{
  "abstract": "Modern smart grids generate increasingly rich operational data and deploy greater numbers of remotely controlled switching assets than ever before, creating new opportunities for data-driven distribution network reconfiguration (DNR). DNR optimizes grid topology to improve efficiency, reduce power losses, and maintain operational constraints, but finding optimal switch configurations remains a computationally demanding combinatorial problem. In this paper, we propose a deep reinforcement learning (DRL) framework for static DNR to systematically investigate how the Q-network architecture affects both solution quality and learning performance. We evaluate multiple architectures within a controlled DRL framework, varying only the Q-network to isolate the contribution of each architectural choice. Our results show that quantum-enhanced Q-networks can achieve higher rewards with fewer parameters than their classical counterpart, suggesting that such architectures are a promising direction for combinatorial power systems optimization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.40327",
  "author_total": 8,
  "authors": [
    {
      "name": "Daniel Germain"
    },
    {
      "name": "Mason Blanchard"
    },
    {
      "name": "Audrey Versteegen"
    },
    {
      "name": "Nora Bauer"
    },
    {
      "name": "Jonathan Mei"
    },
    {
      "name": "Claudio Girotto"
    },
    {
      "name": "Paul Smith"
    },
    {
      "name": "Martin Roetteler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2px9egkj6tw7t.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.40327",
  "fulltext_url": null,
  "id": "2px9egkj6tw7t",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.40327",
  "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": "W7215064902",
  "pdf_url": "https://arxiv.org/pdf/2609.40327",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2px9egkj6tw7t.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.40327",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.40327"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Enhanced Deep Reinforcement Learning for Distribution Network Reconfiguration",
  "title_text": "Quantum Enhanced Deep Reinforcement Learning for Distribution Network Reconfiguration",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/2px9egkj6tw7t",
  "venue": "arXiv",
  "versions": []
}