Superfluid Drag in Multicomponent Bose-Einstein Condensates on a Square Optical Lattice
Stian Hartman, Eirik Erlandsen, and Asle Sudb{\o}

TL;DR
This paper analyzes the superfluid drag in a three-component Bose-Einstein condensate on a square lattice, revealing complex non-monotonic behavior influenced by inter-component interactions, supported by analytic, mean-field, and Monte Carlo methods.
Contribution
It provides the first detailed analytic and numerical study of superfluid drag in three-component BECs, highlighting non-monotonic interaction effects absent in two-component systems.
Findings
Drag increases monotonically with inter-component interaction in two-component BECs.
In three-component BECs, drag can be strengthened or weakened depending on interaction strengths.
The drag between two components can decrease with increasing interaction with a third component, showing non-monotonic behavior.
Abstract
The superfluid drag-coefficient of a weakly interacting three-component Bose-Einstein condensate is computed deep into the superfluid phase, starting from a Bose-Hubbard model with component-conserving, on-site interactions and nearest-neighbor hopping. Rayleigh-Schr\"odinger perturbation theory is employed to provide an analytic expression for the drag density. In addition, the Hamiltonian is diagonalized numerically to compute the drag within mean-field theory at both zero and finite temperatures to all orders in inter-component interactions. Moreover, path integral Monte Carlo simulations have been performed to support the mean-field results. In the two-component case the drag increases monotonically with the magnitude of the inter-component interaction between the two components A and B. This no longer holds when an additional third component C is included. Instead of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
