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.
Cite
@article{arxiv.2006.11031,
title = {Semi-supervised time series classification method for quantum computing},
author = {Sheir Yarkoni and Andrii Kleshchonok and Yury Dzerin and Florian Neukart and Marc Hilbert},
journal= {arXiv preprint arXiv:2006.11031},
year = {2021}
}