Semi-supervised time series classification method for quantum computing
This addresses time series analysis problems for quantum computing applications, but it appears incremental as it adapts existing quantum methods to a specific domain.
The paper tackles time series reconstruction and classification using quantum computing by formulating reconstruction as a QUBO problem via discretization and set cover, then extends it to semi-supervised classification. Results show the method is competitive with classical techniques while using less data.
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.