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2篇 您的检索式：作者名="Daryl Ryan Chong"

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		题名	作者	年代	出处	被引量
	1	Machine learning identification of symmetrized base states of Rydberg atoms显示文摘Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.	Daryl Ryan Chong Minhyuk Kim Jaewook Ahn Heejeong Jeong	2022	Frontiers of physics2022，17，1：	1
	2	Erratum to: Machine learning identification of symmetrized base states of Rydberg atoms显示文摘ERRATUM TO:Front.Phys.17(1),12504(2022),http://gffzzd3cc09b8251d45dfsv6p5bqxqp60k6fwu.ffgz.tsg.suse.edu.cn/10.1007/s11467-021-1099-0.The online version of the original article can be found at http://gffzzd3cc09b8251d45dfsv6p5bqxqp60k6fwu.ffgz.tsg.suse.edu.cn/10.1007/s11467-021-1099-0 and http://gffzzb86625cb60604fc3hv6p5bqxqp60k6fwu.ffgz.tsg.suse.edu.cn/fop/EN/10.1007/s11467-021-1099-0.The x-axis of Fig.3(b)should be labelled'Observation duration'instead of'Total evolution time'as shown be-low.	Daryl Ryan Chong Minhyuk Kim Jaewook Ahn Heejeong Jeong	2022	Frontiers of physics2022，17，1：	0

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