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#!/usr/bin/env python3
"""Mechanics checks for the Appendix-C physical imperfection model."""
from __future__ import annotations
from pathlib import Path
import sys
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from sdil.physical_coupled import Circuit, Task # noqa: E402
from sdil.physical_imperfection import ( # noqa: E402
DifferentialSquareLawImperfection,
LocalPolynomialPredictor,
edge_voltage_drops,
fit_polynomial_predictor,
simulate_imperfect_alternating_tasks,
)
def main() -> None:
circuit = Circuit()
ideal = DifferentialSquareLawImperfection.ideal()
output_free = 0.23
output_clamped = 0.26
assert np.array_equal(
ideal.observed_rate(circuit, output_free, output_clamped),
ideal.ideal_rate(circuit, output_free, output_clamped),
)
assert np.array_equal(
ideal.neutral_bias(circuit, output_free), np.zeros(2))
hardware = DifferentialSquareLawImperfection.sample_appendix_c(20260829)
outputs = np.linspace(circuit.low + 0.01, circuit.high - 0.01, 81)
local_states = np.asarray([
edge_voltage_drops(circuit, output) for output in outputs])
neutral = np.asarray([
hardware.neutral_bias(circuit, output) for output in outputs])
center = np.mean(local_states, axis=0)
scale = np.ptp(local_states, axis=0)
constant = LocalPolynomialPredictor.zeros(center, scale, degree=0)
quadratic = LocalPolynomialPredictor.zeros(center, scale, degree=2)
constant_count = fit_polynomial_predictor(
constant,
local_states,
neutral,
)
quadratic_count = fit_polynomial_predictor(
quadratic,
local_states,
neutral,
)
assert constant_count == quadratic_count
constant_rmse = float(np.sqrt(np.mean([
np.square(measurement - constant.predict(state))
for state, measurement in zip(local_states, neutral)
])))
quadratic_rmse = float(np.sqrt(np.mean([
np.square(measurement - quadratic.predict(state))
for state, measurement in zip(local_states, neutral)
])))
assert quadratic_rmse < 1e-6
assert constant_rmse > 1e-3
tasks = (
Task("alpha", circuit.high, 0.31),
Task("beta", circuit.low, 0.14),
)
shared = {
"circuit": circuit,
"tasks": tasks,
"imperfection": hardware,
"period_seconds": 0.01,
"cycles": 12,
"initial_gates": np.asarray((4.0, 4.0)),
"overclamp_nudging": 0.05,
"overclamp_magnitude": circuit.high,
}
oracle = simulate_imperfect_alternating_tasks(
method="overclamp_oracle_neutral", **shared)
sdil = simulate_imperfect_alternating_tasks(
method="overclamp_sdil", predictor=quadratic, **shared)
assert np.allclose(
oracle["final_gates"], sdil["final_gates"], atol=1e-8, rtol=0.0)
print({
"constant_neutral_rmse_v_per_s": constant_rmse,
"quadratic_neutral_rmse_v_per_s": quadratic_rmse,
"neutral_observations_each": constant_count,
"sdil_matches_neutral_oracle": True,
"autodiff_used": False,
})
if __name__ == "__main__":
main()
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