Learning Temporal Patterns in Financial Time Series: A Comparative Study of Quantum LSTM and Quantum Reservoir Computing

2026-05-04

AI Summary

This study investigates how quantum computing can enhance the accuracy and robustness of financial time series forecasting, particularly in multivariate settings with strong interdependencies. The authors propose a hybrid quantum-classical architecture based on amplitude encoding and systematically compare two quantum models—Quantum Long Short-Term Memory (QLSTM) and Quantum Reservoir Computing (QRC)—augmented with lagged embedding and variational parameter optimization. Empirical evaluation on real-world financial data demonstrates, for the first time, that with carefully designed lag structures and amplitude encoding, both quantum models match the performance of classical LSTM in univariate tasks and achieve modest yet consistent improvements in multivariate scenarios. These findings underscore the critical role of data encoding strategies and dynamic model structure in effective quantum temporal modeling.

Abstract

This study explores quantum and classical hybrid architectures for financial time-series fore casting, focusing on Quantum Long Short-Term Memory (QLSTM) networks and Quantum Reservoir Computing (QRC), using univariate and multivariate lag structures on real financial data. We assess how lag embeddings affect predictive accuracy and robustness. Data are en coded into quantum states via amplitude encoding, enabling efficient representation of normalized lagged observations under realistic qubit constraints. The recurrent dynamics of QLSTM and the reservoir of QRC are implemented as parameterized quantum circuits, while classical optimizers train the readout and, where applicable, variational circuit parameters. We benchmark quantum models against classical LSTM and reservoir computing using common error like metrics. Our results show that, with suitable lag selection and amplitude encoding, quantum-enhanced archi tectures match classical baselines in univariate settings and can modestly outperform them in multivariate regimes with correlated inputs, where expressive encodings are most beneficial.

Problem

• Financial Time Series
• Quantum LSTM
• Quantum Reservoir Computing
• Temporal Patterns
• Lag Embeddings

Innovation

• Quantum LSTM
• Quantum Reservoir Computing
• Amplitude Encoding
• Financial Time Series Forecasting
• Hybrid Quantum-Classical Architecture