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https://repositorio.ufrn.br/handle/123456789/30124| Título: | Machine learning nonlocal correlations |
| Autor(es): | Silva, Askery Alexandre Canabarro Barbosa da Brito, Samuraí Gomes de Aguiar Araújo, Rafael Chaves Souto |
| Palavras-chave: | Quantum mechanics |
| Data do documento: | 22-Mai-2019 |
| Editor: | American Physical Society |
| Referência: | CANABARRO, 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: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.200401. Acesso em: 03 set. 2020. http://dx.doi.org/10.1103/physrevlett.122.200401. |
| Resumo: | The 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 nonlocality |
| URI: | https://repositorio.ufrn.br/jspui/handle/123456789/30124 |
| ISSN: | 0031-9007 1079-7114 |
| Aparece nas coleções: | ECT - Artigos publicados em periódicos IIF - Artigos publicados em periódicos |
Arquivos associados a este item:
| Arquivo | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| MachineLearningNonlocal_ARAUJO_2019.pdf | Artigo | 1,25 MB | Adobe PDF |  Visualizar/Abrir |
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