Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised time series classification method for quantum computing   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.

37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat