Machine Learning Bell Nonlocality in Quantum Many-body Systems

Understand

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.