Quantum annealing for semi-supervised learning  

作　　者：Yu-Lin Zheng Wen Zhang Cheng Zhou Wei Geng 郑玉鳞;张文;周诚;耿巍(Hisilicon Research,Huawei Technologies Co.,Ltd.,Shenzhen,China)

机构地区：[1]Hisilicon Research,Huawei Technologies Co.,Ltd.,Shenzhen,China

出　　处：《Chinese Physics B》2021年第4期74-80,共7页中国物理B（英文版）

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.

关 键 词：quantum annealing semi-supervised learning machine learning 

分 类 号：O413[理学—理论物理] TP181[理学—物理]