Articles in 2025

This work addresses the challenges of radio frequency interference (RFI) in radio astronomy. The authors train spiking neural networks on synthetic and real data, demonstrating a viable path for real-time, energy-efficient RFI detection.

Optical skyrmions, as topologically stable structures, offer promising applications in spintronics and quantum computing, yet their dynamic transitions remain underexplored. Here, the authors demonstrate controllable transitions of two-dimensional optical skyrmions by adjusting Bessel beam components, revealing potential for advanced information processing and transmission technologies.

Quantum metrology is one of the most promising applications of quantum science and technology, yet noise often constrains its precision and sensitivity. We propose enhanced virtual purification methods to address the significant noise accumulation and additional noise from the implementations, which demonstrate quantum advantage under specific scenarios.

Ferrimagnetic materials offer a promising platform for fast and energy-efficient spin dynamics in next-generation spintronic devices. Here, the authors experimentally demonstrate spin–orbit-torque-driven perpendicular standing spin waves in AuPt/GdFeCo and identify the Ta capping layer as a crucial antioxidant barrier that enhances interfacial pinning and stabilizes higher-order spin-wave modes.

The study of ion-atom interactions often requires the ion to be trapped, however the effects of the trapping potential have been elusive. The authors find that if the trapped ion is described as a delocalized charged distribution according to its motional ground state in the trap, the trap frequency modifies the scattering and transport properties of the ion in a neutral bath.

The growing demand for computational and energy resources in neural network training presents a significant challenge. The authors demonstrate that quantum annealing platforms, like D-Wave, can efficiently train classical neural networks, achieving superior performance scaling compared to classical methods, with potential implications for more sustainable AI development.

Traditionally, material topology is classified by approximating a system as infinite and using methods based on band theory, prohibiting the study of effects due to finite size. Here, the authors develop an approach rooted in a real-space classification framework to show how material topology can change as a finite system’s shape is altered.

Predicting stable structures of large crystals, unit cells containing tens or even hundreds of atoms, has been a long-standing challenge. In this article, the authors present a combinatorial optimization-based algorithm that leverages the inherent symmetry of crystals, making it potentially achievable, especially with future implementation on quantum hardware.

High dimensional quantum key distribution (QKD) systems will allow for higher key generation rate, but with added complexity for creating and detecting high dimensional quantum states. The authors demonstrate a QKD protocol using “qubit-like” qudit states, “F-qubits”, with simpler generation and detection, maintaining the benefits of high dimensional QKD protocols.

Multifractality occurs in the local density of states (LDOS) in disordered electronic systems near a metal-insulator transition, and despite an abundance of theoretical studies, experimental analyses are rare. Here, using scanning tunneling microscopy, the authors investigate the correlations of the LDOS and multifractal spectra for Sn/Si monolayers and connect the localization patterns of disordered electronic systems with the Weyl group symmetry of nonlinear sigma-models.

Steering nanobots in crowded biological environments raises questions about the limits on the size of a steerable propeller under thermal noise. Here, the authors theoretically investigate torque-driven actuation of magnetic nanohelices in a viscous fluid, revealing that weak rotational diffusion significantly disrupts their orientation and propulsion, offering insights into the challenges of controllable steering at the nanoscale.

Antiferromagnetic order in iridates arising from unusual combinations of strong electron-correlation and spin-orbit coupling has drawn broad attention in the condensed matter community. In this work, the authors uncover the interplay between antiferromagnetic and nematic orders, highlighting its fundamental importance and potential functional applications.

The interplay between gain saturation, nonlinearity, and band topology is investigated in SSH laser arrays. The authors show that, in certain regimes, the lasing zero mode no longer inherits the spatial profile of the localized defect state dictated by the underlying topological lattice. Instead, it becomes delocalized, with its intensity distributed nearly uniformly across the entire array.

In quantum-logic spectroscopy schemes, the internal state of an atom or molecule without easily accessible closed-cycling transitions is projected onto a co-trapped atom that serves as a probe. Here, the authors investigate the effect of the motion of trapped ions on the state detection of a single nitrogen molecular ion with such a scheme.

Exciton-polariton condensates in optical microcavities enable next-generation all-optical devices, but simulating their complex nonlinear dynamics remains computationally challenging. The authors demonstrate that Fourier Neural Operators trained on experimental and numerical data predict condensate behavior three orders of magnitude faster than conventional solvers while maintaining high accuracy.

Spin-triplet superconductivity offers a promising route to topological quantum computation, yet its realization and control in solids remain challenging. Here, through the framework of Floquet engineering, the authors propose using polarized-laser irradiation to selectively manipulate p- wave pairing correlations for transition metal oxide systems and verify its feasibility using a dynamical simulation.

When negative-SET occurs in resistive random access memory it causes an irreversible hard breakdown. Here, the authors demonstrate that hydrogen ions moved directionally towards the electrodes under an applied electric field are an underlying factor in the occurrence of negative-SET.

Biological systems must encode time intervals crucial for tasks like navigation and communication, yet mechanisms for intervals of hundreds of milliseconds to minutes remain unclear. The authors develop a Bayesian framework using neural fatigue and cellular heterogeneity to optimize interval representation, enhancing our understanding of timing memory and its computational roles.

The authors developed a new inverse design approach using automatic differentiation with molecular dynamics to program patchy particles to self-assemble into curved structures. By optimizing anisotropic interactions through spherical harmonics, they successfully formed clusters with the desired curvature, offering a promising way to control curvature-directed self-assembly.

3D spin textures are promising for future data storage but remain hard to detect, especially in antiferromagnets. Here, the authors show that electronic signals in electron microscopy can reveal these hidden structures, opening new paths to study and use 3D magnetic textures.