Reinforcement Learning in Ultracold Atom Experiments

Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In this work, we introduce reinforcement learning to cold atom experiments and demonstrate a flexible and adaptive approach to control a magneto-optical trap. Instead of following a set of predetermined rules to accomplish a specific task, the objectives are defined by a reward function. This approach not only optimizes the cooling of atoms just as an experimentalist would do, but also enables new operational modes such as the preparation of pre-defined numbers of atoms in a cloud. The machine control is trained to be robust against external perturbations and able to react to situations not seen during the training. Finally, we show that the time consuming training can be performed in-silico using a generic simulation and demonstrate successful transfer to the real world experiment.Comment: 11 pages, 5 figure

Trapping of ultracold atoms in a He-3/He-4 dilution refrigerator

International audienceWe describe the preparation of ultracold atomic clouds in a dilution refrigerator. The closed-cycle 3He/4He cryostat was custom made to provide optical access for laser cooling, optical manipulation and detection of atoms. We show that the cryostat meets the requirements for cold atom experiments, specifically in terms of operating a magneto-optical trap, magnetic traps and magnetic transport under ultrahigh vacuum conditions. The presented system is a step toward the creation of a quantum hybrid system combining ultracold atoms and solid-state quantum devices