{
  "id": "oai:papers.qnfo.org:auditing-bqnn",
  "slug": "auditing-bqnn",
  "title": "Auditing the BQNN: Does a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware Demonstrate a Route to Near-Term Quantum Advantage?",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "We audit the BQNN paper by Lakhdar-Hamina et al. (2025, PRL / arXiv:2507.21222v2) which implements a tunable quantum neural network on three quantum computing platforms. Through a complete Phase 1-4 research pipeline including due diligence, external literature search across 32 papers, and a 9-stage Bayesian deep-dive cascade with 5-adversary red-team audit, we evaluate every claim against the Physics of Computation criterion (joules-per-solution < classical alternative). We find the paper's strongest claim (multi-platform QNN benchmarking methodology) is sustained (confidence 0.95), while its quantum advantage claims are not sustained. Includes 5 calibration register predictions for future verification.",
  "abstract_markdown": "We audit the BQNN paper by Lakhdar-Hamina et al. (2025, PRL / arXiv:2507.21222v2) which implements a tunable quantum neural network on three quantum computing platforms. Through a complete Phase 1-4 research pipeline including due diligence, external literature search across 32 papers, and a 9-stage Bayesian deep-dive cascade with 5-adversary red-team audit, we evaluate every claim against the Physics of Computation criterion (joules-per-solution < classical alternative). We find the paper's strongest claim (multi-platform QNN benchmarking methodology) is sustained (confidence 0.95), while its quantum advantage claims are not sustained. Includes 5 calibration register predictions for future verification.",
  "date": "2026-07-25",
  "first_published": "2026-07-25",
  "last_revised": "2026-07-25T00:00:00Z",
  "modified": "2026-07-25T00:00:00Z",
  "version": "1.0.0",
  "version_count": 1,
  "language": "en",
  "keywords": [
    "quantum neural network",
    "BQNN",
    "quantum advantage audit",
    "QNFO",
    "trapped-ion",
    "superconducting qubits",
    "quantum machine learning",
    "joules-per-solution"
  ],
  "quality_tags": [
    "High-quality scholarly research",
    "open-access scholarly work",
    "versioned research publication"
  ],
  "quality_label": "High-quality scholarly research",
  "quality_basis": "QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review.",
  "quality_evidence_url": "https://qnfo.org/about",
  "peer_review_status": "Not independently peer-reviewed",
  "resource_type": "ScholarlyArticle",
  "doi": null,
  "doi_url": null,
  "url": "https://papers.qnfo.org/papers/auditing-bqnn",
  "license_url": "https://legal.qnfo.org/",
  "license_spdx": null,
  "datacite": {
    "schemaVersion": "http://datacite.org/schema/kernel-4",
    "identifiers": [
      {
        "identifier": "https://papers.qnfo.org/papers/auditing-bqnn",
        "identifierType": "URL"
      }
    ],
    "creators": [
      {
        "name": "Rowan Brad Quni-Gudzinas",
        "nameType": "Personal",
        "nameIdentifiers": [
          {
            "nameIdentifier": "https://orcid.org/0009-0002-4317-5604",
            "nameIdentifierScheme": "ORCID",
            "schemeUri": "https://orcid.org"
          }
        ]
      }
    ],
    "titles": [
      {
        "title": "Auditing the BQNN: Does a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware Demonstrate a Route to Near-Term Quantum Advantage?"
      }
    ],
    "publisher": "QNFO",
    "publicationYear": "2026",
    "resourceType": {
      "resourceTypeGeneral": "Text",
      "resourceType": "ScholarlyArticle"
    },
    "subjects": [
      {
        "subject": "quantum neural network"
      },
      {
        "subject": "BQNN"
      },
      {
        "subject": "quantum advantage audit"
      },
      {
        "subject": "QNFO"
      },
      {
        "subject": "trapped-ion"
      },
      {
        "subject": "superconducting qubits"
      },
      {
        "subject": "quantum machine learning"
      },
      {
        "subject": "joules-per-solution"
      },
      {
        "subject": "High-quality scholarly research",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
      },
      {
        "subject": "open-access scholarly work",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
      },
      {
        "subject": "versioned research publication",
        "subjectScheme": "QNFO publisher classification",
        "schemeUri": "https://qnfo.org/about"
      }
    ],
    "dates": [
      {
        "date": "2026-07-25",
        "dateType": "Issued"
      },
      {
        "date": "2026-07-25",
        "dateType": "Updated"
      }
    ],
    "language": "en",
    "version": "1.0.0",
    "rightsList": [
      {
        "rightsUri": "https://legal.qnfo.org/"
      }
    ],
    "descriptions": [
      {
        "description": "We audit the BQNN paper by Lakhdar-Hamina et al. (2025, PRL / arXiv:2507.21222v2) which implements a tunable quantum neural network on three quantum computing platforms. Through a complete Phase 1-4 research pipeline including due diligence, external literature search across 32 papers, and a 9-stage Bayesian deep-dive cascade with 5-adversary red-team audit, we evaluate every claim against the Physics of Computation criterion (joules-per-solution < classical alternative). We find the paper's strongest claim (multi-platform QNN benchmarking methodology) is sustained (confidence 0.95), while its quantum advantage claims are not sustained. Includes 5 calibration register predictions for future verification.",
        "descriptionType": "Abstract"
      },
      {
        "description": "Publisher quality classification: High-quality scholarly research. QNFO publisher classification for open-access, versioned research with a public revision history and reader correction channel; this is not a claim of independent peer review.",
        "descriptionType": "Other"
      },
      {
        "description": "Peer review status: Not independently peer-reviewed",
        "descriptionType": "Other"
      }
    ],
    "formats": [
      "text/html"
    ]
  }
}