{
  "abstract": "In a world fraught with uncertainty, it behooves us to consider the most disastrous possible outcomes, in order that we can best mitigate against them. Often these are extreme risk events -- of high consequence, but low probability. Yet, the rarity of such events limits our ability to study them; there is a paucity of past occurrences to draw upon, and sampling such rare events from models is typically costly in time. Here, we introduce an approach for rare-event sampling based on quantum stochastic models. This integrates the memory advantages of quantum stochastic modelling -- and its associated efficient means of preparing quantum sample states -- with the `speed-ups' of quantum amplitude amplification, to yield simultaneous reductions in spatial and temporal computational complexity compared to classical Monte Carlo-based approaches.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.03026",
  "author_total": 7,
  "authors": [
    {
      "name": "Akshat Gupta"
    },
    {
      "name": "Jianjun Chen"
    },
    {
      "name": "Chengran Yang"
    },
    {
      "name": "Mile Gu"
    },
    {
      "name": "Caterina Doglioni"
    },
    {
      "name": "Jayne Thompson"
    },
    {
      "name": "Thomas J. Elliott"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f9bqa01vebd3m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.03026",
  "fulltext_url": null,
  "id": "f9bqa01vebd3m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.03026",
  "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",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.03026",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f9bqa01vebd3m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:45Z",
      "native_id": "2610.03026",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.03026"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Spatiotemporal quantum advantages for rare-event sampling",
  "title_text": "Spatiotemporal quantum advantages for rare-event sampling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:45Z",
  "url": "https://research.draconitic.com/paper/f9bqa01vebd3m",
  "venue": "arXiv",
  "versions": []
}