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Machine learning and the physical sciences*

Giuseppe Carleo†, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová‡

Giuseppe Carleo†

  • Center for Computational Quantum Physics, Flatiron Institute, 162 5th Avenue, New York, New York 10010, USA

Ignacio Cirac

  • Max-Planck-Institut fur Quantenoptik, Hans-Kopfermann-Straße 1, D-85748 Garching, Germany

Kyle Cranmer

  • Center for Cosmology and Particle Physics, Center of Data Science, New York University, 726 Broadway, New York, New York 10003, USA

Laurent Daudet

  • LightOn, 2 rue de la Bourse, F-75002 Paris, France

Maria Schuld

  • University of KwaZulu-Natal, Durban 4000, South Africa, National Institute for Theoretical Physics, KwaZulu-Natal, Durban 4000, South Africa, and Xanadu Quantum Computing, 777 Bay Street, M5B 2H7 Toronto, Canada

Naftali Tishby

  • The Hebrew University of Jerusalem, Edmond Safra Campus, Jerusalem 91904, Israel

Leslie Vogt-Maranto

  • Department of Chemistry, New York University, New York, New York 10003, USA

Lenka Zdeborová‡

  • Institut de Physique Théorique, Université Paris Saclay, CNRS, CEA, F-91191 Gif-sur-Yvette, France

  • †gcarleo@flatironinstitute.org
  • ‡lenka.zdeborova@cea.fr
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Rev. Mod. Phys. 91, 045002 – Published 6 December, 2019

DOI: https://doi.org/10.1103/RevModPhys.91.045002

Abstract

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences. This includes conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields. After giving a basic notion of machine learning methods and principles, examples are described of how statistical physics is used to understand methods in ML. This review then describes applications of ML methods in particle physics and cosmology, quantum many-body physics, quantum computing, and chemical and material physics. Research and development into novel computing architectures aimed at accelerating ML are also highlighted. Each of the sections describe recent successes as well as domain-specific methodology and challenges.

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Physics Subject Headings (PhySH)

  • *This article reviews and summarizes the topics discussed at the APS Physics Next Workshop on Machine Learning held in October 2018 in Riverhead, NY.

Article Text

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Rev. Mod. Phys. 91, 045002– Published 6 December, 2019
  • Received 15 March 2019
Article part of CHORUS
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