2018/08/21 by Askery Canabarro, Samuraí Brito, Rafael Chaves · 73 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Perceptron #Physics #Quantum #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum entanglement #Quantum mechanics #Quantum nonlocality #Relevance (law) #Statistical Mechanics and Entropy #Theoretical computer science #quant-ph #stat.ML
paper · pdf · doi:10.1103/physrevlett.122.200401
published in Physical Review Letters 122(20), 200401 (American Physical Society) · 12 pages, 5 figures
arxiv created 2018/08/21 · openalex publication_date 2019/05/22 · arxiv updated 2019/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.