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In this paper we develop methods to solve two problems related to time series(TS) analysis using quantum computing: reconstruction and classification. Weformulate the task of reconstructing a given TS from a training set of data asan unconstrained binary optimization (QUBO) problem, which can be solved byboth quantum annealers and gate-model quantum processors. We accomplish this bydiscretizing the TS and converting the reconstruction to a set cover problem,allowing us to perform a one-versus-all method of reconstruction. Using thesolution to the reconstruction problem, we show how to extend this method toperform semi-supervised classification of TS data. We present resultsindicating our method is competitive with current semi- and unsupervisedclassification techniques, but using less data than classical techniques.

Semi-supervised time series classification method for quantum computing