{
  "checks": [
    {
      "check": "classical sample exponent",
      "value": 0.9998465165925013,
      "criterion": "> 0.95",
      "passed": true
    },
    {
      "check": "quantum sample exponent",
      "value": 0.4890890034261018,
      "criterion": "0.45 to 0.60",
      "passed": true
    },
    {
      "check": "large-m runtime advantage",
      "value": 1244.9049652622857,
      "criterion": "> 100",
      "passed": true
    },
    {
      "check": "speedup grows with m",
      "value": 0.32112649675354366,
      "criterion": "> 0",
      "passed": true
    }
  ],
  "scope": "Independent audit of the stated m-dependence in the classical and quantum runtime formulas; no quantum hardware was used.",
  "paper_id": "TBSyYj4VV6",
  "title": "Accelerating Regression Tasks with Quantum Algorithms",
  "seed": 1082026,
  "executed_at": "2026-08-01T09:33:31.052022+00:00",
  "all_checks_passed": true,
  "environment": {
    "python": "3.10.12",
    "numpy": "1.24.4",
    "scipy": "1.14.0",
    "platform": "Linux-5.15.0-139-generic-x86_64-with-glibc2.35"
  },
  "reference_evidence": {
    "space": "ai-sherpa/quantum-regression-sparsifiers-repro",
    "sha": "adadc54a2097bf77e6e9766d5bf6322c32374204",
    "relationship": "separately attributed public reference"
  }
}