{
  "artifact": "Leakage-resistant QML benchmark card",
  "chapter": "Quantum Machine Learning and Benchmark Discipline",
  "chapter_url": "https://www.steven-geller.com/playground/quantum/book/69-quantum-machine-learning-and-benchmark-discipline/",
  "date_modified": "2026-08-14",
  "dependencies": [],
  "expected_exit_code": 0,
  "expected_stdout": "PASS: 69 QML card interval=(-0.013859292911256331, 0.013859292911256331) invalid=['feature_contract'] shifted=directional\n",
  "fixture_sha256": "a8ccbe0de33f104d5753aa39c21f6f8dca88450ecc7bebcfe64a9ccf86d0459b",
  "manifest_test_command": "python3 tools/validate_briefs.py --briefs data/editorial_briefs_64_87.json --from 64 --through 87 --check-rewritten-sources --execute-artifacts",
  "pass_condition": "Train/test hashes differ, the quantum and classical models receive the same features, all seeds are recorded, and the advantage flag remains false when the confidence interval crosses zero.",
  "python_requires": ">=3.11",
  "run_command": "python3 fixture.py",
  "schema_version": "1.1.0",
  "test_method": "Run the isolated fixture; require exit code 0 and exact stdout."
}