Theory Seminar: Combining Tensor Networks and Monte Carlo for Lattice Gauge Theories

Patrick Emonts (MPQ)
By combining Monte Carlo sampling and Tensor Networks, specifically Gauged Gaussian Projected Entangled Pair States (GGPEPS), we show an efficient way to compute expectation values for lattice gauge theories in two and three spatial dimensions

June 05, 2019

Patrick Emonts (MPQ)
Herbert-Walther Lecture Hall G0.25
Wed 5. June 2019, 11:30 am

Abstract:

By combining Monte Carlo sampling and Tensor Networks, specifically Gauged Gaussian Projected Entangled Pair States (GGPEPS), we show an efficient way to compute expectation values for lattice gauge theories in two and three spatial dimensions. The method can be applied to arbitrary gauge groups, in the talk, however, we will focus on the U(1) gauge group. In the first part of the talk, we will explore the difference between a path integral approach to lattice gauge theories and the Hamiltonian approach that we are using. The second part details the interplay between Monte Carlo sampling and the tensor network construction of a locally gauge invariant state. Further details can be found in arXiv:1710.11013.

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