PIRSA:22050030

Video URL

https://pirsa.org/22050030

Reducing the Sign Problem with Complex Neural Networks

Ostmeyer, J. (2022). Reducing the Sign Problem with Complex Neural Networks. Perimeter Institute for Theoretical Physics. https://pirsa.org/22050030

Ostmeyer, Johann. Reducing the Sign Problem with Complex Neural Networks. Perimeter Institute for Theoretical Physics, May. 16, 2022, https://pirsa.org/22050030 

          @misc{ scivideos_PIRSA:22050030,
            doi = {10.48660/22050030},
            url = {https://pirsa.org/22050030},
            author = {Ostmeyer, Johann},
            keywords = {Quantum Matter},
            language = {en},
            title = {Reducing the Sign Problem with Complex Neural Networks},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {may},
            note = {PIRSA:22050030 see, \url{https://scivideos.org/pirsa/22050030}}
          }

Johann Ostmeyer University of Liverpool

Talk numberPIRSA:22050030
DOI10.48660/22050030
Source RepositoryPIRSA
Collection
Talk Type Conference
Subject

Abstract

The sign problem is arguably the greatest weakness of the otherwise highly efficient, non-perturbative Monte Carlo simulations. Recently, considerable progress has been made in alleviating the sign problem by deforming the integration contour of the path integral into the complex plane and applying machine learning to find near-optimal alternative contours. This deformation however requires a Jacobian determinant calculation which has a generic computational cost scaling as volume cubed. In this talk I am going to present a new architecture with linear runtime, based on complex-valued affine coupling layers.