{
  "abstract": "Quantum computing offers a promising approach to accelerating partial differential equation (PDE) solvers for large-scale, real-world problems. However, reconstructing the classical representation of a solution from a quantum state remains a significant computational bottleneck. We propose a problem-specific method, termed proper orthogonal decomposition-based readout (PODR), to improve this efficiency. This method comprises offline and online stages. In the offline stage, a set of basis functions capturing the dominant features of the target problem is constructed using classical computations. In the online stage, the quantum state is projected onto this reduced basis, and only a small number of coefficients is extracted to reconstruct the solution. PODR is particularly advantageous for simulations involving varying parameters, common in computational fluid dynamics (CFD), because the POD basis functions are constructed only once in the offline stage and reused during the online stage. Applying PODR to benchmark CFD problems demonstrates a significant reduction in online computational cost compared with conventional readout methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.27915",
  "author_total": 10,
  "authors": [
    {
      "name": "Kota Ichiki"
    },
    {
      "name": "Xinchi Huang"
    },
    {
      "name": "Gekko Budiutama"
    },
    {
      "name": "Masari Watanabe"
    },
    {
      "name": "Yoshifumi Kawada"
    },
    {
      "name": "Ryunosuke Terasawa"
    },
    {
      "name": "Hirofumi Nishi"
    },
    {
      "name": "Takayuki Suzuki"
    },
    {
      "name": "Nagai Ryutaro"
    },
    {
      "name": "Yu-ichiro Matsushita"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ds23nk5q47yw1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.27915",
  "fulltext_url": null,
  "id": "ds23nk5q47yw1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.27915",
  "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",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7162644469",
  "pdf_url": "https://arxiv.org/pdf/2605.27915",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ds23nk5q47yw1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2605.27915",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.27915"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition",
  "title_text": "Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/ds23nk5q47yw1",
  "venue": "arXiv",
  "versions": []
}