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arXiv · 2606.30765

Deep Reinforcement Learning for Individual Atomic Control and Cooling

Matthew L. Peters·Guoqing Wang·David C. Spierings·Niv Drucker·Meng-Wei Chen·Audrey Bartlett·Isaac Chuang·Vladan Vuletić
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Abstract

Real-time feedback control of quantum systems is often limited by partial observations, nonlinear dynamics and measurement noise, which make accurate model-based controllers difficult to design. Here we show that deep reinforcement learning can cool the motion of a single neutral atom coupled to a high-finesse optical cavity using only the continuously monitored cavity transmission. We first train the controller in simulation and then transfer it to the experiment, where online fine-tuning adapts it to unmodeled experimental dynamics. The learned policy damps the atom's motion in real time and achieves a cooling time constant of 388 +/- 14 microseconds, corresponding to only two motional periods in the trap. It also outperforms a standard linear differentiator controller in cooling speed while maintaining comparable atom retention over a broad range of operating conditions. These results establish reinforcement learning as a practical strategy for feedback control in quantum-limited experiments where compact analytical models are incomplete.

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Matthew L. Peters, Guoqing Wang, David C. Spierings, Niv Drucker, Meng-Wei Chen, Audrey Bartlett, Isaac Chuang, Vladan Vuletić. 2026-06-29. Deep Reinforcement Learning for Individual Atomic Control and Cooling. https://arxiv.org/abs/2606.30765

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