Machine-learning-based device-independent certification of quantum networks

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dc.contributor.authorD’Alessandro, Nicola
dc.contributor.authorPolacch, Beatrice
dc.contributor.authorMoreno Filho, Marcos George Magalhães
dc.contributor.authorPolino, Emanuele
dc.contributor.authorAraújo, Rafael Chaves Souto
dc.contributor.authorAgresti, Iris
dc.contributor.authorSciarrino, Fabio
dc.date.accessioned2025-04-22T17:06:17Z
dc.date.available2025-04-22T17:06:17Z
dc.date.issued2023-04-10
dc.description.resumoWitnessing nonclassical behavior is a crucial ingredient in quantum information processing. For that, one has to optimize the quantum features a given physical setup can give rise to, which is a hard computational task currently tackled with semidefinite programming, a method limited to linear objective functions and that becomes prohibitive as the complexity of the system grows. Here, we propose an alternative strategy, which exploits a feedforward artificial neural network to optimize the correlations compatible with arbitrary quantum networks. A remarkable step forward with respect to existing methods is that it deals with nonlinear optimization constraints and objective functions, being applicable to scenarios featuring independent sources and nonlinear entanglement witnesses. Furthermore, it offers a significant speedup in comparison with other approaches, thus allowing to explore previously inaccessible regimes. We also extend the use of the neural network to the experimental realm, a situation in which the statistics are unavoidably affected by imperfections, retrieving device-independent uncertainty estimates on Bell-like violations obtained with independent sources of entangled photon states. In this way, this work paves the way for the certification of quantum resources in networks of growing size and complexitypt_BR
dc.identifier.citationD'ALESSANDRO, Nicola; POLACCHI, Beatrice; MORENO, George; POLINO, Emanuele; ARAUJO, Rafael Chaves Souto; AGRESTI, Iris; SCIARRINO, Fabio. Machine-learning-based device-independent certification of quantum networks. Physical Review Research, v. 5, p. 023016, 2023. DOI 10.1103/PhysRevResearch.5.023016. Disponível em: https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.023016. Acesso em: 17 mar. 2025.pt_BR
dc.identifier.doihttps://doi.org/10.1103/PhysRevResearch.5.023016
dc.identifier.issne2643-1564
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/63491
dc.languagept_BRpt_BR
dc.publisherPhysical Review Researchpt_BR
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learningpt_BR
dc.subjectQuantum communicationpt_BR
dc.subjectQuantum networkspt_BR
dc.subjectQuantum computationpt_BR
dc.titleMachine-learning-based device-independent certification of quantum networkspt_BR
dc.typearticlept_BR

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