Proofs of Network Quantum Nonlocality in Continuous Families of Distributions

The study of nonlocality in scenarios that depart from the bipartite Einstein-Podolsky-Rosen setup is allowing one to uncover many fundamental features of quantum mechanics. Recently, an approach to building network-local models based on machine learning led to the conjecture that the family of quantum triangle distributions of [Renou et al., Phys. Rev. Lett. 123, 140401 (2019)] did not admit triangle-local models in a larger range than the original proof. We prove part of this conjecture in the affirmative. Our approach consists of reducing the family of original, four-outcome distributions to families of binary-outcome ones, and then using the inflation technique to prove that these families of binary-outcome distributions do not admit triangle-local models. This constitutes the first successful use of inflation in a proof of quantum nonlocality in networks for distributions whose nonlocality could not be proved with alternative methods. Moreover, we provide a method to extend proofs of network nonlocality in concrete distributions of a parametrized family to continuous ranges of the parameter. In the process, we produce a large collection of network Bell inequalities for the triangle scenario with binary outcomes, which are of independent interest.Received 9 April 2022Revised 13 October 2022Accepted 18 January 2023DOI:https://doi.org/10.1103/PhysRevLett.130.090201© 2023 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasNonlocalityQuantum correlations in quantum informationQuantum information theoryQuantum networksQuantum Information, Science & TechnologyNetworks