Superlattice engineering, the introduction of periodic potentials to a material, can induce selective scattering events that modify the electrical properties of the material in predictable ways. Extending superlattice engineering to three-dimensional (3D) electronic materials can expand the tunability of their transport properties beyond traditional means such as doping. Here, we show that magnetotransport can be modified by superlattices in 3D materials based on the relative symmetry between the Fermi surface and nanostructured superlattice. We demonstrate commensuration oscillations in the ballistic transport regime of a nanostructured 3D material with the Weyl semimetal NbP. The oscillations encode information about the shared properties between the quasiparticles at the Fermi surface—including their momentum, charge, mass, and rotational symmetry—and the structure of the superlattice. These results point toward methods to engineer quantum transport in 3D electronic materials based on the mutual properties of the superlattice and Fermi surface.

The non-Hermitian skin effect (NHSE), characterized by the accumulation of a macroscopic number of bulk states at system boundaries, is a hallmark of non-Hermitian physics. However, in higher dimensions, achieving deterministic control over where skin modes accumulate remains a major challenge. Here, we propose a versatile route to program the skin-mode localization site in two-dimensional non-Hermitian lattices by combining disorder with a static electric field. While the electric field alone suppresses the NHSE in a clean system, the introduction of disorder induces transverse wave-packet transport perpendicular to the field. In nonreciprocal lattices, when the nonreciprocal hopping is misaligned with the electric field, the hopping component perpendicular to the field guides wave-packet propagation and produces boundary localization. By tuning the relative orientation between the electric field and the nonreciprocal hopping direction, the boundary localization position can be continuously and arbitrarily controlled. We further demonstrate distinct geometry-dependent manipulation of skin modes in reciprocal lattices, where controllable boundary localization emerges solely from the lattice geometry. Our results establish a robust and broadly applicable route to engineer boundary accumulation and directed transport along prescribed directions in two-dimensional non-Hermitian systems, enabling reconfigurable wave routing in classical platforms and programmable transport functionalities in quantum settings.

We investigate the nonequilibrium glassy dynamics of dense, topologically constrained ring-polymer systems subjected to instantaneous thermal quenches, using large-scale molecular dynamics simulations. We show that the onset of dynamical arrest is strongly controlled by polymer stiffness, with increasing rigidity enhancing configurational constraints and shifting the estimated glass transition temperature to higher values. Below this threshold temperature, the system exhibits clear aging behavior, manifested through the waiting-time dependence of two-time correlation functions, with the corresponding structural relaxation approximately displaying a simple aging scenario. A key feature of these systems is the emergence of long-lived threaded rings, upon quenching, which act as persistent topological constraints, and the threading relaxation is shown to be correlated with aging dynamics. At the collective level, persistent threading leads to pronounced spatial heterogeneity, manifested as clustering of centers of mass of the rings. Cluster formation is maximized near the glass transition and is enhanced by polymer stiffness, while aging primarily affects the kinetics of cluster participation rather than their characteristic size. Overall, our study establishes a direct connection between polymer stiffness, topological constraints, spatial heterogeneity, and aging dynamics in ring-polymer glasses.

Some atomic nuclei exhibit enhanced octupole collectivity, reflected in finite reflection-asymmetric multipole correlations rather than necessarily in a rigid static pear-shaped ground state. Low-energy studies indicate finite octupole strength in uranium-238, commonly interpreted as soft or vibrational in nature, in addition to its large prolate quadrupole collectivity [F. K. McGowan and W. T. Milner, Nucl. Phys. A 571, 569 (1994).; T. Kibédi and R. H. Spear, At. Data Nucl. Data Tables 80, 35 (2002)]. Here, we investigate how such octupole correlations can be encoded in the initial geometry of relativistic heavy-ion collisions and mapped to final-state flow observables. Using state-of-the-art hydrodynamic calculations, we demonstrate quantitative sensitivity to octupole-induced features encoded in the initial-state geometry and suggest a modest effective octupole-correlation strength in uranium-238 that is compatible with the latest high-energy experimental measurements [B. E. Aboona et al. (STAR), Rep. Prog. Phys. 88, 108601 (2025)]. These findings provide a complementary probe of odd-order nuclear collectivity and help constrain quark-gluon plasma initial conditions.

As a nontrivial state of solid, Schwarz crystal manifests itself in extremely refined nanograins and triply periodic minimal surface (TPMS) grain boundaries, and therefore exceptional thermal and mechanical stability. However, it remains unexploited how the structural units interact and thermal transport is influenced by such complex atomic structures. Here, we investigate Schwarz crystals of Cu and Pt using neutron scattering and thermal transport measurements. Pair distribution function analysis reveals the coexistence of compressed grains and expanded TPMS grain-boundary regions, forming a spatially confined atomic environment. This leads to an unusual phonon hardening suggested by inelastic neutron scattering. As bulk materials, Schwarz crystals exhibit a quasilinear temperature dependence of thermal conductivity, which was previously only observed in low-dimensional materials. This contrast indicates that the spatial confinement of the Schwarz crystals plays a crucial role in determining the unique thermal conductivity, which also provides an emergent route to regulate thermal properties in bulk materials.

We propose and analyze a scheme to generate squeezed light by storing a classical probe pulse in a Bose-Einstein condensate (BEC) and exploiting the nonlinear evolution caused by atom-atom collisions during the storage time. A $\mathrm{\Lambda }$-type optical memory interface maps a chosen temporal probe mode onto a single phase-matched collective spin wave; for a coherent input, this prepares a tunable coherent spin state of a two-component BEC, with its initial spin orientation set by the stored mean excitation number and the phase relation between the probe and control fields. Collisional interactions during storage then implement one-axis-twisting dynamics and generate spin squeezing in the atomic ensemble. We account for realistic loss and finite memory and retrieval efficiencies, and model readout as a single-mode beam-splitter mapping that transfers the atomic quadrature squeezing onto a propagating optical mode. The model identifies optimal storage times and predicts that, under realistic conditions, several decibels of squeezing can be transferred to the retrieved light.

We introduce a linear-scaling stochastic method to compute real-space maps of any positive local spectral operator in a tight-binding model. By employing positive semidefinite estimators, the sampling error at each site can be rigorously bounded relative to the mean via the Markov inequality, overcoming the lack of self-averaging and enabling accurate estimates even under strong spatial fluctuations. The approach extends to nondiagonal observables, such as local currents, through local unitary transformations and its effectiveness is showcased by benchmark calculations in the disordered two-dimensional $\pi$-flux model, where the local density of states and steady-state current maps are computed. This method will enable simulations of disorder-driven mesoscopic physics in realistically large lattices and accelerate real-space self-consistent mean-field calculations.

We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on reduced density matrix data from $L$-site Fermi-Hubbard models, we identify minimal latent spaces with a sharp reconstruction quality threshold at $L-1$ latent dimensions, matching the system's intrinsic degrees of freedom. We demonstrate the use of the trained decoder as a differentiable variational ansatz to minimize energy directly within the latent space. Crucially, this approach circumvents the $N$-representability problem, as the learned manifold implicitly restricts the optimization to physically valid quantities.

Alternating shear rotations in dense suspensions have recently shown the ability to reduce both viscosity and dissipation per strain (at a fixed global shear rate). Here, we study alternating shear rotations, with extensive numerical simulations, at various angles and up to their corresponding jamming points. For increasing shear rotation angles, we find that the jamming point is continuously shifted to higher packing fractions for frictional particles, while it remains constant for frictionless particles. As a consequence, the alternating shear rotation is unable to reduce the dissipation per strain for suspensions composed of frictionless particles. We detail the individual contributions, hydrodynamic or contact, to the shear stress, which are uncharted for this protocol. As the angle of rotation increases, the average contact stress decreases. However, we find that the hydrodynamic stress shows the opposite trend, instead increasing with increasing angle. Hence, hydrodynamic stress will dominate up to much higher packing fractions as the angle of rotation increases. In addition, we report how the microstructure varies and establish a one-to-one mapping between the contact number and its contribution to the total stress for both frictionless and frictional particles.

Cells control their size to cope with noise during growth and division. Eukaryotic cells exhibiting “sizer” control (targeting a specific size before dividing) may rely on molecular concentration thresholds, but simple implementations of this strategy are not stable. We derive a general criterion for concentration-based sizer control and demonstrate it with a mechanistic model that resolves the instability by using multistage progression toward division. We show that if molecular dynamics in one stage satisfy the sizer criterion, then sizer control follows for the whole progression. We predict that perturbations to the molecular dynamics in nonsizer stages shift the size statistics without disrupting sizer control, consistent with recent experiments in fission yeast.

The kinetic energy dissipation rate is of central importance for small-scale statistics in turbulent flows. Here, we report a transition to the dilatation-dominated regime of three-dimensional isothermal fully compressible, homogeneous, isotropic turbulence by moments of energy dissipation and its components up to order 4 for root-mean-square (rms) Mach numbers $0.1\le M_{\mathrm{rms}}\le 10$ and for Reynolds numbers $100\le \text{Re}\le 2400$. Our high-resolution numerical simulations show a crossover from incompressible to $M_{\mathrm{rms}}$-independent, Burgers turbulencelike scaling of energy dissipation rate moments with respect to Reynolds number $\text{Re}$. This confirms the statistical dominance of shocks for rms Mach numbers $M_{\mathrm{rms}}\gtrsim 1$.

We propose a quantum sensor for electric fields based on networks of Rydberg atoms. The sensing mechanism exploits the strong dependence of the Rydberg blockade on the applied electric field near a Förster resonance. In this regime, variations of the electric field across the array lead to local changes in the blockade radius. Therefore, owing to its spatially distributed architecture, the device can operate as a gradiometer. Our analysis shows that our scheme enables detection of spatial variations in the electric field with a resolution of few micrometers. We analyze the dynamics of Rydberg excitations for systems with different spatial geometries and electric field configurations to establish the relation between the applied field and the blockade response. For spatially inhomogeneous fields, we also provide another observable, density-density correlations, that can probe the field’s spatial structure.

Quantum Monte Carlo (QMC) methods are uniquely capable of providing exact simulations of quantum many-body systems. Unfortunately, the applications of a QMC simulation are limited because extracting dynamic properties requires solving the analytic continuation problem. Across the many fields that use QMC methods, there is no universally accepted analytic continuation algorithm for extracting dynamic properties, but many publications compare to the maximum entropy method. We investigate when entropy maximization is an acceptable approach. We show that stochastic sampling algorithms reduce to entropy maximization when the Bayesian prior is near to the true solution. We investigate when is Bryan’s controversial optimization algorithm [Bryan, Eur. Biophys. J. 18, 165 (1990)] for entropy maximization (sometimes known as the maximum entropy method—MEM) appropriate to use. We show that Bryan’s algorithm is appropriate when the noise is near zero or when the Bayesian prior is near the true solution. We also investigate the mean squared error, finding a better scaling when the Bayesian prior is near the true solution than when the noise is near zero. Together, these three results indicate that enhancing the quality of the Bayesian prior enhances the validity of the MEM and has better return on investment than reducing the noise. We point to examples of improved data-driven Bayesian priors that have already leveraged this advantage. We support these results by solving the double Gaussian problem using both Bryan’s algorithm and the newly formulated dual approach to entropy maximization [Chuna et al., J. Phys. A: Math. Theor. 58, 335203 (2025)].

We investigate the impact of an isotropic antiferromagnetic Heisenberg perturbation on the toric code, focusing on the resulting quantum phase transition and the nature of the phase that emerges beyond topological order. Using neural-network quantum states, we compute ground states over a wide range of Heisenberg couplings while fully respecting the exact symmetries of the model. In the weak-coupling regime, the numerical results are in excellent agreement with an effective low-energy description derived from a Schrieffer-Wolff (SW) transformation, providing analytic control over the perturbative breakdown of topological order. We show that the Heisenberg perturbation only renormalizes local operators at low orders, whereas mixing between topological sectors occurs only at a perturbative order proportional to the system size. At intermediate values of the Heisenberg interaction, the topological phase breaks down. We estimate the critical point through a combination of the fidelity susceptibility and the logarithmic susceptibility of noncontractible Wilson loops for various system sizes. Furthermore, we utilize the topological entanglement entropy to provide a comprehensive characterization of the phase transition. Beyond the transition, an antiferromagnetic $\pm X/\pm Z$ Néel phase emerges, characterized by a fourfold-degenerate symmetry-broken manifold, which is explicitly probed using staggered-magnetization-based diagnostics. Our results show how local two-spin interactions, which naturally arise in realistic implementations of the toric code, drive the breakdown of topological order. Moreover, we establish the SW approach as a systematic framework for analyzing such perturbations in combination with variational many-body methods.

Biomolecular condensates frequently form near surfaces, including lipid membranes. However, the effect of a surface on phase separation within a cell, a confined system, remains elusive. In this work, we study surface phase separation under the constraint of a finite number of molecules in a confined space and identify three wetting states: thin, intermediate, and thick. Notably, the intermediate state, which is unstable in a fixed-chemical-potential system, can be stabilized in confinement below a critical width. Intriguingly, in wider systems, the intermediate solution becomes unstable due to a negative relationship between the chemical potential and particle number, leading to partial-wetting or prewetting droplets. Importantly, as the particle volume fraction increases from a lower value starting from a homogeneous surface, droplets form when the average volume fraction reaches the threshold of the intermediate state, which can be much lower than the threshold of bulk spinodal decomposition. Our theory elucidates the kinetic pathways of droplet formation in a confined system, providing crucial insights into how biomolecular condensates near surfaces respond to cellular events, such as the synthesis of biomolecules.

Quantum machine learning is an emergent field that continues to draw significant interest for its potential to offer improvements over classical algorithms in certain areas. However, training quantum models remains a challenging task, largely because of the difficulty in establishing an effective inductive bias when solving high-dimensional problems. In this work, we propose a training framework that prioritizes informative data points over the entire training set. This approach draws inspiration from classical techniques such as curriculum learning and hard example mining to introduce an additional inductive bias through the training data itself. By selectively focusing on informative samples, we aim to steer the optimization process toward more favorable regions of the parameter space. This data-centric approach complements existing strategies such as warm-start initialization methods, providing an additional pathway to address performance challenges in quantum machine learning. We provide theoretical insights into the benefits of prioritizing informative data for quantum models, and we validate our methodology with numerical experiments on selected recognition tasks of quantum phases of matter. Our findings indicate that this strategy could be a valuable approach for improving the performance of quantum machine learning models.

Critical quantum metrology aims to harness critical properties near quantum phase transitions to enhance parameter estimation precision. However, critical slowing down inherently limits the achievable precision within a finite evolution time. To address this challenge, we establish a fundamental scaling limit of critical quantum metrology with respect to the total evolution time. We find that the effective winding number of the system's phase-space trajectory determines the scaling bound of quantum Fisher information. Furthermore, we demonstrate that the exponential scaling of the quantum Fisher information can be obtained, and for this, it is necessary to increase the effective winding number by the total evolution time. Based on the topological nature of the effective winding number, we explicitly derive an optimal time-dependent control whose form is, surprisingly, a simple on-off scheme. We highlight that such an exponential scaling of quantum Fisher information remains valid even without reaching the critical point and in the presence of thermal dissipation, albeit with a decreased exponent.

A Bernoulli factory is a model for randomness manipulation that transforms an initial Bernoulli random variable into another Bernoulli variable by applying a predetermined function relating the output bias to the input one. In literature, quantum-to-quantum Bernoulli factory schemes have been proposed, which encode both the input and output variables using qubit amplitudes. This fundamental concept can serve as a subroutine for quantum algorithms that involve Bayesian inference and Monte-Carlo methods, or that require data encryption, like in blind quantum computation. In this work, we present a characterization of the complexity of the quantum-to-quantum Bernoulli factory by providing a lower bound on the required number of qubits needed to implement the protocol, an upper bound on the success probability, and the quantum circuit that saturates the bounds. We also formalize and analyze two different variants of the original problem that address the possibility of increasing the number of input biases or the number of functions implemented by the quantum-to-quantum Bernoulli factory. The obtained results can be used as a framework for randomness manipulation via such an approach.

Machine-learning surrogates are increasingly used to accelerate computational fluid dynamics (CFD), yet their robustness to small, structured input perturbations remains insufficiently characterized. Conventional adversarial training is largely impractical for artificial intelligence for CFD (AI4CFD), since perturbed inputs alter the underlying partial differential equation (PDE) solutions and require costly resimulation for valid labels. We propose a systematic post hoc robustness audit and apply it to the Fourier neural operator (FNO) and UNet on three canonical two-dimensional flows—laminar tube flow, transient dam break, and cylinder wake—using Gaussian, random, and fast gradient sign method (FGSM) perturbations together with normalized mean-squared error, attack gain, and signed-bias diagnostics. Adversarial vulnerability is found to arise from a coupling between flow physics and architectural inductive bias: The transient dam break exhibits heavy-tailed gains with rare amplification of order ${10}^2$, while steadier flows remain bounded. A spectral analysis with a proposed attack separability index shows that FNO behaves like a spectral solver with global mode coupling, whereas UNet acts as a dissipative low-pass scheme, with the distinction modulated by PDE type. A physical-realizability audit, including a divergence-free Helmholtz projection control, rules out continuity violation and distributional shift, indicating that the vulnerability is intrinsic to the learned operator rather than a generalization artifact. We therefore argue that robustness auditing should accompany accuracy reporting in AI4CFD, and provide a practical three-step audit protocol.

The one-way quantum computation utilizing bosonic modes of light offers unmatched scalability of light modes, and it has seen rapid experimental development recently. Scalability requires robust and low-error gates and measurements. Squeezing gate is one of the necessary Gaussian operations. We find the optimal squeezing gate in cluster-state architecture. Our approach newly uses amplitude transmission coefficients of unbalanced beam splitters and homodyne detection with subsequent unity-gain feedforward to squeeze the input state. The approach outperforms the current method based on optimally rotated homodyne detection but fixed balanced beam splitters. The performance of both cluster-state squeezers is evaluated for Gaussian and non-Gaussian input states. We use different metrics to benchmark the quality of squeezed output states. The result opens a road to low-noise squeezing gates in experimentally achievable cluster states.