题目：Tensor Network Holography and Deep Learning

　　报告人：尤亦庄，博士（哈佛大学）

　　时  间：2017年7月10日(周一)下午3:00

　　地  点：best365注册 中心教学楼610

　　Abstract:

　　The close connection between holographic duality and entanglement renormalization group (RG) has been investigated recently. One perspective to understand the connection is to formulate the entanglement RG transformation as a tensor network that recursively resolves entanglement structures in a quantum many-body state at larger and larger scales.  The tensor network lives in a higher dimensional holographic bulk where the extra dimension corresponds to the RG scale. On the other hand, the resemblance between deep learning and variational RG was also recently proposed. Deep learning is a class of machine learning algorithms based on the architecture known as the deep neutral network, which is a statistical mechanics model on a hierarchical network that can be trained to encode input data features in the network connectivity. Given the connections of RG with both holographic duality and deep learning, we wish to establish a connection between holographic duality and deep learning. In this talk, I will introduce the recent development of entanglement feature learning on random tensor networks, by which the holographic geometry can emerge by learning the entanglement features in a quantum many-body state.

　　简历：

　　尤亦庄，美国哈佛大学博士后，即将赴加州大学圣迭戈分校任职。博士毕业于清华大学高等研究中心，之后分别于美国加州大学圣塔芭芭拉分校和哈佛大学从事博士后研究。主要领域为凝聚态理论，集中研究关联电子系统和超导理论，在包括Phys. Rev. Lett.在内的国际一流刊物发表论文多篇，受到国际学术界的广泛关注。

　　联系方式：best365平台办公室 （68913163）

　　邀请人：杨帆 教授

　　网    址：http:/