{
  "abstract": "Optical computing offers a promising route to high-speed, energy-efficient information processing. However, implementing nonlinear operations typically requires strong light-matter interactions. Here, we introduce structural nonlinearity into an optical reservoir computing architecture, using only linear components. A digital micromirror device acts as a reconfigurable scattering potential, while the propagation of light through a scattering medium provides the high-dimensional mode mixing. By enforcing a second interaction with the wavefront shaper, we obtain a quadratic mapping of the input within the reservoir. A single experimental apparatus simultaneously implements conventional and structurally nonlinear reservoir architectures, enabling a direct comparison on forecasting of the chaotic Mackey-Glass time series. We demonstrate that the structurally nonlinear reservoir improves short-term prediction accuracy and better preserves the long-term dynamics of the chaotic system, demonstrating the potential of structural nonlinearity for low-power optical computing.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02733",
  "author_total": 4,
  "authors": [
    {
      "name": "Leandro R. Venâncio"
    },
    {
      "name": "Jonathan Dong"
    },
    {
      "name": "Mickael Mounaix"
    },
    {
      "name": "Jacopo Bertolotti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/015nr8a8nxk6q.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02733",
  "fulltext_url": null,
  "id": "015nr8a8nxk6q",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02733",
  "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": "W7207815427",
  "pdf_url": "https://arxiv.org/pdf/2609.02733",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/015nr8a8nxk6q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2609.02733",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02733"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series",
  "title_text": "Optical Reservoir Computing with Structural Nonlinearity for Forecasting Chaotic Time Series",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/015nr8a8nxk6q",
  "venue": "arXiv",
  "versions": []
}