报告人:谢志远 副教授 （中国人民大学）
时间：2018年7月11号（星期三）下午2:30
地点：物理学院LE201（理论物理平台）
Abstract: In the tensor-network framework, the expectation values of two-dimensional quantum states or three-dimensional classical model are evaluated by contracting a double-layer tensor network constructed from initial and final tensor-network states. The computational cost of carrying out this contraction is generally very high, which limits the largest bond dimension of tensor-network states that can be accurately studied to a relatively small value. We propose Nested Tensor Network Method to solve this problem by mapping the double-layer tensor network onto an intersected single-layer tensor network. This reduces greatly the bond dimensions of local tensors to be contracted and improves dramatically the efficiency and accuracy of the evaluation of expectation values of tensor-network states. It almost doubles the largest bond dimension of tensor-network states whose physical properties can be efficiently and reliably calculated, and it extends significantly the application scope of tensor-network methods. We demonstrate its performance by the application in the spin-1/2 Kagome anti-ferromagnet system and the Ising model at simple cubic lattice.
Brief CV:
I graduated from Harbin Institute of Technology in 2007, and obtained my Ph.D. in 2012 at Institute of Theoretical Physics, Chinese Academy of Sciences (CAS). Then after that I moved to Institute of Physics, CAS, to do postdoctoral research. From 2015, I joined the department of physics at Renmin University of China. My main interest lies in developing effective numerical algorithms and applying them to study the rich physics in strongly correlated systems, such as frustrated quantum magnets, high-Tc superconductivity, and classical critical systems, etc.
