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

被引:2
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
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共 45 条
  • [1] Quantum Boltzmann Machine
    Amin, Mohammad H.
    Andriyash, Evgeny
    Rolfe, Jason
    Kulchytskyy, Bohdan
    Melko, Roger
    [J]. PHYSICAL REVIEW X, 2018, 8 (02):
  • [2] Quantum supremacy using a programmable superconducting processor
    Arute, Frank
    Arya, Kunal
    Babbush, Ryan
    Bacon, Dave
    Bardin, Joseph C.
    Barends, Rami
    Biswas, Rupak
    Boixo, Sergio
    Brandao, Fernando G. S. L.
    Buell, David A.
    Burkett, Brian
    Chen, Yu
    Chen, Zijun
    Chiaro, Ben
    Collins, Roberto
    Courtney, William
    Dunsworth, Andrew
    Farhi, Edward
    Foxen, Brooks
    Fowler, Austin
    Gidney, Craig
    Giustina, Marissa
    Graff, Rob
    Guerin, Keith
    Habegger, Steve
    Harrigan, Matthew P.
    Hartmann, Michael J.
    Ho, Alan
    Hoffmann, Markus
    Huang, Trent
    Humble, Travis S.
    Isakov, Sergei V.
    Jeffrey, Evan
    Jiang, Zhang
    Kafri, Dvir
    Kechedzhi, Kostyantyn
    Kelly, Julian
    Klimov, Paul V.
    Knysh, Sergey
    Korotkov, Alexander
    Kostritsa, Fedor
    Landhuis, David
    Lindmark, Mike
    Lucero, Erik
    Lyakh, Dmitry
    Mandra, Salvatore
    McClean, Jarrod R.
    McEwen, Matthew
    Megrant, Anthony
    Mi, Xiao
    [J]. NATURE, 2019, 574 (7779) : 505 - +
  • [3] Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
    Benedetti, Marcello
    Realpe-Gomez, John
    Perdomo-Ortiz, Alejandro
    [J]. QUANTUM SCIENCE AND TECHNOLOGY, 2018, 3 (03):
  • [4] Quantum machine learning
    Biamonte, Jacob
    Wittek, Peter
    Pancotti, Nicola
    Rebentrost, Patrick
    Wiebe, Nathan
    Lloyd, Seth
    [J]. NATURE, 2017, 549 (7671) : 195 - 202
  • [5] Characterizing quantum supremacy in near-term devices
    Boixo, Sergio
    Isakov, Sergei, V
    Smelyanskiy, Vadim N.
    Babbush, Ryan
    Ding, Nan
    Jiang, Zhang
    Bremner, Michael J.
    Martinis, John M.
    Neven, Hartmut
    [J]. NATURE PHYSICS, 2018, 14 (06) : 595 - 600
  • [6] Buza K., 2010, Proceedings 2010 IEEE 13th International Conference on Computational Science and Engineering (CSE 2010), P48, DOI 10.1109/CSE.2010.16
  • [7] A deep learning framework for time series classification using Relative Position Matrix and Convolutional Neural Network
    Chen, Wei
    Shi, Ke
    [J]. NEUROCOMPUTING, 2019, 359 : 384 - 394
  • [8] Quantum convolutional neural networks
    Cong, Iris
    Choi, Soonwon
    Lukin, Mikhail D.
    [J]. NATURE PHYSICS, 2019, 15 (12) : 1273 - +
  • [9] Cui ZC, 2016, Arxiv, DOI arXiv:1603.06995
  • [10] Quantum generative adversarial networks
    Dallaire-Demers, Pierre-Luc
    Killoran, Nathan
    [J]. PHYSICAL REVIEW A, 2018, 98 (01)