{
  "abstract": "Distribution network reconfiguration (DNR) is a combinatorial optimization problem that seeks a low-loss network topology subject to topological and electrical operating constraints. In this work, we propose a two-stage quantum-classical method for DNR. First, an iterative linear-ramp Quantum Alternating Operator Ansatz (LR-QAOA) searches topologically feasible subspaces constructed using a novel cycle-edge encoding. The encoding guarantees that every represented configuration is a spanning tree while allowing the size and coverage of each subspace to be adjusted to the available qubit budget. Second, the resulting sample distribution is used to guide a mixed-integer second-order-cone programming (MISOCP) solver. The method is evaluated on six distribution networks ranging from 33 to 417 buses through ideal simulation and execution on trapped-ion quantum hardware. For the 417-bus network, quantum hardware sampling identifies a configuration within approximately $1.4\\%$ of the literature reference loss. Across all six test systems, the MISOCP solver guided by hardware-derived distributions reaches an incumbent within $1\\%$ of the target with a lower median solver time than the corresponding unguided solver. These findings establish a concrete framework for applying current quantum hardware to DNR, with the potential to become increasingly competitive as hardware improves.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40264",
  "author_total": 9,
  "authors": [
    {
      "name": "Nowar Alashkar"
    },
    {
      "name": "Cade Kennedy"
    },
    {
      "name": "Phillip C. Lotshaw"
    },
    {
      "name": "Shaked Regev"
    },
    {
      "name": "Miguel Angel Lopez-Ruiz"
    },
    {
      "name": "Ananth Kaushik"
    },
    {
      "name": "Paul Smith"
    },
    {
      "name": "Claudio Girotto"
    },
    {
      "name": "Martin Roetteler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ef03n0cpdh8wd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40264",
  "fulltext_url": null,
  "id": "ef03n0cpdh8wd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40264",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "2208|Optimal Power Flow Distribution"
    ]
  ],
  "openalex": "W7215121163",
  "pdf_url": "https://arxiv.org/pdf/2609.40264",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ef03n0cpdh8wd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2609.40264",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40264"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Two-Stage Quantum-Classical Distribution Network Reconfiguration via Cycle-Edge Encoding",
  "title_text": "Two-Stage Quantum-Classical Distribution Network Reconfiguration via Cycle-Edge Encoding",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/ef03n0cpdh8wd",
  "venue": "arXiv",
  "versions": []
}