{
  "abstract": "Optimizing continuous-variable functions on qubit-based quantum hardware requires a finite representation of the search space and a solver-compatible representation of the objective function. Fixed-grid discretizations provide a direct encoding, but restrict the search to a predefined range and resolution; at the same time, constructing a compact Hamiltonian for a general continuous-variable objective can be impractical. Here, we introduce a framework for continuous-variable optimization tailored to qubit-based quantum architectures. The method combines an adaptive discretization with an iterative local mapping of the objective function onto an effective spin-glass Hamiltonian. We assess the approach on unconstrained and constrained instances of the $n$-dimensional Gramacy-Lee benchmark, and on a proof-of-concept inverse-planning problem in intensity-modulated radiotherapy (IMRT) for cancer treatment. The results show that this framework can produce competitive solutions, supporting its use as a flexible mapping strategy for continuous-variable optimization in qubit-based quantum and quantum-inspired pipelines.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03987",
  "author_total": 7,
  "authors": [
    {
      "name": "Samuele Cavinato"
    },
    {
      "name": "Marco Tesoro"
    },
    {
      "name": "Gianluca Morettini"
    },
    {
      "name": "Marta Paiusco"
    },
    {
      "name": "Alessandro Scaggion"
    },
    {
      "name": "Simone Montangero"
    },
    {
      "name": "Ilaria Siloi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4443y7gphd51c.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03987",
  "fulltext_url": null,
  "id": "4443y7gphd51c",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03987",
  "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",
      "physics.med-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03987",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4443y7gphd51c.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.03987",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03987"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Adaptive Qubit Encoding for Continuous-Variable Quantum Optimization",
  "title_text": "Adaptive Qubit Encoding for Continuous-Variable Quantum Optimization",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/4443y7gphd51c",
  "venue": "arXiv",
  "versions": []
}