{
  "abstract": "Quantum amplitude estimation can estimate expected discounted payoffs with quadratically fewer oracle calls than Monte Carlo, but each call requires coherent computation. We use language-model-assisted circuit design to reduce reversible payoff arithmetic for a three-asset worst-of autocallable and a target accrual redemption forward (TARF). The constructions retain compact integer representations of market paths alongside contract history, reconstructing quantities temporarily for contract decisions and payoff-angle evaluation. Relative to matched internal references, the factor-sum autocallable and windowed TARF circuits reduce the product of Toffoli count and peak logical qubits $(N_{Tof} Q_{max})$ by factors of 2.39 and 4.84, respectively. At a second autocallable setting, fresh search recovers a 1.35-fold saving after the tested fixed adaptation loses its advantage. For the factor-sum circuit, we prove exact coherent execution of its integer specification for every input, incoming accumulator and measurement record, with clean workspace and phase cancellation. A separate lattice calculation gives a numerical angle-tolerance-violation probability of 6.248e-8 and evaluates pricing bias under the declared loading law, with bounded omission but no certified numerical enclosure. The results connect compact representations to lower-cost payoff arithmetic and separately assessed logical and financial correctness. Their resource scope excludes loading, payoff rotation and estimation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.04431",
  "author_total": 2,
  "authors": [
    {
      "name": "Monit Sharma"
    },
    {
      "name": "Hoong Chun Lau"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/94cq7q36phnfp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.04431",
  "fulltext_url": null,
  "id": "94cq7q36phnfp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.04431",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "quant-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.04431",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/94cq7q36phnfp.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:25Z",
      "native_id": "2611.04431",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.04431"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Reducing the arithmetic cost of quantum derivative pricing with language-model circuit design",
  "title_text": "Reducing the arithmetic cost of quantum derivative pricing with language-model circuit design",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/94cq7q36phnfp",
  "venue": "arXiv",
  "versions": []
}