The generation of cold atom clouds is a complex process which involves the
optimization of noisy data in high dimensional parameter spaces. Optimization
can be challenging both in and especially outside of the lab due to lack of
time, expertise, or access for lengthy manual optimization. In recent years, it
was demonstrated that machine learning offers a solution since it can optimize
high dimensional problems quickly, without knowledge of the experiment itself.
In this paper we present results showing the benchmarking of nine different
optimization techniques and implementations, alongside their ability to
optimize a Rubidium (Rb) cold atom experiment. The investigations are performed
on a 3D $^{87}$Rb molasses with 10 and 18 adjustable parameters, respectively,
where the atom number obtained by absorption imaging was chosen as the test
problem. We further compare the best performing optimizers under different
effective noise conditions by reducing the Signal-to-Noise ratio of the images
via adapting the atomic vapor pressure in the 2D+ MOT and the detection laser
frequency stability.

在本文中，我们展示了对九种不同优化技术和实现方法进行基准测试的结果，以及它们在铷（Rb）冷原子实验中的优化能力。我们使用 3D $^{87}$Rb molasses 进行实验，并将吸收成像得到的原子数作为测试问题。我们还通过调整 2D+ MOT 中的原子蒸汽压力和检测激光频率稳定性来减小图像的信噪比，对不同有效噪声条件下表现最佳的优化器进行了比较。