run_shvqe_qiskit_ncz0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gj3jZbCBSn71"
   },
   "source": [
    "# Solutions (SHVQE + readout error mitigation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6yqcs2Aejtv7",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "import time\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from qiskit import *\n",
    "import qiskit\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "import qiskit.providers.aer.noise as noise\n",
    "from qiskit.providers.fake_provider import *\n",
    "from qiskit.utils import algorithm_globals\n",
    "from qiskit import QuantumCircuit, transpile, QuantumRegister, ClassicalRegister, Aer\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit_aer.noise import (NoiseModel, QuantumError, ReadoutError,\n",
    "    pauli_error, depolarizing_error, thermal_relaxation_error)\n",
    "\n",
    "import stim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "_z5kT9vNSWus"
   },
   "outputs": [],
   "source": [
    "seeds = 170\n",
    "algorithm_globals.random_seed = seeds\n",
    "seed_transpiler = seeds\n",
    "shot = int(np.floor(1800000 / 631))\n",
    "# shot = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "_z5kT9vNSWus"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2852"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_z5kT9vNSWus"
   },
   "source": [
    "The total number of shots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "_z5kT9vNSWus"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1799612"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shot * 631"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprossing: Clifford Hamiltonian conversion\n",
    "\n",
    "By using SHVQE, we need to apply the inversal of Heisenberg part to the original Hamiltonian. First, load the original Hamiltonian from `Hamiltonian/OHhamiltonian.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Hamiltonian/OHhamiltonian.txt', 'r') as file:\n",
    "    pauli_text_lines = file.readlines()\n",
    "paulis = []\n",
    "weight = []\n",
    "for line in pauli_text_lines:\n",
    "    line = line.replace(' ', '')\n",
    "    coeff, pauli_text_string = line.split(\"*\")\n",
    "    coeff = float(coeff)\n",
    "    weight.append(coeff)\n",
    "    # pauli_text_string = pauli_text_string[::-1] # TODO: this is a quick but wrong fix for qubits order\n",
    "    pauli_text_string = pauli_text_string.replace('\\n', '')\n",
    "    paulis.append(pauli_text_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we use `Stim` to apply the inversal of Heisenberg part to Hamiltonian. In our protocal, the Heisenberg part consists of `cz` gates, which commute with each other. The inversal of the Heisenberg part is itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "heisenberg_text_combined = ''\n",
    "\n",
    "with open('saved_models/shvqe_clifford_ncz0_hei.qasm', 'r') as file:\n",
    "    heisenberg_text_lines = file.readlines()\n",
    "    heisenberg_text_combined = ''.join(heisenberg_text_lines[3:]) # omit the header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "# Define the regular expression pattern\n",
    "pattern = r\"q\\[(\\d+)\\],q\\[(\\d+)\\]\"\n",
    "\n",
    "# Define the replacement string\n",
    "replacement = r\"\\1 \\2\"\n",
    "\n",
    "# Use the sub() function to replace the matches\n",
    "stim_text = re.sub(pattern, replacement, heisenberg_text_combined).replace(';', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the Heisenberg circuit to each Pauli string. For any scale of quantum devices, classical simulation of Clifford circuits is efficient due to the Gottesman–Knill theorem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 21.4 ms, sys: 0 ns, total: 21.4 ms\n",
      "Wall time: 21.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "heisenberg_circ = stim.Circuit(stim_text)\n",
    "paulis_new = []\n",
    "\n",
    "for p in paulis:\n",
    "    ps  = stim.PauliString(p.replace('I', '_'))\n",
    "    ps = ps.after(heisenberg_circ)\n",
    "    paulis_new.append(ps.__str__().replace('_', 'I'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct the new Hamiltonian as observable. The number of Pauli strings is not changed by Clifford."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ld6bpObI-l0n",
    "outputId": "18744ccd-0234-425a-e3cf-741452d8cad5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Observable size: 631\n"
     ]
    }
   ],
   "source": [
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "observable = SparsePauliOp(paulis_new, weight)\n",
    "print(f\">>> Observable size: {observable.size}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vPEQx7yh_dDE"
   },
   "source": [
    "## Circuit Transpile and Execution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vPEQx7yh_dDE"
   },
   "source": [
    "To implement readout error mitigation, we hack into the `Estimator` class provided by `qiskit`. The source code can be found as `estimator_rem.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171
    },
    "id": "lJ8S2H4F2uUl",
    "outputId": "efd7a7dd-cc85-4fc8-9fdc-9680c15ba8a7"
   },
   "outputs": [],
   "source": [
    "from qiskit import qasm2\n",
    "\n",
    "# our implementation of Estimator with REM\n",
    "from estimator_rem import EstimatorRem\n",
    "\n",
    "# the original implementation of Estimator\n",
    "from qiskit_aer.primitives import Estimator\n",
    "\n",
    "circuit = qasm2.load('saved_models/shvqe_clifford_ncz0_sch.qasm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the circuits and reverse its qubits to respect qiskit convention. The circuit we use consists of only single-qubit rotations. Because the entanglement is virtually created by Clifford Heisenberg circuits in SHVQE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "circuit = circuit.reverse_bits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 556.358x1036.78 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ailYlmao_jBV"
   },
   "source": [
    "Transpile the circuit based on given system model from IBMQ_Montreal. **We use default transpiler from qiskit.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "SI275sum_rFg"
   },
   "outputs": [],
   "source": [
    "system_model = FakeMontreal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "65IYl_7U_zOh"
   },
   "outputs": [],
   "source": [
    "transpiled_circuit = transpile(circuit, backend=system_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 787.401x2290.94 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transpiled_circuit.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1UmOoQMZ_42L"
   },
   "source": [
    "### Noiseless\n",
    "\n",
    "Evaluate on the qiskit Estimator. Consider noiseless first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "1UmOoQMZ_42L"
   },
   "outputs": [],
   "source": [
    "def get_error_rate(val):\n",
    "    ref_value = -74.38714627\n",
    "    nuclear_repulsion_energy = 4.365374966545\n",
    "    energy_with_nuclear_repulsion = val + nuclear_repulsion_energy\n",
    "    error_rate = abs(abs(ref_value - energy_with_nuclear_repulsion) / ref_value * 100)\n",
    "    return error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "M6ACRPs4_GRi"
   },
   "outputs": [],
   "source": [
    "estimator_noiseless = Estimator(\n",
    "    backend_options = {\n",
    "        # simulation options, use multiple cpus\n",
    "        'method': 'statevector',\n",
    "        'device': 'CPU',\n",
    "        'max_parallel_threads' : 0,\n",
    "        'max_parallel_shots' : 0,\n",
    "        'statevector_parallel_threshold' : 5,\n",
    "        'coupling_map' : system_model.configuration().coupling_map,\n",
    "        'noise_model': None # noiseless\n",
    "    },\n",
    "    run_options = {\n",
    "        'shots': shot,\n",
    "        'seed': seeds,\n",
    "    },\n",
    "    transpile_options = {\n",
    "        'seed_transpiler' : seed_transpiler,\n",
    "    },\n",
    "    skip_transpilation=False,\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy: [-78.67793043]\n",
      "CPU times: user 1h 48min 59s, sys: 17 s, total: 1h 49min 16s\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "job_noiseless = estimator_noiseless.run(circuit, observable)\n",
    "result_noiseless = job_noiseless.result()\n",
    "print('Energy:', result_noiseless.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy error: 0.10027378508814073\n"
     ]
    }
   ],
   "source": [
    "print('Energy error:', get_error_rate(result_noiseless.values)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "### Noisy\n",
    "\n",
    "For readout error mitigation, we first load the readout error process matrices of noise models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_model_name = ['fakecairo', 'fakekolkata', 'fakemontreal']\n",
    "measure_noise_matrix = {}\n",
    "representations = {}\n",
    "for noise_name in noise_model_name:\n",
    "    measure_noise_matrix[noise_name] = []\n",
    "    noise_dict = pd.read_pickle('./NoiseModel/'+noise_name+'.pkl')\n",
    "    single_qubit_noise_operator = []\n",
    "\n",
    "    for qubit in range(27):\n",
    "        single_qubit_noise_operator.append([])\n",
    "        \n",
    "    for noise_subdict in noise_dict['errors']:\n",
    "        if len(noise_subdict['gate_qubits'][0])==1:\n",
    "            qubit = noise_subdict['gate_qubits'][0][0]\n",
    "            single_qubit_noise_operator[qubit].append(noise_subdict)\n",
    "    single_noisy_super_op_list = []\n",
    "    for qubit in range(27):\n",
    "        single_noisy_super_op_list.append([])\n",
    "\n",
    "        for noisy_operator in single_qubit_noise_operator[qubit]:\n",
    "            if noisy_operator['operations'][0]=='measure':\n",
    "                measure_noise_matrix[noise_name].append(np.array(noisy_operator['probabilities']).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fake Kolkata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "2CHRkoGUxv9D"
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_noise_matrix_inv(readout_noise_matrix, qubits):\n",
    "    readout_noise_matrix_inv = []\n",
    "    for i in qubits:\n",
    "        matrix = readout_noise_matrix[i]\n",
    "        readout_noise_matrix_inv.append(np.linalg.inv(matrix))\n",
    "    return readout_noise_matrix_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_noise_matrix_fakekolkata = get_noise_matrix_inv(measure_noise_matrix['fakekolkata'], range(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "kHk6FZU1ypz2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67897/3864961774.py:2: DeprecationWarning: from_dict has been deprecated as of qiskit-aer 0.10.0 and will be removed no earlier than 3 months from that release date.\n",
      "  noise_model_fakekolkata = noise.NoiseModel.from_dict(pickle.load(file))\n"
     ]
    }
   ],
   "source": [
    "with open('NoiseModel/fakekolkata.pkl', 'rb') as file:\n",
    "    noise_model_fakekolkata = noise.NoiseModel.from_dict(pickle.load(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator_fakekolkata = EstimatorRem(\n",
    "    backend_options = {\n",
    "        # simulation options\n",
    "        'method': 'statevector',\n",
    "        'device': 'CPU',\n",
    "        'max_parallel_threads' : 0,\n",
    "        'max_parallel_shots' : 0,\n",
    "        'statevector_parallel_threshold' : 5,\n",
    "        'coupling_map' : system_model.configuration().coupling_map,\n",
    "        'noise_model': noise_model_fakekolkata,\n",
    "    },\n",
    "    run_options = {\n",
    "        'shots': shot,\n",
    "        'seed': seeds,\n",
    "    },\n",
    "    transpile_options = {\n",
    "        'seed_transpiler' : seed_transpiler,\n",
    "    },\n",
    "    skip_transpilation=False,\n",
    "    measurement_noise_matrix_inv = readout_noise_matrix_fakekolkata\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 5min 4s, sys: 18min 22s, total: 1h 23min 27s\n",
      "Wall time: 2min 26s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-78.68981691925806"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "job_fakekolkata = estimator_fakekolkata.run(circuit, observable)\n",
    "result_fakekolkata = job_fakekolkata.result()\n",
    "\n",
    "result_fakekolkata.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08429455951885581"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_error_rate(result_fakekolkata.values)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fake Cairo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67897/1409486957.py:2: DeprecationWarning: from_dict has been deprecated as of qiskit-aer 0.10.0 and will be removed no earlier than 3 months from that release date.\n",
      "  noise_model_fakecairo = noise.NoiseModel.from_dict(pickle.load(file))\n"
     ]
    }
   ],
   "source": [
    "with open('NoiseModel/fakecairo.pkl', 'rb') as file:\n",
    "    noise_model_fakecairo = noise.NoiseModel.from_dict(pickle.load(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "readout_noise_matrix_fakecairo = get_noise_matrix_inv(measure_noise_matrix['fakecairo'], range(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator_fakecairo = EstimatorRem(\n",
    "    backend_options = {\n",
    "        # simulation options\n",
    "        'method': 'statevector',\n",
    "        'device': 'CPU',\n",
    "        'max_parallel_threads' : 0,\n",
    "        'max_parallel_shots' : 0,\n",
    "        'statevector_parallel_threshold' : 5,\n",
    "        'coupling_map' : system_model.configuration().coupling_map,\n",
    "        'noise_model': noise_model_fakecairo,\n",
    "    },\n",
    "    run_options = {\n",
    "        'shots': shot,\n",
    "        'seed': seeds,\n",
    "    },\n",
    "    transpile_options = {\n",
    "        'seed_transpiler' : seed_transpiler,\n",
    "    },\n",
    "    skip_transpilation=False,\n",
    "    measurement_noise_matrix_inv = readout_noise_matrix_fakecairo\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 6min 1s, sys: 18min 16s, total: 1h 24min 18s\n",
      "Wall time: 2min 26s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-78.69202675045504"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "job_fakecairo = estimator_fakecairo.run(circuit, observable)\n",
    "result_fakecairo = job_fakecairo.result()\n",
    "result_fakecairo.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08132384306072264"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_error_rate(result_fakecairo.values)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fake Montreal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "kHk6FZU1ypz2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_67897/3624331980.py:2: DeprecationWarning: from_dict has been deprecated as of qiskit-aer 0.10.0 and will be removed no earlier than 3 months from that release date.\n",
      "  noise_model_fakemontreal = noise.NoiseModel.from_dict(pickle.load(file))\n"
     ]
    }
   ],
   "source": [
    "with open('NoiseModel/fakemontreal.pkl', 'rb') as file:\n",
    "    noise_model_fakemontreal = noise.NoiseModel.from_dict(pickle.load(file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "kHk6FZU1ypz2"
   },
   "outputs": [],
   "source": [
    "readout_noise_matrix_fakemontreal = get_noise_matrix_inv(measure_noise_matrix['fakemontreal'], range(12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator_fakemontreal = EstimatorRem(\n",
    "    backend_options = {\n",
    "        # simulation options\n",
    "        'method': 'statevector',\n",
    "        'device': 'CPU',\n",
    "        'max_parallel_threads' : 0,\n",
    "        'max_parallel_shots' : 0,\n",
    "        'statevector_parallel_threshold' : 5,\n",
    "        'coupling_map' : system_model.configuration().coupling_map,\n",
    "        'noise_model': noise_model_fakemontreal,\n",
    "    },\n",
    "    run_options = {\n",
    "        'shots': shot,\n",
    "        'seed': seeds,\n",
    "    },\n",
    "    transpile_options = {\n",
    "        'seed_transpiler' : seed_transpiler,\n",
    "    },\n",
    "    skip_transpilation=False,\n",
    "    measurement_noise_matrix_inv = readout_noise_matrix_fakemontreal\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 6min 5s, sys: 18min 17s, total: 1h 24min 23s\n",
      "Wall time: 2min 29s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-78.68849846419418"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "job_fakemontreal = estimator_fakemontreal.run(circuit, observable)\n",
    "result_fakemontreal = job_fakemontreal.result()\n",
    "result_fakemontreal.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08606698275323509"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_error_rate(result_fakemontreal.values)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hw8SxNyclYyT"
   },
   "source": [
    "Obtain the Duration of Quantum Circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "F-djGu7_BGiY"
   },
   "outputs": [],
   "source": [
    "from qiskit import pulse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rvKZMDXUBLfA"
   },
   "source": [
    "Do remember to set the optimization_level to 0 if your circuit is already transpiled!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "_julUiabi5iI"
   },
   "outputs": [],
   "source": [
    "transpiled_circuit = transpile(circuit, backend=system_model)\n",
    "\n",
    "with pulse.build(system_model) as program:\n",
    "  with pulse.transpiler_settings(optimization_level=0):\n",
    "    pulse.call(transpiled_circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E3jGzVpNkT00",
    "outputId": "4cf0d4da-f938-49c0-a382-7d5253ba6152"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "program.duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('saved_models/shvqe_clifford_ncz0_sch_transpiled.qasm', 'w+') as file:\n",
    "    file.write(transpiled_circuit.qasm())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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