Realizing a deep reinforcement learning agent for real-time quantum feedback
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Date
2023-11-06
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
14 (1)
Pages / Article No.
7138
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Organisational unit
03720 - Wallraff, Andreas / Wallraff, Andreas
02205 - FIRST-Lab / FIRST Center for Micro- and Nanoscience
Notes
Funding
184686 - Quantum Photonics with Microwaves in Superconducting Circuits (SNF)
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