{
  "abstract": "Real-time, scalable, and accurate decoding is a critical component for realizing a fault-tolerant quantum computer. While Transformer-based neural decoders such as AlphaQubit have demonstrated high accuracy, the computational complexity of their core attention mechanism, which scales as $${{{\\mathcal{O}}}}({d}^{4})$$ O ( d 4 ) with code distance d , results in decoding speeds insufficient for practical real-time applications. In this work, we introduce and evaluate a Mamba -based decoder, a state-space model with $${{{\\mathcal{O}}}}({d}^{2})$$ O ( d 2 ) complexity. In memory experiments using Sycamore hardware data, our Mamba decoder matches the performance of its Transformer-based counterpart, demonstrating that its superior efficiency does not come at the cost of performance. Crucially, in simulated real-time scenarios that account for decoder-induced noise, the Mamba decoder significantly outperforms the Transformer, with the performance gap increasing across the tested code distances up to d = 9. These results demonstrate that Mamba decoders offer a compelling balance between speed and accuracy, making them a promising architecture for scalable, real-time quantum error correction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Changwon Lee",
      "orcid": "0009-0003-0183-4939"
    },
    {
      "name": "Tak Hur",
      "orcid": "0009-0004-0071-0907"
    },
    {
      "name": "Daniel K. Park",
      "orcid": "0000-0002-3177-4143"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9p3v70f81b4n3.bib",
  "cited_by": 0,
  "doi": "10.1038/s41534-026-01403-y",
  "fulltext_url": null,
  "id": "9p3v70f81b4n3",
  "issns": [
    "2056-6387"
  ],
  "landing_url": "https://www.nature.com/articles/s41534-026-01403-y",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/9p3v70f81b4n3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:08Z",
      "native_id": "10.1038/s41534-026-01403-y",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41534-026-01403-y"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "issn:2056-6387",
      "subfield": "quantum"
    }
  ],
  "title": "Scalable neural decoders for practical real-time quantum error correction",
  "title_text": "Scalable neural decoders for practical real-time quantum error correction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:08Z",
  "url": "https://research.draconitic.com/paper/9p3v70f81b4n3",
  "venue": "npj Quantum Information",
  "versions": []
}