{
  "abstract": "Hyperdimensional computing (HDC) is a computing framework that represents information as high-dimensional pseudorandom vectors called hypervectors (HVs), enabling learning and inference through simple algebraic operations. The HV dimensionality provides computational capacity and error robustness, but the required high-dimensional randomness must be stored or regenerated, imposing a memory--computation tradeoff. Here, we address this tradeoff by physically embodying the randomness required for HV generation in the static disorder of a scattering medium. Specifically, a silicon photonic circuit combined with the scattering medium generates high-dimensional HVs from a small number of input values in a single optical shot. Detector-side encoding programs HV correlations for continuous and categorical representations. The resulting HVs reproduce key statistical and compositional properties of ideal i.i.d.\\ random HVs, including pairwise similarity statistics, associative-memory capacity, and factorization capacity. We further demonstrate an optically assisted inference path using the generated HVs and optical similarity evaluation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.24108",
  "author_total": 3,
  "authors": [
    {
      "name": "Takuya Iwata"
    },
    {
      "name": "Namthip Srisuthep"
    },
    {
      "name": "Satoshi Sunada"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6anzjw6v81dmc.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24108",
  "fulltext_url": null,
  "id": "6anzjw6v81dmc",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24108",
  "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",
      "cs.ET"
    ],
    [
      "arxiv",
      "physics.optics"
    ],
    [
      "openalex",
      "1702|Neural Networks and Reservoir Computing"
    ]
  ],
  "openalex": "W7214010365",
  "pdf_url": "https://arxiv.org/pdf/2609.24108",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6anzjw6v81dmc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:19Z",
      "native_id": "2609.24108",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24108"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Hypervectors from Optical Disorder: Programmable Encoding and Optical Inference for Hyperdimensional Computing",
  "title_text": "Hypervectors from Optical Disorder: Programmable Encoding and Optical Inference for Hyperdimensional Computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:19Z",
  "url": "https://research.draconitic.com/paper/6anzjw6v81dmc",
  "venue": "arXiv",
  "versions": []
}