{
  "abstract": "Bayesian inversion is widely used in engineering applications, including material characterisation, structural damage identification, and medical diagnostics. These inverse problems are typically ill-posed, because localised variations in material properties must be inferred from noisy observations, and they are computationally intensive, since they often require many evaluations of a high-fidelity forward solver. Quantum computing has recently attracted considerable interest, partly because some near-term algorithms are feasible on NISQ hardware. In particular, PQC combined with classical optimisation enable a hybrid quantum-classical surrogate model. A central obstacle to applying such circuits to inversion is that the discretised parameter and response fields are far too high-dimensional to be embedded in a near-term quantum register. In this work we propose a quantum-assisted latent surrogate model for the Bayesian inversion of inhomogeneous material properties, in which latent compression bridges these high-dimensional fields and the small registers accessible to near-term circuits. The low intrinsic dimensional structure of the spatially varying conductivity and potential fields is exploited by reducing the conductivity through PCA and the response through a convolutional autoencoder. A PQC is then trained to map the latent conductivity to the latent response, and stochastic variational inference is performed entirely in latent space. The methodology is demonstrated on one- and two-dimensional inverse problems under both informative and non-informative priors. We further evaluate the model on IonQ simulator and Rigetti Cepheus-1-108Q, and use a lightweight learned denoising stage that recovers the noiseless accuracy to within a small residual. (The abstract has been shortened to meet arXiv's 1,920-character limit. For the full Abstract, please download the full Article.)",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.18942",
  "author_total": 3,
  "authors": [
    {
      "name": "Dewangga Alfarisy"
    },
    {
      "name": "Catherine F. Higham"
    },
    {
      "name": "Eky Febrianto"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/07sy4dbht5esc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.18942",
  "fulltext_url": null,
  "id": "07sy4dbht5esc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.18942",
  "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.comp-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7222088144",
  "pdf_url": "https://arxiv.org/pdf/2610.18942",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/07sy4dbht5esc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "2610.18942",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.18942"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    }
  ],
  "title": "A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware",
  "title_text": "A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/07sy4dbht5esc",
  "venue": "arXiv",
  "versions": []
}