{
  "abstract": "Hybrid quantum–classical machine learning on noisy intermediate-scale quantum (NISQ) devices suffers from three coupled overheads: hardware miscalibration, classical syndrome-decoding latency and shot noise on gradient estimators. We present quantum calibration and observable processing (QCOP), a unified closed-loop control layer integrating a spectral calibration engine (SCE), a hierarchical syndrome decoder (HSD) and an observable compression layer (OCL) within a single GPU-accelerated pipeline. Three theoretical bounds characterize the joint effect on logical error rate (LER), convergence rate and observable variance. On device-calibrated noise-model simulators (n=20 seeds), QCOP achieves up to 2× wall-clock training speed-up and approximately 40% variational quantum eigensolver (VQE) energy error reduction relative to the unaugmented baseline. A hardware pilot on ibm_nairobi at surface-code distance d=5 (2000 syndrome volumes, 8192 shots each, single calibration window) yields a 1.6× latency reduction and 1.9× LER reduction for the HSD over minimum-weight perfect matching (MWPM)—directionally consistent with simulation but quantitatively smaller, indicating noise sources beyond the model. This pilot exercises the HSD alone: the SCE and the OCL have not been run on physical hardware, and the three modules have never been executed jointly on a device. The end-to-end QCOP benefits—speed-up, energy error reduction and variance reduction—are therefore not hardware-validated, and end-to-end hardware validation of all three modules remains future work. Code and model weights are available at https://github.com/hekimoghlu/QCOP.",
  "abstract_shown": true,
  "affiliations": [
    "Omnira , Baku, Azerbaijan"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tunjay Akbarli",
      "orcid": "0009-0001-2052-6574"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/acbmx7atsqt1b.bib",
  "cited_by": 0,
  "doi": "10.1098/rsos.260812",
  "fulltext_url": "https://europepmc.org/articles/PMC13682866",
  "id": "acbmx7atsqt1b",
  "issns": [
    "2054-5703"
  ],
  "landing_url": "https://royalsocietypublishing.org/rsos/article/13/10/260812/483801/A-unified-framework-for-calibration-decoding-and",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7155091713",
  "pdf_url": "https://royalsocietypublishing.org/rsos/article-pdf/doi/10.1098/rsos.260812/6172520/rsos.260812.pdf",
  "pmcid": "PMC13682866",
  "pmid": "42904015",
  "published": "2026-10-21",
  "publisher": "The Royal Society",
  "ris": "https://research.draconitic.com/paper/acbmx7atsqt1b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:45Z",
      "native_id": "10.1098/rsos.260812",
      "source": "crossref",
      "url": "https://royalsocietypublishing.org/rsos/article/13/10/260812/483801/A-unified-framework-for-calibration-decoding-and"
    },
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "MED:42904015",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42904015"
    }
  ],
  "subjects": [],
  "title": "A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning",
  "title_text": "A unified framework for calibration, decoding and resource-aware processing in hybrid quantum–classical machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:53Z",
  "url": "https://research.draconitic.com/paper/acbmx7atsqt1b",
  "venue": "Royal Society Open Science",
  "versions": []
}