Video URL
https://pirsa.org/16120022Tensor Network Algorithms for 2D Strongly Correlated Systems
APA
Osorio, J. (2016). Tensor Network Algorithms for 2D Strongly Correlated Systems. Perimeter Institute for Theoretical Physics. https://pirsa.org/16120022
MLA
Osorio, Juan. Tensor Network Algorithms for 2D Strongly Correlated Systems. Perimeter Institute for Theoretical Physics, Dec. 14, 2016, https://pirsa.org/16120022
BibTex
@misc{ scivideos_PIRSA:16120022, doi = {10.48660/16120022}, url = {https://pirsa.org/16120022}, author = {Osorio, Juan}, keywords = {Other Physics}, language = {en}, title = {Tensor Network Algorithms for 2D Strongly Correlated Systems}, publisher = {Perimeter Institute for Theoretical Physics}, year = {2016}, month = {dec}, note = {PIRSA:16120022 see, \url{https://scivideos.org/pirsa/16120022}} }
Juan Osorio ETH Zurich
Abstract
In this talk I will give a short introduction into Projected Entangled-Pair States (PEPS), and their infinite variant iPEPS, a class of tensor network Ansatz targeted at the simulation of 2D strongly correlated systems. I will present work on two recent
projects: the first will be an application of the iPEPS algorithm to a Kitaev-Heisenberg model, a model which through-out recent years has received a lot of attention due to its potential connection to the physics of a subclass of the so-called Iridate compounds. The second will be work related to the development of the iPEPS method to specifically target cylindrical geometries. Here I will present some preliminary results where we apply the methods to the Heisenberg and Fermi-Hubbard models and evaluate their performance in comparison to infinite Matrix Product States. As a final part of my talk I will, depending on time, elaborate somewhat on potential future topics including (but not restricted to): the main challenges of iPEPS simulations from a numerical perspective and what pre-steps we have experimented with to tackle these, the possibility of applying recent proposals for finite-temperature calculations within the PEPS framework to frustrated spin systems and the use of Tensor Network Renormalization for the study of RG flows.