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

被引:3
作者
Xie, Jianshe [1 ]
Dong, Yumin [1 ]
机构
[1] Chongqing Normal Univ, Coll Comp & Informat Sci, Chongqing 401331, Peoples R China
基金
中国国家自然科学基金;
关键词
quantum neural networks; time series classification; time-series images; feature fusion; 03.67.-a; 03.67.Ac; 03.67.Lx; SUPREMACY;
D O I
10.1088/1674-1056/accb45
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 45 条
[11]   Quantum generative adversarial networks [J].
Dallaire-Demers, Pierre-Luc ;
Killoran, Nathan .
PHYSICAL REVIEW A, 2018, 98 (01)
[12]   Quantum circuit architecture search for variational quantum algorithms [J].
Du, Yuxuan ;
Huang, Tao ;
You, Shan ;
Hsieh, Min-Hsiu ;
Tao, Dacheng .
NPJ QUANTUM INFORMATION, 2022, 8 (01)
[13]   RECURRENCE PLOTS OF DYNAMIC-SYSTEMS [J].
ECKMANN, JP ;
KAMPHORST, SO ;
RUELLE, D .
EUROPHYSICS LETTERS, 1987, 4 (09) :973-977
[14]  
Geler Z., 2020, INT C INNOVATIONS IN, P1
[15]  
Hatami N., 2017, 10 INT C MACHINE VIS, V696, P242
[16]   Supervised learning with quantum-enhanced feature spaces [J].
Havlicek, Vojtech ;
Corcoles, Antonio D. ;
Temme, Kristan ;
Harrow, Aram W. ;
Kandala, Abhinav ;
Chow, Jerry M. ;
Gambetta, Jay M. .
NATURE, 2019, 567 (7747) :209-212
[17]   The UCR Time Series Archive [J].
Hoang Anh Dau ;
Bagnall, Anthony ;
Kamgar, Kaveh ;
Yeh, Chin-Chia Michael ;
Zhu, Yan ;
Gharghabi, Shaghayegh ;
Ratanamahatana, Chotirat Ann ;
Keogh, Eamonn .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2019, 6 (06) :1293-1305
[18]   Hybrid quantum-classical convolutional neural network model for COVID-19 prediction using chest X-ray images [J].
Houssein, Essam H. ;
Abohashima, Zainab ;
Elhoseny, Mohamed ;
Mohamed, Waleed M. .
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, 2022, 9 (02) :343-363
[19]   Quantum generative adversarial learning in a superconducting quantum circuit [J].
Hu, Ling ;
Wu, Shu-Hao ;
Cai, Weizhou ;
Ma, Yuwei ;
Mu, Xianghao ;
Xu, Yuan ;
Wang, Haiyan ;
Song, Yipu ;
Deng, Dong-Ling ;
Zou, Chang-Ling ;
Sun, Luyan .
SCIENCE ADVANCES, 2019, 5 (01)
[20]   CondenseNet: An Efficient DenseNet using Learned Group Convolutions [J].
Huang, Gao ;
Liu, Shichen ;
van der Maaten, Laurens ;
Weinberger, Kilian Q. .
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, :2752-2761