Evolutionary optimization of an experimental apparatus

Abstract

In recent decades, cold atom experiments have become increasingly complex. While computers control most parameters, optimization is mostly done manually. This is a time-consuming task for a high-dimensional parameter space with unknown correlations. Here we automate this process using a genetic algorithm based on Differential Evolution. We demonstrate that this algorithm optimizes 21 correlated parameters and that it is robust against local maxima and experimental noise. The algorithm is flexible and easy to implement. Thus, the presented scheme can be applied to a wide range of experimental optimization tasks.

arXiv commentminor revision

References in corpus (3)

• Stochastic optimization of a cold atom experiment using a genetic algorithm
• A hexapole-compensated magneto-optical trap on a mesoscopic atom chip
• Optimizing the Stark-decelerator beamline for the trapping of cold molecules using evolutionary strategies

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• Approaching the adiabatic timescale with machine-learning
• Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
• All-optical production of Bose-Einstein condensates with 2 Hz repetition rate
• Rapid generation and number-resolved detection of spinor Rubidium Bose-Einstein condensates
• A continuously pumped reservoir of ultracold atoms