{
  "abstract": "Selecting anomalous samples and explanatory features under fixed cardinalities defines a coupled optimization problem: feature-first selection can overlook features whose usefulness depends on the selected samples. In the analyzed model, joint calibration-error sensitivity is sample-count independent, while the sum-column feature-first rule's sensitivity grows linearly. To optimize this formulation, we introduce Coupling-Grouped XY Quantum Approximate Optimization Algorithm (CG-XY-QAOA), with constraint-preserving mixers and block-structured phase angles. On matched sparse IBM Heron R3 circuits, our implementation reduces circuit depth by 35.1%-54.8% against Qiskit's optimization-level 3 preset pass manager on the CZ-basis target and enables runs at 64 decision qubits (p=2) and 36 (p=3). At 20 decision qubits (p=5), fully grouped angles raise the fixed low-energy hit probability 25-fold to 3.7%, with 64.7% of measured samples satisfying both constraints. Benchmarks favor joint selection when feature usefulness varies across anomalies. Noiseless simulations show problem-structured grouping improves over same-depth XY-QAOA and parameter-matched type-preserving randomizations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.13244",
  "author_total": 1,
  "authors": [
    {
      "name": "Pauli Taipale"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/672bfdy7wdbb2.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.13244",
  "fulltext_url": null,
  "id": "672bfdy7wdbb2",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.13244",
  "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",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7164517269",
  "pdf_url": "https://arxiv.org/pdf/2606.13244",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/672bfdy7wdbb2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:16Z",
      "native_id": "2606.13244",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.13244"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection",
  "title_text": "Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:16Z",
  "url": "https://research.draconitic.com/paper/672bfdy7wdbb2",
  "venue": "arXiv",
  "versions": []
}