We have investigated the time-resolved low-energy charge dynamics in counterpropagating edge channels formed in the quantum Hall regime of an electron-hole bilayer system in band-inverted InAs/InGaSb composite quantum wells. In the studied magnetic field range, the system remains in the topological phase where an equal number of counterpropagating electronlike and holelike edge channels exist in the bulk energy gap, analogous to the helical edge channels in a quantum spin Hall insulator. When the Fermi level was set in the conduction band, the injected charge pulses propagated unidirectionally as chiral edge magnetoplasmon (EMP) wave packets. In contrast, in the nonchiral regime with the Fermi level set in the band gap, the injected charge pulses propagated identically in both directions with significantly reduced velocity. In addition, as the channel length increased, we observed a sharp crossover from EMP transport to diffusive transport, manifested as pronounced waveform broadening and dissipation. Simulations using a distributed capacitance model including interchannel tunneling well reproduced the observed behavior in both chiral and nonchiral regimes, revealing that interchannel charge transfer between counterpropagating channels plays a crucial role. The slow EMP transport in the nonchiral regime and its crossover to diffusive transport are consequences of the strong interchannel interaction and interchannel tunneling, respectively. Our results will thus provide insights into charge dynamics and scattering mechanism in analogous one-dimensional systems, including a quantum spin Hall insulator with helical edge modes and its expected Tomonaga-Luttinger liquid behavior.

High-order harmonic generation (HHG) using femtosecond lasers is an efficient and cost-effective method for producing attosecond and femtosecond light in the extreme ultraviolet and soft x-ray regions. This technique generates high-brightness harmonic beams with stable spectra, intensity, and wavefronts, making it a valuable tool for applications such as nanoimaging, material metrology, and studies of ultrafast dynamics. Accurate wavefront measurement of HHG beams is essential for analyzing complex incident fields, understanding atomic responses, and enhancing applications such as attosecond-driven ultrafast processes. However, quantifying the wavefronts of each harmonic component remains a challenge. Here, we present a multicolor wavefront sensing for HHG beams using the Talbot effect, validated theoretically and experimentally. Our method reconstructs individual high-order harmonic wavefronts with a spectral resolution of 4.8 eV, capturing multiple harmonic wavelengths in a single scan along the propagation direction, without the need for additional spectrometers or complex calibrations. This approach surpasses conventional Talbot effect wavefront sensing, which typically relies on quasimonochromatic waves or wavelength-averaging. Additionally, we introduce a high-accuracy centroid detection strategy that reduces tracking errors to the thousandths of a pixel while maintaining high computational efficiency, paving the way for broader applications in imaging and metrology.

Transverse thermoelectric power generation has emerged as a topic of immense interest in recent years owing to the orthogonal geometry which enables better scalability and fabrication of devices. Here, we investigate the thickness dependence of longitudinal and transverse responses in film-substrate systems, i.e., the Seebeck coefficient, the Hall coefficient, the Nernst coefficient, and the anomalous Nernst coefficient in a unified and general manner based on the circuit model, which describes the system as the parallel setup. By solving the parallel circuit model, we show that the transverse responses exhibit a significant peak, indicating the importance of a cooperative effect between the film and the substrate, arising from circulating currents that occur in these multilayer systems in the presence of a temperature gradient. Finally, on the basis of realistic material parameters, we predict that the Nernst effect in bismuth thin films on doped silicon substrates is boosted to unprecedented values if the thickness ratio is tuned accordingly, motivating experimental validation.

In the context of Monte Carlo sampling for lattice models, the complexity of the energy landscape often leads to Markov chains being trapped in local optima, thereby increasing the correlation between samples and reducing sampling efficiency. This study proposes a Monte Carlo algorithm that effectively addresses the irregularities of the energy landscape through the introduction of the estimated density of states. This algorithm enhances the accuracy in the study of phase transitions and is not model specific. Although our algorithm is primarily demonstrated on the two-dimensional square lattice model, the method is also applicable to a broader range of lattice and higher-dimensional models. Furthermore, the study develops a method for estimating the density of states of large systems based on that of smaller systems, enabling high-precision density of states estimation within specific energy intervals in large systems without additional sampling. For regions of lower precision, a reweighting strategy is employed to adjust the density of states to enhance the precision further. This algorithm is not only significant within the field of lattice model sampling but may also inspire applications of the Monte Carlo method in other domains.

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.

Bilayer and few-layer $\mathrm{Mo}{\mathrm{S}}_2$ show intrinsically higher electronic quality and substantially improved device performance than monolayer, and high-quality $\mathrm{Mo}{\mathrm{S}}_2$ wafers with controlled layer number have been grown. In addition, $\mathrm{Mo}{\mathrm{S}}_2$ has different polymorphs such as the semiconducting H phase and semimetallic distorted T (dT) phase, and vertically stacked dT-/H-$\mathrm{Mo}{\mathrm{S}}_2$ metal-semiconductor junctions (MSJs) have been synthesized. However, the contact mechanism of dT-/H (few layer)-$\mathrm{Mo}{\mathrm{S}}_2$ MSJs remains to be elucidated, and $p$-type ohmic contact to $\mathrm{Mo}{\mathrm{S}}_2$ is difficult to realize due to the high ionization energy of $\mathrm{Mo}{\mathrm{S}}_2$. In the current work, we reveal a mechanism of two-dimensional (2D) semiconductors (2DSCs) layer-number-assisted metal-semiconductor (SC) interface engineering for Schottky barrier height (SBH) reduction and $p$-type ohmic contact is achieved based on this mechanism; 2DSCs here mean H (few layer)-$\mathrm{Mo}{\mathrm{S}}_2$ and metal-SC interfaces are the dT-/H-$\mathrm{Mo}{\mathrm{S}}_2$ interfaces with increasing 2DSC layers. Specifically speaking, the mechanism is as follows: (1) two competing effects coexist, namely, the interface dipole (Δ$V$) at the metal-SC interface and the quasibonding (QB) between all adjacent layers, with Δ$V$ (QB) increasing (decreasing) SBH; (2) the effect of QB beats Δ$V$ and hence the overall effect is decreasing SBH at the metal-SC interface; and (3) the SBH reduction effect increases with increasing 2DSC layers. The mechanism of 2DSC layer-number-assisted metal-SC interface engineering should apply also for other 2DSCs with a suitable metal and hence our current work paves an avenue for ohmic contact to few-layer 2DSCs by the accumulated interlayer QBs that widely present in 2DSCs.

In recent years, applications of quantum simulation have been developed to study the properties of strongly interacting theories. This has been driven by two factors: on the one hand, needs from theorists to have access to physical observables that are prohibitively difficult to study using classical computing; on the other hand, quantum hardware becoming increasingly reliable and scalable to larger systems. In this work, we discuss the feasibility of using quantum optical simulation for studying scattering observables that are presently inaccessible via lattice QCD and are at the core of the experimental program at Jefferson Laboratory, the future Electron-Ion Collider, and other accelerator facilities. We show that recent progress in measurement-based photonic quantum computing can be leveraged to provide deterministic generation of required exotic gates and implementation in a single photonic quantum processor.

Biological functionality of cellular aggregates is largely influenced by the activity and displacements of individual constituent cells. From a theoretical perspective this activity can be characterized by hydrodynamic transport coefficients of diffusivity and conductivity. Motivated by the clustering dynamics of bacterial microcolonies we propose a model of active multicellular aggregates and use recently developed macroscopic fluctuation theory to derive a fluctuating hydrodynamics for this model system. Both semianalytic theory and microscopic simulations show that the hydrodynamic transport coefficients are affected by nonequilibrium microscopic parameters and significantly decrease inside of the clusters. We further find that the Einstein relation connecting the transport coefficients and fluctuations breaks down in the parameter regime where the detailed balance is not satisfied. This study offers valuable tools for experimental investigation of hydrodynamic transport in other systems of cellular aggregates such as tumor spheroids and organoids.

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins et al., Nature (London) 603, 416 (2022)] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling postprocessing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [Commun. Math. Phys. 404, 629 (2023)], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical postprocessing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm.

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime.

Recently reported van der Waals layered honeycomb rare-earth chalcohalides REChX (RE = rare earth, Ch = chalcogen, and $X$ = halogen) are considered to be promising Kitaev spin liquid (KSL) candidates. The high-quality single crystals of YbOCl, a representative member of the family with an effective spin of 1/2, are available now. The crystalline electric field (CEF) excitations in a rare-earth spin system are fundamentally important for understanding both finite-temperature and ground-state magnetism but remain unexplored in YbOCl so far. In this paper, we conduct a comprehensive Raman scattering study to unambiguously identify the CEF excitations in YbOCl and determine the CEF parameters and wave functions. Our Raman experiments further reveal the anomalous nonlinear CEF splitting under magnetic fields. We have grown single crystals of YbOCl, the nonmagnetic LuOCl, and the diluted magnetic ${\mathrm{Lu}}_{0.86}{\mathrm{Yb}}_{0.14}\text{OCl}$ to make a completely comparative investigation. Polarized Raman spectra on the samples at 1.8 K allow us to clearly assign all the Raman-active phonon modes and explicitly identify the CEF excitations in YbOCl. The CEF excitations are further examined using temperature-dependent Raman measurements and careful symmetry analysis based on Raman tensors related to CEF excitations. By applying the CEF Hamiltonian to the experimentally determined CEF excitations, we extract the CEF parameters and eventually determine the CEF wave functions. The study experimentally pins down the CEF excitations in the Kitaev compound YbOCl and sets a foundation for understanding its finite-temperature magnetism and exploring the possible nontrivial spin ground state.

Soft lubrication has been shown to drastically affect the mobility of an object immersed in a viscous fluid in the vicinity of a purely elastic wall. In this theoretical article, we develop a minimal model incorporating viscoelasticity, carrying out a perturbation analysis in both the elastic deformation of the wall and its viscous damping. Our approach reveals the singular-perturbation nature of viscoelasticity to soft lubrication. Numerical resolution of the resulting nonlinear, singular, and coupled equations of motion reveals peculiar effects of viscoelasticity on confined colloidal mobility, opening the way towards the description of complex migration scenarios near realistic polymeric substrates and biological membranes.

To apply the powerful many-body techniques of tensor networks to massless Dirac fermions, one wants to discretize the $\mathit{p}·\mathit{\sigma }$ Hamiltonian and construct a matrix-product-operator (MPO) representation. We compare two alternative discretization schemes, one with a sine dispersion, the other with a tangent dispersion, applied to a one-dimensional Luttinger liquid with Hubbard interaction. Both types of lattice fermions allow for an exact MPO representation of low bond dimension, so they are efficiently computable, but only the tangent dispersion gives a power law decay of the propagator in agreement with the continuum limit: The sine dispersion is gapped by the interactions, evidenced by an exponentially decaying propagator. Our construction of a tensor network with an unpaired Dirac cone works around the fermion-doubling obstruction by exploiting the fact that the nonlocal Hamiltonian of tangent fermions permits a local generalized eigenproblem.

Collective behaviors leading to various fascinating movement patterns are believed to be the product of complex interplay among individuals. Previous studies have identified two types of leadership structures in pigeon flocks, i.e., hierarchical networks and reciprocal relationships. However, both of these leadership structures are predicated based on data analysis and lack substantial empirical evidence. Additionally, it is difficult to delineate a direct correspondence between leadership structures and trajectory data for pigeon flocks because birds cannot report their leadership structure. Herein, based on experiments involving volunteers, we found that tree structures may serve as the leadership structures employed by group motions exhibiting hierarchy. In the tree structure, each follower follows its only leader during collective motions, and the single top leader determines the direction of collective motions. By employing the tree structure, we introduced a model for describing collective motions. A method for obtaining the leadership structure through experimental trajectory data was proposed. This strategy was used to analyze flight trajectory data from a pigeon flock and elucidate the pigeons' leadership relationships. Our model can simulate the collective behavior of pigeon flocks, thus accurately replicating the findings of previous experimental studies. The results of this study provide insights regarding the leadership structure in pigeon flocks and have implications for artificial collective systems, e.g., autonomous formation control of multiple unmanned aerial vehicles or unmanned surface vehicles.