PIRSA:17110108

Renormalization of tensor networks using graph independent local truncations

Hauru, M. (2017). Renormalization of tensor networks using graph independent local truncations. Perimeter Institute. https://pirsa.org/17110108

Hauru, Markus. Renormalization of tensor networks using graph independent local truncations. Perimeter Institute, Nov. 06, 2017, https://pirsa.org/17110108 

          @misc{ pirsa_PIRSA:17110108,
            doi = {10.48660/17110108},
            url = {https://pirsa.org/17110108},
            author = {Hauru, Markus},
            keywords = {Condensed Matter},
            language = {en},
            title = {Renormalization of tensor networks using graph independent local truncations},
            publisher = {Perimeter Institute},
            year = {2017},
            month = {nov},
            note = {PIRSA:17110108 see, \url{https://pirsa.org}}
          }

Markus Hauru

The Alan Turing Institute

Talk number
PIRSA:17110108
Collection
Talk Type
Subject
Abstract

I will describe our recent work from 1709.07460, where we introduce a new renormalization group algorithm for tensor networks. The algorithm is based on a novel understanding of local correlations in a tensor network, and a simple method to remove such correlations from any network. It performs comparably with the best competing algorithms on 2D/(1+1)D systems, but is significantly simpler to implement, and easier to generalize to different lattices and graphs, including to higher dimensions. I will begin the talk by discussing renormalization group methods for tensor networks in general, then describe our algorithm and its advantages, show some benchmark results, and finally comment on the status of implementing real-space renormalization for 3D tensor networks.