PIRSA:19070030

Video URL

https://pirsa.org/19070030

Machine learning phase discovery in quantum gas microscope images

Khatami, E. (2019). Machine learning phase discovery in quantum gas microscope images. Perimeter Institute for Theoretical Physics. https://pirsa.org/19070030

Khatami, Ehsan. Machine learning phase discovery in quantum gas microscope images. Perimeter Institute for Theoretical Physics, Jul. 11, 2019, https://pirsa.org/19070030 

          @misc{ scivideos_PIRSA:19070030,
            doi = {10.48660/19070030},
            url = {https://pirsa.org/19070030},
            author = {Khatami, Ehsan},
            keywords = {Quantum Matter},
            language = {en},
            title = {Machine learning phase discovery in quantum gas microscope images},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {jul},
            note = {PIRSA:19070030 see, \url{https://scivideos.org/index.php/pirsa/19070030}}
          }

Ehsan Khatami San Jose State University

Talk numberPIRSA:19070030
DOI10.48660/19070030
Source RepositoryPIRSA
Collection
Talk Type Conference
Subject

Abstract

Site resolution in quantum gas microscopes for ultracold atoms in optical lattices have transformed quantum simulations of many-body Hamiltonians. Statistical analysis of atomic snapshots can produce expectation values for various charge and spin correlation functions and have led to new discoveries for the Hubbard model in two dimensions. Conventional approaches, however, fail in general when the order parameter is not known or when an expected phase has no clear signatures in the density basis. In this talk, I will introduce our efforts in using machine learning techniques to overcome this challenge with snapshots of fermionic atoms. Collaborators: Richard Scalettar (UC Davis), Waseem Bakr (Princeton), and Juan Carrasquilla (Vector Institute)