Magnetoencephalography (MEG) measures the magnetic fields generated by neural currents using quantum sensors such as superconducting quantum interference devices and atomic magnetometers. Here, we combine the energy resolution limit of magnetic sensing with the metabolic power available to neural currents to derive a technology-independent bound on the information capacity of MEG. The bound factorizes into geometry, metabolism, and Planck's constant, and gives an estimated maximum information rate of 2.2 Mbit/s for representative human-brain parameters. Further, we show that the externally measurable magnetic field has a finite angular bandwidth, with high multipole components being geometrically attenuated and falling below the quantum-limited noise floor. This yields an information-limited spatial scale of order $1\mathrm{cm}$ and renders the accessible measurement space effectively finite dimensional. The energy resolution limit therefore defines an information-theoretic Nyquist scale for magnetoencephalography, beyond which denser spatial sampling provides redundant measurements rather than additional recoverable information. Since the energy resolution limit also makes the noise variance grow linearly with measurement bandwidth, temporal and spatial bandwidths compete, producing a fundamental spatiotemporal trade-off. These results show how quantum-limited measurements constrain the observable complexity and information content of noninvasive brain imaging, providing a quantitative link between fundamental physics and neuroscience.

The complex dynamics of liquid water are manifested in its unique dielectric relaxation and nonlinear shear rheology. We investigate the dielectric and rheological responses of TIP4P/$\varepsilon$ water using molecular dynamics simulations across a wide temperature range, including the deeply supercooled regime. We demonstrate that the fast secondary Debye relaxation is faithfully captured by the continuous, history-dependent hydrogen-bond lifetime, ${\tau }_{\text{HB}}$, which directly accounts for the high-frequency deviation of the dielectric loss, ${\varepsilon }^{\prime \prime }\left(\omega \right)$, from classical Debye behavior. Unlike the history-independent intermittent lifetime ${\widehat{\tau }}_{\text{HB}}$, which scales linearly with the primary relaxation time ${\tau }_1$ and the $\alpha$-relaxation time ${\tau }_{\alpha }$, the lifetime ${\tau }_{\text{HB}}$ exhibits a distinct nonlinear dependence that characterizes the local kinetic rupture of individual bonds. We establish a link between dielectric relaxation and shear thinning, revealing that the onset of thinning occurs at a critical shear rate ${\dot{\gamma }}_0\lesssim 1/{\tau }_1\sim 1/{\tau }_{\alpha }$, governed by the competition between the shear deformation and the slowest mode of structural relaxation. Furthermore, we explore the impact of ionic solutes on the primary relaxation time ${\tau }_1$ in NaCl solutions. We find that ${\tau }_1$ exhibits a nonmonotonic dependence on salt concentration, reflecting a competition between the disruption of bulk hydrogen bonds and the formation of rigid hydration shells. Our study elucidates the connection between hydrogen bonding, dielectric relaxation, and shear rheology, providing insights applicable to other tetrahedral liquids.

t-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction algorithm that preserves local similarity by matching probability distributions between high- and low-dimensional spaces. Owing to its ability to capture complex manifold structures, t-SNE has become a widely used technique for high-dimensional data visualization. However, it still faces significant computational challenges when dealing with large-scale datasets. In this paper, we present a quantum-enhanced t-SNE algorithm that achieves a quadratic speedup with respect to the number of data samples. Specifically, three unitary operators are constructed using quantum arithmetic and comparators to compute the probability distributions. By integrating them with the Hadamard transform, the gradients of the Kullback-Leibler divergence can be efficiently computed. Moreover, since a complete quantum procedure is provided, our method surpasses the performance of existing quantum variants.

Any coarse-grained description of a nonequilibrium system should faithfully represent its latent irreversible degrees of freedom. However, standard dimensionality reduction methods typically prioritize accurate reconstruction over physical relevance. Here, we introduce a model-free approach to identify irreversible degrees of freedom in stochastic systems that are in a nonequilibrium steady state. Our method leverages the insight that a black-box classifier, trained to differentiate between forward and time-reversed trajectories, implicitly estimates the local entropy production rate. By parametrizing this classifier as a quadratic form of learned state representations, we obtain nonlinear embeddings of high-dimensional state-space dynamics, which we term latent embeddings of nonequilibrium systems (LENS). LENS effectively identifies low-dimensional irreversible flows and provides a scalable, learning-based strategy for estimating entropy production rates directly from high-dimensional time series data.

Shear thickening fluid exhibits complex properties in a wide range of external conditions, leading to the significance for probing its state-transition property at the molecular level. Here, we propose a constitutive model that unifies the three rheological stages of shear thinning, shear thickening, and secondary shear thinning by considering the short-range attractive forces governed by particle surface chemistry in shear thickening dense suspensions. The proposed model reproduces the secondary shear thinning phenomenon precisely and predicts the evolution of cluster number, size, and dispersion with a marked error reduction. Besides, based on the proposed model, the distribution of particles can be divided into isolated, uniform-aggregated, and overaggregated states according to the dissipative particle dynamics simulation results. This model provides a framework to design rheological behavior via particle surface chemistry, bridge the relationship between microstructure and macroscopic performance at various scales, and accelerate the understanding to the state-transition phenomenon in shear thickening dense suspension.

We show that the standard textbook description of (restricted) Hartree-Fock theory for (Fermi) Hubbard-like models is in need of an update, and we present such an update allowing us to correct basic and established results in the condensed matter physics literature that are qualitatively wrong. Our update amounts to adding a test that reliably checks the thermodynamic stability of solutions of Hartree-Fock equations. This stability test makes it possible to detect, by simple means and with certainty, regions in phase space where the model exhibits mixed phases where two conventional phases coexist and translation invariance is broken in complicated ways; in such a mixed phase, unconventional physics is to be expected. Our results show that mixed phases are ubiquitous in Hubbard-like models in arbitrary dimensions.

We propose a scheme to generate two isolated attosecond pulses at controllable angles through the interaction between relativistic laser pulses and a solid target. Two $p$-polarized laser pulses with certain frequency chirps are employed to drive oscillating plasma gratings (OPGs) on the target surface, while an $s$-polarized laser serves as the seed to trigger high-order harmonic generation (HHG) from the OPGs. Unlike HHG from the usual oscillating plasma mirrors, the OPGs not only generate harmonics but also diffract them. The evolution of the emission angles of the diffracted harmonics can be controlled through the laser chirp. At three specific angles, selected harmonics can be synthesized to attosecond pulse trains, and at two of these angles to isolated attosecond pulses. Two-dimensional particle-in-cell simulations are used to demonstrate the feasibility and robustness of the scheme, which shows that isolated attosecond pulse with duration around 30 $\text{as}$ and intensity close to ${10}^{17}\mathrm{W}/{\mathrm{cm}}^2$ can be stably generated. The scheme enables applications that require two isolated attosecond pulses with independent controllability.

We numerically study the transport of Rydberg excitations in chains of neutral atoms. We realize an effective flip-flop interaction using off-resonant driving fields. By tuning the relative distances between atoms and applying atom-selective detuning fields, we fulfill the perfect transport condition. This condition enables the transfer of a single Rydberg excitation from one end of the chain to the other, allowing the distribution of entanglement across the chain at the quantum speed limit. Through numerical simulations, we identify the set of control parameters that maximizes the transport probability under experimentally relevant conditions. We study the various competing trade-offs involved in the hierarchy of approximations used to map the native Rydberg spin model onto the effective model driving spin transport. Our results suggest that entanglement can be distributed over chains of more than 50 atoms spanning hundreds of microns at room temperature. This study informs the selection of parameters for the experimental realization of perfect transport in Rydberg chains, providing an approach to distribute entanglement among distant atoms in quantum processors.

Randomized benchmarking (RB) is a widely adopted protocol for estimating the average gate fidelity in quantum hardware. However, its standard formulation relies on the assumption of temporally uncorrelated noise, an assumption often violated in current devices. In this work, we derive analytic expressions for the average sequence fidelity (ASF) in the presence of temporally correlated (non-Markovian) noise with classical memory, including cases where such correlations originate from interactions with a quantum environment. We show how the ASF can be interpreted to extract meaningful benchmarking parameters under such noise and identify classes of interaction Hamiltonians that render temporal correlations completely invisible to RB. We further provide operational criteria for witnessing temporal correlations due to quantum memory through RB experiments. Importantly, while classical correlations may remain undetectable in the ASF data, they can nonetheless significantly affect worst-case errors quantified by the diamond norm, a metric central to fault-tolerant quantum computing. In particular, we demonstrate that temporal correlations may suppress worst-case errors, highlighting that temporal correlations may not always have detrimental effects on gate performance.

The bulk band structure of an in situ prepared ferromagnetic ${\mathrm{Fe}}_4\mathrm{N}$ thin film has been investigated using spin- and angle-resolved photoelectron spectroscopy with synchrotron radiation. It was observed that most of the bands near the Fermi level are of minority spin. The experimental results are well reproduced by first-principles calculations. Our orbital analysis of the band structure has revealed that the electron pocket near the $\mathrm{\Gamma }$ point is primarily attributed to the Fe $3d_{xy}$ orbital of the Fe(II) site, while the electron pocket near the M point is associated with the Fe(II) $3d_{z^2}$ orbital. Our observation of real band structure in ${\mathrm{Fe}}_4\mathrm{N}$ thin films could provide valuable insight into the properties of anisotropic magnetoresistance and anomalous Nernst effect.

We investigate the feasibility of satellite-to-ground quantum key distribution (QKD) at multi-GHz clock rates, where secure key generation time is constrained, due to low-Earth orbit (LEO) satellite overpasses ($\approx 300$ s). Higher repetition rates offer a direct route to increased secure key rate (SKR), though system performance is fundamentally limited by detector timing jitter, optical coupling losses, and atmospheric turbulence, depending on receiver architecture. We combine finite-key modeling, detailed detector timing characterization, adaptive optics (AO) modeling, and real-time free-space QKD experiments at 1 GHz to evaluate practical receiver configurations based on multi-mode fiber-coupled avalanche photodiodes (MMF-APDs) and single-mode fiber-coupled superconducting nanowire single-photon detectors (SMF-SNSPDs), at projected rates >1 GHz. The results indicate clear operating regimes across a range of atmospheric conditions: MMF-APDs maximize secure key rates below $\sim 1$ GHz, mainly due to their low coupling loss. However, low-jitter SMF-SNSPDs are required to obtain positive key rates at higher clock rates—with modeling and measured timing responses of our hardware indicating an estimated optimal operating point near $\sim 7.2$ GHz—though AO correction is required for effective single-mode coupling under turbulence. The experimental and projected results provide quantitative performance bounds and realistic design targets for future high-rate satellite QKD systems, while highlighting the technological requirements needed to achieve end-to-end operation beyond the GHz regime.

Entanglement depth quantifies how many qubits share genuine multipartite entanglement, but certification typically relies on tailored witnesses or full tomography, both of which scale poorly with system size. We recast entanglement-depth and non-$k$-separability certification as likelihood-based model selection among neural quantum states whose architecture enforces a chosen entanglement constraint. A hierarchy of separable neural quantum states is trained on finite-shot local Pauli outcomes and compared against an unconstrained reference model trained on the same data. When all constrained models are statistically disfavored, the data certify entanglement beyond the imposed limit directly from measurement statistics, without reconstructing the density matrix. We validate the method on simulated six- and ten-qubit datasets targeting Greenberger–Horne–Zeilinger state (GHZ), Dicke, and Bell-pair states, and demonstrate robustness for mixed states under local noise. Finally, we discuss lightweight interpretability diagnostics derived from trained parameters that expose coarse entanglement patterns and qubit groupings directly from bitstring statistics.

The energy cost of computation has emerged as a central challenge at the intersection of physics and computer science. Recent advances in statistical physics—particularly in stochastic thermodynamics—enable precise characterizations of work, heat, and entropy production in information-processing systems driven far from equilibrium by time-dependent control protocols. A key open question is then how to design protocols that minimize thermodynamic cost while ensuring correct outcomes. To this end, we develop a unified framework to identify optimal protocols using fluctuation-response relations (FRRs) and machine learning. Unlike previous approaches that optimize either distributions or protocols separately, our method unifies both using FRR-derived gradients. Moreover, our method is based primarily on iteratively learning from sampled noisy trajectories, which is generally much easier than solving for the optimal protocol directly from a set of governing equations. We apply the framework to canonical examples—bit erasure in a double-well potential and translating harmonic traps—demonstrating how to construct loss functions that trade-off energy cost against task error. The framework extends trivially to underdamped systems, and we show this by optimizing a bit flip in an underdamped system. In all computations we test, the framework achieves the theoretically optimal protocol or achieves work costs comparable to relevant finite-time bounds. In short, the results provide principled strategies for designing thermodynamically efficient protocols in physical information-processing systems. Applications range from quantum gates robust under noise to energy-efficient control of chemical and synthetic biological networks.

Radiation forces and torques enable the manipulation of objects with acoustic and electromagnetic waves. Yet, harnessing them in complex scattering media remains a formidable challenge, especially when multiple objects must be controlled under competing objectives. Here, we demonstrate that sound or light can be shaped to tailor momentum transfer to multiple objects simultaneously in a complex scattering medium. For a single object, our theory yields the maximal achievable force or torque; for multiple objects, it produces Pareto-optimal actuation and exact bounds on the simultaneous realization of incompatible objectives. This opens applications for wave tweezers, enabling selective and precise manipulation of objects within complex media, ranging from the handling of cells, organoids, or microrobots to targeted drug delivery in biological media.

We present a cluster kinetic Monte Carlo algorithm for active matter systems of self-propelled particles with special focus on steric interactions. The kinetic event-chain algorithm is based on the event-chain Monte Carlo method and is applied to active Brownian disks in two dimensions. The algorithm assigns Monte Carlo moves of active disks a mean time based on a comparison between Brownian dynamics and the dynamics of the event-chain Monte Carlo method. This time is used to perform diffusional rotation of their propulsion force. We show that the algorithm correctly and efficiently reproduces various physical results ranging from single-particle dynamics to many-body effects. In particular, we reproduce the phase diagram of active disks and the motility-induced phase-separated region with high accuracy. The kinetic event-chain algorithm is shown to be much faster—at comparable accuracy—than (event-driven) Brownian dynamics algorithms, enabling large-scale simulations up to giant systems with ${10}^5$ particles on standard desktop hardware.

In recurrent cortical networks, inhibitory kinetics are often assumed to tune oscillation frequency continuously. Using a recurrent excitatory-inhibitory (E-I) model with fixed excitatory synaptic dynamics, we systematically vary inhibitory decay time constants and show instead that inhibitory timescale acts as a control parameter that drives sharp transitions between distinct collective states. The network switches between a coherent high-frequency regime and a coherent gamma-band regime, separated by an intermediate transition state with reduced synchrony and unstable phase coordination. Phase analysis demonstrates that the coherent regimes exhibit distinct, internally consistent E-I coordination patterns, whereas these relationships break down in the transition regime. Dissecting inhibitory pathways reveals that inhibitory-to-inhibitory decay times determine the operative regime, while inhibitory-to-excitatory kinetics primarily adjust oscillation frequency and the width of the transition region without altering the global phase structure. Our results link physiological diversity in inhibitory synaptic timescales to discrete oscillatory regimes in recurrent cortical circuits.