{
  "abstract": "Quantum algorithms solve a linear system at a cost that grows only polylogarithmically in its dimension, but on present-day hardware that advantage is realised only if the quantum and the classical parts of a solve are apportioned deliberately. We present PyQLAS, a quantum--classical hybrid library for linear systems that exposes this apportionment as a design parameter rather than a fixed assumption. PyQLAS builds three nested solvers around a single variational core---a variational solver, an outer residual iteration, and a Krylov-subspace projection---so that the quantum subroutine can be held at a fixed circuit width while the classical host absorbs the growth of the problem. Around them, the library supplies preconditioning at two independent levels, dimension alignment, checkpointing, and device-budget control, and a solve written against the interface runs unchanged on an exact state-vector simulator, on a local sampler, and on a real superconducting processor. The library is validated on the IEEE 118-bus power flow, a standard test system in which the linear solver is embedded in a classical Newton iteration. There, the iteration converges to the specified tolerance and reproduces the classical reference solution to within $2.3\\times10^{-8}$ p.u.\\ in voltage magnitude and $3.4\\times10^{-6}$ degrees in voltage angle.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41317",
  "author_total": 5,
  "authors": [
    {
      "name": "Meng-Han Dou"
    },
    {
      "name": "Teng-Yang Ma"
    },
    {
      "name": "Chuang-Chao Ye"
    },
    {
      "name": "Zhao-Yun Chen"
    },
    {
      "name": "Guo-Ping Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/caqwtmdf0e1vw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41317",
  "fulltext_url": null,
  "id": "caqwtmdf0e1vw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41317",
  "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",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41317",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/caqwtmdf0e1vw.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41317",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41317"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems",
  "title_text": "PyQLAS: A Quantum-Classical Hybrid Solver Library for Linear Systems",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/caqwtmdf0e1vw",
  "venue": "arXiv",
  "versions": []
}