Efficient Preparation of Fermionic Superfluids in an Optical Dipole Trap through Reinforcement Learning

TL;DR

This paper presents a reinforcement learning approach using Soft Actor-Critic to optimize evaporative cooling in preparing fermionic superfluids, significantly improving atomic density and control efficiency.

Contribution

It introduces a novel RL-based control framework that autonomously discovers optimal cooling trajectories for fermionic gases, outperforming traditional methods.

Findings

Achieves up to 130% increase in atomic density

Learns non-trivial control strategies balancing evaporation and thermalization

Demonstrates RL's potential for automated quantum control

Abstract

We demonstrate a reinforcement learning (RL) based control framework for optimizing evaporative cooling in the preparation of strongly interacting degenerate Fermi gases of Li6. Using a Soft Actor-Critic (SAC) algorithm, the system autonomously explores a high-dimensional parameter space to learn optimal cooling trajectories. Compared to conventional exponential ramps, our method achieves up to 130% improvement in atomic density within a 0.5 second, revealing non-trivial control strategies that balance fast evaporation and thermalization. While our current optimization focuses on the evaporation stage, future integration of other cooling stages, such as grey molasses cooling, could further extend RL to the full preparation pipeline. Our result highlights the promise of RL as a general tool for closed-loop quantum control and automated calibration in complex atomic physics experiments.