{
  "mode": "classical-benchmark",
  "truth_contract": {
    "implementation_kind": "classical-baseline-with-optional-qsvt-proxy",
    "truth_status": "classical_timing_reference",
    "is_quantum_runtime_benchmark": false,
    "implemented_components": [
      "classical_dense_or_iterative_numpy_computation",
      "wall_clock_timing_for_the_classical_path",
      "residual_or_matrix_function_diagnostics",
      "optional_polynomial_resource_proxy"
    ],
    "validation_scope": "The timing fields measure only the classical baseline path in this Python/NumPy environment. Attached QSVT fields are polynomial proxies and are not quantum runtimes."
  },
  "algorithm": "dense-spectral-matrix-function",
  "problem": "exponential-matrix-function",
  "matrix_dimension": 10,
  "repeats": 3,
  "best_time_seconds": 9.104999980991124e-05,
  "mean_time_seconds": 0.00010143599987107639,
  "benchmark_environment": {
    "timing_kind": "python_wall_clock_microbenchmark",
    "timer": "time.perf_counter",
    "python_version": "3.13.12",
    "numpy_version": "2.4.5",
    "platform_system": "Linux",
    "platform_release": "6.8.0-1052-azure",
    "platform_machine": "x86_64",
    "processor": "x86_64",
    "stability_note": "Timing fields are environment-dependent snapshots. Use metrics and QSVT proxy fields for stable schema checks; regenerate timings deliberately for benchmark studies."
  },
  "metrics": {
    "function": "exponential",
    "min_eigenvalue": 0.02067219782410493,
    "max_eigenvalue": 0.9999999999999998,
    "output_frobenius_norm": 2.121440863180591
  },
  "qsvt_proxy": null,
  "notes": [
    "Dense spectral matrix functions are exact small-system references for QSVT polynomial approximation studies."
  ]
}