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

Machine learning, the core of artificial intelligence, 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 talk, I will 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, I will demonstrate that RBM is capable of finding the maximum quantum violations of multipartite Bell inequalities with given measurement settings. This result builds a novel bridge between computer-science-based machine learning and quantum many-body nonlocality, which will benefit future studies in both areas.

[1] D.-L. Deng, Phys. Rev. Lett. 120, 240402 (2018).