Authors:
Rebh Soltani
1
;
Emna Benmohamed
2
and
Hela Ltifi
3
Affiliations:
1
Research Groups in Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia
;
2
Department of Cyber Security, College of Engineering and Information Technology, Onaizah Colleges, P.O. Box 5371, Onaizah, K.S.A.
;
3
Computer Science and Mathematics Department, Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Tunisia
Keyword(s):
Echo State Network, Quantum Computing, Reservoir Computing, Quanvolution Filter.
Abstract:
Quantum Machine Learning (QML) combines quantum physics with machine learning techniques to enhance algorithm performance. By leveraging the unique properties of quantum computing, such as superposition and entanglement, QML aims to solve complex problems beyond the capabilities of classical computing. In this study, we developed a hybrid model, the quantum convolutional Echo State Network, which incorporates QML principles into the Reservoir Computing framework. Evaluating its performance on benchmark time-series datasets, we observed improved results in terms of mean square error (MSE) and reduced time complexity compared to the classical Echo State Network (ESN). These findings highlight the potential of QML to advance time-series prediction and underscore the benefits of merging quantum and machine learning approaches.