docs/tutorials/h37_ising_4q_hardware_single.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json, os\n",
    "import glob, pickle\n",
    "\n",
    "from tqdm.notebook import tqdm\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\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, modify_and_add_noise_to_model\n",
    "from gnn import ExpValCircuitGraphModel, ExpValCircuitGraphModel_2, ExpValCircuitGraphModel_3\n",
    "from mlp import MLP1, MLP2, MLP3, encode_data\n",
    "from collections import defaultdict\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from noise_utils import AddNoise, RemoveReadoutErrors\n",
    "\n",
    "plt.style.use({'figure.facecolor':'white'})\n",
    "D = {'output': 'mpl', 'fold': -1}"
   ]
  },
  {
   "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 import *\n",
    "from typing import TypedDict\n",
    "\n",
    "import pandas as pd\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit.result import sampled_expectation_value, Counts\n",
    "from typing import Union, List, Dict\n",
    "\n",
    "class CountsProcess:\n",
    "\n",
    "    @classmethod\n",
    "    def pauli_exp_val_all1Q_for_counts(cls, counts:Union[List[Counts], Counts], nolabels=False):\n",
    "        \"\"\" Pauli expectaitoin value for all isngle qubit Z observables across all counts.\n",
    "            - Simplest version\n",
    "            - Not very efficient\n",
    "        Returns:\n",
    "            - <Z> for qubit Q0...Qn-1 with ordering qubit index is array index.\n",
    "            - Pauli labels returened are in rightmost is Q0 (qiskit convention)\n",
    "              To put ot physics notation when in df columns:\n",
    "                    df.columns = list(map(lambda x: x[::-1],df.columns))\n",
    "            \"\"\"\n",
    "        if isinstance(counts, Counts):\n",
    "            nq = counts.creg_sizes[0][1]\n",
    "            expects = [] if nolabels else {}\n",
    "            for n in range(nq):\n",
    "                # In the string representation qubit-0 corresponds to the right-most Pauli character\n",
    "                pauli = SparsePauliOp.from_sparse_list([(\"Z\", [n], 1)], nq)\n",
    "                expect = sampled_expectation_value(counts, pauli)\n",
    "                if nolabels:\n",
    "                    expects += [expect]\n",
    "                else:\n",
    "                    expects[pauli.paulis.to_labels()[0]] = expect\n",
    "            return expects\n",
    "        elif isinstance(counts, list):\n",
    "            expects = []\n",
    "            for count in counts:\n",
    "                expect = cls.pauli_exp_val_all1Q_for_counts(count)\n",
    "                expects += [expect]\n",
    "            return expects\n",
    "\n",
    "    @classmethod\n",
    "    def pauli_exp_val_all2Q_for_counts(cls, counts:Union[List[Counts], Counts], nolabels=False):\n",
    "        \"\"\" Pauli expectaitoin value for all isngle qubit Z observables across all counts.\n",
    "            - Simplest version\n",
    "            - Not very efficient\n",
    "        Returns:\n",
    "            - <Z> for qubit Q0...Qn-1 with ordering qubit index is array index.\n",
    "            - Pauli labels returened are in rightmost is Q0 (qiskit convention)\n",
    "              To put ot physics notation when in df columns:\n",
    "                    df.columns = list(map(lambda x: x[::-1],df.columns))\n",
    "            \"\"\"\n",
    "        if isinstance(counts, Counts):\n",
    "            nq = counts.creg_sizes[0][1]\n",
    "            expects = [] if nolabels else {}\n",
    "            for n in range(nq):\n",
    "                for m in range(n+1, nq):\n",
    "                    # In the string representation qubit-0 corresponds to the right-most Pauli character\n",
    "                    pauli = SparsePauliOp.from_sparse_list([(\"ZZ\", [n,m], 1)], nq)\n",
    "                    expect = sampled_expectation_value(counts, pauli)\n",
    "                    if nolabels:\n",
    "                        expects += [expect]\n",
    "                    else:\n",
    "                        expects[pauli.paulis.to_labels()[0]] = expect\n",
    "            return expects\n",
    "        elif isinstance(counts, list):\n",
    "            expects = []\n",
    "            for count in counts:\n",
    "                expect = cls.pauli_exp_val_all2Q_for_counts(count)\n",
    "                expects += [expect]\n",
    "            return expects\n",
    "\n",
    "class IsingModel:\n",
    "\n",
    "    class Options(dict):\n",
    "        def __init__(self, *args, **kwargs):\n",
    "            super().__init__()\n",
    "            self['nq'] = 4\n",
    "            self['h'] = 1\n",
    "            self['J'] = 0.15\n",
    "            self['dt'] = 1/4\n",
    "            self['depth'] = 15\n",
    "            self.update(*args, **kwargs)\n",
    "\n",
    "        def config_4q_paper(self):\n",
    "            self['h'] = 1\n",
    "            self['J'] = 0.15\n",
    "            self['dt'] = 1/4*2\n",
    "\n",
    "        def config_10q_paper(self):\n",
    "            self['h'] = 1\n",
    "            self['J'] = 0.5236\n",
    "            self['dt'] = 1/4\n",
    "\n",
    "\n",
    "    @classmethod\n",
    "    def apply_quantum_circuit_layer(cls, qc: QuantumCircuit, ops: 'IsingModel.Options'):\n",
    "        allq = list(range(ops['nq']))\n",
    "\n",
    "        qc.rx(2*ops['h']*ops['dt'], allq)\n",
    "\n",
    "        qc.barrier()\n",
    "        for q0 in allq[0::2]:\n",
    "            q1 = q0+1\n",
    "            qc.cx(q0,q1)\n",
    "        qc.rz(-2*ops['J']*ops['dt'], allq[1::2])\n",
    "        for q0 in allq[0::2]:\n",
    "            q1 = q0+1\n",
    "            qc.cx(q0,q1)\n",
    "\n",
    "        qc.barrier()\n",
    "        for q0 in allq[1:-2:2]:\n",
    "            q1 = q0+1\n",
    "            qc.cx(q0,q1)\n",
    "        qc.rz(-2*ops['J']*ops['dt'], allq[2:-1:2])\n",
    "        for q0 in allq[1:-2:2]:\n",
    "            q1 = q0+1\n",
    "            qc.cx(q0,q1)\n",
    "        qc.barrier()\n",
    "\n",
    "    @classmethod\n",
    "    def make_quantum_circuit(cls, ops: 'IsingModel.Options'):\n",
    "        qc = QuantumCircuit(ops['nq'])\n",
    "        allq = list(range(ops['nq']))\n",
    "\n",
    "        for step in range(ops['depth']):\n",
    "            cls.apply_quantum_circuit_layer(qc, ops)\n",
    "\n",
    "        if ops['measure_basis'] == 'Z':\n",
    "            pass\n",
    "        elif  ops['measure_basis'] == 'X':\n",
    "            qc.h(allq)\n",
    "        elif  ops['measure_basis'] == 'Y':\n",
    "            qc.sdg(allq)\n",
    "            qc.h(allq)\n",
    "        else:\n",
    "            raise Exception(\"Must be X Y or Z\")\n",
    "        qc.measure_all()\n",
    "\n",
    "        return qc\n",
    "\n",
    "    @classmethod\n",
    "    def make_circs_sweep(cls, ops, sweep_steps = None, measure_bases = None):\n",
    "        sweep_steps = sweep_steps or [16]\n",
    "        measure_bases = measure_bases or ['Z']\n",
    "\n",
    "        circs = []\n",
    "        run_params = []\n",
    "        for basis in measure_bases:\n",
    "            for depth in range(*sweep_steps):\n",
    "                ops['measure_basis'] = basis\n",
    "                ops['depth'] = depth\n",
    "                qc = cls.make_quantum_circuit(ops)\n",
    "                qc.metadata = {}\n",
    "                qc.metadata['measure_basis'] = basis\n",
    "                qc.metadata['depth'] = depth\n",
    "                run_params += [qc.metadata.copy()]\n",
    "                circs += [qc]\n",
    "        return circs, run_params"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
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+WiHNXuf+GIdDevwSqXpl/8ay7FspPcm/71Ga6g2k7uVY1O6tmdLWg6U/P/PDUYquXF1HDm7TBXdPKvZ8fl6Ovh3TQ826XKi+w/+nGe/foIyUHbrs4VnuF3GQ1Ly29O+zyx6zZF5Zl7ecAV9avFX6ZpHn4+44S2pRx7+xcA0pm0BcQ47lSGMmSvnO0o9xSOrdQrqyl39jsWv98FZKhvTMT56PO7eDNLSjf2OxYw4iua8jVq0flLX1PosAEIremy1t3C+5u/tQpZL05KVGfw3Kx+WSXpoi7Us3drIsiUNSnWrSg+cb/U/+ZIf23MxcKRiVJf8L9u9fZWXXvB0A7CyvQHp8onQst/RjHJLaN5BGDQxYWEEpM1t6fJJU4KEPuF9L6fKe/o3FLjkIeapvWSXfscN3pEAK5ntfAKxj3AJp2fbS+8okKSpCeuoyqVJkwMIKSu/8Jm056L4PuFqMNOYS//cB26HNJd/zLau073aoe4FEnyeAQPh7t/TJfM/HjRogdWjo/3iC2R+bpAlLPR9397lS45r+jwcoj4MHDx7fwLg0bdq00fr160t9PiEhQbVr1/Z1aBo0aFCRXeKvuuoqffPNN5o6dWqRebelKW0Ce6HbbrtNY8eO1TfffKOrr776+OMDBw70epd3wJcCNjysZk2jVdq0aVORx1988UXt27dP3bp1k2Tsyl6nTp3jE9glqWnTptq5c2eFYzhy5Ig2btyoyZMnez2BXZIuvvhipaSkaPr06ccfy8vL07fffquzzjqLCewAbKlnUykmUqXukuuQ0dkV6AnsktTQT+9Zt1rgJ7BLxgCMiLDSy1qSujTy/wT2YHVav2u0asZbatThnBKfX/DdwwqPiFKvy56QJA36vzd1JGWHlk99NXBBAiGmS2OpaiX3bUz9eKOdMRvXkMCrHC31aeH+GIdD6t8qMPG4Y/f6UTNO6uimM94hKTLc8/lAyexeP+yEsgYA+zmjjfvBi5I06DQmsFeUwyGd2db9oFyXjGP8PYHdE9pzeEIdAQD4W2S4NKC1+2NcMvJUVEyVSlKvZu6PcTik0z2cj0AgB4FZqHsA4B8DWsv9AC0Z47iYwF5xXvUBtzG/D5g2F2ah7gGAf7SrL9WoUvq9T4dDqllFals/sHEFo+5NpcpRbspaUpOaUqMaAQ0L8Lknn3zS7BAk6fi82oULF3o81tME9pNfp/B1AbMFrHugWbNm6tixo5599ll98cUXmjVrlkaPHq1PPvlEkm8+FImJiXK5XCXuwi5J8fHxWrRokccd3U914YUXqn///rrhhhv05Zdf6rffftMVV1yhXbt26Zlnnqlw3ABghsrR0q1nFu+UL/yekVhdur5/oKMy1IqTalf1/eua9YW0RhVjx4bwUybQF36pa1bL/zvNBrMGbQYprmZjtepTfCnNXWtmac2cDzRk9DiFRxiVPSomTufc9qUWTXxcKbtXBzpcICRERUi3nWm0NScrbGNqxEk3DTR/EoXENcQsF3eV2tYzfj61GoQ5pGv7SfXiAx5WMcFQP0b0lhr/01F8allHhEs3D2IhnfIKhvphF5Q1ANhP67rSZd2Nn0/OQQp/7tnMmFiNiuvWRDq7nfFzSWU9uK3Uo2mAgyoB7Tk8oY4AAALhnPZG/iQVz50ckq7oKTWvE/i4gtEl3aTWicbPp94LCHdI150uJVYLfFynIgeBWah7AOAfDWtII/sY95xPVpiPtG8gnd8p8HEFo7b1pUu6Gj8Xya3/+UefFtJACywQRZsLs1D3AMA/wsOkW88wNlk6WWE+Ui3GeN7shXSCQaVIYxxwaXNNaleVbhxgjXHAQEUMHz7c7BAkSY0aNZIkbdy40e1xiYmJHiewn/w6jRs39n2wQDk4XC5Pa+H5zqZNm3TrrbdqyZIlqlGjhq677jrFxcXpkUceUUZGhmJiYpSSkqLGjRsrNTX1+G7sPXr00DPPPFPmyee+lJ6ergcffFATJ05UZmamunTpoueff14DBgwwLSYA8IXDWdLCzdK0f/q9GtUwOpG7NTEmIZpl3gZp0l+ej3t9pPH3XePcHxfmkMZcYu4krUOZ0p+bpdnrjH83q2Ws7tupUeB2iF/2rZSeFJj3OlX1BlL34n2yHr01U9p60P0xR9P2KTa+bvkCc6N5benfZ5fvd80q6/KWM+APmdnSoq3SLyuNfzeIl3o1l3o0C9zK5lxDyiaQ15ACp7QmSfpjk7T5gPHYgNZSv1ZSHT8sZlMSu9aPssovkFbslBZskbYnG4+d1c7IQ+JjAxODHXMQyXMdsWL9oKzLJpCfRQAIRUmpRr63aKvx73b1pdNbSafV5Wa2r207KP2+ycj7JKlLY6Osm9cOXAx2aM/NzJWCUVnyv1D5/uUtu+btABAMnC5pw14jT12313isTwupfytrLKwZTAqc0urdxv3Rwj7ggadJp7eUatEHXAR5qm9ZJd+xw3ekQAqFe18ArGP/YenPTUZ/mWQsrtOvldS+vhTGhCaf2n3IKOcl24x/d2hglHXrxMD1AduhzSXf8y2rtO92qHuBRJ8ngEA6liMt3iZNXm78u251qVczYzH1UzdfQsVkZEkLt0hT/jb+3TBB6t3C2Kk92sS5JoA3Dh48qNTUVLfHtGnTRuvXry/1+YSEBNWu7fuBF4MGDdK8efOO/7tOnTpKSEjQnj17dOTIkVJ/r3Pnzlq6dKl+/fVXDR8+vMQJ7IXatWun7Oxsbd269fhjAwcO1Ny5c33y/wCURUCbjFatWmnOnDlFHrv22mvVtm1bxcTESJJq1qypfv366eOPP9a//vUvLViwQHv27NEZZ5wRyFCLqV69ut5//329//77psYBAL5WLUYa0vHEJPZ7hpgbT6HezaX5G42J377Qr5X5u4zWqCJd1OXEJPY7zzE3nmDij85eABVXpZIxUbZwEvt955kaTqm4hpgjPMxYyKVToxOL0RTu1mklwVA/IsKNxSN6NDtR1hd0NjWkoBEM9cMuKGsAsKcGCdJVvU9MYr95kKnhBLVmtY0/hZPYrzvd3HhKQnsOT6gjAIBACHMYu0a2rX+ir+zKXubGFKzCw6TOjY0/hWV9aTdzYyoJOQjMQt0DAP9JrCYN63FiEvvowebGE8wa1pCu7nNiEvuogebGUxLaXJiFugcA/lM5WjqjzYlJ7A+eb248wSwuRjqnw4lJ7PcONTceIFgdOHBABw4c8HjcypUrVbNmTR0+fNjjsWvXrvVFaIBPmL7uybJly9S7d+8ij7333nu6/vrr9frrrysqKkrffPONoqKiTIoQAGCG6EhjkPM7v7k/bu0ez69VswoTtbyVlLxZn09/TFVja6pVg+46t8f1x59bumGapi75SGP+b4Jy87L12oRbVK1KLYU7wnXzBS+aFzQAAAgae1K26Okvr9B7d68o8vjJecjyzbP0219fSpKWb56pF275TY3rtDEjXAAAAASp574eqd5tL9QZnU9s3XJyTlpoyuIPtTlpuf4zbGyJx9z5Vh81qNVaknT7xW9o4u+v61DGPoU5wnXFoPtUUJCvL2c+oWqxtVTgzNe/L31b3899SakZ+5WecUDXnvOEUg7v0afTHlW9Gi3UtG4HXdz39mL9cjOWfa45K75RfFyiOrc4Q6e3v0wf/HKfYqLjdPhoih646jN9OePJIu+9cdcSLdkwVZL016bp+vC+tfps2mNyOBwKCwvXtWc/rqqVEwJV5AAAAPACeSp5KgAACG7ke+R7AAAAgCfeTGAHrMbUSeyZmZnatGmT/vWvfxV5vFmzZpo/f75JUQEArKJlHWlYd+mHZaUf8+Fc968RGy3dNEiKNn3ZFns4mn1YN573nGpVa6Anvxh2fBL7yi1ztPfQVmXlZEqSMrPSdV6vm9WhWX89/tklys3PUVREtImR29eqrXP1xOeXqlndTtqful3N63VWqwbddVG/27X74Ab9svA9xUTHKT6ujq49e4zZ4QKwsJKuJ0/dMNnssACvpR7Zr6lLPlKlqNgij5+ah3RtOVhdWw7Wn2t+VKuG3ZnA7ielLSgAAAAQ7CbMe1UxUVWKPHZqTipJf2+br90HN8olV4nHJKcnKSs3U5ERUUpMaKrYmGpauXWOXhk9V0ezDuvtH/+t83vfqlFDn1Pt+EYa8+nFOpC2U2u2/6E6CU1UNbamaldvpDkrvlFC1bpyuZxq17hvif1yq7fNV81q9VXgzFebRr2VmrFPC9ZOVrdW5yg6qrIR3ynv/eCILzSo85Wa+Psb6t9xmBxyKPXIXj1x/SRt2/u3Js5/TdcPeTpApQ53Tl14NDGhCd//AQAIQeSp5KmhhBwYABCKyPfI94LdgbSdGvPpxWper7NqVK2r7q3PLTHH+2rm0+rScjDjJgEAQEAMGjTI7BCAkBBm5ptXqVJFBQUF+ve//21mGAAAC+vfWhrRW4oML/vv1q4q3Xm2lFjN93EFq9YNuysyPEqPfny+2jTqffzxzi3O0MX9bj/+74SqierQrL9em3CLHP/8h/Lp0HSAWjfsqVdGz1XHZgN152XvKiMrVVUrJygzK03/vvQd3XnZO1qz/Q+zQwVgcSVdTwA7SaiaqJvOe77YjflT8xBJKijI18xln+vivkUfh2+UtqAAAABAsFuw9idViamuNo37FHn81Jx0f+oO/blmki7ud0epx0RHxujhEeN09+Uf6MjRQ1q7Y4Eu6vsvvfTdDZq84B3l5B1T+6b9VDu+kaYt+URtGvdWasZ+ScbuSM3qdtSs5V+pV5vzde/lH+nuyz/Qp9MeLbFf7tweN+rOy8bqlgte1oe/PqCY6Dg9e9M0PTjiC2XlZGh/6o5i7y1JGcfStClpmfq0vVBxlePVueVgvTbhFi1a/4vSMg/4s6hRBoULj46+6DX9sWYi3/8BAAhB5KnkqaGGHBgAEGrI98j3QsHf2+YrIS5RktS2Sd9Sc7yMrFTGTQIAgIAZO3as2SEAIcHUSewAAHijV3PpgfOlVoneHR8RLg1uK91/nlSHCexlsmX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iRoE8NhYVSVNNruspy6Q2daQSFom2rUDeh1ayW57qS7SR4uzWPtiHgP/NWWXuHb1XbjeOd9dNMW+bngr0PmTmCim/0Lzt/b5OGtjGmvPUgV7XVrDbmGtXtL3i7Nb22IeA/23fJy3ZZN72DudLs1dKZ7Uzb5ueCvQ+ZNUOaf1u87a3L0+av1bqacGioIFe11aw25hrV7S9ktmt/bEfAf9yOqUpS83d5pSlUof6/j/PGOj9R36h6/wGM0xdxiR2APClWrVq6b333lPv3r0lSRs2bND48eM1f/58rVu3Tk6nUw0aNFDnzp118cUXq2HDhnrttdd08cUX68cff9Sjjz4qSbrpppv0xx9/+CVmv19SVVRUpKefflqNGzdWVFSU2rZtq5kzZ6pp06a65pprKrTtnJwcDRgwQEuWLCnxeafTqfvvv19btmyp0PtIUlxcnCIjIxUebsFygKeIj0nSv89/Q7/+PUlzln7l8lxRUZEeGz9cIwc+ptTk+tYEWILVC75Qo87nSZIKC47qw3vaadaHt7qUWTj5//TWTfV05OBeCyL0PzvuR1+xa/uw4z5csE56YYqxutgx+YXGHZKe/l7ammNNXIVF0tw15m/3l5Xmb9NT05dLr8+QVu888djhfGnG39Iz30vZBywLzS07tmv6ECAw/LlJeu4H1xPHBUXSb2uNfs/Mk5xmog/xHzvW9Y9LpTdmSutOar95R41J7c9OlvYdsi42d2gf/kNd+48d6xoAEBg27TEmIJlp7yFj8UIrBeLYuHyrlGNyjrw52/z9FygCcR9aya55qi/RRk6wa/tgHwL+UeQ0JrGbbbaF5xmlwOxDjhQYx/zN5JRxtyYrBWJdW8WuY65d0fZOsGvbYx8C/vOLD/K9eaulAhMX5/FWoPYhvqhrcuvAYdcx165oe67s2v7Yj4B/rNoh7co1d5s79rtex+9vgdp/LN5o7oKgkrH/duwzd5sAAEP9+vX1yy+/qHfv3tq5c6cuueQSpaen6+6779bEiRO1aNEiLV68WF9++aXuueceNWzYUBdddJF27typrKwslwnszz//vN/i9vsk9pEjR+qhhx7SqFGj9P333+uCCy7QxRdfrLVr16pDhw4V2vaRI0f0ww8/KDs7u8Tnc3Jy9NlnnykzM1ObNnm/DGNhYaHy8/O1YcMGjRkzRk6nU9dee22FYjZLQkyyzj3933pr8j0qKio6/vj7Ux5Ug9TW6t5qqHXBneJA9hZFxiQpIspYOjs0LEIDrvtQf057VZuWTZMk7d70p+ZMuEdnXPueImOrWBitf9lpP/qK3duHnfZhzkHpo7nGhQBOp+tzThkTrN/62bjQw9+27ZUOHDZ/uyt3mL9NT2zaI331z+I0p9a1JO3Pkz6Y49+YvGGndk0fAgSGA4eld2cbfd6p3Z5TUn6B9NZMY9GSQEIf4j92rOu1O6XvFhu/lzSe5xyUxs/zb0zeoH34D3XtP3aqawBA4Fix3Tfbteq408kCbWz0WV37aLuBIND2oVXsnqf6Em3E/u2DfQj43u5c8xfSkQIjBwm0PmTTHuOcstmo68Bg9zHXrmh79m977EPAP1ZuM3+bB44Y16xZKdD6EKdTWuWD3GznfusXiA+0uraC3cdcu6LtGeze/tiPgO/5Igfx5XY9FYj9h6+Oxa0KgPPngCdmzpxpdQiAx2JjY/Xjjz+qXr16mjNnjlq2bKnx48e7jCmnKioq0ieffKInn3zy+GO7du3SW2+95Y+Qj/PrJPbx48frnXfe0ddff63bbrtNvXr10r333quMjAwVFBSoffv2przP0aNHdfjw4WI/MTExmjx5siIiIpSZman169d7td3MzExFRESofv36mjhxor777ju1adPGlJjNcM7pNyl7/zZN+f09SdIfq37S7yt/1NVnPlnGK33r6OEDOpp3YhmkFXM+UrPul7qUSandUt3Of1Q/vn6FDu7drskvD1fbfterdvNMf4druUDdj74SjO3DLvtwzir3E9SdTuMiDyu+rG0ueS2SCtu13zcXUpTl55WSw83zTklrd1l353tPBGq7pg8BAtP8tcZd10sbZpyS9h+W/tzsz6iKow/xn2Coa0/G87+3mb8CrJloH/5DXftPoNY1ACBw+eq406YAuTt4II2NPqtrH203UATSPvSXYMxTfamytZFgbB+VbR8C/uarXGHvISnXB4tweyuQ+hBf1fXWnMBYBDeQ6tofgnHMtSvanv3bXmXbh4C/Hck3JkH7QiAcdwqkPmTPAenQUd9sm7r2v2Acc+2qsrU9KTjbX2Xcj4A/+SpX2BgA53QDrf/w1TndQKhrwBPLli2zOgTAY48//rgaN26sJUuWaMCAAdq9e7dHrxs1apSefvppSdLmzZtVrVo1l0nt/uBwOku6h5xvtG7dWrVr19b333/v8vidd96psWPHKjc3V5GRkRo6dKjWrl2r0NBQhYeH69FHH1Xfvn3L3P727duVlpbmcTw9e/b0asWMFStWaN++fdqyZYveeOMNzZo1S998842ysrI8ev2ZZ56p7777rsxyT187XW0berbN0mTv367bXuulR0d+r9Tk+h69ZvGaGbrt1V7ler9z752u2s2zij2+buEkzZlwr5p1G64OZ90uSfrqqTN19q3fyBHiuoaC0+nUl08O0K4NCxWXVFsXPjhPoWERZb735uUz9Pkj5Yvbl+y4H32ppDYS6O3Dqn0o+Wc/nn/fL0prnCGHo/TpWE5nkRZ887jmfHqvT2M5VZdz7lPXcx90eezqLKllLe+2s2yLNG6G62Pv3t5Ue7etrFB83hrx3HolVK1XZrkZ796gxVNe9GksdmzXdh1jGAdQmQ25bZLqtumvkJDQUssUFRXqz59e1Yx3r/dpLPQhfCcwyzUv71R0QrUyy00dN1LLZvp2dTrah3/ahxn1LJE7ecqO7RoAYE/n3zdbNZt0d3nM2+NOJR1z2r9rvd6+pUGF45OszUPMHBuveHaNEqunuzxmRl1vWzVXnz7YrcLx+Rr5jSs75Klmffb8qbzfN/zF0/Zoh/ZREj7nQGBqf+ZtOv3ip1weM+s840f3nqZdGxZVJLzjgqEPOX34M2o/8N8uj5lV1+PGpOrQvorfrilYcmsz2fHaDTPZLecLlnxPsmfbs+P1FUBlkFijoa54ZrXLY2blIL9+8aDmffFARcI7LhjyvZpNe+j8//7s8phZdT3trWv157TXKhTfMcFQ12ay45hrJvI9c1U035MCu/3xnREIXMMf+1NV67RyecyM84y7Ny7Rh/e0rXB8UvB8Z7zmld2Kjk85/rdZ+d76JZP11ZMDKxwfUF7//ve/PZo3OnbsWN1yyy2lPp+ZmalZs2bp2WefNTtEr935uPEd6om7Rrn8HujsGHcgxty8eXP99ddfys/PV8eOHbVkyRKPXjdq1Ci9+uqrkqSbbrpJ06ZN0++//66IiAi1bt1aS5cuLVc8gwYN0qRJkzwuH1audymHzZs3a+nSpSV+sDdu3KiWLVsqMjJSkvTOO++oSpUqkqSFCxcqKytL2dnZCg0tfRLMyR599FG1bt26xOeKiop01113ac2aNbr99tu9+jc0bdr0+O9DhgxRRkaGbrrpJi1evNir7fjDB1Mf0sHD+/TUJ1ccf6xOtaa6+TxzDvp4qsFpZ6rg6CHN/+phdTjrdu3asEjV6rUr9qVTkhwOh2o3z9LGP39Ux8F3e/SlM9gFyn70lcrQPgJ5H4aEhLqdwC5JckoOh2d9r6nKiqsim3Z7D1XfcDeJ82QOD8tZLVDaNX0IELgcIaFl97dOp8f9oy/Qh/hPsNS1p+M047l3gqV9uENd+0+g1DUAIND55tiQw1F8TLWa9WNj5alrX7F+H/peZchTfSnY20hlaB/Bvg8BK/j0XKAPz2GWh9V9CHUdPP11ZRhz7Yq2d4Jd216w70PACuQg9j++Z2yauvanyjDm2lWwtz2pcrS/yrAfAX8rc45D+Tfsm+2WUyD0H76q68p0ThcA/GH06NGSpLfeeqvcE9iff/55SdK4ceM0ZswYjR49WmPGjPFNwKfw253Y582bp4yMDE2aNEmDBg06/nheXp4aNmyogQMH6s033yz2uhkzZmjo0KHavXu3wsLcz7k/dif26dOnl3h39MLCQo0YMUKffvqpJk6cqIEDK7aqy5gxY/TWW28pLy+vQts51YKPpb2bTd2kR6rUljpeVL7XvjBFWrOz5OcKjh7WuOtTdd5/Zmn5z++qde9RSkprUqzc7k1/6pMHuqptvxv057RXNfzRJUqoWrfM925YXbqhX/ni9iU77kdfKq2NBHL7sGofSv7Zj5/9Jv2yUiprELi8h3Ra2TcRN9XslUZ8nnhuuPH/mz/0rPwj50mxkeWLq7zemiX9uansur6hn9FmfcmO7dquYwzjACqzSYukKcvKLndhFymjkW9joQ/xDt8JSvfqNGnFtrLH81sHSHVSyihUQbQP/7QPK/MmidzJX8idAKDyeWOmtNTDMceb4061k6XbTFpI3o7Hb0ry7GRp456yy3l7fK9FTekaG9ychPzGlR3yVKu/AwQjT9ujHdpHSficA4Hp1zXS+HmelfU2D7l/qJQUW66wigmGPmTKUmmSh/d88KauQxzSExdK4SasFxosubWZ7HjthpnI+czlTTu3Y9ujDwEC06Ej0j2feVbW23xvWEepZ9Oyy3kiGPK97fukx7/1rKy3dX3F6VK7srtxjwRDXZvJjmOumcj3zGVGvicFbvsj3wMC14tTpdU7yi7nbQ7SuIY0pm/54zpZsPQhj34j7dxfdjlv67pDfemy7uUOCzDFzp07lZ2d7bZM8+bNtXz58lKfT05OVvXqPp7k46G7nnhdkvT4nde4/B7o7Bi31TFnZWVp5syZLo/t3LlT1apVU9u2bT2axF7aBHZJatWqlf78809lZ2crJcX1ovfMzEzNmDGj4v+IU/htaZOqVatKklauXOny+JNPPqlt27apQ4cOLo+PGTNG6enpOvfcc/X555+XOYHdE/v379eKFSv01VdfVXgCe35+vmbPnq1GjXw86yYIhEVEqVGnc7X853e1f+faEr90FuQf0eSXh+u0/jerx0WPq2GHczTltcvlLCqyIGL4E+3DOt0bu5+E5ZAUFym1ru2viE6oneyb7SbF+n8Cu+RZXddIlNKr+Sui4EEfAgSmjEbu1+N2SIoMk9rX91NApaAP8Z9gqOseTcoYzx1SnWTfT2APRsHQPuyCugYAQKqd5Jvt1vHR8Sw781ldk3MHHfJUuEP7AOAtX+VlcZFSlRjfbNuufHVONzXRnAns8A5jLqxC2wPgrZhIKSXON9v21fEsu6oeL0VU/NLtElHX/seYCyvR/gB4y1e5gq+OZ9lZLeoaldyDDz5odQhAmerVq6dq1app165dFZ7ALklLly7V9u3blZycrPT0dJ/EfCq/TWJPT09XmzZt9Oijj+q9997TTz/9pNGjR+utt96SpGKT2F966SWtXbtWX3zxhW6//XYdOHCgzPdITU2V0+ks8S7skpSUlKR58+apf//+XsU+dOhQPfDAA5o4caJmzpyp999/X71799bSpUv18MMPe7WtyqpZ90u1+McXVLf1GSU+P+eTuxUaFqEuwx6QJGX963nt371ef3z/rP+ChGVoH9ZIqyINaGP8fupEQ4eMiViXdpfCLLhAoGYVY3Kj2RpaNEm8SarUrXHJzzkcRh0PzzB+h/foQ4DAkxwnDf0nvS9pjJFDuiTDN329t+hD/Mfudd2yltSplO/pxxZmuLirX0MKKnZvH3ZCXQMAKruGPlogm8UJi6Ou4Q3yVLhD+wDgjRqJvlnUOr065/JOVS9FCvXBVUfpgXFTm0qJMRdWoe0B8JYv8oWIMCbanCokRGrgg2NxidG+W4gA7jHmwkq0PwDe8NV5Rl9t186oa1R2F1xwgdUhAGVq2bKlJGnx4sVllh05cqTbCezHLFq0yGXbvua3SewhISGaMGGCWrZsqdGjR2vEiBGqWrWqxowZo9DQULVp06bE12VmZiokJES//PKLKXE4ynFWsVu3bpo0aZJGjhypvn376rbbblNKSopmzpypIUOGmBJXsKvdPEvxVeupScZFxZ7buPQnLZ3+ugaM/lChYeGSpIjoeJ1x7fua98X92r3pT3+HCz+jfVhnQGtjslVKvOvj6dWl6/tKzdKsiSsiTOrUwPztljaR3NccDun8TsaEzsRo1+eap0m39JfqcgepcqMPAQJTZjPpX92l6omuj9etKl3bS2pb15q4TkUf4j92r2uHw8ibzj5Nio866XFJLWtLN/eXarJafLnZvX3YCXUNAKjsGtaQqsWXXc4b0RGB8x0nkLSpK8VEmLvNqnFS41Rzt4nAQJ4Kd2gfALwRGiJ18cFNI6w6zxjIYiKl03yQB3drZP424RnGXFiFtgfAW77IFzo28N1dx+3MF3Wd0ZgFoqzCmAsr0f4AeKNFLSkhuuxy3kiMlprXNHebwaBDfSnC5Jse1k6S6rBAFGyiefPmVocAlGnt2rV68MEH9c4775RZ9oEHHpDkfgK7JL3//vt68MEHtXr1apOidM+vh1yaNGmi6dOnuzx22WWXqUWLFoqONjKMAwcOaM+ePapXr54kaeHChVqzZo2lncIdd9yhO+64w7L3DwYOh0Pn/2eWomKLz+qo26qPrnvzQLHHazXtoTFvHvRHeLAY7cNaXRpKndOlrXulvKNSlRipqskX8pbH6c2kuWukwiJztlcvxTcrw3rK4ZCymkk9m0ibc6T8AuNOxUmx1sUULOhDgMDVvr50Wj1p+z7p4BHjIGC1BKujckUf4j/BUNchDql3C2ORhi3/jOcp8Ub+hIoJhvZhF9Q1AKCyC3FIWc2lCfPN2+bpTbjAtSThodLpTaUfTLy2LKu5sQ8RfMhT4Q7tA4C3ujeRZq2UCgrN2V7NKlITFtIpUWZz6ff1ktOk7TVNY7FQKzHmwiq0PQDeql/V+Fm/25zthTiknk3N2VawaVXbWBR0V64524sMY9EiKzHmwkq0PwDeCA2RMptK3ywyb5tZzY3twlV0hLHI0My/zdtmr+YsWgQAZvr777+PT04vyxVXXKGIiAh9//33bst99NFHJkTmOcsvq1qwYIG6du16/O+DBw/qwgsv1IEDBxQWFqaoqCh98MEHqluX25jYXWySRbd0hi3QPqzlcEi1AuxigBoJUv9W0ndL3JdbtqXsbYWGSBd1DYwvQyEh3HXdF+hDgMDlcEhpVayOwj36EP8JlroOZTz3iWBpH3ZAXQMAKruMRtIf66U1O92X8+S4U40EqV8rU8IKSv1aSos3GoublcaTepak9GrcATXYkafCHdoHAG+kxElntZW+/MN9OU/ykBCHdHEGC+mUpk6ycVHqtOXuy3lS1xFh0gWdzYkL5ceYC6vQ9gB4w+EwrgV7+jupwM0NUjw97nRGayk10ZzYgs2x6+5enOJ+4SJP63poB/PvqgrvMObCSrQ/AN7Iai4t3Chtzi69jKc5SJ1kFi1yZ1AbaelmaU/x9USO87SuW9Q0boAFALDGTz/9ZHUIJbJ0EvuBAwe0cuVKXXfddccfq1GjhubNm2dhVPazZfdqPfT++Xr1loUuj2/etUrv/vBfJcRWVZPaHdWyfneXv/t3usKagFFMaftw197Nen/Kg4qJMm5ZOrT7Dfpw6kOKiUpQfHSyLu33XyvCxSkWr5mhB949R+lpbbU9e50a1mynJrU76uzuY7Rp59/6du6rio6MV1J8DV3W7z6rw7WVPi2Ni4lXbC+9zLgZZW9nWMfAn0BpFyW19/+N+MrqsIJGafX7wZSHdFrjPvQnAEpUWi55zGMfDVfXFoPVq91FJf6N4EWe6h+7923Ra9/cqviYZNWr0VJDuo85/tyOnA267+0haliznVIS0lQ1sbZWbPpNhUX5Wrputj68d4Oksj/HOIF2DQAwQ4hDGp4hPfejtD+v9HJlHXeKCpcu627ccdwq7nIRyTXPODU3GTnoMZ/HFxYq/au79PwU6XB+yWU8Ob4XHyUN7xack8fIb+yppO/Wv/09Wd/Pf0P3/euz44999+s4rdr8h24695USy9z4QoZqVzOumhoz5P/0xc/PaU/uNoU4QnV+1m0qLCzQ+1MeUGJsNRUWFeiGc17UpzOeUnbudu3N3aHLznhAu/dt0duT71XNlEZqkNZaQ7qN0djPrlFiXDWFOkJ19VlP6scF72r6wvFKik9Vu0a91KPVML3+7W2KjozXvoO7dcdF7+j9Hx90ee8VG+dr/t/G6uy/r/xB425bpncm/1cOh0MhIaG6rN/9SohJ9leV29Kp/W7Hpv05/gv4Uc+m0qod7i+u9CQPObu9cZGrVQI935OkAW2kdbukdW7uhOpJXV/YxViAwCrkZTgZ+R75nifI9wBrpSZK53aSPvm19DKe5CBNUo2FGK0U6Dlfw+pGzve9m5vReFLXp9WTujY0LSyvke/hZOR75HueIucDrBMaYpyL/b8fpENHSy7jSQ4SGyld2t3au7AHer4X+c9575emSvmFJZfxpK6TYqULA+TGg4CnsrKyrA4BqBQsHIaluLg4FRYW6oYbbrAyDFvL3r9d389/Q1ERscWeO3h4n64c9JhGnz1Ws5d+UexvBAZ3+3DTzr+1cPVP2rV3k+JjkvXZrGeUmpKu3EM5atWghwXRoiStG/RU0zqd9czoGWqTnqkbh72s3LxsJcQk60Bejm445yXdOOwlLV032+pQbSc0RLoyU2pWzsUXHTImsHfnDk2mKam9wzyl1W9uXjb9CYASucslJemzmc8qOiKu1L8R3MhT/ePbea9paI8bdeOwl/Xr35NUUHhidtaStbOUHJ8qSWpRv5uGdB+jOy56R1UTa+uByydKKvtzDFe0awCAWZLjpDF9paSY8r0+JkK6trdU2+Lru9zlIqfmGafmJv5SM0m6ro9RZ+VRJUa6vq+1E5p8ifzGfkr6br1o9XRt3bNGeUdO3KJiydpZ2rRzhZz/3Kfs1DK79m5W3tEDCg+LUJ3qTRUbnahFa6br5nNf1ciBj+q9H+7X3gM7NXLgY7puyHPatXeTduRs0NJ1s1VYVKCE2KqqXqWu/lw7S8kJaXI6i9SyXjcdyNurQV2u1rWDn9Hm3St1tOCI/lw7S1UTa0lyqnndrsrO3aY5y77S3gM7FREeZcR3yntntbtQd1z0jhrVOk03n/e6HHIoe/9W3XDOixrYaaS+mDXWPxVuY6f2uxz/BfwrJES64nSpVe3yb2NwOymrmWkhlYsd8r2IMOmaXlJ6tfK9PsQhXdxV6lDf1LC8Rl6GY8j3yPc8Rb4HWC+jkXReJ+MasfJomiqN7GnthCbJHjnfGa2k/q3L//rT6hkLi1o5oYl8D8eQ75HveYOcD7BWjQTjnG58VPleHx8ljeljbMdKdsj36leVRvUyFnIvj6pxxjndxGhz4wJ87ZVXXrE6BKBSsPRO7PBc9v7teuRD1zslJsen6t5LP9ZVgx7X3eMGFHtN0zodtXvfFt375plq2zCr2N/wr/Lsw2pV6ujJa6YqNbmB7n5jgAoKj2pApytVP6uV7hp3hto16uWv8OHGtuy1SktOlyTt2rdJuXk5qp/aSpLUpfmZcjqd+vCnR9Sn/XArw7StyDDpmixp5gpp0mKpoJTVvU5VLd642CG9uk/DC1ql9VlXDHjYpb0bB0bhLW/qd932paqf2or+BKjkypNLzln2teKiq6h5vYwS/0bwI081V2mfw5ioBFWrUkeSFB+dpIOH9ykxtqokqVmdzmrfuK+S4mroztf7qlPTAdq6Z40KiwrUuHZ7YxsJqaV+jlEc7RoAYKYaCdLtg6SJv0u/rfP8dS1qShd0MSZX+0t5cpFT84yScpOw0HJeheCluinSnWdJn/7q/m6op+pYXzqno3GHhGBFfmMvpX23bteol9o16qV5f30jSdqevV6/LJ2oc3rcpI+nP15imcjwaN198YdKr9lGr31zm5atn6Ozu12npz4ZoVpVG+t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     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.quantum_info import random_statevector\n",
    "from qiskit.circuit.library import RYGate, RXGate, RZGate, CXGate\n",
    "from qiskit import QuantumRegister\n",
    "\n",
    "def construct_ising_circuit_random_init(J, basis, num_steps):\n",
    "    cls = IsingModel\n",
    "    ops = cls.Options()\n",
    "    ops.config_4q_paper()\n",
    "    ops.update({'J': J})\n",
    "    qc_init = QuantumCircuit.from_qasm_str('OPENQASM 2.0;\\ninclude \"qelib1.inc\";\\nqreg q[4];\\nrz(0.0007186381718527407) q[1];\\nrz(2.4917901988569855) q[1];\\nrz(3.3854853863523835) q[3];\\nrx(1.2846113715328817) q[3];\\ncx q[3],q[0];\\nrx(4.212671608894216) q[2];\\ncx q[2],q[3];\\n')\n",
    "    qc_init.barrier()\n",
    "    max_step = 10\n",
    "    qc_list = []\n",
    "    for i in range(max_step):\n",
    "        qc_list.append(qc_init.compose(cls.make_circs_sweep(ops, sweep_steps=[max_step], measure_bases=['Z'])[0][i], list(range(4))))\n",
    "    return qc_list\n",
    "\n",
    "from qiskit.transpiler.passes import RemoveBarriers\n",
    "np.random.seed(5)\n",
    "J = 0.15\n",
    "basis = 'Z'\n",
    "random_ising = construct_ising_circuit_random_init(J, basis, 2)\n",
    "random_ising = [RemoveBarriers()(x) for x in random_ising]\n",
    "######################## NEED TO CHANGE THE BACKEND HERE for OLS Full to work #############################\n",
    "qc_list = transpile(random_ising, backend=real_backend, optimization_level=3, initial_layout=[0, 1, 2, 3])\n",
    "qc_list[5].draw('mpl', fold=-1, idle_wires=False)\n",
    "###########################################################################################################"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "10"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(qc_list)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService, Session, Options\n",
    "from qiskit_ibm_runtime import Estimator as RuntimeEstimator\n",
    "\n",
    "service = QiskitRuntimeService()\n",
    "options = Options()\n",
    "options.resilience_level = 2\n",
    "options.optimization_level = 1\n",
    "options.execution.shots = 10000\n",
    "options.resilience.noise_factors = (1, 3)\n",
    "options.resilience.noise_amplifier = 'TwoQubitAmplifier'\n",
    "options.resilience.extrapolator = 'LinearExtrapolator'\n",
    "\n",
    "runtime_estimator = RuntimeEstimator(session=Session(service=service, backend=real_backend.name), options=options)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ciiaabk60l54kvc6b3og\n"
     ]
    }
   ],
   "source": [
    "padded_obs_all_circs = []\n",
    "for trans_circuit in qc_list:\n",
    "    padded_obs_all_circs += [SparsePauliOp(form_all_qubit_observable(obs, get_measurement_qubits(trans_circuit, 4), 27)) for obs in obs_list]\n",
    "\n",
    "circs_to_submit = []\n",
    "for circ in qc_list:\n",
    "    circ.remove_final_measurements()\n",
    "    circs_to_submit.extend([circ]*len(obs_list))\n",
    "\n",
    "job = runtime_estimator.run(\n",
    "    circuits=circs_to_submit,\n",
    "    observables=padded_obs_all_circs\n",
    ")\n",
    "print(job.job_id())\n",
    "\n",
    "to_save = {\n",
    "    'job_id': job.job_id(),\n",
    "    'shots': 10000,\n",
    "    'backend': real_backend.name,\n",
    "}\n",
    "root = '/Users/haoranliao/GitHub/blackwater/docs/tutorials/data/ising_init_from_qasm_hardware'\n",
    "with open(root+f'/single_J0_15.json', 'w') as file:\n",
    "    json.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": 59,
   "outputs": [],
   "source": [
    "from qiskit_ibm_runtime import QiskitRuntimeService, Session, Estimator, Options\n",
    "service = QiskitRuntimeService()\n",
    "\n",
    "noisy_exp_vals = []\n",
    "zne_mitigated_vals = []\n",
    "ret_job_result = service.job('ciiaabk60l54kvc6b3og').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)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [],
   "source": [
    "noisy_exp_vals = np.array(noisy_exp_vals).reshape(-1, 4).tolist()\n",
    "zne_mitigated_vals = np.array(zne_mitigated_vals).reshape(-1, 4).tolist()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 71,
   "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\n",
    "\n",
    "ideal_exp_vals_estimator = []\n",
    "for qc in qc_list:\n",
    "    qc_new = remove_idle_qubits(qc, layout=[0, 1, 2, 3])\n",
    "    padded_obs = [SparsePauliOp(\n",
    "        form_all_qubit_observable(obs, get_measurement_qubits(qc_new, 4), 4)\n",
    "    ) for obs in obs_list]\n",
    "    qc_new.remove_final_measurements()\n",
    "    ideal_exp_vals_estimator.append(estimator_ideal.run([qc_new]*4, padded_obs).result().values.tolist())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [],
   "source": [
    "with open('./model/ising_init_from_qasm_hardware/rfr_list.pk', 'rb') as file:\n",
    "    model = pickle.load(file)\n",
    "\n",
    "from mlp import MLP1, MLP2, MLP3, encode_data, encode_data_v2_ecr\n",
    "\n",
    "rf_mitigated = []\n",
    "for circ, noisy, ideal in zip(qc_list, noisy_exp_vals, ideal_exp_vals_estimator):\n",
    "    X, _ = encode_data_v2_ecr([circ], ideal, [noisy], obs_size=4, two_q_gate='cx')\n",
    "    mitigated_exp_val = []\n",
    "    for q, m in enumerate(model):\n",
    "        mitigated_exp_val.append(m.predict(X))\n",
    "    rf_mitigated.append(np.array(mitigated_exp_val).transpose().tolist()[0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2822944097650154, 1.0000000000000004, -0.4791774896080656, -0.13526912660159063]\n",
      "[0.226, 0.966, -0.2762, -0.1008]\n",
      "[0.2523999999999999, 1.0567, -0.36329999999999996, -0.10880000000000001]\n",
      "[0.2822944223880768, 1.0, -0.47917747497558594, -0.13526912033557892]\n",
      "-----\n",
      "[0.15252432052972315, 0.5403023058681392, -0.25890070255534486, -0.07308622101560824]\n",
      "[0.0988, 0.5212, -0.2478, 0.076]\n",
      "[0.13289999999999996, 0.5597999999999999, -0.2917999999999999, 0.0982]\n",
      "[0.15296715721488, 0.5443364772200584, -0.2591287505626678, -0.07112939174287021]\n",
      "-----\n",
      "[-0.11523143062901367, -0.4103863370425921, 0.16721084119108584, -0.03765781843070077]\n",
      "[-0.1054, -0.279, 0.1376, 0.0878]\n",
      "[-0.1402, -0.4213000000000001, 0.13789999999999994, 0.09179999999999998]\n",
      "[-0.11635724939405918, -0.41014212757349017, 0.17941885724663734, -0.013910148256691172]\n",
      "-----\n",
      "[-0.2722203474119803, -0.9812729489675625, 0.3956107741356004, -0.06858814977336664]\n",
      "[-0.137, -0.7718, 0.219, 0.0232]\n",
      "[-0.18610000000000002, -0.9877999999999998, 0.30269999999999997, 0.024799999999999996]\n",
      "[-0.2631942531466484, -0.9752284330129624, 0.3827772617340088, -0.0650816785322968]\n",
      "-----\n",
      "[-0.17781866107689515, -0.6462624451598431, 0.25287344167392645, -0.05084409479326804]\n",
      "[0.0036, -0.4806, 0.0568, 0.0736]\n",
      "[-0.03890000000000003, -0.6251, 0.11770000000000001, 0.0874]\n",
      "[-0.15109503015875816, -0.6297059160470962, 0.2044296058267355, -0.07127550102712121]\n",
      "-----\n",
      "[0.07221913652941217, 0.2703440710222269, -0.0794621294586616, 0.09618499159789556]\n",
      "[0.1584, 0.2466, -0.0868, 0.2216]\n",
      "[0.15689999999999996, 0.2944999999999999, -0.07839999999999997, 0.2826]\n",
      "[0.08025929294759408, 0.2411897722631693, -0.09026240638224409, 0.19637239910662174]\n",
      "-----\n",
      "[0.24363655039063886, 0.9202176573892165, -0.27905009240947076, 0.2540579953323488]\n",
      "[0.1562, 0.6746, -0.1008, 0.234]\n",
      "[0.20020000000000004, 0.8712999999999995, -0.09949999999999998, 0.32920000000000005]\n",
      "[0.22473924338817597, 0.8670938038825988, -0.20944238148629665, 0.21238295400049537]\n",
      "-----\n",
      "[0.18687021435664514, 0.7101751965116472, -0.20418877868222624, 0.2040117676244232]\n",
      "[0.0058, 0.4724, -0.1124, 0.0494]\n",
      "[0.0025999999999999973, 0.6325999999999998, -0.10829999999999998, 0.0864]\n",
      "[0.012262508961721324, 0.36925686072558167, -0.2114598225802183, 0.0067044896678999065]\n",
      "-----\n",
      "[-0.030505881542772298, -0.12787861825949173, 0.017579230757742786, -0.09680216472491726]\n",
      "[-0.087, -0.0942, -0.0904, -0.1498]\n",
      "[-0.14459999999999998, -0.1463, -0.09509999999999998, -0.2251]\n",
      "[-0.038527898504398764, -0.1279436263628304, -0.04352261502870533, -0.3314917004108429]\n",
      "-----\n",
      "[-0.20111206498631212, -0.7947923417065272, 0.15940032543718075, -0.3938665365315277]\n",
      "[-0.0532, -0.4846, -0.0022, -0.1212]\n",
      "[-0.11040000000000001, -0.7114999999999999, 0.049300000000000024, -0.2005]\n",
      "[-0.16999070774763822, -0.6692541795969009, 0.12499418454244733, -0.311337516317144]\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "for noisy, zne, ideal, rf in zip(noisy_exp_vals, zne_mitigated_vals, ideal_exp_vals_estimator, rf_mitigated):\n",
    "    print(ideal)\n",
    "    print(noisy)\n",
    "    print(zne)\n",
    "    print(rf)\n",
    "    print('-'*5)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [],
   "source": [
    "noisy_exp_vals = np.array(noisy_exp_vals)\n",
    "ideal_exp_vals_estimator = np.array(ideal_exp_vals_estimator)\n",
    "zne_mitigated_vals = np.array(zne_mitigated_vals)\n",
    "rf_mitigated = np.array(rf_mitigated)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2160x360 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "ms, fs, lw, elw, cs = 9, 17, 2.5, 2, 0\n",
    "mpl.rc('xtick', labelsize=14)\n",
    "mpl.rc('ytick', labelsize=14)\n",
    "mpl.rc('font', weight='normal')\n",
    "\n",
    "fig = plt.figure(figsize=(30, 5))\n",
    "ax = [fig.add_subplot(1,4,i+1) for i in range(4)]\n",
    "plt.style.use({'figure.facecolor':'white'})\n",
    "\n",
    "for q in range(4):\n",
    "    ax[q].plot(-ideal_exp_vals_estimator[:, q], label='ideal')\n",
    "    ax[q].plot(-zne_mitigated_vals[:, q], label='zne')\n",
    "    ax[q].plot(-rf_mitigated[:, q], label=f'rf')\n",
    "    ax[q].grid(False)\n",
    "\n",
    "for a in ax[1:]:\n",
    "    a.set_yticklabels([])\n",
    "for a in ax:\n",
    "    a.tick_params(direction=\"in\")\n",
    "    a.set_ylim([-1.05, 1.05])\n",
    "    a.set_xlim([0, 9])\n",
    "    a.set_xlabel('Trotter steps', fontsize=fs)\n",
    "    # a.set_xticks(np.arange(0, 29, 4))\n",
    "for a in ax:\n",
    "    for axis in ['top','bottom','left','right']:\n",
    "        a.spines[axis].set_linewidth(2.6)\n",
    "\n",
    "for q in range(4):\n",
    "    ax[q].set_title(f'q{q}', fontsize=fs)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.legend(loc='lower right', fontsize=fs)\n",
    "ax[0].set_ylabel(f'<Z>', fontsize=fs)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [],
   "source": [
    "# to_dump = {\n",
    "#     'exp_Z_all_ideal': exp_Z_all_ideal,\n",
    "#     'exp_Z_all_noisy': exp_Z_all_noisy,\n",
    "#     'exp_Z_all_mitigated_dict': exp_Z_all_mitigated_dict,\n",
    "#     'models': [model[1] for model in models],\n",
    "# }\n",
    "# with open('/Users/haoranliao/GitHub/blackwater/docs/paper_figures/no_readout_single_ising.pk', 'wb') as file:\n",
    "#     pickle.dump(to_dump, file)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}