  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.
