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1 results

  • 6 - Reinforcement learning

  • Anna Dawid, Uniwersytet Warszawski, Poland, Julian Arnold, Universität Basel, Switzerland, Borja Requena, ICFO - The Institute of Photonic Sciences, Alexander Gresch, Heinrich-Heine-Universität Düsseldorf, Marcin Płodzień, ICFO - The Institute of Photonic Sciences, Kaelan Donatella, Université de Paris VII (Denis Diderot), Kim A. Nicoli, University of Bonn, Paolo Stornati, ICFO - The Institute of Photonic Sciences, Rouven Koch, Aalto University, Finland, Miriam Büttner, Albert-Ludwigs-Universität Freiburg, Germany, Robert Okuła, Gdańsk University of Technology, Gorka Muñoz-Gil, Universität Innsbruck, Austria, Rodrigo A. Vargas-Hernández, McMaster University, Ontario, Alba Cervera-Lierta, Centro Nacional de Supercomputación, Juan Carrasquilla, Swiss Federal Institute of Technology in Zurich, Vedran Dunjko, Universiteit Leiden, Marylou Gabrié, Institut Polytechnique de Paris, Patrick Huembeli, Evert van Nieuwenburg, Universiteit Leiden, Filippo Vicentini, Institut Polytechnique de Paris, Lei Wang, Chinese Academy of Sciences, Beijing, Sebastian J. Wetzel, University of Waterloo, Ontario, Giuseppe Carleo, École Polytechnique Fédérale de Lausanne, Eliška Greplová, Technische Universiteit Delft, The Netherlands, Roman Krems, University of British Columbia, Vancouver, Florian Marquardt, Max-Planck-Institut für die Wissenschaft des Lichts, Michał Tomza, Uniwersytet Warszawski, Maciej Lewenstein, ICFO - Institute of Photonic Sciences, Alexandre Dauphin, Instituto de Ciencias Fotónicas
  • Book:
    Machine Learning in Quantum Sciences
    Published online:
    13 June 2025
    Print publication:
    12 June 2025, pp 145-183

  • Summary

    In this chapter, we introduce the field of reinforcement learning and some of its most prominent applications in quantum physics and computing. First, we provide an intuitive description of the main concepts, which we then formalize mathematically. We introduce some of the most widely used reinforcement learning algorithms. Starting with temporal-difference algorithms and Q-learning, followed by policy gradient methods and REINFORCE, and the interplay of both approaches in actor-critic algorithms. Furthermore, we introduce the projective simulation algorithm, which deviates from the aforementioned prototypical approaches and has multiple applications in the field of physics. Then, we showcase some prominent reinforcement learning applications, featuring some examples in games; quantum feedback control; quantum computing, error correction and information; and the design of quantum experiments. Finally, we discuss some potential applications and limitations of reinforcement learning in the field of quantum physics.