Semi-supervised time series classification method for quantum computing

Springer Science and Business Media LLC - Tập 3 - Trang 1-11 - 2021
Sheir Yarkoni1,2, Andrii Kleshchonok1, Yury Dzerin1, Florian Neukart2,3, Marc Hilbert1
1Volkswagen Data:Lab, Munich, Germany
2LIACS, Leiden University, Leiden, Netherlands
3Volkswagen Group of America, San Francisco, USA

Tóm tắt

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.

Tài liệu tham khảo

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