{
  "abstract": "Increasing renewable-energy penetration heightens power-system variability and complicates disturbance containment. Controlled islanding mitigates cascading failures by partitioning a stressed network to limit disrupted power transfer while preserving each island's operational integrity, but this constrained partitioning problem is NP-hard. Although QAOA offers a complementary search strategy, limited near-term qubit capacity restricts conventional formulations. This paper presents a qubit-efficient hybrid quantum framework combining a physics-informed compact encoding with Lagrangian constraint handling and classical feasibility refinement. The encoding exploits grid structure while formally preserving the original feasible solution space and objective. For a fixed island count on sparse working graphs, the formulation reduces phase-separator and per-layer gate complexity from quadratic to linear scaling with system size. Tests on eight IEEE systems ranging from 9 to 89 buses and multiple quantum-provider backends produce feasible, high-quality islanding solutions under practical circuit and sampling budgets. Factorial ablation attributes resource and runtime improvements to the complementary effects of compact encoding and qubit-efficient constraint handling. Noise analysis shows stable solution quality under tested device noise, while landscape diagnostics reveal smoother, more consistently scaled QAOA cost surfaces and improved parameter-optimization behavior. These results offer a transferable approach for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.02789",
  "author_total": 5,
  "authors": [
    {
      "name": "Yuqi Jiang"
    },
    {
      "name": "Zhiding Liang"
    },
    {
      "name": "Qiang Guan"
    },
    {
      "name": "Yan Li"
    },
    {
      "name": "Ganesh Kumar Venayagamoorthy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5endqjz2k1ed8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.02789",
  "fulltext_url": null,
  "id": "5endqjz2k1ed8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.02789",
  "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.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7172513605",
  "pdf_url": "https://arxiv.org/pdf/2608.02789",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5endqjz2k1ed8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:11Z",
      "native_id": "2608.02789",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.02789"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding",
  "title_text": "PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:11Z",
  "url": "https://research.draconitic.com/paper/5endqjz2k1ed8",
  "venue": "arXiv",
  "versions": []
}