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Bell Non-locality Using Tensor Networks and Sparse Recovery by Eliëns, I. S.; Brito, S. G. A.; Chaves, R. is a scholarly article available to read on EtoBox.

What is Bell Non-locality Using Tensor Networks and Sparse Recovery about?

Bell's theorem, stating that quantum predictions are incompatible with a local hidden variable description, is a cornerstone of quantum theory and at the center of many quantum information processing protocols. Over the years, different perspectives on non-locality have been put forward as well as different ways to to detect non-locality and quantify it. Unfortunately and in spite of its relevance, as the complexity of the Bell scenario increases, deciding whether a given observed correlation is non-local becomes computationally intractable. Here, we propose to analyse a Bell scenario as a tensor network, a perspective permitting to test and quantify non-locality resorting to very efficient algorithms originating from compressed sensing and that offer a significant speedup in comparison with standard linear programming methods. Furthermore, it allows to prove that non-signalling correlations can be described by hidden variable models governed by a quasi-probability.

Author

Eliëns, I. S.; Brito, S. G. A.; Chaves, R.

Published

2020

Language

EN