Realizing a reinforcement learning agent for real-time quantum feedback

Eichler C (2022)


Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

City/Town: Lyon, France

Event location: Lyon, France

URI: https://www.ens-lyon.fr/PHYSIQUE/seminars/autres-seminaires/2022-12-07

Abstract

Rapid advancements in building quantum information processing devices at scale call for radical paradigm shifts in the real-time control of quantum systems. Reinforcement learning offers a powerful approach to learn sophisticated control strategies in the absence of detailed models and promises to become a game changer in quantum technology as it did in many other disciplines ranging from board games, to robotics, to fundamental science. In my talk, I will show how we achieve real-time feedback control of a quantum system by using a reinforcement learning agent. We realize the agent as a novel low-latency neural-network on a field-programmable gate array interacting with a superconducting quantum system at MHz rates, which is more than 100 times faster than any other previous implementation of a reinforcement learning agent deployed in any physics experiment. Our work paves the way towards using reinforcement learning for real-time control of quantum computers, most notably for quantum error correction and fault-tolerant gate operations. Beyond that, the unprecedented speed of the agent marks an important engineering achievement , which can be deployed in a wide range of other applications, such as real-time the control of optical systems.

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How to cite

APA:

Eichler, C. (2022). Realizing a reinforcement learning agent for real-time quantum feedback. In Proceedings of the ENS Lyon, seminar hosted by Benjamin Huard. Lyon, France: Lyon, France.

MLA:

Eichler, Christopher. "Realizing a reinforcement learning agent for real-time quantum feedback." Proceedings of the ENS Lyon, seminar hosted by Benjamin Huard, Lyon, France Lyon, France, 2022.

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