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Variational quantum semi-supervised classifier based on label propagation

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【Author】 Yan-Yan Hou;Jian Li;Xiu-Bo Chen;Chong-Qiang Ye;College of Information Science and Engineering, Zao Zhuang University;School of Artificial Intelligence, Beijing University of Posts and Telecommunications;School of Cyberspace Security, Beijing University of Posts and Telecommunications;Information Security Center, State Key Laboratory Networking and Switching Technology,Beijing University of Posts and Telecommunications;

【通讯作者】李剑;

【机构】 College of Information Science and Engineering, Zao Zhuang University； School of Artificial Intelligence, Beijing University of Posts and Telecommunications； School of Cyberspace Security, Beijing University of Posts and Telecommunications； Information Security Center, State Key Laboratory Networking and Switching Technology,Beijing University of Posts and Telecommunications；

【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.更多还原

【Abstract】 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.更多还原

【关键词】 semi-supervised learning； variational quantum algorithm； parameterized quantum circuit；
【Key words】 semi-supervised learning； variational quantum algorithm； parameterized quantum circuit；

【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)

【文献出处】 Chinese Physics B ,中国物理B , 编辑部邮箱 ,2023年07期

【分类号】O413;TP181
【下载频次】2

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