Hybrid Quanvolutional Echo State Network for Time Series Prediction

Rebh Soltani, Emna Benmohamed, Hela Ltifi

2024

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.

Paper Citation

in Harvard Style

Soltani R., Benmohamed E. and Ltifi H. (2024). Hybrid Quanvolutional Echo State Network for Time Series Prediction. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 40-46. DOI: 10.5220/0012271600003636

in Bibtex Style

@conference{icaart24,
author={Rebh Soltani and Emna Benmohamed and Hela Ltifi},
title={Hybrid Quanvolutional Echo State Network for Time Series Prediction},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={40-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012271600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Hybrid Quanvolutional Echo State Network for Time Series Prediction
SN - 978-989-758-680-4
AU - Soltani R.
AU - Benmohamed E.
AU - Ltifi H.
PY - 2024
SP - 40
EP - 46
DO - 10.5220/0012271600003636
PB - SciTePress