PIRSA:22100132

Video URL

https://pirsa.org/22100132

Bridging physical intuition and neural networks for variational wave-functions

Valenti, A. (2022). Bridging physical intuition and neural networks for variational wave-functions. Perimeter Institute for Theoretical Physics. https://pirsa.org/22100132

Valenti, Agnes. Bridging physical intuition and neural networks for variational wave-functions. Perimeter Institute for Theoretical Physics, Oct. 14, 2022, https://pirsa.org/22100132 

          @misc{ scivideos_PIRSA:22100132,
            doi = {10.48660/22100132},
            url = {https://pirsa.org/22100132},
            author = {Valenti, Agnes},
            keywords = {Other Physics},
            language = {en},
            title = {Bridging physical intuition and neural networks for variational wave-functions},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {oct},
            note = {PIRSA:22100132 see, \url{https://scivideos.org/pirsa/22100132}}
          }

Agnes Valenti ETH Zurich

Talk numberPIRSA:22100132
DOI10.48660/22100132
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Variational methods have proven to be excellent tools to approximate the ground states of complex many-body Hamiltonians. Generic tools such as neural networks are extremely powerful, but their parameters are not necessarily physically motivated. Thus, an efficient parametrization of the wave function can become challenging. In this talk I will introduce a neural-network-based variational ansatz that retains the flexibility of these generic methods while allowing for a tunability with respect to the relevant correlations governing the physics of the system. I will illustrate the ansatz on a model exhibiting topological phase transitions: The toric code in the presence of magnetic fields. Additionally, I will talk about the use of variational wave functions to gain physical insights beyond lattice models, in particular for the real use-case of two-dimensional materials.