Quantum annealing for semi-supervised learning

被引:0
作者
Zheng, Yu-Lin [1 ]
Zhang, Wen [1 ]
Zhou, Cheng [1 ]
Geng, Wei [1 ]
机构
[1] Huawei Technol Co Ltd, Hisilicon Res, Shenzhen, Peoples R China
关键词
quantum annealing; semi-supervised learning; machine learning; REDUCTION;
D O I
10.1088/1674-1056/abe298
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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 utilizes the quantum resources while maintaining good accuracy. We illustrate two classification examples, suggesting the feasibility of this method even with a small portion (30%) of labeled data involved.
引用
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页数:7
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