A Variational Quantum Neural Network Framework for Learning Asymmetric Patterns and Anomaly Detection in High-Frequency Financial and Economic Systems

Abstract

Financial markets are complex nonlinear systems characterized by high-dimensional interactions, temporal dependencies, and rapidly evolving asymmetric patterns. Detecting hidden structures and abnormal behaviors in high-frequency financial data remains a challenging problem due to market instability and dynamic uncertainty. This study proposes an innovative quantum neural network (QNN)-based framework for adaptive forecasting and anomaly detection by exploiting quantum representation learning for complex financial systems. The proposed architecture is developed in the Qiskit environment using a hybrid classical-quantum optimization strategy based on a Variational Quantum Circuit (VQC). A quantum feature-mapping mechanism transforms multidimensional financial variables into quantum states, enabling extraction of latent relationships and structural patterns from high-dimensional data. A parameterized quantum circuit is optimized using the ADAM algorithm within a classical-quantum learning loop. The framework consists of two stages: first, multivariate financial dynamics are forecasted using market microstructure, volatility-related, and macroeconomic indicators obtained from the Electronic Data Distribution System (EDDS) of the Central Bank of the Republic of Türkiye; second, deviations between predicted and observed states are analyzed for anomaly identification. Comprehensive experiments on the BIST All-100 Index, trading volume, and monetary indicators demonstrate that the proposed QNN model achieves superior performance compared with ANN, LSTM, Bi-LSTM, LSTM-GRU, CNN, and Transformer-based approaches. The findings indicate that quantum-enhanced learning provides an effective computational framework for modeling complex asymmetric patterns and discovering hidden structures in dynamic systems.