Semi-supervised time series classification method for quantum computing

Quantum Machine Intelligence, vol. 3

Abstract

Abstract 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.

Authors 1

1. Florian Neukart Aachen

   Leiden University · Volkswagen Group (United States)

   Affiliation as printed

   LIACS, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, Netherlands

   Volkswagen Group of America, San Francisco, CA, USA