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dc.contributor.authorSilva, Askery Alexandre Canabarro Barbosa da-
dc.contributor.authorBrito, Samuraí Gomes de Aguiar-
dc.contributor.authorAraújo, Rafael Chaves Souto-
dc.identifier.citationCANABARRO, Askery; BRITO, Samuraí; CHAVES, Rafael. Machine Learning Nonlocal Correlations. Physical Review Letters, [s.l.], v. 122, n. 20, p. 200401, 22 maio 2019. Disponível em: Acesso em: 03 set. 2020.
dc.publisherAmerican Physical Societypt_BR
dc.subjectQuantum mechanicspt_BR
dc.titleMachine learning nonlocal correlationspt_BR
dc.description.resumoThe ability to witness nonlocal correlations lies at the core of foundational aspects of quantum mechanics and its application in the processing of information. Commonly, this is achieved via the violation of Bell inequalities. Unfortunately, however, their systematic derivation quickly becomes unfeasible as the scenario of interest grows in complexity. To cope with that, here, we propose a machine learning approach for the detection and quantification of nonlocality. It consists of an ensemble of multilayer perceptrons blended with genetic algorithms achieving a high performance in a number of relevant Bell scenarios. As we show, not only can the machine learn to quantify nonlocality, but discover new kinds of nonlocal correlations inaccessible with other current methods as well. We also apply our framework to distinguish between classical, quantum, and even postquantum correlations. Our results offer a novel method and a proof-of-principle for the relevance of machine learning for understanding nonlocalitypt_BR
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