{
  "abstract": "We introduce a sparse Walsh-Pauli correlation encoding for MaxCut, in which graph variables are represented by diagonal Pauli-string expectation values computed classically as normalized XOR autocorrelations of Walsh coefficients. The complete Walsh basis can reproduce the diagonal correlators of arbitrary quantum states, while a structured sparse support reduces the number of trainable coefficients to $m+1$ for $m$ graph variables. We evaluate the method on $1{,}400$ held-out random graphs with up to $7{,}000$ vertices, obtaining empirical approximation ratios near $0.98$ relative to MQLib heuristic references. On Gset instances G14, G23, G60, and G64, the sparse model achieves mean refined approximation ratios of $0.9828$, $0.9926$, $0.9781$, and $0.9369$, respectively, with $80.4\\%$-$89.3\\%$ fewer parameters than the complete Walsh representation. The trained coefficients are transferred without retraining to quantum circuits. Statevector simulations of the four Gset instances achieve fidelities above $0.99994$ and preserve all correlator signs. Separately, a seven-qubit experiment on a $15$-vertex graph executed on IBM quantum hardware achieves a correlator-sign agreement of $0.927\\pm0.049$ relative to the corresponding noiseless circuit. These results demonstrate the potential of sparse Walsh-PCE for large-instance quantum-inspired MaxCut optimization while validating the transfer of classically trained parameters to quantum hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.08907",
  "author_total": 6,
  "authors": [
    {
      "name": "Cesar Augusto do Amaral"
    },
    {
      "name": "Nicolás Legnazzi"
    },
    {
      "name": "Marcos Vinicius Reballo"
    },
    {
      "name": "Marcus Ritt"
    },
    {
      "name": "Alexsandro Santos da Rosa Júnior"
    },
    {
      "name": "Fernando Augusto Caletti de Barros"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bf5zrpfrsqq77.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.08907",
  "fulltext_url": null,
  "id": "bf5zrpfrsqq77",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.08907",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7212066720",
  "pdf_url": "https://arxiv.org/pdf/2609.08907",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bf5zrpfrsqq77.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:52Z",
      "native_id": "2609.08907",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.08907"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Sparse Walsh-Pauli Correlation Encoding for MaxCut: A Quantum-Inspired Formulation with Quantum-Hardware Validation",
  "title_text": "Sparse Walsh-Pauli Correlation Encoding for MaxCut: A Quantum-Inspired Formulation with Quantum-Hardware Validation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:41Z",
  "url": "https://research.draconitic.com/paper/bf5zrpfrsqq77",
  "venue": "arXiv",
  "versions": []
}