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Quantum-Enhanced Deep Learning for Financial Anomaly Detection

Author

Listed:
  • Rayane Aggoune

    (Mohamed Khider University of Biskra, LINFI Laboratory, Computer Sciences Department)

  • Abdelhak Merizig

    (Mohamed Khider University of Biskra, IMPIA Laboratory)

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 (

Suggested Citation

  • Rayane Aggoune & Abdelhak Merizig, 2026. "Quantum-Enhanced Deep Learning for Financial Anomaly Detection," Advances in Economics, Business and Management Research,, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-711-8_9
    DOI: 10.2991/978-94-6239-711-8_9
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