{
  "abstract": "Analog quantum computing with neutral atom computers is rapidly evolving into a promising way to perform combinatorial optimization tasks at scale. As technology matures, exploring real use cases leveraging such unique information processors becomes warranted; one example is logistics management where vehicles are assigned routes to perform a given task. Autonomous aerial vehicles (drones) are increasingly used for surveillance over a given territory. In this paper, we propose an innovative framework integrating classical machine learning forecasts and quantum annealing to find the best combination of tours for a given surveillance criterion. The risk maps produced by machine learning are used to find possible surveillance tours, while a quantum-assisted heuristic chooses the best combination, producing a full surveillance plan. By modelling our decision problem for drone paths as a Team Orienteering Problem (TOP) with additional constraints, we construct a new heuristic using quantum annealing to find good solutions. We apply this framework to wildfire prevention, where finding tours most likely to detect a starting fire has clear benefits. We study the effect of larger quantum resources on heuristic performance and compare those results to the use of classical resources.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.03890",
  "author_total": 4,
  "authors": [
    {
      "name": "Dorian Lauwerier"
    },
    {
      "name": "Alexis Vieloszynski"
    },
    {
      "name": "Yves Bérubé-Lauzière"
    },
    {
      "name": "Victor Drouin-Touchette"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8gmp628awn7pt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.03890",
  "fulltext_url": null,
  "id": "8gmp628awn7pt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.03890",
  "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",
      "math.OC"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "2209|Vehicle Routing Optimization Methods"
    ]
  ],
  "openalex": "W7220734872",
  "pdf_url": "https://arxiv.org/pdf/2610.03890",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8gmp628awn7pt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.03890",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.03890"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing",
  "title_text": "Quantum-Assisted Optimization Guided by Machine-Learned Risk Maps for Aerial Surveillance Routing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:10Z",
  "url": "https://research.draconitic.com/paper/8gmp628awn7pt",
  "venue": "arXiv",
  "versions": []
}