Realizing a deep reinforcement learning agent for real-time quantum feedback

Kevin Reuer (), Jonas Landgraf, Thomas Fösel, James O’Sullivan, Liberto Beltrán, Abdulkadir Akin, Graham J. Norris, Ants Remm, Michael Kerschbaum, Jean-Claude Besse, Florian Marquardt, Andreas Wallraff and Christopher Eichler ()
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Kevin Reuer: ETH Zurich
Jonas Landgraf: Max Planck Institute for the Science of Light
Thomas Fösel: Max Planck Institute for the Science of Light
James O’Sullivan: ETH Zurich
Liberto Beltrán: ETH Zurich
Abdulkadir Akin: ETH Zurich
Graham J. Norris: ETH Zurich
Ants Remm: ETH Zurich
Michael Kerschbaum: ETH Zurich
Jean-Claude Besse: ETH Zurich
Florian Marquardt: Max Planck Institute for the Science of Light
Andreas Wallraff: ETH Zurich
Christopher Eichler: ETH Zurich

Nature Communications, 2023, vol. 14, issue 1, 1-7

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-42901-3

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