arXiv subjects

Kaiyue Wang

Publications and source records attributed to Kaiyue Wang.

4 recordsLinked to original sources

arXiv2026

Binary Dipolar Condensates of Dysprosium Isotopes with Tunable Spatial Order

Dipolar quantum mixtures provide a route to many-body phases in which long-range anisotropic interactions couple with density, composition and spatial order. Here we realize a new quantum-degenerate dipolar mixture of $^{162}$Dy and $^{164}$Dy in a single-species-like apparatus. The mixture combines nearly matched single-particle Hamiltonians, tunable interactions and composition parameters, and isotope-resolved characterization. Tuning the interaction balance and relative composition reorganizes the coupled condensates from a miscible state into core--shell-like, side-by-side, and exchanged core--shell-like immiscible configurations. These results establish dysprosium isotope mixtures as a compact and versatile platform for multicomponent dipolar quantum matter, ranging from impurity physics to binary supersolidity.

cond-mat.quant-gas↗

arXiv2024

A Customizable Modular Control System for Ultracold Experiments

We implemented a control system for ultracold atom experiments. The system includes hardware modules that generate synchronized experiment signals of different kinds, and a protocol to communicate with all the modules. We also implemented software that can automatically generate experiment sequences from declarative tables of parameters with variations. Both the hardware and the software are open-source for adaptation and customization in other experiment platforms.

physics.atom-ph↗

arXiv2023

Instability and Momentum Bifurcation of molecular BEC in Exotic Dispersion with Shaken Lattice

We place a molecular Bose-Einstein condensate in a 1D shaken lattice with a Floquet-engineered dispersion, and observe the dynamics in both position and momentum space. At the initial condition of zero momentum, our engineered dispersion is inverted, and therefore unstable. We observe that the condensate is destabilized by the lattice shaking as expected, but rather than decaying incoherently or producing jets, as in other unstable condensates, under our conditions the condensate bifurcates into two portions in momentum space, with each portion subsequently following semi-classical trajectories that suffer minimal spreading in momentum space as they evolve. We can model the evolution with a Gross-Pitaevskii equation, which suggests the initial bifurcation is facilitate by a nearly linear "inverted V"-shaped dispersion at the zone center, while the lack of spreading in momentum space is facilitated by interactions, as in a soliton. We propose that this relatively clean bifurcation in momentum space has applications for counter-diabatic preparation of exotic ground states in many-body quantum simulation schemes.

cond-mat.quant-gas↗

arXiv2022

QAGAN: Adversarial Approach To Learning Domain Invariant Language Features

Training models that are robust to data domain shift has gained an increasing interest both in academia and industry. Question-Answering language models, being one of the typical problem in Natural Language Processing (NLP) research, has received much success with the advent of large transformer models. However, existing approaches mostly work under the assumption that data is drawn from same distribution during training and testing which is unrealistic and non-scalable in the wild. In this paper, we explore adversarial training approach towards learning domain-invariant features so that language models can generalize well to out-of-domain datasets. We also inspect various other ways to boost our model performance including data augmentation by paraphrasing sentences, conditioning end of answer span prediction on the start word, and carefully designed annealing function. Our initial results show that in combination with these methods, we are able to achieve $15.2\%$ improvement in EM score and $5.6\%$ boost in F1 score on out-of-domain validation dataset over the baseline. We also dissect our model outputs and visualize the model hidden-states by projecting them onto a lower-dimensional space, and discover that our specific adversarial training approach indeed encourages the model to learn domain invariant embedding and bring them closer in the multi-dimensional space.

cs.CL↗