name in ['rfr_list']:\n",
    "            row[name] = []\n",
    "            for q, m in enumerate(model):\n",
    "                row[name].append(m.predict(X))\n",
    "            row[name] = np.array(row[name]).transpose()[0]\n",
    "        elif name in ['zne']:\n",
    "            row[name] = zne\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "\n",
    "    to_df.append(row)\n",
    "\n",
    "df = pd.DataFrame(to_df)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kj/mfqyg_tj77nf4gt67lvscdkw0000gn/T/ipykernel_65830/1969995707.py:10: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_mean = df.groupby('step').agg('mean')\n",
      "/var/folders/kj/mfqyg_tj77nf4gt67lvscdkw0000gn/T/ipykernel_65830/1969995707.py:11: FutureWarning: The default value of numeric_only in DataFrameGroupBy.std is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_ste = df.groupby('step').agg('std') / np.sqrt(len(df[df['step']==0]))\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(to_df)\n",
    "\n",
    "def l2_dist(list1, list2):\n",
    "    return np.sqrt(np.sum([(a - b)**2 for a, b in zip(list1, list2)]))\n",
    "\n",
    "for col in df.columns:\n",
    "    if col not in ['step', 'ideal']:\n",
    "        df[f'L2_{col}'] = df.apply(lambda row: l2_dist(row['ideal'], row[col]), axis=1)\n",
    "\n",
    "df_mean = df.groupby('step').agg('mean')\n",
    "df_ste = df.groupby('step').agg('std') / np.sqrt(len(df[df['step']==0]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use({'figure.facecolor':'white'})\n",
    "plt.figure(figsize=(8, 5))\n",
    "xs = sorted(set(df.step))\n",
    "legend_labels = ['Unmitigated', 'ZNE', 'RF']\n",
    "for i, col in enumerate(['L2_noisy', 'L2_zne', 'L2_rfr_list']):\n",
    "# legend_labels = ['Unmitigated', 'RF Mitigated']\n",
    "# for i, col in enumerate(['L2_noisy', 'L2_rfr_list']):\n",
    "    plt.plot(xs, df_mean[col], label=legend_labels[i])\n",
    "    plt.fill_between(x=xs, y1=df_mean[col]-df_ste[col], y2=df_mean[col]+df_ste[col], alpha=0.2)\n",
    "plt.xlabel('Trotter Step')\n",
    "plt.ylabel(r'Mean $L_2$-distance from the Ideal')\n",
    "plt.ylim([0, 0.35])\n",
    "plt.xlim([0, 9])\n",
    "ymin, ymax = plt.gca().get_ylim()\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.legend()\n",
    "plt.grid(None)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# to_save = {\n",
    "#     'df_mean': df_mean,\n",
    "#     'df_ste': df_ste,\n",
    "#     'df': df\n",
    "# }\n",
    "# with open('/Users/haoranliao/GitHub/blackwater/docs/paper_figures/hardware_over_depth.pk', 'wb') as file:\n",
    "#     pickle.dump(to_save, file)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2950 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e64323f777034aa8ac6518feaa3fc94b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 10\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2900 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7301689a850c491cb92bf4d80820e8e4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 15\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2850 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6a42831b888f4232b5d43cc30035aaf5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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6GPh/WMG5CFVNVG08f/58Pvjgg1AOaTE+Ov4R646ua9Jzjuo0irvS7rrsflu3bmXixIkUFxcjiiJjxoxh0KBBnD9/nvH33Yfbq6Co8OiTz5CR0a2m/Ix2A6sKKKqiBdrUmjJ1cyEKApJY42LLkogp4HKLIAmC36XWtnuKjfy/B/tiN8mYDRKni6vYkJXPpuwCNh8rJtqWzW1d23Bvn3ZkxIdhM13crulaotrtpbDcxXdnznPw7AWOFWhFllMllXh82udulERSoq3clBZNaqwmHCnRNix+jyxQNDEZRCwGCZNBxCiLmGQJQ2OFVVHAWw2eaqgohNwdcG4/5H8H+YfAdUHbz2CFuAzocD/EdAJrrF8oIhCkeoa6qSMSAogGEGWQ/HPRAJIMggSSBIJBExxJhtKsek0OOYbyxBNP0KFDh2DbEz2x7WICRZ7S0lIeeughEhMT8fgU7I4wnnv5TQySSIzdhN0kIwrUitoHqCsegVdBgVFrov3KJdepwX1VLhaMmmW/F/MDT1yo55Xg//0ZZYFuCWHIoogkCqREW+mVEsHDgzvw7fFivsjKZ9XOHFbsyKFHu3DuyIznl5nxxDrMGOWW91oURaXc6WFfbhmbsgvZfqKErHMXglWlDrNMWqydOzMTSI2xkRprp12EBSkQr/ALrcUoYTZoomHyi4d4JcUar1sTEHeVJhhndvrF4yCUnCT4S4hIgfaDoU1XiOuqvQ54F4JIHW9CMlxaJETJv84vIk00lniDBKW8vByHw8E999zTJBe9GtyVdleDvInmJDIykjlz5jDxgQf488L3UVWVSKuRKJsRgyQgS1qALkBAAALVibWrD/2rg1WOtfe7nBhdTiA0TdEWhNrbaq2rHa+RRZG24ZY6Z42wGmkTZiYxysLPu7Uhp6SKDVkFbMjKZ/anh3l941GGpscyqlc7BnaIwmExNMkY1g3F7VU4e76af2UX8M3RInaeKqWsSgs4psbauKd3Il3ahpEaayPaZqxTnDTIAhaDhFmW/F6HiMnQBLEidzX4nFCeDznbIW+vJiAFWeAq1/Yx2v3ex0818YjLAFOgLZ2gCYhkBKMVZAsYzCAaNeG4ZBGmeWmQoDz66KMsX76cvXv3Mn369JAvoigK06ZNIzs7G6PRyMyZM0lJSQluf+edd/j4448BGDp06HUzRk+ly4sa1pbb/2MMS96ci+iv7zdIAtIlytKCP25Sc883/Ia7nBj9mEA0FQZJJMpmIspmom24mS7xDib0T2Jvznm+OJTPxsMFfHrgHMlRVn7RvS339G5HSrStaW7OH6CqKpUuL7tOlfLVkUK2Hi8mO78cRQW7SaZXcgR9kiPpnRxJpM0Y9NwCRZaAcBivpMjyQ9yV2nTugCYg+fs176P0FEHpj2wPHYZq4tGmG0Qk+b0OajwLgw0MFpDNmoDI105ta4MERZZl7rnnHk6dOkV2dnadbStWrLjs8Rs2bMDtdrNy5Ur27t3L7NmzmT9/PgA5OTl89NFHrF69GlEUGT9+PD/72c/o0qVLI95OyzNgwAD69O1HTkkVpVVuZFHkiUmTcJhlZElE9lf5NTVXIkbNgdUoYzXKxNrNtIuwcnOnaArOu9iUXciGrHwWfn2Ct/99kgEdtEDubV3bEGk1XlGRwaeo5JZWselwjRdyvtqDAHSMszOmbxJ9kiPp1MaBLGrFQKtJxGY0YDGKWAzylRVZfoiiaHGO/ANw5HM4vUUryngqte2mME040obVeB9GLS0DQdQmg9U/mUHyC8hV9OxCpUGC8s4775Cfn8+0adP405/+FPJFdu3axZAhQwDo2bMnBw4cCG5r27Ytb7/9NpKkPaW8Xm+rzW9RVZXiSjf5F5woikqE1Uh0oHgjik37Y20liKJAmNVAmNVAmzAzHWJt/KpPAtl55fzzUAGbsgv49ngxcz7P5raMNtzbtx3dEsKxGhsW3qtyedn+fQlf+c9zNL8CFS2Zq3dyJH1SIumVHEm4xeD3QARsfrGzGqWmr4nyuqGqGI5/Ccc3wqlvoTxP2xbZHjoO98c+ukF4Yq1ypwSSqVbRxQSyFVrZON8N+tYkSSIhIYFFixY16iIVFRXY7fY65/N6vciyjMFgICoqClVVmTNnDl27dr1oyNPWQJXLy5myaqo9PiwGidgIE2aDVG/x5kbEJEuY7BLRdhNtw8z0Tonk4cHt2eIP5K7elcPKnTl0Swjjzsx47vpJO9qEmerc9Kqqcrqkki8PF/L1kUJ2niql3OlFFCC9jYPx/ZPpkxJJxzg7sqhV2dpNMlaTjNUgNUvxCnc1FGbD0c/gxL/gzC7wubQiSUJv+Mk4SB4I9jYE4x6iQfNGDGZ/0cWiBUdbOVdlGA273U5lZWXwtaIoyHLNpV0uF3/4wx+w2WyN8oBaEq+icO68k9JKN6Io0CbMTFgzF2+uB2wmAzaTgViHmaQoK7d3b8Ppomq+OJzPhkP5vPxZNq9vPOYP5CZgkEW+PlLIv48Vc6ygAoAIq4F+7aPokxxJz6QIwq0GZFHAYpSwm2QsRgmLQWr670BRwFkGJ/8Nx/4J3/8bSk9q2xwJ0OUOTUDif6LFNwRJEw2TQyu+yGbNA7kOaZSglJWVERER0eD9e/fuzaZNmxg5ciR79+4lPT09uE1VVR5//HEGDBjAo48+2hhz6qCq6lW5iVVVpbTKzbnzTnyKSpjFQLRdC7garuPiTVPnw0iiQITVSITVSNswC10SHNw/IJndp8v44pCW2/LZwXOAluvRpW0YEwem0CclktQYG7IkYjaI2ExaEcZqlJs0IzWIz6tV3R79XCvK5GwHd4VW7RqfCZ1/oYlIeFJNlazJodXSGGzXrYD8kJAEZfv27cyYMQOfz8eIESNISEhg9OjRlz3utttuY/PmzYwbNw5VVZk1axZLliwhOTkZRVHYvn07brebb775BtByW2q3eWkoZrOZ4uJioqOjm1VUqt1ezpQ5qXJ7MRsk4sNNWIyS38W+fos3qqpSXFyM2fzjTdgbi8kgEWuQiLGbiI+wMKRTDPkXnHxztBiTLPKTpAi/9ycQZjZgNcnBPJBmwV0Fp7dqInLiX1DoT+iyRmvVuMkDoV0fTTREfwDV6PAXZVpf/KMpENQQHjn33Xcfb775Jk8++SRvv/0248ePZ+3atc1pX71kZWWRkZFRZ53H4yE3Nxen09ks11RUlQvVHipdPgSBYKaoJNbkLFzvmM1mEtvGYZDQGpwpPm2SjP6qzHoyMxuJx6dQ7vRS7fZiMUhaMaaBAduQUVUtgHrkn3DsCzi1GapLAUELpCYNgORBEN3R74UYwWyv8UKa+L1fq1zq3gsQ0jcjiiIREREIgoDJZAq2PL5WMBgMzRLQVVWVf+w5y4ufHKKwws3Pu7bhwUHtaRNpJi7M3OAaCapK4Ow+f/LSIS3/wGjzN76K0Z58thht2R4Dtjgtjdpg1n7AzSlYaqBFqsc/d0F5AZSfg4p8qCyAykJtqiqB6hL/vFQLJsZ0hrjOENcD2vXWajAMZq3G4gqe1FpuixFszXCzelxw/jTk7Ye8ffD919qy6tOKK4n9IXkAJPUHc6T2HRhtmoAEvJAb4CESCiEJSnJyMnPnzqWsrIxFixaRkJDQXHZdMxwrKOcP6w6w/WQJqbE2/mdEF3q0CycuzEyktZ5sT3cVnPtOmwoOQeFhKMqGyqKafYx2CG8HF3KhusyfGVmPs2i0aw26LJG1Wn9G1wiRLQZssZr42KK1qkdB1m5kUfZ7EV7wVEF5IVSe07IzK87ViERlUS2RKAHnhUvbI5trbAlLgLbdtbYmhdnaEz1wjL0NxHaG2AyI76mJjCO+ZRKxVFX7fPMPwbl9WmJZYRYUH/N7IH6i0vw1MgO0vBDJpHlegViI0aZloOrUS0hFHq/Xy+rVqzly5AhpaWmMGTPmkoN/Xw1+zO1qCqpcXl794gjvbPkegyQycWAKd/SIJ9ZhIsZh0rInfV5NLM7521sUZkHRESjLIXhjSUYt/yCyA0R1gKhUzWW2xYHRAoiaV+B1gus8VJWCs1T7oVeXabUJ1WXgPF9r2f9arafVp8EKlggwR2gCUF3qF4nzl95fNvuFKvJi4bLUWmeN0s4N1LQX8S/jv2mLjmriWeifLpypuY4jHmK7aBmgCT0hoS84Yps230JR4HyuJhx5/kzUomwo/V4TVdA8qsgOmoBE+6eoNO0zEyQw2bVYiMHq/450atNkRZ6DBw/i8Xj405/+xJQpU+jduzddu3ZtEiOvFU4VV7Ji+2nW7D5DQbmLWzvH8sjNHWgvFxJd8iXmY4e1J13REa2q0Ocf51kQIaydJhppw7QfbHRHzfWXLTWp0rL/qXepjEef1x+X8McmVF9Npzi1O8hRPNo2Z7lffMp+ID5lmnhUl2lCFZ4IbXvUbbZuiawREIONmjTbWkIRaIEqynUbk4mSPw1c1OaCBIobvC5NPNsP0Rq5qYrm6RQd0cSlKFsT4BO1BjoLa6dliLbpoeVstOuteVsNqRXxurQs1Lz9fm8wS7tGVXHNPtYYiE6FxH7a9xGdptXESAZ/O5ha6euyWfNCWqANzPVCSB7KPffcw6uvvkpycjI5OTk8++yzLdadQVN6KE6Pj/X7z7J6Rw47vi+lLcVMjDnMiPBc2rlPYjx/AsFdUXOALUbzNCL9HkdgOZDlGGxjYdR+sE3tJqtqTaxD9Wnd9am1Bcfn3+4XHvAXgeSaRmPCJcQhuF66cpsVRRMznxs8Tm3ZU6XZ7CyFwiM1XkxRthan0QzV2q/EdtWqY9v1gQR/jV/eHsj7ThORwsNQcqJG0EUZIlMgqmNdr8Ma5U8kCwSNTVpRRjZq39MNWBPTGFRVxel14vQ5OXP8DN27db/kfiH9agwGQ7CXtqSkpDrDZ7Y2VFVlX+55lm07xSff5dHOfZJfWfYwL2I3Cc6jUAGKyw5RHRA6Dq8Rjeg0rTMayVTL6/ALh2y8OkE6QfDXKDSguKkoNendVxNR1AQWK9QuNXhd2hTb1S8y1VoAuKpEKzIGRObsTi3zFKjpxsmPJVITi26j/OLRESLag2zQ+u2o/b00l6jfADi9Tqq91VR7qylzlXG45DCHig9xt6P+fqRD+pQTEhL4y1/+Qs+ePdm/fz9xcfWPz3GtUlzhYvXOHNbuOk1U8S5ul3bxe+NuYoV8VEXAbetCdcbDSB2GYIxNrfVk82c3SoEnWytxi6810ZdNFwdlFZ8mLm17+AUn0KlQARRmoxRl45OMqNFpEN0J0RaLJMoIsqXmfJJc43noNIqAgDi9TkqcJRwsPsjB4oNkFWdxrOwYHn//snf3rV9QQiryuFwuli9fzsmTJ0lLS2PcuHGtIijr9Sn860gha7Zmw7EvGS7u5DZpD2FUoIgGXHE9UVJuRuowBHNUYq0Mx6bPq9BpGNXuKiqc59lbsIMdeTuo9lZjNzkIM4ZjN0XgMEcQZgoj3BhOmCkMk2RCFEQkQUIU/XNEBEFAEiR/p84ioiAiimLNvkJogqv1pKcS+Kvpn6bWcrDvXrXuvoCIiCzKyKKM1MIPJZfXFfRAiqqLOFB8gINFBzlUcogTZSfwqT4EBDqEdyAjOoOMqAy6RHXBWmKlR7celzxnSIJyLdEQQTlRWMH6zXu5sH89gzzbGCwdwIwHj2zHldAfJWUwhg6DsEQm+AXE6nfTda42Hp+Ham81x8qOseXsFnYX7OZA0QGqvFUICIiCiC8QD7oEFtmCzWDDYXRgN9ixG+3BucPgCM4dxpplm8GGQTQEBUZEDN78QVHwC4Lir1FTVAWP4sGjePAqXm1SvTXLP5jciju4rKISYYog1hpLtDkaq8GKQTQEJ1nQRCYgOAaxaTuhcvlcOL1OqrxVFFQWsL9of9ADOXn+JCoqkiCRGpFKRlQGGdEZdI7sjNVQc0+IiHjPeesVlJCKPB9++CELFy7E7XYH28xs3Ljxyt5lE1Ph8vL1t5sp2rmOruWbeVI4iiioVFjiqE4ciavDEAztB2INi0Y0+ZOUWkvx5TrCp/io9lVTWFnIt3nfsit/F/sL95NfpQVnYywxDIwfSGZsJt1iumE32Kn2VlPhqaDCXVFnXu4uv2h9UXVR8LVK/c9Mm8GGzWDDbtBaw/+oSKjeoLA0BWHGMGIsMUSbo4m2+CdzNLGWWGKsMUSZojDJJgxSXdEJCE5gqo+AgFR7qzlTcYbvCr/jYPFBDhUfIqc8BxUVg2igY0RHRnUaRUZUBp0iO2GWa5pWBDw5q2zFIlswS2ZO5Z+q95ohCcrixYtZsGAB8fHxoRzW7KiKj6wdmzi3fQ0pRV8xUjgLwDlLKnnJ92FJH4opsQ9hjggkk/2Gaah1LRGoJSh3l7O7YDfb8raxr3Afx8qOoaJikS10je7KyNSR9IjpQbwtXvMaBBGTZMIsm1FUBZ/iw6N48Km+4M39w2JFbRRVocpTVa8QVXoqg3NBELQbVpIvvnEvcSPXu76efQFKnaUUVxdT7CymqLqI4upi8qryOFB8gOofDG8hCiIRpghNdPxiE23xC44lhhhLDJGmSIySMSg6siDjVtzkXMhhX9E+DhYdJKskizMVWj6QSTKRHpnOgPgBdI3uSlpEGsZaHVmLgohBNNQRELNsruMp/ZjXFPK4PLW7bmxRVIXSPes5u30NbfM20ZUyOqkSx81dOZB0F3GZP8MW3xWz3YHBZL/2gpM3AG6fmypPFUdLj7Ilbwt7CvZwsPgg1d5qBAQ6RnTkV51+RY/YHnSM6IgsysEftE22YTVoP2pjPT23exUvPtWHT/EFBSawXNvLsMgWIkwRFxdnfsRzCRXhop57hcBCnXVJjqSgAP6QKk8VxdXFFDk1oQkIT3F1MSfLTrLTuTMYGA1gEA1EmaOCoiMgcLjkcNDTs8gWOkd25qeJPyUjOoPU8NSguAWKkkbJWEdATFeQyRySoJjNZh555BEyMjKCKtVSvd4red8R+c1vMahm9hl74UwcTFq/24lvm4LZFobJ1DwtYnXqx6t4qfZWc67yHFvytrA7fzf7CvdRVK01OWhjbcPN7W4mM0YrxtgMNgQ0r8BqsGKTbZjli5+I9SGLMjIyNKDEWltsgnO1RnwCy4FAroBQZx5YD1ocoaZ/3rr7BbYDdc/jX1ZVtW6RSvXi8WkxGaNoxGF0kBKecknRUVWVC+4LF3k4AdE5UHQAr+IlPTKdn7f/ORlRGaSEpQSDvwEBMUtmbAb/Zy2ZMTRhx04hCcrQoUPrfNFut7vJDAmV89h5N3E6XfrfRkZSAharHcs1Pv7L9YjL66LUWcrO/J1sP7edfYX7ggE+q2yle0x3/qPjf9AjpgdtbG2CP2qTZMJusGORLVhkS7PXeIiCiCiJGGj534iJ+j2AQLzGp/iCohMIAHsUD0bJSJQ5io50rNfTCSAKIhISFoOlTvHlx+IuV0pIZ3Y6nbzzzjt4vV5UVUWWZcaMGdNctv0oYW07cO/g27Ea5Rui24BrCZfPRWFlIRtzNvKv3H+xv3A/Lp8LSZDoGNmRe9PvJTM2k9TwVCRRqinGGGzYZBsW2dKkT8XricsFWoE6geKAh1W75skoGbFKVkyyFnsKtWr8SghJUJYtW8bSpUuZP38+I0aM4N13320uuy6LJIrX/Ih01xNun5vCqkK+yv2KTTmb2J2/G4/iIdIUydDEoWTGZtI1uitWg/WSxRiLQW9k11Q0RHRaipCsiouLIy4ujsrKSgYMGMAbb7zRXHbpXAO4fW5KqkvYlLOJTbmaiLh8LiJMEQxLHsbA+IF0juocTBAzS2bsRjsWyYLFYLmqT0ada4OQBMXhcLBhwwYEQWDFihWUlZU1k1k6LYXH56HYWczXOV+zKXcTO8/txOlzEmYM46eJP2Vg/EAyojOQBAlJkIJJYnoxRgdCzJStqKjg9OnTREdHs2TJEoYNG0b//v2b0756ae7+UG4kPD4Ppc5Svj7zNZtyNrHjnJbq7jA46Bffj0EJg+ga1VVLFxekYAaqzWC7Zl1vneajyfpDee+993j88ccBePbZZ5k7d26LCYrOleFRPJx3nuffZ/7NxpyN7Di3g0pPJTaDjQHxAxgUP4huMd201HRBDKavB9LFdXQuRYMEZfXq1fz973/n+PHjfP311wD4fD68Xi9TpkxpVgN1mg6v4uW86zzfnvmWjTkb2Za3jQpPBRbZQr+2/RgUP4gesT3qeiIGOzajTRcRnQbRIEG5++67GTRoEAsXLuSxxx4DtA6ro6Ojm9U4nSvHq3gpd5Xz7VlNRLbmbaXcXY5ZMtO3bV8GJQwiMyYTg2TQRUTnimmQoBiNRhITE5k8eTLl5eVIksTixYt54IEHWu2g5tcrgWzV/Mp8dufvZnv+drae3cp593lMkok+bfowMH4gPeN6YpSMSIIUbBxnN9j1wKrOFRFSDGXKlClMnjyZZcuWcfvtt/Piiy+ydOnSyx6nKArTpk0jOzsbo9HIzJkzL2oTVFJSwvjx4/noo49a7WDpLYFX8VLlqeLUhVPsyt/FvsJ9HC45TG5FLgBG0UivNr0YFD+IXm16YZJMmojItmATf11EdJqKkARFEAT69evHggULuOOOO1i1alWDjtuwYQNut5uVK1eyd+9eZs+ezfz584Pbv/nmG+bOnUthYWFo1t+AeBUvFe4KDpceDjb5P1xymBJnCQBW2UrnqM4MThxM58jOwdakgSboDqPWD0h9De50dK6EkATF6/Xy5z//mb59+7J161Y8Hs/lDwJ27drFkCFDAOjZsycHDhyos10URZYsWcI999wTijk3BF7FS5mrjH2F+9idv5v9hfvJLs0ONnWPMkfRJaoLXaK60DmqM0mOpGCz/0A7DrvBrouIzlUhJEF56aWX+Pbbb7n33nvZuHEjL7/8coOOq6iowG63B19LkoTX60WWtcvffPPNoZhxXeNVvORX5bMrfxe783fzXdF3HC87HuytLNGRyM3tbqZLpCYgsdbYi5qhmyTTJfux0NFpbhpcbTx69GjWrFmDIAi88cYbqKpKVlZWg7ovsNvtVFZWBl8rihIUkxsdr8/LyfMn2Vmwk935WreHgfiHJEikRaRxR+oddI7qTOfIztiNdkRBRBZkrQWpv7m/STLptTI6LU6D7uq2bdsCkJqa2qiL9O7dm02bNjFy5Ej27t1Lenp6o85zveDyudjw/QY++/4z9hftD8Y/Ap3h3NzuZrpEdSEtIi3Y+bJJMtVpgm6STLr3oXPN0SBBCcQ/Ro0a1aiL3HbbbWzevJlx48ahqiqzZs1iyZIlJCcnM3z48EadszVysOgga46u4Z/f/5Pz7vOEG8PpFtONzlGd6RLVhWRHstZJsd/7qN2Dlp7irtMaaFBbnmHDhtV5GsqyjNfrxWg08umnnzargfXRWtrylFSX8NHxj1h/Yj1HSo8gCRK92/TmlqRb6BXbC4NkwCxpzfvNktaDllEy6t6HzjXLFbfl+eyzz1BVlenTpzNu3DgyMzM5dOgQy5Yta1JDrxe8ipfNZzaz7tg6vsn9BrfiJtGRyMSuExncbjBR5igcBgdhpjCssrXFx2fR0WkqGpwpC5CTk0NmZiYAXbt25eTJk81nWSvkZNlJ1h1bxycnPyG/Kh+LbOGnST/l1qRb6RjeEZNsIsIUgcPo0Ktwda5LQu4PZd68eWRmZrJnzx5iY2Oby65WQ4Wngs9OfsZHxz9ib8FeVFS6x3RndPpo+sX3wybbCDeG4zA6tN7M9KKMznVMSP2hVFVVsWLFCr7//nvS0tIYP358qxiKtKlRFIXdBbtZe3QtX+Z8SaWnkhhLDEMThzI0aShtbW2xSlbCTGE4jA49oKpzXdFk/aFYrVYeeughHnjgAWbMmNEkxrUmzlWc48PjH/LxiY/5/sL3GEQD/dv255akW+ge0x2TZCLcFK71YKb3oapzA9KoR2crHQ65Ubi8LjblbOLDYx+yNW8rPtVHWngaD3V/iJsSbiLcFI7dYCfMGBZMOtPRuVFplKD06dOnqe245jhedpwVh1fwz1P/pMRZgsPo4Pb2t3NL0i20D2uPUTJq3ojRgUnSW0fr6EAjBeXpp59uYjOuHSo9lczdOZc1R9agotIzrie/7vZrerfpjVkyE2YMw2FyYJNteoBVR+cHhCwomzdv5tNPP+W+++4jIyODlStXMnbs2Oaw7aqiqiqff/85s7fPpsRZws9SfsaoTqOIscRgkSxa0cZo19vL6Oj8CCELypo1a5g2bRrz58+nrKyMrKys5rDrqpJbnsuMLTPYkreF9mHtebrP02REZRBhjiDMEKYHWHV0GkjIgmKz2QgLC2Pq1Km88sorfPfdd81h11XBo3j423d/Y/F3iwGY2HUiIzuMDI5mryef6eiERsiCMnTo0ODy7373uwZ1AXktsuvcLqZvmc7JCyfp17Yfv+72a1IcKcRYY7AZbC1tno5OqySkxLZricYmtpU5y3hl5yv84/g/iLHE8J/d/5Ob4m8i2hJNhClCD7Tq6FyGJkts+yHjxo1jxYoVV3KKq4aqqvzj+D94ZecrlLvL+WXqLxnTeQwJ9gSizFF6NquOThNwRXdRQUFBU9nRrJwoO8H0LdPZXbCb9Mh0/jjgj3SP6U60JRqzbG5p83R0rhsuKygvvPAC6enppKen06lTpzp9w17rxQOn18mi/YtYcnAJZsnMoz0eZWTqSOKscTiMjpY2T0fnuuOygpKens6RI0dYv349R48exWazkZ6eTufOnev0E3utsfnMZl7Y+gJnKs4wpN0Q/rP7f5IWnkaEOUJPj9fRaSYuKyg/TFo7d+4c2dnZZGdnM3jw4GYzrLEUVhUyZ8ccPvv+MxJsCfxp0J8YkjiEKHOUnpSmo9PMhBxDadu2LW3btq1TfXwt4FN8rDqyitd2v4bb52Zs57FM6DKBeFu8npimo3OVuC6qNg4VH2LGlhkcLD5IZkwmk34yiczYTMJMYS1tmo7ODUWrFpQKdwVv7n2TZYeXEWYM45nez3B32t1EWiL1OImOTgsQkqB8+OGHLFy4ELfbjaqqCILAxo0bm8u2H+WC+wJ3fXgXRdVF3N7+dn6b+VtSwlL0gb91dFqQkARl8eLFLFiwgPj4+Oayp8HklOfgMDp4rv9zDG43WI+T6OhcA4QkKElJSaSkpDTqQoqiMG3aNLKzszEajcycObPOuVatWsWKFSuQZZlJkyZx6623/uj54qxxLLl9CVGWqEbZo6Oj0/SEJChms5lHHnmEjIyMYFJbQ8Y2BtiwYQNut5uVK1eyd+9eZs+ezfz58wEoLCxk6dKlrFmzBpfLxYQJE7j55pt/tAPsWEusLiY6OtcYIQnKlVQV79q1Kzikac+ePTlw4EBw2/79++nVqxdGoxGj0UhycjKHDx8OjgGko6PTOghJUH44trHH42nwsRUVFXXS9iVJwuv1IssyFRUVOBw1qfA2m42KiopQTNPR0bkGCElQVqxYwZIlS/B6vaiqisFg4PPPP2/QsXa7vU6qvqIoyLJ8yW2VlZV1BOZSuFyu66K3OB2d1obL5ap3W0iC8sEHH7B06VLmz5/PiBEjePfddxt8bO/evdm0aRMjR45k7969pKenB7dlZmYyb948XC4Xbreb48eP19l+KXr27BmK6To6OleBkAQlLi6OuLg4KisrGTBgAG+88UaDj73tttvYvHkz48aNQ1VVZs2axZIlS0hOTmb48OFMnDiRCRMmoKoqzzzzDCaTPjSFjk5rI6Qe255++mnuvPNOvvjiC3r16sUHH3zA+vXrm9M+HR2dVkRIglJRUUFOTg7h4eG89957DBs2jP79+zenfTo6Oq2IkBq8ZGVl8fvf/56HH34Ys9nMqVOnmssuHR2dVkhIgjJv3jzef/99YmJimDRpEsuXL28uu3R0dFohIQmKKIpERGg9w5tMJmy2qz/chKIoPP/884wdO5aJEye2Si/J4/Hw+9//ngkTJnDvvfe2WAPLpqC4uJihQ4dy/Pjxljal0SxcuJCxY8fyq1/9itWrV7e0OY3C4/EwZcoUxo0bx4QJE1rs+whJUJKTk5k7dy6lpaUsWrSIhISE5rKrXmqn8E+ZMoXZs2dfdRuulI8++oiIiAiWLVvG22+/zQsvvNDSJjUKj8fD888/j9ncejv63rZtG3v27GH58uUsXbqUc+fOtbRJjeJf//oXXq+XFStW8MQTTzBv3rwWsSMkQZk+fToJCQn07dsXq9XKzJkzm8uuevmxFP7WwogRI/iv//ovQBveQ5KkFraocbz88suMGzeOuLi4ljal0fz73/8mPT2dJ554gscee4xbbrmlpU1qFB06dMDn86EoChUVFcGk0atNg6763HPPXbTu4MGDPP/887z00ktNbtSP8WMp/K2FQFGxoqKCp556iqeffrplDWoEa9euJSoqiiFDhrBo0aKWNqfRlJaWcvbsWRYsWEBubi6TJk3is88+u+ZHdPghVquVM2fO8Itf/ILS0lIWLFjQInY06C48cOAATqeTu+66i169etGSgw3+WAp/ayIvL48nnniCCRMm8Mtf/rKlzQmZNWvWIAgCW7ZsISsri6lTpzJ//nxiY2Nb2rSQiIiIIDU1FaPRSGpqKiaTiZKSEqKjo1vatJB45513GDx4MFOmTCEvL48HH3yQ9evXX/UE0QYVedavX8+bb76Jy+Vi0aJF7N27l+Tk5GDR42rSu3dvvv76a4CLUvhbC0VFRTz00EP8/ve/5957721pcxrFBx98wPvvv8/SpUvJyMjg5ZdfbnViAtCnTx+++eYbVFUlPz+f6upqIiIiWtqskAkLCwu2fwsPD8fr9eLz+a66HY0a23jHjh3BANaqVauaw656CXTUdOTIkWAKf1pa2lW14UqZOXMmn376KampqcF1ixcvbrXBzYkTJzJt2rRW9z0EmDNnDtu2bQs2+2iJB+WVUllZyR/+8AcKCwvxeDw88MADLeL5hpwp+8UXX/B///d/VFdXM3LkSO6///7mtE9HR6cV0SBB+eSTT/jkk084e/YsP//5z7nzzjtJTEy8Gvbp6Oi0IhokKF26dCE1NZUuXbpoB9WKgM+dO7f5rNPR0WlVNKh65L333mtuO3R0dK4DGhWU1dHR0bkU+vB6Ojo6TUbrywjTaTZmz57NwYMHKSwsxOl0kpSURGRkJK+//vpljz179iyHDx9m2LBhZGdnc+HCBfr16xeyDevWrWPdunWoqorH42Hy5MkMHjy4zvl1rl10QdEJ8uyzzwJaWv2JEyf43e9+1+Bjt27dyokTJxg2bBj//Oc/iYmJCVlQysvLeeutt/j4448xGo3k5+czevRovvrqqzrn17l20QVF57I8++yzlJWVUVZWxsKFC5k/fz67du0C4M477+T+++9n0aJFOJ1O0tLSWLduHQaDgW7duuF0Onn11VeRJImkpCRmzJjB+vXrWbNmDYqi8NRTTzFo0CAAjEYjHo+H5cuXc+utt5KcnMyGDRtQVTV4/l69epGYmBhsmBoREcGsWbM4dOgQCxYsQBRFCgsLGTt2LPfdd1+LfWY3Krqg6DSIgQMH8utf/5pNmzaRm5vLqlWr8Hq9TJgwgYEDB/Loo49y4sQJRo0aRW5uLjExMfTo0YMRI0awbNkyoqOjmTdvHuvWrUOWZcLCwoIjRwYwmUy8++67vPvuuzzyyCN4PB5+85vfMGHChOD5hw8fzpgxY5g1axYdO3Zk9erVvP3229x0003k5+fz4YcfoigKv/zlLxkxYkSra5PT2tEFRadBdOjQAYDjx4/Tt29fBEHAYDDwk5/8pN7OfEpKSigoKAi2pnY6ndx0002kpKQEz1eb/Px8nE4nzz//PAAnT57kkUceoU+fPnX2O378ONOnTwe0Plnat28PEBx9EqBTp06cPn1aF5SrjC4oOg0ikMyYlpbG2rVr+fWvf43H42HPnj2MGjWKo0ePoihKcF9FUYiMjKRt27a89dZbOBwONm7ciNVqJS8vD1G8uIKxqKiI5557jmXLlmG322nXrh2RkZEYDAZEUQyev0OHDrz88sskJCSwa9cuCgsLAa3PY5/Ph9vt5tixY6SkpFylT0cngC4oOiFx6623sn37dsaOHYvH42HEiBF069YNQRCYP38+3bp1o3v37syZM4e0tDT+93//l0cffRRVVbHZbMyZM4e8vLxLnrtbt25MnDiR+++/H7PZjM/nY/To0aSmpuJ0OoPnnzZtGlOnTsXr9SIIAi+++CIFBQV4vV5+85vfUFZWxqRJk4iKirrKn46Ontimc12wbds2VqxYwauvvtrSptzQ6IltOjo6TYbuoejo6DQZuoeio6PTZOiCoqOj02TogqKjo9Nk6IKio6PTZOiCoqOj02TogqKjo9Nk/H9ZAQqTTeY1ZgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 20\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2800 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "51630158b5e144239035dd45ba5a3cd7"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 30\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2700 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fba66ead32a24f42819dda0dc5ad17f3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 40\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2600 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "79b169ff83a9406ba10bcd67b725ef02"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 50\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2500 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "541e7dd433ae4e8a9af2c02eca3d99b6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 60\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2400 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "aa45ad44ee024a1299ee76f619f8a4a3"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 70\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2300 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "42376dce1db442caa542ff801c18ef2a"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 80\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2200 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "fc0fafb6903e46d1ad133dc04fbecfbf"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 100\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/2000 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6e62f6f24b0d4d319c58057145a1be90"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 150\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1500 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2abf6e3d9b9c438cbb9dc495b4ee01a6"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------\n",
      "train size per step: 200\n"
     ]
    },
    {
     "data": {
      "text/plain": "  0%|          | 0/1000 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0135eb5396054d8c92a214b491af7c1c"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 288x144 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "all_ideal, all_noisy, all_zne, all_circuits, all_steps = [], [], [], [], []\n",
    "train_ideal, train_noisy, train_zne, train_circuits, train_steps = [], [], [], [], []\n",
    "test_ideal, test_noisy, test_zne, test_circuits, test_steps = [], [], [], [], []\n",
    "for step, i in enumerate(range(0, 2000, 200)):\n",
    "    all_ideal.extend(ideal_exp_vals_estimator[i:i+200])\n",
    "    all_noisy.extend(noisy_exp_vals[i:i+200])\n",
    "    all_zne.extend(zne_mitigated_vals[i:i+200])\n",
    "    all_circuits.extend(all_circuits_unshuffled[i:i+200])\n",
    "    all_steps.extend([step]*200)\n",
    "\n",
    "for step, i in enumerate(range(2000, 3000, 100)):\n",
    "    all_ideal.extend(ideal_exp_vals_estimator[i:i+100])\n",
    "    all_noisy.extend(noisy_exp_vals[i:i+100])\n",
    "    all_zne.extend(zne_mitigated_vals[i:i+100])\n",
    "    all_circuits.extend(all_circuits_unshuffled[i:i+100])\n",
    "    all_steps.extend([step]*100)\n",
    "\n",
    "rows = []\n",
    "for ideal, noisy, zne, circ, s in zip(all_ideal, all_noisy, all_zne, all_circuits, all_steps):\n",
    "    rows.append([ideal, noisy, zne, circ, s])\n",
    "\n",
    "rows = sorted(rows, key=lambda x: (x[-1]))\n",
    "\n",
    "df = pd.DataFrame(rows)\n",
    "df.columns = ['ideal', 'noisy', 'zne', 'circ', 'step']\n",
    "\n",
    "for train_size_per_step in [5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 100, 150, 200]:\n",
    "    print('-'*30)\n",
    "    print('train size per step:', train_size_per_step)\n",
    "    test_size_per_step = 300 - train_size_per_step\n",
    "    train_rows = df.groupby('step').head(train_size_per_step).reset_index(drop=True)\n",
    "    test_rows = df.groupby('step').tail(test_size_per_step).reset_index(drop=True)\n",
    "\n",
    "    train_ideal, train_noisy, train_zne, train_circuits, train_steps = list(train_rows['ideal']), list(train_rows['noisy']), list(train_rows['zne']), list(train_rows['circ']), list(train_rows['step'])\n",
    "    test_ideal, test_noisy, test_zne, test_circuits, test_steps = list(test_rows['ideal']), list(test_rows['noisy']), list(test_rows['zne']), list(test_rows['circ']), list(test_rows['step'])\n",
    "\n",
    "    X_train, y_train = encode_data_v2_ecr(train_circuits, train_ideal, train_noisy, obs_size=4, two_q_gate='cx')\n",
    "    X_test, y_test = encode_data_v2_ecr(test_circuits, test_ideal, test_noisy, obs_size=4, two_q_gate='cx')\n",
    "\n",
    "    BATCH_SIZE = 32\n",
    "    train_dataset = TensorDataset(torch.Tensor(X_train), torch.Tensor(y_train))\n",
    "    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "    test_dataset = TensorDataset(torch.Tensor(X_test), torch.Tensor(y_test))\n",
    "    test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE*1000, shuffle=False)\n",
    "\n",
    "    X_train = pd.DataFrame(X_train)\n",
    "    y_train = pd.DataFrame(y_train)\n",
    "    X_test = pd.DataFrame(X_test)\n",
    "    y_test = pd.DataFrame(y_test)\n",
    "\n",
    "\n",
    "    rfr_tree_list = []\n",
    "    for q in range(4):\n",
    "        rfr = RandomForestRegressor(n_estimators=100)\n",
    "        rfr.fit(X_train, y_train.iloc[:, q])\n",
    "        rfr_tree_list.append(rfr)\n",
    "\n",
    "    to_df = []\n",
    "\n",
    "    start = 0 #300*9\n",
    "    end = 100000000 #300*10\n",
    "\n",
    "    models = [(rfr_tree_list, 'rfr_list')]\n",
    "    models.append((None, 'zne'))\n",
    "\n",
    "    for circ, ideal, noisy, zne, s in tqdm(zip(test_circuits, test_ideal, test_noisy, test_zne, test_steps), total=len(test_circuits)):\n",
    "        row = dict()\n",
    "        row['ideal'] = ideal\n",
    "        row['noisy'] = noisy\n",
    "        row['step'] = s\n",
    "\n",
    "        X, _ = encode_data_v2_ecr([circ], ideal, [noisy], obs_size=4, two_q_gate='cx')\n",
    "\n",
    "        for j, (model, name) in enumerate(models):\n",
    "            if name in ['rfr_list']:\n",
    "                row[name] = []\n",
    "                for q, m in enumerate(model):\n",
    "                    row[name].append(m.predict(X))\n",
    "                row[name] = np.array(row[name]).transpose()[0]\n",
    "            elif name in ['zne']:\n",
    "                row[name] = zne\n",
    "            else:\n",
    "                raise NotImplementedError\n",
    "\n",
    "        to_df.append(row)\n",
    "\n",
    "    df_rf = pd.DataFrame(to_df)\n",
    "\n",
    "    def l2_dist(list1, list2):\n",
    "        return np.sqrt(np.sum([(a - b)**2 for a, b in zip(list1, list2)]))\n",
    "\n",
    "    for col in df_rf.columns:\n",
    "        if col not in ['step', 'ideal']:\n",
    "            df_rf[f'L2_{col}'] = df_rf.apply(lambda row: l2_dist(row['ideal'], row[col]), axis=1)\n",
    "\n",
    "    df_mean = df_rf.groupby('step').agg('mean')\n",
    "    df_ste = df_rf.groupby('step').agg('std') / np.sqrt(len(df_rf[df_rf['step']==0]))\n",
    "\n",
    "    plt.style.use({'figure.facecolor':'white'})\n",
    "    plt.figure(figsize=(4, 2))\n",
    "    xs = sorted(set(df_rf.step))\n",
    "    legend_labels = ['Unmitigated', 'ZNE', 'RF']\n",
    "    for i, col in enumerate(['L2_noisy', 'L2_zne', 'L2_rfr_list']):\n",
    "    # legend_labels = ['Unmitigated', 'RF Mitigated']\n",
    "    # for i, col in enumerate(['L2_noisy', 'L2_rfr_list']):\n",
    "        plt.plot(xs, df_mean[col], label=legend_labels[i])\n",
    "        plt.fill_between(x=xs, y1=df_mean[col]-df_ste[col], y2=df_mean[col]+df_ste[col], alpha=0.2)\n",
    "    plt.xlabel('Trotter Step')\n",
    "    plt.ylabel(r'Mean $L_2$-distance from the Ideal')\n",
    "    plt.ylim([0, 0.35])\n",
    "    plt.xlim([0, 9])\n",
    "    ymin, ymax = plt.gca().get_ylim()\n",
    "    plt.ylim(ymin, ymax)\n",
    "    plt.legend()\n",
    "    plt.grid(None)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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