PIRSA:21030016

Quantum many-body dynamics in two dimensions with artificial neural networks

APA

Heyl, M. (2021). Quantum many-body dynamics in two dimensions with artificial neural networks. Perimeter Institute. https://pirsa.org/21030016

MLA

Heyl, Markus. Quantum many-body dynamics in two dimensions with artificial neural networks. Perimeter Institute, Mar. 05, 2021, https://pirsa.org/21030016

BibTex

          @misc{ pirsa_21030016,
            doi = {},
            url = {https://pirsa.org/21030016},
            author = {Heyl, Markus},
            keywords = {Quantum Information},
            language = {en},
            title = {Quantum many-body dynamics in two dimensions with artificial neural networks},
            publisher = {Perimeter Institute},
            year = {2021},
            month = {mar},
            note = {PIRSA:21030016 see, \url{https://pirsa.org}}
          }
          

Abstract

In the last two decades the field of nonequilibrium quantum many-body physics
has seen a rapid development driven, in particular, by the remarkable progress
in quantum simulators, which today provide access to dynamics in quantum
matter with an unprecedented control. However, the efficient numerical
simulation of nonequilibrium real-time evolution in isolated quantum matter
still remains a key challenge for current computational methods especially
beyond one spatial dimension. In this talk I will present a versatile and
efficient machine learning inspired approach. I will first introduce the
general idea of encoding quantum many-body wave functions into artificial
neural networks. I will then identify and resolve key challenges for the
simulation of real-time evolution, which previously imposed significant
limitations on the accurate description of large systems and long-time
dynamics. As a concrete example, I will consider the dynamics of the
paradigmatic two-dimensional transverse field Ising model, where we observe
collapse and revival oscillations of ferromagnetic order and demonstrate that
the reached time scales are comparable to or exceed the capabilities of state-
of-the-art tensor network methods.