{
  "abstract": "The Pauli twirl of amplitude damping (AD) is generalized amplitude damping at infinite temperature: it keeps the contraction of AD and removes its non-unital term, so comparing the two in variational circuits isolates the zero-temperature bias, which acts mainly through the scale of the features. For random parameters, features under AD settle on a floor, which at strong damping is set by the last layer, has a closed form, and at fixed $T_1$ falls with temperature as $\\tanh(\\hbarω/2k_BT)$; under the twirls they shrink by a constant factor per layer, up to eight qubits. A trainable output scale removes most of the resulting accuracy differences, leaving AD ahead of its twirls by at most about three percentage points in our simulations; what it removes reappears as a cost in measurement shots: trained and tested with $10^3$ shots per image, a four-qubit classifier under AD at $p=0.3$ stays within 1.5 points of noiseless accuracy, while the twirled classifiers lose up to 33. At weaker damping the separation depth grows roughly as $(np)^{-1}\\ln(1/p)$. The damping direction is a gauge when the damping follows complete entangling layers and the circuit boundaries are trainable; in an eigensolver it becomes physical inside a decomposed two-qubit gate.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.01466",
  "author_total": 4,
  "authors": [
    {
      "name": "Vu-Quoc-Minh Nguyen"
    },
    {
      "name": "Tuan-Vu Truong"
    },
    {
      "name": "Hoang-Long Nguyen"
    },
    {
      "name": "Trung-Khanh Le"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/02wwfjd4t6gnr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.01466",
  "fulltext_url": null,
  "id": "02wwfjd4t6gnr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.01466",
  "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",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7216523676",
  "pdf_url": "https://arxiv.org/pdf/2610.01466",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/02wwfjd4t6gnr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.01466",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.01466"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Zero- Versus Infinite-Temperature Damping in Variational Quantum Circuits: Feature Scale, Sampling Cost, and Frame Gauge",
  "title_text": "Zero- Versus Infinite-Temperature Damping in Variational Quantum Circuits: Feature Scale, Sampling Cost, and Frame Gauge",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/02wwfjd4t6gnr",
  "venue": "arXiv",
  "versions": []
}