Iterative shaping of optical potentials for one-dimensional Bose-Einstein condensates

TL;DR

This paper introduces an iterative learning control approach to precisely shape optical potentials for one-dimensional Bose-Einstein condensates, improving control accuracy amidst uncertainties and disturbances.

Contribution

It applies iterative learning control to optimize optical potentials for Bose-Einstein condensates, offering a systematic and faster convergence method compared to heuristic algorithms.

Findings

ILC effectively refines optical potentials in simulations

Enhanced control precision over traditional heuristic methods

Potential for improved experimental manipulation of BECs

Abstract

The ability to manipulate clouds of ultra-cold atoms is crucial for modern experiments on quantum manybody systems and quantum thermodynamics as well as future metrological applications of Bose-Einstein condensate. While optical manipulation offers almost arbitrary flexibility, the precise control of the resulting dipole potentials and the mitigation of unwanted disturbances is quite involved and only heuristic algorithms with rather slow convergence rates are available up to now. This paper thus suggests the application of iterative learning control (ILC) methods to generate fine-tuned effective potentials in the presence of uncertainties and external disturbances. Therefore, the given problem is reformulated to obtain a one-dimensional tracking problem by using a quasicontinuous input mapping which can be treated by established ILC methods. Finally, the performance of the proposed concept is illustrated in a simulation scenario.