{
  "abstract": "Numerical integration with Monte Carlo methods is a central computational task in many scientific and industrial applications, including financial derivative pricing and risk management. Classical Monte Carlo algorithms are computationally demanding: achieving an accuracy $ε$ typically requires a number of function evaluations scaling as $O(1/ε^2)$. Quantum-accelerated Monte Carlo methods based on quantum amplitude estimation can in principle quadratically improve this dependence. However, \\textit{quasi}-Monte Carlo methods have not been explored in the quantum context. In this work, we introduce a quantum quasi-Monte Carlo algorithm that combines low-discrepancy nets with quantum amplitude estimation. The proposed method prepares the quasi-random point set coherently in superposition. The method does not yield an asymptotic improvement over classical quasi-Monte Carlo, since the total error separates into a discretization error, determined by the finite net, and a quantum estimation error. Instead, we explore a pre-asymptotic advantage window: for a target accuracy that would classically require $2^q$ low discrepancy points, one can prepare a higher-resolution net of size $2^Q$, with $Q>q$, in superposition and reach the same accuracy using significantly fewer function queries. This window can be controlled by tuning the circuit resolution and amplitude-estimation parameters, making the approach relevant for practical regimes where the number of queries is finite rather than asymptotically large.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.03625",
  "author_total": 4,
  "authors": [
    {
      "name": "Paolo Recchia"
    },
    {
      "name": "Zhan Yu"
    },
    {
      "name": "Kelvin Koor"
    },
    {
      "name": "Patrick Rebentrost"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b9ttsrpzrxfea.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.03625",
  "fulltext_url": null,
  "id": "b9ttsrpzrxfea",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.03625",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.comp-ph"
    ],
    [
      "arxiv",
      "quant-ph"
    ],
    [
      "openalex",
      "1702|Quantum Computing Algorithms and Architecture"
    ]
  ],
  "openalex": "W7208743932",
  "pdf_url": "https://arxiv.org/pdf/2609.03625",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b9ttsrpzrxfea.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:31Z",
      "native_id": "2609.03625",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.03625"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.comp-ph",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Quantum Quasi-Monte Carlo: a window for pre-asymptotic quantum advantage",
  "title_text": "Quantum Quasi-Monte Carlo: a window for pre-asymptotic quantum advantage",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:31Z",
  "url": "https://research.draconitic.com/paper/b9ttsrpzrxfea",
  "venue": "arXiv",
  "versions": []
}