论文标题
通过物理启发的学习算法优化光电位
Optimizing optical potentials with physics-inspired learning algorithms
论文作者
论文摘要
我们介绍了新的实验和理论框架，该框架结合了宽带超发光二极管（Sled/SLD）与快速学习算法，以提供速度和准确性提高，以优化1D光学偶极电位，此处由数字微旋转器设备（DMD）生成。为了表征连贯性引起的设置和潜在的斑点模式，我们通过研究干扰性能将超亮发光二极管与单模激光器进行比较。我们采用机器学习（ML）工具来训练由物理启发的模型充当光学系统的数字双胞胎，以预测光学设备的行为，包括其所有缺陷。基于迭代学习控制（ILC）实施迭代算法，我们优化光电位比启发式优化方法更快。我们比较基于迭代模型的离线优化和基于实验反馈的在线优化。我们的方法为快速优化光电位提供了新的途径，这与超电气气体的动态操作有关。
We present our new experimental and theoretical framework which combines a broadband superluminescent diode (SLED/SLD) with fast learning algorithms to provide speed and accuracy improvements for the optimization of 1D optical dipole potentials, here generated with a Digital Micromirror Device (DMD). To characterize the setup and potential speckle patterns arising from coherence, we compare the superluminescent diode to a single-mode laser by investigating interference properties. We employ Machine Learning (ML) tools to train a physics-inspired model acting as a digital twin of the optical system predicting the behavior of the optical apparatus including all its imperfections. Implementing an iterative algorithm based on Iterative Learning Control (ILC) we optimize optical potentials an order of magnitude faster than heuristic optimization methods. We compare iterative model-based offline optimization and experimental feedback-based online optimization. Our methods provide a new route to fast optimization of optical potentials which is relevant for the dynamical manipulation of ultracold gases.
