Efficient Preparation of Fermionic Superfluids in an Optical Dipole Trap through Reinforcement Learning
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Abstract
We demonstrate a reinforcement learning (RL)-based control framework for optimizing evaporative cooling in the preparation of strongly interacting degenerate Fermi gases of 6Li. 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 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 gray 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.