Diffuse laser cooling (DLC) provides a compact, robust, and scalable approach to generating cold-atom ensembles, offering significant potential for ground-based and spaceborne microwave atomic clocks. However, the performance of DLC systems remains limited by the weak atom-light interaction arising from their relatively low atomic density. This limitation is characterized by the optical density (OD), which quantifies the effective interaction strength between the atomic ensemble and the probe light and strongly influences the achievable signal-to-noise ratio. Although quasi-one-dimensional (quasi-1D) DLC extends the probe interaction length and improves atomic utilization, increasing the local atomic density within the interrogation region remains challenging. Here, we demonstrate a magnetically enhanced quasi-1D DLC system that simultaneously maintains the extended interaction length and increases the atomic density through a two-dimensional magnetic-field gradient. By spatially compressing and accumulating atoms near the probe axis, we enhance the OD of a 70-cm-long cold rubidium ensemble from 2.49 to 3.94, representing a 58% enhancement and an equivalent increase in the local atomic density. The dependence of the OD on the magnetic-field gradient, probe intensity, beam size, and cooling-light polarization is systematically investigated. This work establishes a practical pathway for enhancing optical interrogation efficiency and could pave the way for the development of high-performance DLC-based cold-atom clocks and quantum sensing systems.

Quantum transport in disordered systems poses intriguing fundamental questions, such as the interplay of disorder, interactions, and decoherence, and has attracted significant interest. These fundamental aspects also have important implications in real-world scenarios, such as energy transfer in nanoscale systems (e.g., light-harvesting complexes) and quantum information transfer. This large body of work spanning broad research areas in physics, chemistry, and nanotechnology has led to major insights and advances, yet open questions remain, namely, in terms of the emergence of multiple timescales observed in the dynamics of such systems. Here, we investigate the emergence of multiple transport timescales in the dissipative dynamics of a spin impurity coupled to a small, spatially disordered network of spins. Using a two-dimensional tight-binding model with dipolar interactions and local dephasing, we demonstrate that geometric heterogeneity leads to hierarchical coupling strengths. This results in pronounced separation of dynamical timescales, far beyond those expected in homogeneous environments. By analyzing different metrics for dynamics, we identify distinct relaxation timescales associated with cluster-level equilibration and global equilibration. A minimal three-site model reveals the physical origin of the longest timescale: Strong internal hybridization generates an effective detuning that suppresses transfer to other weakly coupled sites, yielding a parametrically enhanced relaxation time in the weak dephasing regime. We corroborate this picture with nonequilibrium steady-state transport calculations and extensive simulations of disordered spin configurations, demonstrating orders-of-magnitude slowing of relaxation when hierarchical couplings are present. Our results highlight the central role of geometry and connectivity in spin networks and open quantum systems in general, and provide experimentally relevant predictions for relaxation times in small spin baths.

Quantum correlations can be harnessed to improve the precision in parameter estimation beyond classical capabilities. Under a standard interferometric or rotation protocol, it is well established that the optimal single-mode Gaussian state is a standard squeezed vacuum, which enables Heisenberg-limited precision. In this work, we investigate the potential metrological advantage of two distinct families involving high-order squeezing, namely, $m\mathrm{th}$-phase and multisqueezed states. Our results show that these non-Gaussian states can grant a significant metrological advantage with respect to the optimal squeezed vacuum under equivalent conditions, i.e., at equal occupations. Their advantage holds at both low and large occupations, but its behavior critically depends on the chosen family of high-order squeezing. While higher squeezing orders enhance the advantage, this comes at the cost of higher-order observables in the measurement for full metrological performance. Finally, we study their robustness to standard decoherence channels, i.e., pure dephasing and zero-temperature damping. Employing standard squeezing as reference state, our results indicate a reasonable robustness against damping up to a certain noise strength, while their metrological advantage becomes fragile under pure dephasing. Our work shows the potential enhancement in quantum metrology beyond Gaussian states, carefully detailing the main challenges and limitations.

Structural phase transitions in metals are conventionally described in terms of external pressure and density, while electronic entropy is typically regarded as a secondary correction to the free energy. Here, we show that electronic entropy constitutes a thermodynamic control parameter of comparable importance to external pressure in determining structural stability. Using finite-temperature density functional theory, we construct pressure–electronic-temperature phase diagrams for 15 transition metals spanning the hcp, fcc, and bcc families and systematically investigate the competition between compressive and entropic contributions to the free energy. We find that increasing electronic entropy progressively reshapes the free-energy landscape, reducing structural competition and driving universal trends in phase stability across chemically diverse metals. In contrast, external pressure often acts in opposition, stabilizing structures that are suppressed by electronic excitation. The resulting phase behavior cannot be understood from pressure alone but emerges from the balance between these two thermodynamic driving forces. Our results establish a general framework for understanding structural transformations in electronically excited matter, extend the conventional pressure-temperature description of metals by explicitly incorporating electronic entropy, and provide quantitative predictions for ultrafast laser and x-ray free-electron laser experiments operating far from equilibrium.

We show that van Hove singularities in radio-frequency spectra of the three-dimensional (3D) attractive Fermi-Hubbard model provide a robust route to extracting the pairing gap. Four types of singularities are classified, and their spectral positions are shown to depend solely on the pairing gap $\mathrm{\Delta }$ and chemical potential $\mu$ through simple algebraic relations. Measuring two well-resolved singularities therefore determines both parameters without requiring full spectral fitting. Numerical simulations incorporating phenomenological lifetime and scattering broadenings confirm that these features remain visible in both momentum-integrated and $k_z$-integrated spectra, and become more pronounced at stronger coupling where conventional backbending methods lose sensitivity. At half filling, particle-hole symmetry fixes $\mu$, reducing the extraction to a single singularity measurement. These results establish van Hove singularity analysis as a practical spectroscopic diagnostic for pairing in quantum-simulated 3D Fermi-Hubbard systems.

This paper explores an efficient method for entanglement quantification in two-qubit and qubit-qutrit quantum systems based upon the framework of collective measurements in conjunction with machine learning. We introduce an adaptive measurement procedure in which measurement settings are dynamically adjusted based on prior measurement outcomes aiming to optimize the inference precision given a limited number of these measurement settings. The procedure makes use of the long short-term memory networks to recurrently process collective measurements on two copies of the investigated states. Obtained results demonstrate the tangible benefits of the adaptive measurements in comparison to previously described nonadaptive strategies.

While elongated particles in viscous fluids follow Jeffery orbits, their dynamics in dense granular flows remain unresolved. We demonstrate that sheared, low-friction granular rods do not follow these orbits, but instead reach a quasiequilibrium state quantitatively described by classical liquid crystal theory, with collisions providing effective thermal noise. This quasiequilibrium analogy breaks down at two limits: low aspect ratios, where the theory incorrectly predicts isotropy, and high interparticle friction, which drives a transition from steric screening to frictional gearing. We map this far-from-equilibrium breakdown using a phenomenological effective Ericksen number, establishing the limits of thermal thermodynamic analogies in driven athermal matter.

Living cells and tissues can generate complex patterns of electric fields and fluid flows, which can play important role in physiology. Both fields and flows are rooted in ion transport across biological interfaces—cell membranes and epithelial cell layers. Here, we develop a unified electrohydraulic framework that combines electric fields, osmotic pressures, and fluid flows, emphasizing their couplings. We consider an active, permeable interface that drives electrohydraulic fields in the surrounding bulk. We show that spatially heterogeneous ion transport acts as a distributed current source, generating long-range electric fields, osmotic gradients, and fluid flows. Using this framework, we show that patterns of ion pumping at cell and tissue boundaries can simultaneously produce large-scale electric fields and fluid flows due to electrohydraulic coupling. A key insight is that an external electric field and an internal dipolar pumping pattern can be physically equivalent—They can generate the same pattern of ion current and fluid flows. The induced dipolar osmotic pressure can drive self-propulsion through bulk osmotic coupling, with a mobility determined by interfacial permeability and system size, a mechanism distinct from classical electrophoresis or electro-osmosis. We further show that for strong fields a new effect emerges. Nonlinear coupling can lead to isotropic swelling of a hollow ball of cells. This can explain recent experiments on epithelial organoids. Finally, we show that feedback between ion transport and resulting electric fields can drive spontaneous symmetry breaking, generating dipolar or multipolar fields and patterns. Our work highlights the importance of electrohydraulic coupling in the emergence of currents and fields in the biological systems.

A central challenge in movement ecology is to describe ecologically meaningful residence sites from raw tracking data due to heterogeneous sampling frequency and uncertain site boundaries. Here, we develop a greedy nearest-neighbor clustering approach with local reassignment and polygon-based site construction that generates timestamped sequences of site visits for each tracked individual, and apply it to 9 years of global positioning system (GPS) data from two sympatric raven species in the Dead Sea region—the fan-tailed raven (Corvus rhipidurus) and the brown-necked raven (C. ruficollis). Using the resulting visitation sequences, we quantify recursion patterns using the non-Markovian individual mobility model (IMM), which captures the balance between novel-site discovery $\beta$ and preferential return $\alpha$. The inferred dynamics are consistent with IMM predictions and reveal clear interspecific differences: brown-necked ravens continue to discover new sites at a higher rate (lower $\beta$), whereas fan-tailed ravens show a steeper concentration of visits among top-ranked sites. Entropy analyses further separate the species, with brown-necked ravens exhibiting higher site entropy and higher conditional entropy of site-to-site transitions. Together, these results provide a robust framework for describing interspecific differences in movement strategies that can be used to infer memory from GPS data and suggest distinct space-use strategies in two closely related sympatric species.

The burst approximation is a widely used technique to simplify stochastic gene expression models. However, the dynamics and analytical properties of the protein number distribution in gene expression models under the burst approximation remain largely unexplored. In this study, we propose and systematically analyze surrogate models with multiple gene states and arbitrary burst size distributions. An analytical time-dependent solution to the chemical master equation is derived and then exploited in two directions. First, several fine properties of the protein number distribution are established using functional analysis. For geometrically distributed burst sizes, the distribution is dominated by a scaled negative binomial distribution and is light tailed in certain parameter regimes. Second, we develop efficient algorithms in three settings, enabling fast calculation of the protein number distribution. Furthermore, the approximation error relative to full gene expression models is estimated in terms of low-order moments of the distribution, thereby clarifying the validity of the burst approximation.

Building on the alternating-waveguide closed-mechanical-subsystem architecture introduced by Kuzyk and Wang [Phys. Rev. X 8, 041027 (2018)], we develop a diamond phononic network that overcomes the coupling-hierarchy bottleneck in the implementation analyzed there. Beyond replacing nitrogen-vacancy centers with silicon-vacancy (SiV) centers, we independently engineer the two relevant couplings: Localized defect modes and strong SiV orbital-strain interaction enhance the spin-phonon coupling $G$, whereas cavity-waveguide engineering suppresses the cavity-waveguide coupling $g$. The resulting coupling hierarchy reverses that found in earlier physical implementations of closed-mechanical-subsystem networks and places the system in the $G\gg g$ regime required for direct state transfer. This hierarchy reversal qualitatively changes the appropriate control strategy from bichromatic Mølmer-Sørensen driving to a simple direct triple swap. Within our model, the resulting protocol enables state transfer with a fidelity of 0.999 on a microsecond timescale and remains robust against mechanical dissipation, spin dephasing, and finite thermal occupation. Our results establish a distinct route for scalable solid-state phononic quantum networks.

We investigate the effect of remote band mixing on the stability of fractional Chern insulators in a family of models that approximate continuum descriptions of moiré materials. Our results suggest that the experimentally observed asymmetry between filling fractions $\nu =1/3$ and $\nu =2/3$ in twisted ${\mathrm{MoTe}}_2$ originates from a competition between a fractional Chern insulator, an electron Wigner crystal, and a hole Wigner crystal. In the absence of band mixing, the leading instability at $\nu =1/3$ is the electron crystal, whereas at $\nu =2/3$ the main competing phase is the hole crystal. Remote band mixing substantially lowers the energy of the electron crystal but has only a weak effect on the hole crystal. Consequently, it brings competing crystalline states much closer in energy to the fractional Chern insulator at $\nu =1/3$ than at $\nu =2/3$, leading to a stronger suppression of the many-body gap. This mechanism also provides an explanation for the emergence of reentrant integer quantum anomalous Hall states in moiré ${\mathrm{MoTe}}_2$ for fillings $\nu >1/2$.

We study a generalized geometric Brownian motion framework that incorporates both entries of new units and exit mechanisms for the current population, extending earlier stochastic resetting models where these rates are treated as identical. The model captures realistic features observed in many economic observables, which can be explained as market-driven firm entries/exits, worker inflow/outflow, and income growth/loss. This model is not conservative and, despite the asymmetry in the entry and exit rates, we find that the system eventually relaxes to a stationary distribution. Moreover, our analysis reveals three distinct dynamical regimes in the moments of the distribution, arising from the interplay between volatility, drift, entry, and exit rates. We further derive the survival probability and the mean first-passage time associated with the observed variable reaching certain threshold under the competing entry-exit processes. Interestingly, we identify an optimal exit rate that minimizes the mean first-passage time, providing insights into how entry and exit policies can influence the outcome of the system. These results should be useful for understanding the long-run behavior of economic systems in which growth, volatility, entry, and exit jointly shape the evolution of heterogeneous units.

We study the dynamical scaling of long-range $\mathrm{O}\left(N\right)$ models after a sudden quench to the critical temperature, using the functional renormalization group approach. We characterize both short-time aging and long-time relaxation as a function of the symmetry index $N$, the interaction range decay exponent $\sigma$, and the dimension $d$. Our results substantially improve on perturbative predictions, as demonstrated by benchmarks against Monte Carlo simulations and the large-$N$ limit. Finally, we demonstrate that long-range systems increase the performance of critical heat engines with respect to a local active medium.

Transfer and multinucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the particle identification, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays significantly enhances experimental capabilities but introduces major challenges for detector calibration, as conventional channel-by-channel approaches become inefficient, difficult to scale, and potentially inconsistent. In this work, we present a fully automated, physics-informed calibration framework based on neural networks, specifically designed for highly segmented silicon detector arrays. The method formulates calibration as a global optimization problem, in which detector gains and geometrical corrections are determined simultaneously by minimizing the width of the reconstructed excitation energy under two-body kinematic constraints. The approach relies exclusively on experimental data and well-established physical principles, without requiring explicit modeling of detector response. A distinctive feature of the method is the use of multiple neural network submodels sharing a common loss function with embedded physics constraints, enabling coherent and self-consistent calibration across all detector channels. This strategy ensures scalability, robustness, and reproducibility, making it particularly suitable for next-generation detector systems with increasing complexity. The performance of the method is demonstrated using experimental data from the Particle-Identification Silicon-Telescope Array in high-resolution fission studies in inverse kinematics. The results show excellent agreement with predicted kinematics, high-quality particle identification, and a significant improvement in calibration efficiency. The proposed framework provides a general and adaptable solution for the calibration of complex detector systems in modern nuclear physics experiments.

We use bosonization to analyze the ground-state Hall response of interacting bosonic $N$-leg ladders threaded by a flux. We derive an explicit expression of the Hall imbalance in a perturbative expansion in the band curvature, retaining fully the interactions. For small magnetic field, the Hall resistance is proportional to the derivative of the logarithm of the charge stiffness with respect to density, generalizing the result obtained in the two-leg case. We also consider the effect of temperature, and establish that at low temperature, corrections to the Hall resistance are exponentially small in the Meissner phase.

Persistent current is a hallmark of quantum phase coherence. We study the fate of the persistent current in a nonequilibrium setting, where a tight-binding ring is subjected to stochastic disorder as well as a fermionic reservoir attached to each site. We evaluate the current using the Keldysh technique and find that it exhibits nonmonotonic behavior, suggesting two distinct mechanisms of decoherence. While coupling to the reservoirs introduces a coherence length scale given by the inverse of the coupling strength, the other mechanism is more subtle and driven by the ratio of noise strength to reservoir coupling. The interplay of noise and reservoir constitutes a purely nonequilibrium steady state with a flatter distribution function that we effectively describe using classical rate equations. We also discuss possibilities of realizing our findings in ultracold-atom experiments.

We derive thermodynamic speed limits for isolated quantum systems undergoing finite-time unitary driving. The key ingredient is a set of generalized second-law inequalities: For the Gibbs entropy and for several thermodynamically motivated observational entropies, the entropy production is bounded from below by a function of the Vu-Saito quantum Wasserstein distance between the initial and final coarse-grained states. These inequalities yield lower bounds on the operation time in terms of the average entropy-production rate. We apply the framework to system-bath coarse graining, local-energy coarse graining, and diagonal entropy, and illustrate the resulting bounds with numerical and analytically solvable examples. Our results provide a thermodynamic characterization of finite-time state transformations in isolated quantum systems and clarify how coarse-grained entropy production constrains macroscopic reachability.

The functional renormalization group (fRG) is an established tool in the treatment of correlated electron systems, notably for the description of competing instabilities. In recent years, methodological advancements led to the multiloop extension of the fRG, which systematically includes loop corrections beyond the conventional one-loop truncation and yields a quantitatively accurate description of two-dimensional lattice systems. At the same time, the single-boson exchange (SBE) decomposition of the two-particle vertex has been shown to offer both computational and interpretative advantages, paving the way to more affordable approximation schemes. We here apply their combination coined as multiloop SBE fRG to the two-dimensional Hubbard model at weak coupling. After providing a detailed account of the underlying formalism in physical channels, we analyze the results for the frequency- and momentum-dependent vertex functions. We find that the SBE approximation, i.e., neglecting the flow of the multiboson exchange contributions, accurately reproduces the parquet approximation at loop convergence. The presented algorithmic improvement opens the route for the treatment of more challenging parameter regimes and more realistic models.