{
  "abstract": "Accurate modeling of midcircuit measurements (MCMs) is essential for dynamic-circuit operations such as syndrome extraction, measurement-based reset, and the separation of state-preparation and measurement error. Unlike terminal measurement, a noisy MCM both produces a classical outcome and alters the incoming quantum state, thereby influencing subsequent circuit operations. This makes conventional confusion-matrix or fidelity-level characterization insufficient. Here, we introduce an efficient, self-consistent protocol for learning a single-qubit Z-twirled MCM instrument, retaining the readout-backaction correlations and excitation-decay asymmetry that are erased in Pauli-error descriptions. Remarkably, readout bit strings from only three repeated MCMs on a maximally mixed input determine all learnable parameters of the reduced instrument, up to a single unidentifiable gauge degree of freedom. Physicality constraints convert this nonidentifiability into narrow, gauge-aware error intervals. Implemented on IBM superconducting processors, the learned instrument improves Pauli-observable prediction by $\\sim 100\\times$ over a conventional confusion-matrix model and reveals a $T_1$-decay dominated backaction. Our protocol provides a compact characterization layer for state-preparation and measurement error separation, reset optimization, and noise-aware quantum error correction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.00433",
  "author_total": 7,
  "authors": [
    {
      "name": "Chia-Tung Chu"
    },
    {
      "name": "Su-un Lee"
    },
    {
      "name": "Han Zheng"
    },
    {
      "name": "Senrui Chen"
    },
    {
      "name": "Bibek Pokharel"
    },
    {
      "name": "Alireza Seif"
    },
    {
      "name": "Liang Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cb5886sth3bb3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.00433",
  "fulltext_url": null,
  "id": "cb5886sth3bb3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.00433",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2606.00433",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cb5886sth3bb3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:32Z",
      "native_id": "2606.00433",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.00433"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Learning Midcircuit Measurement Backaction from Three Repeated Measurements",
  "title_text": "Learning Midcircuit Measurement Backaction from Three Repeated Measurements",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:28Z",
  "url": "https://research.draconitic.com/paper/cb5886sth3bb3",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/y6x3-348l",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}