典型文献
Dynamical learning of non-Markovian quantum dynamics
文献摘要：
We study the non-Markovian dynamics of an open quantum system with machine learning.The observable physical quantities and their evolutions are generated by using the neural network.After the pre-training is completed,we fix the weights in the subsequent processes thus do not need the further gradient feedback.We find that the dynamical properties of physical quantities obtained by the dynamical learning are better than those obtained by the learning of Hamiltonian and time evolution operator.The dynamical learning can be applied to other quantum many-body systems,non-equilibrium statistics and random processes.
文献关键词：
中图分类号：
作者姓名：
Jintao Yang;Junpeng Cao;Wen-Li Yang
作者机构：
Beijing National Laboratory for Condensed Matter Physics,Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China;School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;Songshan Lake Materials Laboratory,Dongguan 523808,China;Peng Huanwu Center for Fundamental Theory,Xi'an 710127,China;Institute of Modern Physics,Northwest University,Xi'an 710127,China;School of Physical Sciences,Northwest University,Xi'an 710127,China;Shaanxi Key Laboratory for Theoretical Physics Frontiers,Xi'an 710127,China
文献出处：
引用格式：
[1]Jintao Yang;Junpeng Cao;Wen-Li Yang-.Dynamical learning of non-Markovian quantum dynamics)[J].中国物理B（英文版）,2022(01):189-194
A类：
B类：
Dynamical,learning,Markovian,quantum,dynamics,We,study,open,machine,observable,physical,quantities,their,evolutions,are,generated,by,using,neural,network,After,pre,training,completed,fix,weights,subsequent,processes,thus,not,need,further,gradient,feedback,find,that,dynamical,properties,obtained,better,than,those,Hamiltonian,operator,can,applied,other,many,body,systems,equilibrium,statistics,random
AB值：
0.598957
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