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

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. Howev...

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Veröffentlicht in:Nature communications 2023-11, Vol.14 (1), p.7138-7138, Article 7138
Hauptverfasser: Reuer, Kevin, Landgraf, Jonas, Fösel, Thomas, O’Sullivan, James, Beltrán, Liberto, Akin, Abdulkadir, Norris, Graham J., Remm, Ants, Kerschbaum, Michael, Besse, Jean-Claude, Marquardt, Florian, Wallraff, Andreas, Eichler, Christopher
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Sprache:eng
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Zusammenfassung: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. Real-time feedback control of quantum systems without relying on a description of the system itself is usually challenging. Here, the authors exploit deep reinforcement learning to realise feedback control for initialisation of a superconducting qubit on a submicrosecond timescale.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-42901-3