{
  "abstract": "Analyzing quantum systems is essential for quantum information processing, but can require exponentially many measurements without exploiting the structure of the system or task. Quantum machine learning offers a promising way to reduce this cost by learning from quantum data. Here, we theoretically establish a framework for efficient quantum-state classification and experimentally demonstrate the resulting scheme on a programmable photonic processor. Our analysis links learning efficiency to the structure of quantum data, without relying on a physical description tailored to each task. We derive sufficient conditions for accurate classification with a number of measurements polynomial in system size. We apply this guarantee to a resource problem in continuous-variable (CV) quantum computing: classifying stellar rank, a measure of non-Gaussianity. Experimentally, we implement the classification of single-mode CV quantum states with stellar rank zero and one. This is enabled by developing a programmable CV photonic processor capable of preparing and processing diverse Gaussian and non-Gaussian states and performing virtual projection onto such states, going beyond the Gaussian-only capabilities of most existing photonic platforms. The experiment demonstrates accurate classification despite experimental imperfections. Our results establish a theory-guided and experimentally validated framework for quantum-state classification that can be implemented on quantum devices available today.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.39350",
  "author_total": 12,
  "authors": [
    {
      "name": "Keitaro Anai"
    },
    {
      "name": "Akimoto Nakayama"
    },
    {
      "name": "Shintaro Ooka"
    },
    {
      "name": "Hiroko Tomoda"
    },
    {
      "name": "Yuto Morohoshi"
    },
    {
      "name": "Yohji Chin"
    },
    {
      "name": "Shigehito Miki"
    },
    {
      "name": "Masahiro Yabuno"
    },
    {
      "name": "Hirotaka Terai"
    },
    {
      "name": "Peter van Loock"
    },
    {
      "name": "Kosuke Mitarai"
    },
    {
      "name": "Shuntaro Takeda"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fq8rsatmf2534.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.39350",
  "fulltext_url": null,
  "id": "fq8rsatmf2534",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.39350",
  "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/2610.39350",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fq8rsatmf2534.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:23Z",
      "native_id": "2610.39350",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.39350"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Provably efficient experimental classification of quantum data with quantum kernels",
  "title_text": "Provably efficient experimental classification of quantum data with quantum kernels",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:45Z",
  "url": "https://research.draconitic.com/paper/fq8rsatmf2534",
  "venue": "arXiv",
  "versions": []
}