| { |
| "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" |
| } |
| } |
|
|
