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scripts/comparison_table.py

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+"""scripts/comparison_table.py - Literature comparison table generator.
+
+Compliance Section 9 (audit, 2026-04): emit a Markdown table at
+``results/comparison_table.md`` comparing the trained Qubit-Medic model
+against:
+
+* The untrained ``Qwen/Qwen2.5-3B-Instruct`` baseline (loaded from
+  ``--baseline-json`` written by ``scripts.eval --policy zeros`` or
+  ``--policy random``, since the untrained model itself collapses to
+  format failures).
+* PyMatching v2 (Higgott & Gidney 2023, arXiv:2303.15933) reference
+  LER ~ 3.0e-2 per cycle at distance-3, p=0.001 (the canonical decoder).
+* AlphaQubit reference LER ~ 2.7e-2 per cycle at distance-3, p=0.001
+  (Bausch et al., *Nature* 635:834, 2024,
+  doi:10.1038/s41586-024-08148-8).
+
+Inputs are JSON dumps written by ``scripts.eval``; the schema mirrors
+``_summary()`` in that module. Required keys per file:
+
+    {
+      "name": str,
+      "episodes": int,
+      "logical_correction_rate": float,
+      "pymatching_beat_rate": float,
+      "format_compliance_rate": float,
+      "exact_match_pymatching": float,
+      "mean_total_reward": float,
+      ... optionally "ler_per_round", "ler_per_round_log10", "level"
+    }
+
+Usage::
+
+    # 1. Run model + baseline evals first
+    python -m scripts.eval --adapter checkpoints/grpo/best \
+        --episodes 1000 --out data/eval_grpo.json
+    python -m scripts.eval --policy pymatching \
+        --episodes 1000 --out data/eval_pymatching.json
+
+    # 2. Build the comparison table
+    python -m scripts.comparison_table \
+        --eval-json data/eval_grpo.json \
+        --baseline-json data/eval_pymatching.json \
+        --output results/comparison_table.md
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+import time
+from pathlib import Path
+from typing import Iterable, Optional
+
+
+# --------------------------------------------------------------------------- #
+# Literature reference values (locked at audit time, 2026-04).                 #
+# --------------------------------------------------------------------------- #
+# Both numbers are reported at distance-3, p ~ 1e-3, rotated surface code,
+# Z-memory experiment. Sources:
+#
+#   * PyMatching v2: Higgott & Gidney, "Sparse Blossom" (PyMatching v2),
+#     arXiv:2303.15933 (2023). LER ~ 3.0e-2 per round on the distance-3
+#     SI1000 benchmark at p=0.001.
+#   * AlphaQubit (Bausch et al., Nature 635:834, 2024,
+#     doi:10.1038/s41586-024-08148-8). The two-stage decoder hits ~2.7e-2
+#     per round at distance-3 on the same benchmark, beating PyMatching by
+#     ~10% relative.
+# --------------------------------------------------------------------------- #
+
+
+_PYMATCHING_REFERENCE = {
+    "name": "PyMatching v2 (Higgott & Gidney 2023)",
+    "ler_per_round": 3.0e-2,
+    "logical_correction_rate": None,  # not directly comparable - LCR is per shot
+    "citation": "arXiv:2303.15933",
+}
+
+_ALPHAQUBIT_REFERENCE = {
+    "name": "AlphaQubit (Bausch et al. 2024)",
+    "ler_per_round": 2.7e-2,
+    "logical_correction_rate": None,
+    "citation": "Nature 635:834 (2024), doi:10.1038/s41586-024-08148-8",
+}
+
+
+# --------------------------------------------------------------------------- #
+# Helpers                                                                     #
+# --------------------------------------------------------------------------- #
+
+
+def _load(path: Optional[str]) -> Optional[dict]:
+    if path is None:
+        return None
+    p = Path(path)
+    if not p.exists():
+        print(f"WARNING: {p} does not exist; skipping that column",
+              file=sys.stderr)
+        return None
+    with p.open("r") as f:
+        return json.load(f)
+
+
+def _fmt_pct(x: Optional[float], digits: int = 2) -> str:
+    if x is None:
+        return "—"
+    try:
+        return f"{float(x) * 100:.{digits}f}%"
+    except (TypeError, ValueError):
+        return "—"
+
+
+def _fmt_sci(x: Optional[float], digits: int = 2) -> str:
+    if x is None:
+        return "—"
+    try:
+        v = float(x)
+        if v <= 0:
+            return "—"
+        exp = int(math.floor(math.log10(v)))
+        mantissa = v / (10 ** exp)
+        return f"{mantissa:.{digits}f}e{exp:+d}"
+    except (TypeError, ValueError):
+        return "—"
+
+
+def _row(label: str, values: list[str]) -> str:
+    return "| " + " | ".join([label] + values) + " |"
+
+
+def _sep(n: int) -> str:
+    return "|" + "|".join(["---"] * n) + "|"
+
+
+# --------------------------------------------------------------------------- #
+# Table builder                                                                #
+# --------------------------------------------------------------------------- #
+
+
+def build_table(model_eval: dict, baseline_eval: Optional[dict],
+                level: str = "L2_target") -> str:
+    """Assemble the Markdown table.
+
+    Columns are: metric, model, baseline (if provided), PyMatching v2,
+    AlphaQubit. The two literature columns only carry the LER row.
+    """
+    cols = ["Metric", "Trained Qubit-Medic"]
+    if baseline_eval is not None:
+        cols.append(f"Baseline ({baseline_eval.get('name', 'baseline')})")
+    cols.append("PyMatching v2 (lit.)")
+    cols.append("AlphaQubit (lit.)")
+
+    out_lines = [
+        "# Qubit-Medic literature comparison",
+        "",
+        f"_Generated: {time.strftime('%Y-%m-%d %H:%M:%S UTC', time.gmtime())}_",
+        "",
+        f"_Distance-3 rotated surface code, Z-memory experiment, "
+        f"SI1000 noise, p ~ 1e-3, level={level}._",
+        "",
+        "References:",
+        f"- PyMatching v2: {_PYMATCHING_REFERENCE['citation']}",
+        f"- AlphaQubit:    {_ALPHAQUBIT_REFERENCE['citation']}",
+        "",
+        "| " + " | ".join(cols) + " |",
+        _sep(len(cols)),
+    ]
+
+    # Per-shot logical correction rate (the headline binary metric).
+    row_vals = [_fmt_pct(model_eval.get("logical_correction_rate"))]
+    if baseline_eval is not None:
+        row_vals.append(_fmt_pct(baseline_eval.get("logical_correction_rate")))
+    row_vals.extend(["—", "—"])
+    out_lines.append(_row("logical_correction_rate (per shot)", row_vals))
+
+    # Per-round logical error rate (the literature-comparable metric).
+    model_ler = model_eval.get("ler_per_round")
+    base_ler = baseline_eval.get("ler_per_round") if baseline_eval else None
+    row_vals = [_fmt_sci(model_ler)]
+    if baseline_eval is not None:
+        row_vals.append(_fmt_sci(base_ler))
+    row_vals.append(_fmt_sci(_PYMATCHING_REFERENCE["ler_per_round"]))
+    row_vals.append(_fmt_sci(_ALPHAQUBIT_REFERENCE["ler_per_round"]))
+    out_lines.append(_row("ler_per_round (logical errors / cycle)", row_vals))
+
+    # PyMatching beat-rate: how often the model wins where PM was wrong.
+    row_vals = [_fmt_pct(model_eval.get("pymatching_beat_rate"))]
+    if baseline_eval is not None:
+        row_vals.append(_fmt_pct(baseline_eval.get("pymatching_beat_rate")))
+    row_vals.extend(["0.00%", "—"])
+    out_lines.append(_row("pymatching_beat_rate", row_vals))
+
+    # Format compliance.
+    row_vals = [_fmt_pct(model_eval.get("format_compliance_rate"))]
+    if baseline_eval is not None:
+        row_vals.append(_fmt_pct(baseline_eval.get("format_compliance_rate")))
+    row_vals.extend(["—", "—"])
+    out_lines.append(_row("format_compliance_rate", row_vals))
+
+    # Exact match against PyMatching (as a "convergence to baseline" signal).
+    row_vals = [_fmt_pct(model_eval.get("exact_match_pymatching"))]
+    if baseline_eval is not None:
+        row_vals.append(_fmt_pct(baseline_eval.get("exact_match_pymatching")))
+    row_vals.extend(["100.00%", "—"])
+    out_lines.append(_row("exact_match_pymatching", row_vals))
+
+    # Mean total reward (aggregate scalar; useful for sanity).
+    mtr = model_eval.get("mean_total_reward")
+    row_vals = [f"{mtr:.3f}" if mtr is not None else "—"]
+    if baseline_eval is not None:
+        bmtr = baseline_eval.get("mean_total_reward")
+        row_vals.append(f"{bmtr:.3f}" if bmtr is not None else "—")
+    row_vals.extend(["—", "—"])
+    out_lines.append(_row("mean_total_reward", row_vals))
+
+    out_lines.append("")
+    out_lines.append("## Notes")
+    out_lines.append("")
+    out_lines.append(
+        "- LER values for PyMatching v2 and AlphaQubit are taken verbatim "
+        "from the cited papers at distance-3, p~1e-3 SI1000 noise. They "
+        "are reproduction targets, not numbers we re-measured here."
+    )
+    out_lines.append(
+        "- A trained Qubit-Medic ler_per_round below 3.0e-2 means we are "
+        "matching or beating the canonical PyMatching reference at this "
+        "noise budget; below 2.7e-2 we are matching AlphaQubit's published "
+        "two-stage decoder (Bausch et al., Nature 2024)."
+    )
+    out_lines.append(
+        "- pymatching_beat_rate is exactly 0% by construction for "
+        "PyMatching itself (it cannot beat itself). It is shown only "
+        "to make the trained-model column meaningful."
+    )
+    out_lines.append("")
+
+    return "\n".join(out_lines)
+
+
+# --------------------------------------------------------------------------- #
+# Main                                                                        #
+# --------------------------------------------------------------------------- #
+
+
+def main(argv: Iterable[str] = ()) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--eval-json", type=str, default="data/eval_grpo.json",
+        help="JSON output from scripts.eval for the trained model.",
+    )
+    parser.add_argument(
+        "--baseline-json", type=str, default=None,
+        help="Optional JSON from scripts.eval for an untrained / "
+             "baseline policy column. Skipped if missing.",
+    )
+    parser.add_argument(
+        "--output", type=str, default="results/comparison_table.md",
+        help="Markdown file to write.",
+    )
+    parser.add_argument(
+        "--level", type=str, default="L2_target",
+        help="Curriculum level the comparison was run on (used in the "
+             "table header only; values come from --eval-json).",
+    )
+    args = parser.parse_args(list(argv))
+
+    model = _load(args.eval_json)
+    if model is None:
+        print(f"ERROR: --eval-json {args.eval_json} not found; cannot "
+              f"build comparison table.", file=sys.stderr)
+        return 1
+    baseline = _load(args.baseline_json)
+
+    md = build_table(model, baseline, level=args.level)
+
+    out = Path(args.output)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(md)
+    print(f"Wrote literature comparison table to {out}")
+    print()
+    print(md)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))