Proofs of network quantum nonlocality aided by machine learning
- URL: http://arxiv.org/abs/2203.16543v1
- Date: Wed, 30 Mar 2022 18:00:00 GMT
- Title: Proofs of network quantum nonlocality aided by machine learning
- Authors: Alejandro Pozas-Kerstjens, Nicolas Gisin, Marc-Olivier Renou
- Abstract summary: We show that the family of quantum triangle distributions of [DOI40103/PhysRevLett.123.140] did not admit triangle-local models in a larger range than the original proof.
We produce a large collection of network Bell inequalities for the triangle scenario with binary outcomes, which are of independent interest.
- Score: 68.8204255655161
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The study of nonlocality in scenarios that depart from the bipartite
Einstein-Podolsky-Rosen setup is allowing to uncover many fundamental features
of quantum mechanics. Recently, an approach to building network-local models
based on machine learning lead to the conjecture that the family of quantum
triangle distributions of [DOI:10.1103/PhysRevLett.123.140401] 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 in reducing the
family of original, four-outcome distributions to families of binary-outcome
ones, which we prove not to admit triangle-local models by means of the
inflation technique. In the process, we produce a large collection of network
Bell inequalities for the triangle scenario with binary outcomes, which are of
independent interest.
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