Applying machine learning techniques to ultracold quantum gases

Immanuel Bloch, Jean Dalibard, Sylvain Nascimbène

Nicholas Milson, Arina Tashchilina, T Ooi +3 more

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.

Zhihao Lan

Henning Schlömer, Annabelle Bohrdt

As ultracold atom experiments become highly controlled and scalable quantum simulators, they require sophisticated control over high-dimensional parameter spaces and generate increasingly complex measurement data that need to be analyzed and interpreted efficiently. Machine learning (ML) techniques have been established as versatile tools for addressing these challenges, offering strategies for data interpretation, experimental control, and theoretical modeling. In this review, we provide a perspective on how machine learning is being applied across various aspects of quantum simulation, with a focus on cold atomic systems. Emphasis is placed on practical use cases -- from classifying many-body phases to optimizing experimental protocols and representing quantum states -- highlighting the specific contexts in which different ML approaches prove effective. Rather than presenting algorithmic details, we focus on the physical insights enabled by ML and the kinds of problems in quantum simulation where these methods offer tangible benefits.

Niklas Käming, Anna Dawid, Korbinian Kottmann +4 more

Abstract Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries from noisy and imperfect data without the knowledge of the order parameter. Here, we apply different unsupervised machine learning techniques, including anomaly detection and influence functions, to experimental data from ultracold atoms. In this way, we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that these methods can successfully be applied to experimental data at finite temperatures and to the data of Floquet systems when post-processing the data to a single micromotion phase. Our work provides a benchmark for the unsupervised detection of new exotic phases in complex many-body systems.

S. A. Hopkins, Simon L. Cornish