bqc/run_simulations.py at main · nicslabdev/bqc · GitHub

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import os
import time

import numpy as np
import pandas as pd

from simulations.classical_client import run_simulation as run_classical_simulation
from simulations.quantum_client import run_simulation as run_quantum_simulation
from utils.theory_verification import theory_THT


def generate_quantum_data(
    shots,
    readout_err,
    p_trap,
    p_dummies,
    deltas=(np.pi / 4, np.pi / 2, np.pi / 4),
):
    t0 = time.perf_counter()
    (client_results, server_results, server_dummy_results, trap_total, trap_hits) = (
        run_quantum_simulation(shots, p_trap, p_dummies, readout_err, deltas)
    )
    elapsed = time.perf_counter() - t0

    client_total = sum(client_results.values())
    client_p0 = client_results.get("0", 0) / max(1, client_total)

    dummy_total = sum(server_dummy_results.values())

    trap_error = None if trap_total == 0 else 1 - (trap_hits / trap_total)

    gates = ["T1", "H", "T2"]

    row = {
        "shots": shots,
        "c_p0": client_p0,
        **{
            f"server_{g}_p0": sr.get("0", sr.get(0, 0)) / max(1, sum(sr.values()))
            for g, sr in zip(gates, server_results)
        },
        "p_trap": p_trap,
        "p_dummies": p_dummies,
        "d_total": dummy_total,
        "t_total": trap_total,
        "t_hits": trap_hits,
        "t_err": trap_error,
        "r_err": readout_err,
        "elapsed": elapsed,
    }

    df = pd.DataFrame([row])

    return df


def generate_classical_data(
    shots,
    readout_err,
    p_chsh,
    deltas=(np.pi / 4, np.pi / 2, np.pi / 4),
):
    t0 = time.perf_counter()
    (client_results, cnt_A, cnt_B, chsh_tests, chsh_hits) = run_classical_simulation(
        shots, p_chsh, readout_err, deltas
    )
    elapsed = time.perf_counter() - t0

    client_total = sum(client_results.values())
    client_p0 = client_results.get("0", 0) / max(1, client_total)

    serverA_total = sum(cnt_A.values())
    serverA_p0 = cnt_A.get("0", 0) / max(1, serverA_total)

    serverB_total = sum(cnt_B.values())
    serverB_p0 = cnt_B.get("0", 0) / max(1, serverB_total)

    p_pass = chsh_hits / chsh_tests if chsh_tests else 0

    row = {
        "shots": shots,
        "c_p0": client_p0,
        "sA_p0": serverA_p0,
        "sB_p0": serverB_p0,
        "p_pass": p_pass,
        "p_chsh": p_chsh,
        "r_err": readout_err,
        "elapsed": elapsed,
    }

    df = pd.DataFrame([row])
    return df


def generate_theory_data(
    shots,
):
    p0 = theory_THT(shots)
    row = {
        "shots": shots,
        "p0": p0,
    }

    df = pd.DataFrame([row])
    return df


if __name__ == "__main__":
    os.makedirs("data/quantum", exist_ok=True)
    os.makedirs("data/classical", exist_ok=True)

    quantum_path = "data/quantum/quantum_data.csv"
    classical_path = "data/classical/classical_data.csv"
    theory_path = "data/theory/theory_data.csv"

    initial_shots = 32
    shots_multiplier = 2
    rounds = 7

    readout_err = 0
    p_trap = 0.75
    p_dummies = 0
    p_chsh = p_trap

    for i in range(rounds):
        write_quantum_header = (
            not os.path.exists(quantum_path) or os.path.getsize(quantum_path) == 0
        )
        write_classical_header = (
            not os.path.exists(classical_path) or os.path.getsize(classical_path) == 0
        )
        write_theory_header = (
            not os.path.exists(theory_path) or os.path.getsize(theory_path) == 0
        )

        generate_quantum_data(
            initial_shots * (shots_multiplier**i), readout_err, p_trap, p_dummies
        ).to_csv(quantum_path, index=False, mode="a", header=write_quantum_header)

        generate_classical_data(
            initial_shots * (shots_multiplier**i), readout_err, p_chsh
        ).to_csv(classical_path, index=False, mode="a", header=write_classical_header)

        generate_theory_data(initial_shots * (shots_multiplier**i)).to_csv(
            theory_path, index=False, mode="a", header=write_theory_header
        )

        print(f"Completed round {i + 1}/{rounds}", flush=True)