{
  "abstract": "Optical computing is emerging as a promising paradigm for next-generation information processing. Diffractive optical processors rely on spatially distributed trainable degrees of freedom, leading to extended architectures. Here, we propose a compact neuromorphic optical-computing approach where the entire trainable transformation is implemented by a single Mie scatterer. By formulating computation in vector spherical harmonics basis, trainable modal couplings can be concentrated within a finite object through its T-matrix. Since available T-matrix parameters scale as the fourth power of maximal multipole order, this architecture can overcome trainable-parameter-density limitations of conventional spatially distributed diffractive processors. We demonstrate classification of phase-encoded MNIST images using scattered-field intensity. At a particle size parameter of $ka = 15$, the trained T-matrix reaches approximately 90% test accuracy, comparable to a single-layer artificial neural network. Similar performance can be achieved using near-fields, enabling on-chip integration. We show how reciprocity, passivity, and particle symmetry constrain performance: passivity reduces the accessible operator space while improving robustness, whereas symmetry reduces the number of independent parameters. Finally, we inverse-design a non-absorbing dielectric scatterer that realizes the classification task with 84% accuracy. These results demonstrate that nontrivial neuromorphic transformations can be encoded within the multipolar response of a single compact scatterer.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.21891",
  "author_total": 4,
  "authors": [
    {
      "name": "Vsevolod Kleshchenko"
    },
    {
      "name": "Vladimir Igoshin"
    },
    {
      "name": "Costantino De Angelis"
    },
    {
      "name": "Mihail Petrov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4mpmvytay8yrx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.21891",
  "fulltext_url": null,
  "id": "4mpmvytay8yrx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.21891",
  "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",
      "physics.optics"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7204188757",
  "pdf_url": "https://arxiv.org/pdf/2608.21891",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-14",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4mpmvytay8yrx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:25Z",
      "native_id": "2608.21891",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.21891"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Mie Optical Computing",
  "title_text": "Mie Optical Computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:25Z",
  "url": "https://research.draconitic.com/paper/4mpmvytay8yrx",
  "venue": "arXiv",
  "versions": []
}