Machine Learning Bell Nonlocality in Quantum Many-body Systems

TL;DR

This paper applies machine learning, specifically reinforcement learning with restricted Boltzmann machines, to detect quantum nonlocality and find maximum violations of Bell inequalities in many-body quantum systems.

Contribution

It introduces a novel approach combining machine learning and quantum physics to identify quantum nonlocality in complex many-body systems.

Findings

RBM can find maximum quantum violations of Bell inequalities

Reinforcement learning effectively detects quantum nonlocality

Bridges machine learning techniques with quantum many-body physics

Abstract

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.