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Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks |
| Jianshe Xie(谢建设) and Yumin Dong(董玉民)† |
| College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China |
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Abstract Time series classification (TSC) has attracted a lot of attention for 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 use 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 using quantum neural networks to handle TSC tasks has not received enough attention. Therefore, we proposed a learning framework based on multiple imaging and hybrid QNN (MIHQNN) for TSC tasks. 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.
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Received: 20 January 2023
Revised: 19 March 2023
Accepted manuscript online: 07 April 2023
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PACS:
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03.67.-a
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(Quantum information)
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03.67.Ac
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(Quantum algorithms, protocols, and simulations)
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03.67.Lx
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(Quantum computation architectures and implementations)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos.61772295 and 61572270), the PHD foundation of Chongqing Normal University (Grant No.19XLB003), and Chongqing Technology Foresight and Institutional Innovation Project (Grant No.cstc2021jsyj-yzysbAX0011). |
Corresponding Authors:
Yumin Dong
E-mail: dym@cqnu.edu.cn
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Cite this article:
Jianshe Xie(谢建设) and Yumin Dong(董玉民) Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks 2023 Chin. Phys. B 32 120302
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