Semi-supervised time series classification method for quantum computing

TL;DR

This paper introduces a quantum computing-based semi-supervised method for time series reconstruction and classification, leveraging QUBO formulation and discretization, showing competitive results with less data than classical methods.

Contribution

It develops a novel quantum approach to time series analysis, formulating reconstruction as a QUBO problem and extending it to semi-supervised classification.

Findings

Method is competitive with current techniques.

Uses less data than classical methods.

Applicable to quantum annealers and gate-model processors.

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.