Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum Annealing for Semi-Supervised Learning   Recent advances in quantum technology have led to the development and the
manufacturing of programmable quantum annealers that promise to solve certain
combinatorial optimization problems faster than their classical counterparts.
Semi-supervised learning is a machine learning technique that makes use of both
labeled and unlabeled data for training, which enables a good classifier with
only a small amount of labeled data. In this paper, we propose and
theoretically analyze a graph-based semi-supervised learning method with the
aid of the quantum annealing technique, which efficiently utilize the quantum
resources while maintaining a good accuracy. We illustrate two classification
examples, suggesting the feasibility of this method even with a small portion
(20%) of labeled data is involved.

31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th