PIRSA:23110074

Video URL

https://pirsa.org/23110074

Network nonlocality and large linear programs

Gitton, V. (2023). Network nonlocality and large linear programs. Perimeter Institute for Theoretical Physics. https://pirsa.org/23110074

Gitton, Victor. Network nonlocality and large linear programs. Perimeter Institute for Theoretical Physics, Nov. 23, 2023, https://pirsa.org/23110074 

          @misc{ scivideos_PIRSA:23110074,
            doi = {10.48660/23110074},
            url = {https://pirsa.org/23110074},
            author = {Gitton, Victor},
            keywords = {Quantum Foundations},
            language = {en},
            title = {Network nonlocality and large linear programs},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {nov},
            note = {PIRSA:23110074 see, \url{https://scivideos.org/index.php/pirsa/23110074}}
          }

Victor Gitton ETH Zurich

Talk numberPIRSA:23110074
DOI10.48660/23110074
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Network nonlocality, and more specifically, triangle network nonlocality, is a basic feature of modern causal modelling when going beyond Bell scenarios. However, despite the apparent simplicity of the problems one may formulate, relatively little is known due to the hardness of certifying nonlocality in networks. In this talk, I will describe a motivating example of a quantum triangle distribution, the Elegant Joint Measurement due to Nicolas Gisin, that is strongly believed to be nonlocal even in the presence of experimental noise. I will then present the ongoing effort to produce a computer-assisted proof of nonlocality for this distribution, thereby developing a toolkit to tackle general nonlocality problems. This effort is based on the inflation technique for causal inference, but taken to higher levels than what was generally considered tractable. This is made possible by a number of optimization techniques, involving symmetry reductions, branch-and-bound optimization, and most importantly, the use of a Frank-Wolfe algorithm to bypass the need to call a standard linear program solver.

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Zoom link https://pitp.zoom.us/j/97499052021?pwd=R1EyU2pmc1hFSzJ1UEpJQ1h0RnQzdz09