摘要
机器学习方法近年来在许多不同的领域受到广泛的关注,文章回顾了机器学习方法在量子多体物理中的应用的几个有代表性的例子,着重讨论了机器学习方法对于解决量子多体物理中"指数墙"困难的可能的潜在意义。除此以外,量子多体物理中的若干方法和思想反过来可能对理解机器学习领域面临的核心问题有重要的启发作用。
Machine learning methods have attracted considerable interest in various fields. In this paper we review recent progress in the application of machine learning methods to quantum many-body physics. Special attention is devoted to the potential relation between the ＂exponential wall＂ difficulties in this field of physics and machine learning methods. We also discuss the possi- bility that progress in quantum many-body physics will also be beneficial to the machine learning community.
出处
《物理》
北大核心
2017年第9期590-596,共7页
Physics
关键词
机器学习
量子多体物理
人工神经网络
指数墙困难
machine learning, quantum many-body physics, artificial neural network, expo-nential wall difficulty
