Optimizing quantum gas production by an evolutionary algorithm
Optimizing quantum gas production by an evolutionary algorithm
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DOI：
10.1007/s00340-016-6391-2
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发表时间：
2016-05-01
影响因子：
2.1
通讯作者：
Widera, A.
中科院分区：
文献类型：
--
作者：
Lausch, T.;Hohmann, M.;Widera, A.
我们报告的进化算法（EA）的应用，以提高超冷量子气体实验的性能。87铷玻色-爱因斯坦凝聚体（BEC）的产生可以分为基本的冷却步骤，特别是冷原子的磁光捕获，原子加载到远失谐的交叉偶极阱，最后是蒸发冷却的过程。EA分别应用于这些步骤中的每一个，并对反馈进行特定定义，即所谓的适应度。我们讨论了EA的原理，并实现了一种称为差分进化的增强。分析EA需要改进的原因，例如，的原子装载率和增加BEC相空间密度，产生一个最佳的参数设置的BEC生产，使我们能够减少BEC生产时间显着。此外，我们专注于如何提取有关实验和优化可能性的额外信息，以及如何揭示相关性以进一步改进。我们的研究结果表明，EA是强大的优化工具，复杂的实验和应用程序产生有用的信息，这些实验的依赖优化参数。
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a 87 rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.