{
  "abstract": "Lorentz symmetry and the three-prong structure of hadronic top decays provide physical guidance for designing compact quantum jet taggers. We introduce QGrAT (Quantum Grade-Aware Transformer), a quantum transformer that incorporates these structures into its interactions between constituent qubits. Geometric features constructed from the constituent four-momenta are organised by the grades of the spacetime Clifford algebra $C\\ell(1,3)$, and their grade-specific inner products determine the pairwise attention couplings. For fixed constituents and co-transforming reference vectors, these couplings are Lorentz invariant, although the component-wise input encoding makes the complete classifier frame dependent. An auxiliary qubit introduces an explicit three-particle interaction through products of pairwise Minkowski inner products, while a trainable in-circuit readout converts the resulting quantum state into a classification score. We evaluate the architecture in state-vector simulation on the Top Tagging benchmarks. With ten constituents per jet, the configuration using grades $0\\text{-}3$ and the three-particle ancilla requires twelve qubits and $144$ trainable parameters, reaching AUC $0.944 \\pm 0.002$ and background rejection $1/\\varepsilon_B = 35.7 \\pm 2.4\\ (96.8 \\pm 7.7)$ at signal efficiency $\\varepsilon_S = 0.50\\ (0.30)$. This performance is competitive with the L-GATr-Slim baseline at matched constituent and parameter budgets. Adding the three-particle ancilla raises background rejection at $50\\%$ signal efficiency from $19.9$ to $35.7$, whereas extending the representation to the tested grade-$4$ feature gives no consistent improvement. The study establishes a framework for incorporating spacetime geometry into quantum attention and identifies explicit three-particle correlations as the principal source of improved background rejection among the architectural variations tested.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.17351",
  "author_total": 2,
  "authors": [
    {
      "name": "Pallavi Bhardwaj"
    },
    {
      "name": "Michael Spannowsky"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e0639zxb3eadk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.17351",
  "fulltext_url": null,
  "id": "e0639zxb3eadk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.17351",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "3106|Particle physics theoretical and experimental studies"
    ]
  ],
  "openalex": "W7221771524",
  "pdf_url": "https://arxiv.org/pdf/2610.17351",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e0639zxb3eadk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.17351",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.17351"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ph",
      "subfield": "high-energy"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "QGrAT: Quantum Grade-Aware Transformer for Jet Tagging",
  "title_text": "QGrAT: Quantum Grade-Aware Transformer for Jet Tagging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/e0639zxb3eadk",
  "venue": "arXiv",
  "versions": []
}