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arXiv:2308.05216 · Machine Learning Science and Technology

High-dimensional reinforcement learning for optimization and control of ultracold quantum gases

Nicholas Milson, Arina Tashchilina, T Ooi, Anna Malgorzata Czarnecka, Zaheen Farraz Ahmad, Lindsay J. LeBlanc

Published Dec 1, 2023• 9 citations
arXivHTMLPDF

Abstract

Abstract Machine-learning (ML) techniques are emerging as a valuable tool in experimental physics, and among them, reinforcement learning (RL) offers the potential to control high-dimensional, multistage processes in the presence of fluctuating environments. In this experimental work, we apply RL to the preparation of an ultracold quantum gas to realize a consistent and large number of atoms at microkelvin temperatures. This RL agent determines an optimal set of 30 control parameters in a dynamically changing environment that is characterized by 30 sensed parameters. By comparing this method to that of training supervised-learning regression models, as well as to human-driven control schemes, we find that both ML approaches accurately predict the number of cooled atoms and both result in occasional superhuman control schemes. However, only the RL method achieves consistent outcomes, even in the presence of a dynamic environment.

DOI
10.1088/2632-2153/ad1437

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Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.
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