{
  "abstract": "Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliability is limited by noise on superconducting hardware. These operations depend on accurate quantum state discrimination (QSD), which assigns measured signals to state labels. Neural network-based (NN-based) QSD improves this assignment, yet its error remains bounded below by the Bayes risk of the available readout distributions. A complementary challenge is state deterioration as successive measurements accumulate and circuit execution advances, making static calibration less representative of the workload. We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration. Encoding supplies joint in-phase and quadrature (I-Q) readout signals from the target and auxiliary qubits, while contextual calibration trains the decoder for the conditions encountered during execution. Our theoretical analysis shows that retaining additional channels from a fixed joint distribution cannot increase Bayes risk and establishes sufficient conditions for contraction of an overlap-based upper bound. Experiments across three IBM processors evaluate the framework on repeated stabilizer measurement, parity-check, and teleportation workloads.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.18482",
  "author_total": 6,
  "authors": [
    {
      "name": "Chi Zhang"
    },
    {
      "name": "Neel Vora"
    },
    {
      "name": "Yilun Xu"
    },
    {
      "name": "Gang Huang"
    },
    {
      "name": "Fan Chen"
    },
    {
      "name": "Lei Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ehjda2gh9zfse.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.18482",
  "fulltext_url": null,
  "id": "ehjda2gh9zfse",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.18482",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.ET"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7221781496",
  "pdf_url": "https://arxiv.org/pdf/2610.18482",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ehjda2gh9zfse.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:11Z",
      "native_id": "2610.18482",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.18482"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements",
  "title_text": "Lifting: Quantum State Discrimination Framework for Mid-Circuit Measurements",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:11Z",
  "url": "https://research.draconitic.com/paper/ehjda2gh9zfse",
  "venue": "arXiv",
  "versions": []
}