Quantum-Fluctuation-Driven Coherent Spin Dynamics in Small Condensates

We have studied quantum spin dynamics of small condensates of cold sodium atoms. For a condensate initially prepared in a mean field ground state, we show that coherent spin dynamics are {\em purely} driven by quantum fluctuations of collective spin coordinates and can be tuned by quadratic Zeeman coupling and magnetization. These dynamics in small condensates can be probed in a high-finesse optical cavity where temporal behaviors of excitation spectra of a coupled condensate-photon system reveal the time evolution of populations of atoms at different hyperfine spin states.Comment: 4 pages, 3 figure

Resonance Scattering in Optical Lattices and Molecules: Interband versus Intraband Effects

We study the low-energy two-body scattering in optical lattices with all higher-band effects included in an effective potential, using a renormalization group approach. As the potential depth reaches a certain value, a resonance of low energy scattering occurs even when the negative s-wave scattering length $(a_s)$ is much shorter than the lattice constant. These resonances can be mainly driven either by interband or intraband effects or by both, depending on the magnitude of $a_s$. Furthermore the low-energy scattering matrix in optical lattices has a much stronger energy-dependence than that in free space. We also investigate the momentum distribution for molecules when released from optical lattices.Comment: 4 figures, version accepted for publication in PR

Implicit Counterfactual Data Augmentation for Deep Neural Networks

Machine-learning models are prone to capturing the spurious correlations between non-causal attributes and classes, with counterfactual data augmentation being a promising direction for breaking these spurious associations. However, explicitly generating counterfactual data is challenging, with the training efficiency declining. Therefore, this study proposes an implicit counterfactual data augmentation (ICDA) method to remove spurious correlations and make stable predictions. Specifically, first, a novel sample-wise augmentation strategy is developed that generates semantically and counterfactually meaningful deep features with distinct augmentation strength for each sample. Second, we derive an easy-to-compute surrogate loss on the augmented feature set when the number of augmented samples becomes infinite. Third, two concrete schemes are proposed, including direct quantification and meta-learning, to derive the key parameters for the robust loss. In addition, ICDA is explained from a regularization aspect, with extensive experiments indicating that our method consistently improves the generalization performance of popular depth networks on multiple typical learning scenarios that require out-of-distribution generalization.Comment: 17 pages, 16 figure

Enhanced fermion pairing and superfluidity by an imaginary magnetic field

We show that an imaginary magnetic field(IMF), which can be generated in non-Hermitian systems with spin-dependent dissipations, can greatly enhance the s-wave pairing and superfluidity of spin-1/2 fermions, in distinct contrast to the effect of a real magnetic field. The enhancement can be attributed to the increased coupling constant in low-energy space and the reduced spin gap in forming singlet pairs. We have demonstrated this effect in a number of different fermion systems with and without spin-orbit coupling, using both the two-body exact solution and many-body mean-field theory. Our results suggest an alternative route towards strong fermion superfluid with high superfluid transition temperature.Comment: 5 pages, 4 figures; version accepted by iScienc