benchmark_id string | benchmark_name string | cards_referenced list | claim_boundary dict | cross_vendor_contrast dict | description string | hf_org string | hf_user string | promotion_state string | public_claim bool | publisher string | reproducibility dict | results dict | scoring_methodology dict | timestamp timestamp[s] | version string | whitespace_analysis dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HF-QGOV-001 | Aevion Quantum Governance Benchmark v0.1 | [
"QGOV-ORIGIN-001 — Bell Origin CPUQVM",
"QGOV-ORIGIN-003 — GHZ-3 Origin CPUQVM",
"QGOV-ORIGIN-004 — ProofCommons Quantum Governance Seed",
"IBM_QUANTUM_BELL_20260525 — IBM Marrakesh Bell Hardware"
] | {
"blocked_claims": [
"Aevion proved quantum advantage.",
"Aevion verified Wukong hardware.",
"Aevion proved Origin or IBM hardware correctness.",
"Aevion solved a useful quantum workload.",
"Aevion has formal proof of quantum behavior.",
"Aevion has quantum supremacy evidence.",
"Aevion h... | {
"bell_simulator_vs_hardware": {
"hardware_correlation": 0.9609,
"interpretation": "Simulator confirms circuit correctness. Hardware confirms real-world noise characteristics. Together they provide a complete picture that neither alone achieves.",
"noise_gap": 0.0391,
"simulator_correlation": 1
}
} | Proof-carrying quantum governance benchmark: cross-vendor Bell and GHZ-3 execution receipts with classical baselines, automated overclaim prevention (EGON), and transparent noise recording. NOT a quantum advantage benchmark — a quantum honesty benchmark. | Aevion-Verifiable-AI | aevionai | PROMOTE_INTERNAL_QUANTUM_GOVERNANCE_BENCHMARK_SEED | false | Aevion LLC (SDVOSB, CAGE 15NV7) | {
"dependencies": [
"pyqpanda3>=0.3.5",
"qiskit-ibm-runtime>=0.46.1",
"jsonschema>=4.0"
],
"ibm_runner": "packages/aevion_real_qpu_witness/ibm_quantum_runner.py",
"origin_runner": "qpan/origin_governance_runner.py",
"schema": "schemas/origin_quantum_governance_probe.schema.json",
"tests": "tests... | {
"ibm_marrakesh_bell": {
"backend": "ibm_marrakesh",
"backend_qubits": 156,
"card": "IBM_QUANTUM_BELL_20260525",
"circuit": "Bell state (2-qubit entanglement) on real superconducting hardware",
"classical_baseline": {
"10": 0,
"11": 0.5,
"00": 0.5,
"01": 0
},
"corr... | {
"correlation_score": "Fraction of shots matching expected support (e.g. |00>+|11> for Bell). Range [0, 1]. Higher is better.",
"hardware_note": "Hardware noise is expected and recorded, not penalized. The governance layer distinguishes simulator evidence from hardware evidence.",
"leakage_score": "Fraction of s... | 2026-05-26T05:49:09 | 0.1.0 | {
"blue_ocean_positioning": {
"analogy": "SSL/TLS didn't make the internet faster — it made it trustworthy. Aevion Quantum Governance doesn't make quantum computers better — it makes quantum claims auditable.",
"what_aevion_is": "Aevion is the governance layer between quantum execution and claims about quantu... |
YAML Metadata Warning:The task_categories "quantum-computing" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_categories "governance" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
YAML Metadata Warning:The task_categories "benchmarking" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Aevion Quantum Governance Benchmark v0.1
NOT a quantum advantage benchmark — a quantum honesty benchmark.
What This Is
Proof-carrying quantum governance: every quantum execution produces a schema-validated receipt with:
- Classical expected-distribution baseline
- Automated EGON (Evidence Gate for Overclaim Neutralization) decision
- Transparent noise recording (leakage is recorded, not hidden)
- Claim boundary enforcement
Results (2026-05-26)
| Circuit | Backend | Correlation | Leakage | Decision |
|---|---|---|---|---|
| Bell (2q) | Origin CPUQVM | 1.0000 | 0.0000 | PROMOTE |
| GHZ-3 (3q) | Origin CPUQVM | 1.0000 | 0.0000 | PROMOTE |
| Bell (2q) | IBM Marrakesh 156q | 0.9609 | 0.0391 | PROMOTE |
Cross-Vendor Contrast
Simulator Bell correlation: 1.0000 Hardware Bell correlation: 0.9609 Noise gap: 0.0391 (3.9%)
Simulator confirms circuit correctness. Hardware confirms real-world noise. Together = complete picture.
What This Proves
- Cross-vendor Bell state execution (Origin + IBM)
- GHZ-3 entanglement on Origin simulator
- Transparent hardware noise recording
- Automated governance decisions on every execution
- Classical-baseline-anchored scoring
What This Does NOT Prove
- Quantum advantage or supremacy
- Hardware correctness
- Production quantum workload capability
- Formal proof of quantum behavior
Reproducibility
pip install pyqpanda3 qiskit-ibm-runtime jsonschema
pytest tests/origin/test_origin_quantum_governance_probe.py -v # 24/24
python qpan/origin_governance_runner.py
Publisher
Aevion LLC (SDVOSB, CAGE 15NV7) Scott Leishman scott@aevion.ai
License
CC BY-NC 4.0 — Attribution-NonCommercial
- Downloads last month
- 25