Emergent spacetimes from Hermitian and non-Hermitian quantum dynamics

We show that quantum dynamics of any systems with $SU(1,1)$ symmetry give rise to emergent Anti-de Sitter spacetimes in 2+1 dimensions (AdS$_{2+1}$). Using the continuous circuit depth, a quantum evolution is mapped to a trajectory in AdS$_{2+1}$. Whereas the time measured in laboratories becomes either the proper time or the proper distance, quench dynamics follow geodesics of AdS$_{2+1}$. Such a geometric approach provides a unified interpretation of a wide range of prototypical phenomena that appear disconnected. For instance, the light cone of AdS$_{2+1}$ underlies expansions of unitary fermions released from harmonic traps, the onsite of parametric amplifications, and the exceptional points that represent the $PT$ symmetry breaking in non-Hermitian systems. Our work provides a transparent means to optimize quantum controls by exploiting shortest paths in the emergent spacetimes. It also allows experimentalists to engineer emergent spacetimes and induce tunnelings between different AdS$_{2+1}$.Comment: 6+3 pages, 3 figure

Multipolar condensates and multipolar Josephson effects

When single-particle dynamics are suppressed in certain strongly correlated systems, dipoles arise as elementary carriers of quantum kinetics. These dipoles can further condense, providing physicists with a rich realm to study fracton phases of matter. Whereas recent theoretical discoveries have shown that an unconventional lattice model may host a dipole condensate as the ground state, fundamental questions arise as to whether dipole condensation is a generic phenomenon rather than a specific one unique to a particular model and what new quantum macroscopic phenomena a dipole condensate may bring us with. Here, we show that dipole condensates prevail in bosonic systems. Because of a self-proximity effect, where single-particle kinetics inevitably induces a finite order parameter of dipoles, dipole condensation readily occurs in conventional normal phases of bosons. Our findings allow experimentalists to manipulate the phase of a dipole condensate and deliver dipolar Josephson effects, where supercurrents of dipoles arise in the absence of particle flows. The self-proximity effects can also be utilized to produce a generic multipolar condensate. The kinetics of the $n$-th order multipoles unavoidably creates a condensate of the $(n+1)$-th order multipoles, forming a hierarchy of multipolar condensates that will offer physicists a whole new class of macroscopic quantum phenomena

Synthetic tensor gauge fields

Synthetic gauge fields have provided physicists with a unique tool to explore a wide range of fundamentally important phenomena in physics. However, only synthetic vector gauge fields are currently available in experiments. The study of tensor gauge fields, which play a vital role in fracton phase of matter, remains purely theoretical. Here, we propose schemes to realize synthetic tensor gauge fields using techniques readily available in laboratories. A lattice tilted by a strong linear potential and a weak quadratic potential naturally yields a rank-2 electric field for a lineon formed by a particle-hole pair. Such a rank-2 electric field leads to a new type of Bloch oscillations, where neither a single particle nor a single hole responds but a lineon vibrates. A synthetic vector gauge field carrying a position-dependent phase could also be implemented to produce the same synthetic tensor gauge field for a lineon. In higher dimensions, the interplay between interactions and vector gauge potentials imprints a phase to the ring-exchange interaction and thus generates synthetic tensor gauge fields for planons. Such tensor gauge fields make it possible to realize a dipolar Harper-Hofstadter model in laboratories.Comment: 6+3 pages, 4+3 figure

Nanophotonic cavity cooling of a single atom

We investigate external and internal dynamics of a two-level atom strongly coupled to a weakly pumped nanophotonic cavity. We calculate the dipole force, friction force, and stochastic force due to the cavity pump field, and show that a three-dimensional cooling region exists near the surface of a cavity. Using a two-color evanescent field trap as an example, we perform three-dimensional Monte-Carlo simulations to demonstrate efficient loading of single atoms into a trap by momentum diffusion, and the stability of cavity cooling near the trap center. Our analyses show that cavity cooling can be a promising method for directly loading cold atoms from free-space into a surface micro-trap. We further discuss the impact of pump intensity on atom trapping and loading efficiency.Comment: 14 pages, 11 figures, 1 tabl

Zero-Shot Aerial Object Detection with Visual Description Regularization

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture.Comment: 13 pages, 3 figure

GPT-NAS: Neural Architecture Search with the Generative Pre-Trained Model

Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorithms inefficient. This work presents a novel architecture search algorithm, called GPT-NAS, that optimizes neural architectures by Generative Pre-Trained (GPT) model. In GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus could learn the fundamental law of building neural architectures. Therefore, GPT-NAS leverages the generative pre-trained (GPT) model to propose reasonable architecture components given the basic one. Such an approach can largely reduce the search space by introducing prior knowledge in the search process. Extensive experimental results show that our GPT-NAS method significantly outperforms seven manually designed neural architectures and thirteen architectures provided by competing NAS methods. In addition, our ablation study indicates that the proposed algorithm improves the performance of finely tuned neural architectures by up to about 12% compared to those without GPT, further demonstrating its effectiveness in searching neural architectures