Artificial intelligence and machine learning for quantum technologies

Krenn M, Landgraf J, Fösel T, Marquardt F (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Physical Review A American Physical Society

Book Volume: 107

Article Number: 010101

Journal Issue: 1

DOI: 10.1103/PhysRevA.107.010101

Abstract

In recent years the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.

Authors with CRIS profile

Jonas Landgraf Lehrstuhl für Theoretische Physik Thomas Fösel Lehrstuhl für Theoretische Physik Florian Marquardt Lehrstuhl für Theoretische Physik

Additional Organisation(s)

FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Krenn, M., Landgraf, J., Fösel, T., & Marquardt, F. (2023). Artificial intelligence and machine learning for quantum technologies. Physical Review A, 107(1). https://doi.org/10.1103/PhysRevA.107.010101

MLA:

Krenn, Mario, et al. "Artificial intelligence and machine learning for quantum technologies." Physical Review A 107.1 (2023).

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