{
  "abstract": "We present an attention-based foundation model architecture for learning and predicting quantum states across Hamiltonian parameters, system sizes, and physical systems. Using only basis configurations and physical parameters as inputs, our trained neural network is able to produce highly accurate ground state wavefunctions. For example, we build the phase diagram for the 2D square-lattice t t – V V model with N N particles, from only 18 training points in parameter space (V/t,N) ( V / t , N ) . Thus, our architecture provides a basis for building a universal foundation model for quantum matter.",
  "abstract_shown": true,
  "affiliations": [
    "Massachusetts Institute of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Timothy Zaklama",
      "orcid": "0009-0005-9324-5983"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniele Guerci"
    },
    {
      "affs": [
        0
      ],
      "name": "Liang Fu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/44km7cxr83qqw.bib",
  "cited_by": 0,
  "doi": "10.21468/scipostphys.21.4.084",
  "fulltext_url": null,
  "id": "44km7cxr83qqw",
  "issns": [
    "2542-4653"
  ],
  "landing_url": "https://scipost.org/10.21468/SciPostPhys.21.4.084",
  "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",
      "3107|Quantum many-body systems"
    ]
  ],
  "openalex": "W4417447048",
  "pdf_url": "https://scipost.org/10.21468/SciPostPhys.21.4.084/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "Stichting SciPost",
  "ris": "https://research.draconitic.com/paper/44km7cxr83qqw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:57Z",
      "native_id": "10.21468/scipostphys.21.4.084",
      "source": "crossref",
      "url": "https://scipost.org/10.21468/SciPostPhys.21.4.084"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3107|Quantum many-body systems",
      "subfield": "amo"
    }
  ],
  "title": "Attention-based foundation model for quantum states",
  "title_text": "Attention-based foundation model for quantum states",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:57Z",
  "url": "https://research.draconitic.com/paper/44km7cxr83qqw",
  "venue": "SciPost Physics",
  "versions": []
}