ABSTRACT
Tensor network states are new kinds of variational wavefunctions that help us to understand quantum phases and phase transitions beyond Landau paradigm. In this talk, I will first review the major development of tensor network simulation in the past two decades. In particular, I will introduce the novel concept of long-range entanglement and entanglement renormalization. Then I will discuss the major breakthroughs made by tensor network simulations in recent years. If time permits, I will also mention the relationship between deep learning and tensor network simulation.
BIOGRAPHY
Prof. Zhengcheng Gu received his bachelor’s degree from Tsinghua University in 2003 and his PhD in Physics from the Center for Advanced Study of Tsinghua University in 2008. He then spent a few years as a postdoc in MIT, an Associate Specialist in the Kavli Institute of Theoretical Physics (UCSB), and the Director’s Fellow in Perimeter Institute, respectively. In 2016 he joined the Chinese University of Hong Kong as an Associate Professor. Prof. Gu has a broad research interest, which includes theoretical physics, condensed matter physics, high-energy physics, and particle physics. In particular, Prof. Gu is a world expert on topological phases in interacting bosonic and fermionic systems and the application of tensor network simulation for strongly correlated systems.
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