Quantum-Enhanced Deep Learning for Financial Anomaly Detection

Rayane Aggoune () and Abdelhak Merizig ()
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Rayane Aggoune: Mohamed Khider University of Biskra, LINFI Laboratory, Computer Sciences Department
Abdelhak Merizig: Mohamed Khider University of Biskra, IMPIA Laboratory

A chapter in Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026), 2026, pp 78-89 from Springer

Abstract: Abstract 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 (

Keywords: Quantum Machine Learning; Financial Anomaly Detection; Hybrid Quantum-Classical Models; Variational Quantum Circuits; NISQ Computing; MENA Region; High-Frequency Trading (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-711-8_9

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