  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.
