Related papers: Optimizing persistent currents in a ring-shaped Bo…
We present a scheme for creating macroscopic superpositions of the direction of superfluid flow around a loop. Using the Bose-Hubbard model we study an array of Bose-Einstein condensates trapped in optical potentials and coupled to one…
The theory of linear wave structures generated in Bose-Einstein condensate flow past an obstacle is developed. The shape of wave crests and dependence of amplitude on coordinates far enough from the obstacle are calculated. The results are…
Reinforcement learning is employed to optimize the periodic forcing signal of a pulsed blowing system that controls flow separation in a fully-turbulent $Re_\theta = 1000$ diffuser flow. Based on the state of the wind tunnel experiment that…
The physics of quantized vortex excitations in atomic Bose-Einstein condensates has been extensively studied in recent years.Although simple vortex lines are relatively easy to create, control, and measure in experiments, it is a lot more…
We create supercurrents in annular two-dimensional Bose gases through a temperature quench of the normal-to-superfluid phase transition. We detect the amplitude and the chirality of these supercurrents by measuring spiral patterns resulting…
We propose an efficient stochastic method to implement numerically the Bogolubov approach to study finite-temperature Bose-Einstein condensates. Our method is based on the Wigner representation of the density matrix describing the non…
By cyclic adiabatic change of two control parameters of an optical trap one can induce a circulating current of condensed bosons. The amount of particles that are transported per period depends on the "radius" of the cycle, and this…
Gaussian process regression is increasingly applied for learning unknown dynamical systems. In particular, the implicit quantification of the uncertainty of the learned model makes it a promising approach for safety-critical applications.…
The collective modes of a rotating Bose-Einstein condensate confined in an attractive quadratic plus quartic trap are investigated. Assuming the presence of a large number of vortices we apply the diffused vorticity approach to the system.…
We study the dynamic generation of persistent current by phase imprinting fermionic atoms in a ring geometry. Mediated by the pairing interaction, the Fermi condensate dynamically acquires a quantized current by developing azimuthal phase…
There is a pressing need for robust and straightforward methods to create potentials for trapping Bose-Einstein condensates which are simultaneously dynamic, fully arbitrary, and sufficiently stable to not heat the ultracold gas. We show…
We propose a simple way to generate nonlinear excitations in a controllable way by managing interactions in Bose-Einstein condensates. Under the action of a quantum analogue of a classical piston the condensed atoms are pushed through the…
We study the dynamics of a supersonically expanding ring-shaped Bose-Einstein condensate both experimentally and theoretically. The expansion redshifts long-wavelength excitations, as in an expanding universe. After expansion, energy in the…
We apply a path integral variational approach to obtain analytical expressions for condensate wave functions of an ultracold, interacting trapped Bose gases. As in many recent experiments, the particles are confined in a 1D or 3D harmonic…
Using the explicit numerical solution of the axially-symmetric Gross-Pitaevskii equation we study the oscillation of the Bose-Einstein condensate induced by a periodic variation in the atomic scattering length $a$. When the frequency of…
We use the quantum kinetic theory to calculate the steady state and the fluctuations of a trapped Bose-Einstein condensate at finite temperature. The system is divided in a condensate and a non-condensate part. A quantum mechanical…
We combine numerical and experimental approaches to study how impurities affect the maximum superflow in an annular Bose-Einstein condensate. By tuning the impurity density, we achieve precise control over the stability of persistent…
In computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervised problems are signals defined on meshes, and a natural…
We study the spatial distributions of the spin and mass currents generated by a moving Gaussian magnetic obstacle in a symmetric, two-component Bose-Einstein condensate in two dimensions. We analytically describe the current distributions…
We investigate a computational device that harnesses the effects of Bose-Einstein condensation (BEC) to accelerate the speed of finding the solution of a given optimization problem. Many computationally difficult problems, including…