Semi-supervised time series classification method for quantum computing

被引：0
作者：
Sheir Yarkoni
Andrii Kleshchonok
Yury Dzerin
Florian Neukart
Marc Hilbert
机构：
[1] Volkswagen Data:Lab,LIACS
[2] Leiden University,undefined
[3] Volkswagen Group of America,undefined
来源：
Quantum Machine Intelligence | 2021年 / 3卷
关键词：
Quantum computing; Quantum annealing; Quantum machine learning; Classification;
D O I：
暂无
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学科分类号：
摘要：
In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.
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共 149 条
[1]  
Aghabozorgi S(2015)Time-series clustering – a decade review Inf Syst 53 16-38
[2]  
Shirkhorshidi AS(2008)Adiabatic quantum computation is equivalent to standard quantum computation SIAM Rev 50 755-787
[3]  
Wah TY(2013)Quantum speed-up for unsupervised learning Mach Learn 90 261-287
[4]  
Aharonov D(2003)Mit-bih long-term ecg database. physionet.org/content/ltdb/1.0.0/. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals Circulation 101 e215-e220
[5]  
Van Dam W(2019)Quantum supremacy using a programmable superconducting processor Nature 574 505-510
[6]  
Kempe J(2017)The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances Data Mining Knowl Disc 31 606-660
[7]  
Landau Z(1982)On the computational complexity of ising spin glass models J Phys A Math Gen 15 3241-77
[8]  
Lloyd S(2018)Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package) Neurocomputing 307 72-368
[9]  
Regev O(1999)Optimal reconstruction of a sequence from its probes J Comput Biol 6 361-181
[10]  
Aimeur E(2011)A review on time series data mining Eng Appl Artif Intell 24 164-18
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