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arXiv:0810.4474 · Applied Physics Letters

Stochastic optimization of a cold atom experiment using a genetic algorithm

Wolfgang Rohringer, Robert Bücker, Stephanie Manz, Thomas Betz, Christian Koller, M. Göbel, A. Perrin, Jörg Schmiedmayer, Thorsten Schumm

Published Dec 29, 2008• 31 citations
arXivHTMLPDF

Abstract

We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces, the automatic optimization outperforms a manual search.

DOI
10.1063/1.3058756

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We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces, the automatic optimization outperforms a manual search.
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Cold atom experiments involve a large number of parameters, and there are even different degrees of coupling between these parameters. To achieve the optimal cooling effect, the optimization of experimental parameters is an indispensable step in the atomic cooling process. An online optimization scheme for atomic cooling experimental parameters based on artificial neural networks and genetic algorithms is proposed in this paper. First, the experimental setup and principle scheme used in the experiment are introduced, with emphasis on elaborating in detail on the laser system, cooling sequence, optimization steps, and algorithm flow. Then, the influence of different network structures on prediction performance is analyzed. Finally, the optimization scheme is verified on an actual atomic cooling experimental setup. Experimental results show that the optimization algorithm can accurately predict the optimal parameters of the experimental setup. Compared with manual optimization, the proposed scheme achieves a more significant cooling effect, with the temperature improved by approximately a factor of two in both horizontal and vertical directions.
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Optimization of quantum systems by closed-loop adaptive pulse shaping offers a rich domain for the development and application of specialized evolutionary algorithms. Derandomized evolution strategies (DESs) are presented here as a robust class of optimizers for experimental quantum control. The combination of stochastic and quasi-local search embodied by these algorithms is especially amenable to the inherent topology of quantum control landscapes. Implementation of DES in the laboratory results in efficiency gains of up to $\ensuremath{\sim}9$ times that of the standard genetic algorithm, and thus is a promising tool for optimization of unstable or fragile systems. The statistical learning upon which these algorithms are predicated also provide the means for obtaining a control problem's Hessian matrix with no additional experimental overhead. The forced optimal covariance adaptive learning (FOCAL) method is introduced to enable retrieval of the Hessian matrix, which can reveal information about the landscape's local structure and dynamic mechanism. Exploitation of such algorithms in quantum control experiments should enhance their efficiency and provide additional fundamental insights.