We demonstrate a method for generating persistent currents in Bose-Einstein condensates by using a Gaussian process learner to experimentally control the stirring of the superfluid. The learner optimizes four different outcomes of the stirring process: (O.I) targeting and (O.II) maximization of the persistent current winding number and (O.III) targeting and (O.IV) maximization with time constraints. The learner optimizations are determined based on the achieved winding number and the number of spurious vortices introduced by stirring. We find that the learner is successful in optimizing the stirring protocols, although the optimal stirring profiles vary significantly depending strongly on the choice of cost function and scenario. These results suggest that stirring is robust and persistent currents can be reliably generated through a variety of stirring approaches.

M. Reeves et. al

Phys. Rev. X 12, 011031

In this work, we explored the relaxation of initially non-equilibrium configurations of vortices. Impressively, the vortex configurations in equilibrium were found to closely match the predictions of the point vortex model.

M Baker et al, 2021

AVS Quantum Sci. 3, 039201 (2021)

Roadmap on Atomtronics: State of the art and perspective, has now been published online in AVS Quantum Sci. 3, 039201 (2021). This is a review of the latest progress in atomtronics-enabled quantum technologies, such as matter-wave circuits and atom chips.