docs/tutorials/h05_tiling_data_gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\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, QuantumRegister, ClassicalRegister, Aer\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\n",
    "from gnn import ExpValCircuitGraphModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "backend = FakeMontreal()\n",
    "properties = get_backend_properties_v1(backend)\n",
    "\n",
    "## Local\n",
    "backend_ideal = QasmSimulator() # Noiseless\n",
    "backend_noisy = AerSimulator.from_backend(backend) # Noisy\n",
    "\n",
    "run_config_ideal = {'shots': 10000, 'backend': backend_ideal, 'name': 'ideal'}\n",
    "run_config_noisy = {'shots': 10000, 'backend': backend_noisy, 'name': 'noisy'}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [],
   "source": [
    "def construct_tiling(circ_to_append, qbs_with_gates, num_qubit):\n",
    "    \"\"\"The circuit to append must already be measured\"\"\"\n",
    "    assert len(qbs_with_gates) <= num_qubit\n",
    "    idle_ind = list(set(range(num_qubit)) - set(qbs_with_gates))\n",
    "    qr = QuantumRegister(num_qubit)\n",
    "    cr = ClassicalRegister(num_qubit)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    qc.append(circ_to_append, qbs_with_gates, qbs_with_gates)\n",
    "    qc.measure(idle_ind, idle_ind)\n",
    "    return qc"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "qbs_with_gates = [0, 1] #list(range(8))\n",
    "num_qubit = 4\n",
    "circ_to_append = random_circuit(len(qbs_with_gates), 10, 2, measure=True)\n",
    "qc = construct_tiling(circ_to_append, qbs_with_gates, num_qubit)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1360.65x325.08 with 1 Axes>",
      "image/png": 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\n"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc = transpile(qc, backend=backend_noisy, optimization_level=3)\n",
    "qc.draw('mpl', fold=-1, idle_wires=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Tiling Data Generation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "def construct_mbl_tiling_circuit(qbs_with_gates, total_circuit_size):\n",
    "    num_spins = len(qbs_with_gates) # Number of spins. Must be even.\n",
    "    assert num_spins <= total_circuit_size\n",
    "    W = 0.8 * np.pi # Disorder strength up to np.pi\n",
    "    theta = 0.05 * np.pi # Interaction strength up to np.pi\n",
    "    steps = 6\n",
    "\n",
    "    disorders = generate_disorder(num_spins, W)\n",
    "    random_mbl_circuit = construct_mbl_circuit(num_spins, disorders, theta, steps)\n",
    "    qc = construct_tiling(random_mbl_circuit, qbs_with_gates, total_circuit_size)\n",
    "    qc = transpile(qc, backend=backend_noisy, optimization_level=3)\n",
    "    return qc, random_mbl_circuit, disorders"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 4912.59x565.88 with 1 Axes>",
      "image/png": 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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qc, random_mbl, _ = construct_mbl_tiling_circuit(list(range(4)), 8)\n",
    "qc.draw('mpl', fold=-1, idle_wires=False, plot_barriers=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "from qiskit.transpiler.exceptions import TranspilerError\n",
    "from scipy.linalg import LinAlgError\n",
    "\n",
    "def generate_data(qbs_with_gates, total_num_qubits, save_path: str, n_entries_per_file: int):\n",
    "    entries = []\n",
    "\n",
    "    for i in tqdm_notebook(range(n_entries_per_file)):\n",
    "        success = False\n",
    "        while not success:\n",
    "            try:\n",
    "                circuit, _, disorder = construct_mbl_tiling_circuit(qbs_with_gates, total_num_qubits)\n",
    "                # print(disorder)\n",
    "                success = True\n",
    "            except (LinAlgError, TranspilerError, np.linalg.LinAlgError) as e:\n",
    "                print(f\"Ran into an error:, {e}\")\n",
    "\n",
    "        job_ideal = execute(circuit, **run_config_ideal)\n",
    "        job_noisy = execute(circuit, **run_config_noisy)\n",
    "\n",
    "        counts_ideal = job_ideal.result().get_counts()\n",
    "        counts_noisy = job_noisy.result().get_counts()\n",
    "\n",
    "        ideal_exp_val = cal_z_exp(counts_ideal)\n",
    "        noisy_exp_val = cal_z_exp(counts_noisy)\n",
    "\n",
    "        graph_data = circuit_to_graph_data_json(\n",
    "            circuit=circuit,\n",
    "            properties=properties,\n",
    "            use_qubit_features=True,\n",
    "            use_gate_features=True,\n",
    "        )\n",
    "\n",
    "        entry = ExpValueEntry(\n",
    "            circuit_graph=graph_data,\n",
    "            observable=[],\n",
    "            ideal_exp_value=ideal_exp_val.tolist(),\n",
    "            noisy_exp_values=[noisy_exp_val.tolist()],\n",
    "            circuit_depth=circuit.depth()\n",
    "        )\n",
    "        to_append = entry.to_dict()\n",
    "        to_append['circuit'] = circuit.qasm()\n",
    "        entries.append(to_append)\n",
    "\n",
    "    path = f\"{save_path}/step6_q{qbs_with_gates[0]}-q{qbs_with_gates[-1]}_total{total_num_qubits}.json\"\n",
    "    with open(path, \"w\") as f:\n",
    "        # pprint(entries)\n",
    "        json.dump(entries, f)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "qbs_with_gates = [4, 5, 6, 7]\n",
    "total_num_qubits = 8\n",
    "circuit, random_mbl, disorders = construct_mbl_tiling_circuit(qbs_with_gates, total_num_qubits)\n",
    "# generate_data(qbs_with_gates, total_num_qubits, './data/tiling2/val', 1000)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 4912.01x565.88 with 1 Axes>",
      "image/png": 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     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuit.draw('mpl', fold=-1, idle_wires=False, plot_barriers=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 3006.48x325.08 with 1 Axes>",
      "image/png": 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     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_mbl.draw('mpl', fold=-1, idle_wires=False, plot_barriers=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "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
}