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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+ magneto-optical trap and the detection laser frequency stability.

Bose-Einstein condensates are a promising platform for optical quantum memories but suffer from several decoherence mechanisms, leading to short memory lifetimes. While some of these decoherence effects can be mitigated by conventional methods, density-dependent atom-atom collisions ultimately set the upper limit of the quantum memory lifetime to timescales of seconds in trapped Bose-Einstein condensates. We propose a quantum memory technique that utilizes microgravity as a resource to minimize such density-dependent effects. We show that by using optical atom lenses to collimate and refocus the freely expanding atomic ensembles, in a semi-ideal environment, the expected memory lifetime is only limited by the quality of the background vacuum. We anticipate that this method can be experimentally demonstrated in Earth-bound microgravity platforms or space missions, eventually leading to storage times of minutes and unprecedented time-bandwidth products of 10^{10}.