Ultracold atom arrays in optical lattices emerge as an excellent playground for the integration of topological photonics and quantum optics. Here, we study high-order topological quantum optics in an ultracold atom metasurface intended to mimic the t
wo-dimensional Su-Schrieffer-Heeger model. We find the existence of long-range interactions beyond nearest-neighbor ones leads to isolated corner states in the band gap, and show a corner atom can be addressed by a laser drive far away from it via these nontrivial states. We demonstrate the Purcell factor can be used as a powerful tool to examine the existence of topological edge and corner states. We predict topological edge states can mediate strong coherent interactions between two remote impurity quantum emitters while suppressing dissipative losses thanks to the higher-order topology, generating robust and long-lived quantum entanglement, without the need for additional photonic structures.
We study the nonlinear optical response in a superconducting NbN thin film with strong terahertz (THz)wave. Besides the expected third harmonic generation, we observe a new transient oscillation which softensin frequency with temperature increasing t
owards superconducting transition temperatureTc. We identify thisnew mode as the Higgs transient oscillation. To verify this proposal, we introduce a time-frequency resolvedtechnique, named spectrogram for visualizing THz spectrum. The dynamic decaying behavior of the mode isobserved, which is consistent with theoretical expectation about intrinsic Higgs oscillation. Moreover, a higherorder nonlinear optics effect,i.e.fifth harmonic generation, has been observed for the first time, which we assignto the higher order coupling between Higgs mode and electromagn
Recently, kagome lattice metal AV$_3$Sb$_5$ (A = K, Rb, Cs) family has received wide attention due to its presence of superconductivity, charge density wave (CDW) and peculiar properties from topological nontrivial electronic structure. With time-res
olved pump-probe spectroscopy, we show that the excited quasiparticle relaxation dynamics can be explained by formation of energy gap below the phase transition being similar to a usual second-order CDW condensate, by contrast, the structure change is predominantly first order phase transition. Furthermore, no CDW amplitude mode is identified in the ordered phase. The results suggest that the CDW order is very different from the traditional CDW condensate. We also find that weak pump pulse can non-thermally melt the CDW order and drive the sample into its high temperature phase, revealing the fact that the difference in lattice potential between those phases is small.
The entropy shows an unavoidable tendency of disorder in thermostatistics according to the second thermodynamics law. This provides a minimization entropy principle for quantum thermostatistics with the von Neumann entropy and nonextensive quantum th
ermostatistics with special Tsallis entropy. Our goal in this work is to provide operational characterizations of general entropy measures. We present the first unified principle consistent with the second thermodynamics law in terms of general quantum entropies for both quantum thermostatistics and nonextensive quantum thermostatistics. This further reveals new features beyond the second thermodynamics law by maximization the cross entropy during irreversible measurement procedures. The present result is useful for asymptotical tasks of quantum entropy estimations and universal quantum source encoding without the state tomography. It is further applied to single-shot state transitions and cooling in quantum thermodynamics with limited information. These results should be interesting in the many-body theory and long-range quantum information processing.
Realization of electromagnetic energy confinement beyond the diffraction limit is of paramount importance for novel applications like nano-imaging, information processing, and energy harvest. Current approaches based on surface plasmon polaritons and
photonic crystals are either intrinsically lossy or with low coupling efficiency. Herein, we successfully address these challenges by constructing an array of nonradiative anapoles that originate from the destructive far-field interference of electric and toroidal dipole modes. The proposed metachain can achieve ultracompact (1/13 of incident wavelength) and high-efficiency electromagnetic energy transfer without the coupler. We experimentally investigate the proposed metachain at mid-infrared and give the first near-field experimental evidence of anapole-based energy transfer, in which the spatial profile of anapole mode is also unambiguously identified at nanoscale. We further demonstrate the metachain is intrinsically lossless and scalable at infrared wavelengths, realizing a 90$^circ$ bending loss down to 0.32 dB at the optical communication wavelength. The present scheme bridges the gap between the energy confinement and transfer of anapoles, and opens a new gate for more compactly integrated photonic and energy devices, which can operate in a broad spectral range.
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning. One problem in this area of research, however, has been a scarcity of adequate benchmark tasks. In ge
neral, the structure underlying past benchmarks has either been too simple to be inherently interesting, or too ill-defined to support principled analysis. In the present work, we introduce a new benchmark for meta-RL research, which combines structural richness with structural transparency. Alchemy is a 3D video game, implemented in Unity, which involves a latent causal structure that is resampled procedurally from episode to episode, affording structure learning, online inference, hypothesis testing and action sequencing based on abstract domain knowledge. We evaluate a pair of powerful RL agents on Alchemy and present an in-depth analysis of one of these agents. Results clearly indicate a frank and specific failure of meta-learning, providing validation for Alchemy as a challenging benchmark for meta-RL. Concurrent with this report, we are releasing Alchemy as public resource, together with a suite of analysis tools and sample agent trajectories.
We report the synthesis and physical properties of the single crystals of TaC, which are proposed to hold topological band structure as a topological superconductor (TSC) candidate. Magnetization, resistivity and specific heat measurements are perfor
med and indicate that TaC is bulk superconductor with critical temperature of 10.3 K. TaC is a strongly coupled type-II superconductor and the superconducting state can be well described by s-wave Bardeen-Cooper-Schrieffer (BCS) theory with a single gap. The upper critical field (Hc2) of TaC shows linear temperature dependence, which is quite different from most conventional superconductors and isostructural NbC, which is proposed to manifest topological nodal-loops or type-II Dirac points as well as superconductivity. Our results suggest that TaC would be a new candidate for further research of TSCs.
In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually
using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various computing techniques and technologies, such as Lidar, have been utilised in order to automate this process and update the collection paradigm for these types of measurements. Techniques such as Structure from Motion (SfM) reconstruct of scenes and objects by generating a point cloud from input images, with detailed reconstruction possible on the decimetre scale. SfM-type techniques provide advantages in areas of cost and usability in more varied environmental conditions, while sacrificing the extreme levels of data fidelity. Here is presented a methodology of data acquisition and a Machine Learning-based software system: GeoStructure, developed to automate the measurement of orientation measurements. Rather than deriving measurements using a method applied to the input images, such as the Hough Transform, this method takes measurements directly from the reconstructed point cloud surfaces. Point cloud noise is mitigated using a Mahalanobis distance implementation. Significant structure is characterised using a k-nearest neighbour region growing algorithm, and final surface orientations are quantified using the plane, and normal direction cosines.
In this article, we first present our vision on the application scenarios, performance metrics, and potential key technologies of the sixth generation (6G) wireless communication networks. Then, 6G wireless channel measurements, characteristics, and
models are comprehensively surveyed for all frequency bands and all scenarios, focusing on millimeter wave (mmWave), terahertz (THz), and optical wireless communication channels under all spectrums, satellite, unmanned aerial vehicle (UAV), maritime, and underwater acoustic communication channels under global coverage scenarios, and high-speed train (HST), vehicle-to-vehicle (V2V), ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels under full application scenarios. Future research challenges on 6G channel measurements, a general standard 6G channel model framework, channel measurements and models for intelligent reflection surface (IRS) based 6G technologies, and artificial intelligence (AI) enabled channel measurements and models are also given.
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within nature, has deep roots in cognitive science and psychology, and is cu
rrently studied in various forms within neuroscience. The aim of this review is to recast previous lines of research in the study of biological intelligence within the lens of meta-learning, placing these works into a common framework. More recent points of interaction between AI and neuroscience will be discussed, as well as interesting new directions that arise under this perspective.
