Realizing a deep reinforcement learning agent for real-time quantum feedback
Reuer K, Landgraf J, Fösel T, O’Sullivan J, Beltrán L, Akin A, Norris GJ, Remm A, Kerschbaum M, Besse JC, Marquardt F, Wallraff A, Eichler C (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 14
Article Number: 7138
Journal Issue: 1
DOI: 10.1038/s41467-023-42901-3
Abstract
Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devices and more generally any physical device requiring low-latency feedback.
Authors with CRIS profile
Additional Organisation(s)
Involved external institutions
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light
Germany (DE)
Switzerland (CH)
Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light
Germany (DE)
How to cite
APA:
Reuer, K., Landgraf, J., Fösel, T., O’Sullivan, J., Beltrán, L., Akin, A.,... Eichler, C. (2023). Realizing a deep reinforcement learning agent for real-time quantum feedback. Nature Communications, 14(1). https://dx.doi.org/10.1038/s41467-023-42901-3
MLA:
Reuer, Kevin, et al. "Realizing a deep reinforcement learning agent for real-time quantum feedback." Nature Communications 14.1 (2023).
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