LAUSR

Time series classification (TSC) has attracted great attention in time series data mining tasks and has been applied in various fields. With the success of deep learning (DL) in computer vision recognition, people are starting to combine deep learning to tackle TSC tasks. Quantum neural networks (QNN) have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing, but research combining quantum neural networks to handle TSC tasks has not received enough attention. Therefore, we propose a learning framework based on multiple imaging and hybrid QNN (MIHQNN) for the TSC task. We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN. We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging. Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks. We tested our method on several standard datasets and achieved significant results compared to several current TSC methods, demonstrating the effectiveness of MIHQNN. This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.