bachelor_thesis/performance/generate_data_scaling_qbits.py

from collections import deque
import matplotlib.pyplot as plt
import numpy as np
import json
from random import shuffle

from pyqcs import State, H, X, S, CZ, M, list_to_circuit
from pyqcs.graph.state import GraphState
from pyqcs.util.random_circuits import random_circuit

from measure_circuit import execution_statistics, measure_all

def S_with_extra_arg(act, i):
    return S(act)

def test_scaling_qbits(state_factory
                    , nstart
                    , nstop
                    , ngates_per_qbit
                    , ncircuits
                    , **kwargs):
    trials = deque()

    N = (nstop - nstart)
    print()
    for n, qbits in enumerate(range(nstart, nstop)):
        print(f"generating test data... {int(n/N * 100)} %", end="\r", flush=True)
        measurement_circuit = list_to_circuit([M(i) for i in range(qbits)])
        circuits = [random_circuit(qbits, ngates_per_qbit * qbits, X, H, S_with_extra_arg, CZ)
                        | measurement_circuit
                        for _ in range(ncircuits)]

        state = state_factory(qbits)
        for circuit in circuits:
            trials.append((qbits, circuit, state))
    print("generating test data... done ")

    print("randomizing tests...", end="", flush=True)
    shuffle(trials)
    print(" done")

    results = measure_all(trials, **kwargs)
    N, avg, std_dev = execution_statistics(results, scale={i:i for i in range(nstart, nstop)}, **kwargs)
    nqbits = [[i] for i in sorted(results.keys())]
    N = [[i] for i in N]
    avg = [[i] for i in avg]
    std_dev = [[i] for i in std_dev]

    return np.hstack([nqbits, N, avg, std_dev])


if __name__ == "__main__":
    nstart = 4
    nstop = 16
    ncircuits = 50
    ngates_per_qbit = 100
    seed = 0xdeadbeef

    np.random.seed(seed)
    results_naive = test_scaling_qbits(State.new_zero_state
                                    , nstart
                                    , nstop
                                    , ngates_per_qbit
                                    , ncircuits
                                    , repeat=10)
    np.random.seed(seed)
    results_graph = test_scaling_qbits(GraphState.new_zero_state
                                    , nstart
                                    , nstop
                                    , ngates_per_qbit
                                    , ncircuits
                                    , repeat=10)

    np.savetxt("qbit_scaling_naive.csv", results_naive)
    print("saved naive results to qbit_scaling_naive.csv")
    np.savetxt("qbit_scaling_graph.csv", results_graph)
    print("saved graph results to qbit_scaling_graph.csv")

    meta = {"nstart": nstart
            , "nstop": nstop
            , "ncircuits": ncircuits
            , "ngates_per_qbit": ngates_per_qbit
            , "seed": seed}

    with open("qbit_scaling_meta.json", "w") as fout:
        json.dump(meta, fout)
    print("saved meta data to qbit_scaling_meta.json")