Variational quantum semi-supervised classifier based on label propagation  

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作  者:侯艳艳 李剑 陈秀波 叶崇强 Yan-Yan Hou;Jian Li;Xiu-Bo Chen;Chong-Qiang Ye(College of Information Science and Engineering,ZaoZhuang University,Zaozhuang 277160,China;School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China;Information Security Center,State Key Laboratory Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China)

机构地区:[1]College of Information Science and Engineering,ZaoZhuang University,Zaozhuang 277160,China [2]School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China [3]School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China [4]Information Security Center,State Key Laboratory Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China

出  处:《Chinese Physics B》2023年第7期279-289,共11页中国物理B(英文版)

基  金:Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202108);the National Natural Science Foundation of China(Grant No.U162271070);Scientific Research Fund of Zaozhuang University(Grant No.102061901).

摘  要:Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.

关 键 词:semi-supervised learning variational quantum algorithm parameterized quantum circuit 

分 类 号:O413[理学—理论物理] TP181[理学—物理]

 

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