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

Dong-Ling Deng
Phys. Rev. Lett. 120, 240402 – Published 14 June 2018
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Abstract

Machine learning, the core of artificial intelligence and big data science, is one of today’s most rapidly growing interdisciplinary fields. Recently, machine learning tools and techniques have been adopted to tackle intricate quantum many-body problems. In this Letter, 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.

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  • Received 17 October 2017
  • Revised 12 April 2018

DOI:https://doi.org/10.1103/PhysRevLett.120.240402

© 2018 American Physical Society

Physics Subject Headings (PhySH)

NetworksQuantum Information, Science & TechnologyGeneral Physics

Authors & Affiliations

Dong-Ling Deng*

  • Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, China and Condensed Matter Theory Center and Joint Quantum Institute, Department of Physics, University of Maryland, College Park, Maryland 20742-4111, USA

  • *dldeng@tsinghua.edu.cn

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Issue

Vol. 120, Iss. 24 — 15 June 2018

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