Hybrid Quantum Algorithms for Trade Surveillance Analytics
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.
Related
- From Wavefunction Regularity to Eigenvector Conditioning: Accuracy--Cost Trade-offs of Quantum Algorithms for Non-Hermitian Transcorrelated Hamiltonians
- Tight bounds for hybrid quantum-classical query algorithms
- Using Learning Analytics to Study Novice Learners' Work on Quantum Circuit Simulator Tasks
- Binary Quantum Hypothesis Testing with a Rejection Option: The Exact Error-Exponent Trade-off
- Accelerating noisy variational quantum algorithms with physics-informed denoising networks
BibTeX
@misc{mehta2026hybrid,
title = {{Hybrid Quantum Algorithms for Trade Surveillance Analytics}},
author = {Bimal Mehta and Daniel musgrove and Devanshu Shekhar and Shiplu Sarkar and Vanshaj Kerni and Varun Puram and Nitesh Kansara and Abinash Pati and Sarthak Chakraborty and Parth Gaglani and Bhaskar Roy Bardhan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41874},
eprint = {2610.41874},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41874},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hybrid Quantum Algorithms for Trade Surveillance Analytics
AU - Bimal Mehta
AU - Daniel musgrove
AU - Devanshu Shekhar
AU - Shiplu Sarkar
AU - Vanshaj Kerni
AU - Varun Puram
AU - Nitesh Kansara
AU - Abinash Pati
AU - Sarthak Chakraborty
AU - Parth Gaglani
AU - Bhaskar Roy Bardhan
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41874
UR - https://arxiv.org/abs/2610.41874
AB - 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.
ER -