Title: Tensor network skeletonization
Speaker: Lexing Ying (Stanford University)
Abstract: This talk introduces tensor network skeletonization, which is a new coarse-graining algorithm for effective computing and transforming tensor networks, with applications in studying partition functions in statistical mechanics, Euclidean path integral of quantum many-body systems, and computational problems in graphical models. Building on top of recent work in tensor networks, this new algorithm introduces a novel skeletonization step for removing local short-range correlations. This key step allows for the bond dimension low so that the computation can be carried out efficiently without sacrificing the accuracy. Numerical examples are provided to demonstrate the effectiveness of the this approach.
