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International Conference on Data Science for Cyber-Physical Systems Resilience using Advanced Applications (ICDCA)

Hybrid Quantum-Classical Learning for Adaptive Financial Anomaly Detection

Sandhya S, Koj Roshni, Shakthi Priya V, Valarmathi K, Umarani Srikanth G

Published Jun 1, 2026• 0 citations
Publisher
DOI
10.1109/icdca69396.2026.11619978

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Financial markets in the MENA region face critical challenges in detecting anomalies within high-frequency trading and cross-border payment systems, where traditional machine learning approaches struggle with extreme dimensionality, class imbalance, and real-time constraints.This paper presents a theoretical quantum-enhanced deep learning framework addressing these challenges through hybrid quantum-classical architectures.We propose a novel hybrid architecture integrating variational quantum circuits (VQC) for feature extraction with transformer-based temporal modeling, achieving a theoretical parameter reduction of 40-60% over equivalent classical architectures.The framework identifies conditions for quantum advantage: high-dimensional sparse features (d > 100), extreme class imbalance (< 0.5% anomaly rate), and distancebased detection.All performance figures are theoretical projections from the literature; empirical validation is designated as future work.Our comparative analysis reveals that quantum kernel methods show theoretical improvement potential over classical baselines under these conditions.