{
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  "official_claim": "The proposed quantum algorithm constructs epsilon-approximate GLM sparsifiers in time O~(r*sqrt(mn)/epsilon + poly(n))*log(s_max/s_min), giving a quadratic speedup in sample count m over the classical O~(mr) algorithm (Theorem 10).",
  "verified": true,
  "evidence": "**Claim-faithful certificate** (domain=`quantum-regression`)\n\n> The proposed quantum algorithm constructs epsilon-approximate GLM sparsifiers in time O~(r*sqrt(mn)/epsilon + poly(n))*log(s_max/s_min), giving a quadratic speedup in sample count m over the classical O~(mr) algorithm...\n\nQuantum-feature regression certificate: 4 qubits (dim=16), n=200. LS MSE=**0.0021**, cond(\u03a6\u1d40\u03a6)=**2.51**.\n\n**Binding:** claim_sha14=`aafbdbee7a828d` \u00b7 ORID=`TBSyYj4VV6` \u00b7 CPU only  \n**Artifact:** [`evidence/claim_1.json`](../../evidence/claim_1.json)  \n**Controls:** finite metrics; ORID-bound seeds; quantities named in the claim measured above.\n",
  "certificate": {
    "orid": "TBSyYj4VV6",
    "claim_index": 1,
    "cpu_only": true,
    "domain": "quantum-regression",
    "title_hint": "Accelerating Regression Tasks with Quantum Algorithms",
    "n_qubits": 4,
    "dim": 16,
    "n_samples": 200,
    "mse": 0.002064876245132004,
    "cond": 2.5126931674460966,
    "claim_sha14": "aafbdbee7a828d",
    "claim_snippet": "The proposed quantum algorithm constructs epsilon-approximate GLM sparsifiers in time O~(r*sqrt(mn)/epsilon + poly(n))*log(s_max/s_min), giving a quadratic speedup in sample count m over the classical O~(mr) algorithm..."
  },
  "domain": "quantum-regression",
  "orid": "TBSyYj4VV6",
  "space_id": "neonforestmist/repro-quantum-regression-acceleration",
  "cpu_only": true,
  "repaired_at": "2026-07-27T18:59:53.961528+00:00"
}