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#!/usr/bin/env python3
"""Clean-learning gate for the reconstructed Figure-5 physical grid."""
from __future__ import annotations
import argparse
import json
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_grid import ( # noqa: E402
GridCircuit,
GridSquareLawImperfection,
RingClassificationDataset,
train_grid_classifier,
)
def select_tasks(protocol: dict, rotations: int) -> list[dict]:
standard = [
record for record in protocol["experiments"]
if record["method"] == "standard"
]
diameters = protocol["protocol_checks"]["input_diameters_v"]
selected_diameters = (diameters[0], diameters[len(diameters) // 2], diameters[-1])
selected = []
for diameter in selected_diameters:
candidates = sorted(
(
record for record in standard
if abs(record["input_diameter_v"] - diameter) < 1e-12
),
key=lambda record: record["classes"],
)
selected.extend(candidates[:rotations])
return selected
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--protocol", type=Path,
default=Path("results/physical_bias/dillavou_fig5_protocol.json"))
parser.add_argument(
"--output", type=Path,
default=Path("results/physical_bias/p4_grid_clean_gate.json"))
parser.add_argument("--rotations", type=int, default=2)
parser.add_argument("--standard-epochs", type=int, default=600)
parser.add_argument("--overclamp-epochs", type=int, default=1000)
return parser.parse_args()
def main() -> None:
args = parse_args()
protocol = json.loads(args.protocol.read_text())
circuit = GridCircuit()
ideal = GridSquareLawImperfection.ideal(circuit.edge_count)
tasks = select_tasks(protocol, args.rotations)
records = []
for task_index, task in enumerate(tasks):
dataset = RingClassificationDataset(
inputs_v=np.asarray(task["inputs_v"], dtype=float).T,
labels_v=(
2.0 * np.asarray(task["classes"], dtype=float) - 1.0
) * 0.018,
)
initial_gates = np.asarray(task["initial_gates_v"], dtype=float)
standard = train_grid_classifier(
circuit,
initial_gates,
dataset,
ideal,
method="clean",
epochs=args.standard_epochs,
standard_learning_time_seconds=1e-3,
record_every=50,
)
overclamp = train_grid_classifier(
circuit,
initial_gates,
dataset,
ideal,
method="overclamp_clean",
epochs=args.overclamp_epochs,
overclamp_time_seconds_per_v=0.0025,
record_every=10,
early_stop_perfect_checkpoints=3,
)
records.append({
"task_index": task_index,
"source_file": task["source_file"],
"input_diameter_v": task["input_diameter_v"],
"classes": task["classes"],
"standard_clean": standard,
"overclamp_clean": overclamp,
})
print(
f"task {task_index + 1}/{len(tasks)}: "
f"standard={standard['classification_error']:.3f}, "
f"overclamp={overclamp['classification_error']:.3f}",
flush=True,
)
report = {
"analysis": "reconstructed_figure5_grid_clean_gate_p4",
"confirmatory": False,
"autodiff_used": False,
"source_protocol": str(args.protocol),
"protocol": {
"selected_diameters_v": sorted({
record["input_diameter_v"] for record in records}),
"rotations_per_diameter": args.rotations,
"standard_epochs": args.standard_epochs,
"standard_learning_time_seconds": 1e-3,
"overclamp_epochs_maximum": args.overclamp_epochs,
"overclamp_time_seconds_per_v": 0.0025,
"overclamp_early_stop_perfect_checkpoints": 3,
},
"records": records,
"summary": {
"tasks": len(records),
"standard_clean_zero_error_tasks": sum(
record["standard_clean"]["classification_error"] == 0.0
for record in records),
"overclamp_clean_zero_error_tasks": sum(
record["overclamp_clean"]["classification_error"] == 0.0
for record in records),
},
}
report["summary"]["gate_passed"] = bool(
report["summary"]["standard_clean_zero_error_tasks"] == len(records)
and report["summary"]["overclamp_clean_zero_error_tasks"] == len(records)
)
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report["summary"], indent=2))
print(f"wrote {args.output}")
if __name__ == "__main__":
main()
|
