Quantum-Enhanced Deep Learning for Financial Anomaly Detection

A Hybrid Theoretical Framework for MENA Financial Markets

DOI

10.2991/978-94-6239-711-8_9How to use a DOI?

Keywords

Quantum Machine Learning; Financial Anomaly Detection; Hybrid Quantum-Classical Models; Variational Quantum Circuits; NISQ Computing; MENA Region; High-Frequency Trading

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 (< 0.5% anomaly rate), and distance-based 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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rayane Aggoune
AU  - Abdelhak Merizig
PY  - 2026
DA  - 2026/06/24
TI  - Quantum-Enhanced Deep Learning for Financial Anomaly Detection
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 78
EP  - 89
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_9
DO  - 10.2991/978-94-6239-711-8_9
ID  - Aggoune2026
ER  -