首页|Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
  • 维普
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.

quantum neural networkstime series classificationtime-series imagesfeature fusion

谢建设、董玉民

展开 >

College of Computer and Information Science,Chongqing Normal University,Chongqing 401331,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaPHD foundation of Chongqing Normal UniversityChongqing Technology Foresight and Institutional Innovation Project

617722956157227019XLB003cstc2021jsyjyzysbAX0011

2023

10.1088/1674-1056/accb45

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDCSCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2023.32(12)
  • 45