{
  "abstract": "Anti-money laundering (AML) and trade surveillance remain largely manual: rule-based monitoring raises alerts of which, by estimate, over 95% are false positives, each reviewed by a human analyst. We benchmark four quantum and hybrid classifiers against classical baselines on a synthetic pump-and-dump detection task: a fidelity-based quantum k-nearest-neighbours classifier (QKNN) with clustering-based routing; a quantum support vector machine (QSVM) on a one-body projected quantum kernel; a 20-qubit quantum neural network (QNN) with a Pauli-ZZ feature map and RealAmplitudes ansatz; and a hybrid quantum neural network (HQNN) in which a small classical network generates every rotation angle of a five-qubit circuit. Baselines are logistic regression, XGBoost, one- and three-hidden-layer neural networks, and classical k-nearest neighbours. All models share one stratified split of 1000 accounts (800 train, 200 test, 20% suspicious) and two 20-feature representations (ANOVA and PCA); QKNN and QSVM are also tested under simulated noise, and the HQNN is run on the ibm_fez processor. The best model is classical XGBoost on ANOVA features, at a suspicious-class F1 of 61.4%. The HQNN is the strongest quantum-hybrid model, at 54.5-57.9% F1 on an ideal simulator and 55.4-59.8% on ibm_fez, ahead of six of eight classical configurations; at a 15% false-positive rate, half its alerts are genuinely suspicious. Versus the QNN (42.0% F1), it raises under half the false alarms at similar recall with a quarter of the qubits. The QKNN reaches 43.5% F1 (44.4% under noise), above classical KNN (35.6%); the QSVM does not exceed 41%. We observe no quantum advantage, but a five-qubit hybrid on today's noisy hardware already matches well-tuned classical models, with room to grow via richer circuits and larger devices. We contribute a like-for-like comparison with classical models on shared data, splits and features.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41874",
  "author_total": 11,
  "authors": [
    {
      "name": "Bimal Mehta"
    },
    {
      "name": "Daniel musgrove"
    },
    {
      "name": "Devanshu Shekhar"
    },
    {
      "name": "Shiplu Sarkar"
    },
    {
      "name": "Vanshaj Kerni"
    },
    {
      "name": "Varun Puram"
    },
    {
      "name": "Nitesh Kansara"
    },
    {
      "name": "Abinash Pati"
    },
    {
      "name": "Sarthak Chakraborty"
    },
    {
      "name": "Parth Gaglani"
    },
    {
      "name": "Bhaskar Roy Bardhan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2ngedxe05aytx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41874",
  "fulltext_url": null,
  "id": "2ngedxe05aytx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41874",
  "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/2610.41874",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2ngedxe05aytx.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:15Z",
      "native_id": "2610.41874",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41874"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:quant-ph",
      "subfield": "quantum"
    }
  ],
  "title": "Hybrid Quantum Algorithms for Trade Surveillance Analytics",
  "title_text": "Hybrid Quantum Algorithms for Trade Surveillance Analytics",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:51Z",
  "url": "https://research.draconitic.com/paper/2ngedxe05aytx",
  "venue": "arXiv",
  "versions": []
}