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Machine Learning Detection of Bell Nonlocality in Quantum Many-Body Systems

2018; American Physical Society; Volume: 120; Issue: 24 Linguagem: Inglês

10.1103/physrevlett.120.240402

1092-0145

Dong-Ling Deng,

Advanced Thermodynamics and Statistical Mechanics

Machine learning, the core of artificial intelligence and big data science, is one of today's most rapidly growing interdisciplinary fields. Recently, its tools and techniques have been adopted to tackle intricate quantum many-body problems. In this work, we introduce machine learning techniques to the detection of quantum nonlocality in many-body systems, with a focus on the restricted-Boltzmann-machine (RBM) architecture. Using reinforcement learning, we demonstrate that RBM is capable of finding the maximum quantum violations of multipartite Bell inequalities with given measurement settings. Our results build a novel bridge between computer-science-based machine learning and quantum many-body nonlocality, which will benefit future studies in both areas.