Browse Articles

Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.

Bipartite networks link two different kinds of nodes, but standard one-mode projections can distort their structure. Here, the authors introduce a geometric model and the B-Mercator algorithm that embed both node types in a shared hyperbolic space, yielding accurate maps that improve analysis, prediction, and synthetic network generation.

Non-Hermitian lattices with asymmetric couplings can steer light toward specific edges, producing the so-called skin effect. The authors show that when optical nonlinearity is added, this directed flow competes with self-trapping, giving rise to position-dependent thresholds and novel two dimensional “skin solitons”.

Many real-world systems involve higher-order, directional interactions that are naturally modeled by directed hypergraphs. The authors develop a framework that characterizes directed hypergraphs through hyperedge pattern frequencies, frequent co-sending and co-receiving nodes, higher-order reciprocity, and motifs, revealing their microscale organization.

The capture of helium by carbon to form oxygen is a key astrophysical reaction. The authors show that measuring this reaction using high-intensity photons and a Time Projection Chamber has significant advantages over conventional techniques, providing data with measured E1-E2 mixing angles that agree with quantum mechanical predictions.

Solar background and multiple scattering degrade the profiling depth and ocean-color parameter retrieval of ocean LiDAR. Our anti-interference circularly polarized LiDAR effectively suppresses these sources of interference and improves the detection performance, highlighting its potential for diurnal ocean remote sensing.

Temporal higher-order interactions are prevalent in real systems, yet the underlying mechanisms remain underexplored. This article provides a potential explanation for the spontaneous emergence of such interactions, by considering collaborative effects between pairwise interactions.

Designing quantum circuits is challenging due to the exponential growth of the state space. The authors introduce gadget reinforcement learning, which combines reinforcement learning with program synthesis to enhance quantum circuit exploration, demonstrating improved accuracy and scalability in complex tasks, thus bridging algorithmic design and practical implementation for hardware-specific optimizations.

Intensity-rotating beams are a class of structured beams that can potentially enable advanced optical manipulation techniques, as well as applications in free-space optical communication and high-resolution imaging, but their exploration has remained mostly theoretical. The authors demonstrate galactic-form spinning beams, i.e a stable, intensity-rotating beam whose dynamics and divergence can be precisely controlled.

Rhythmic phenomena, ubiquitous in biological systems, are often modeled as networks of coupled limit-cycle oscillators. This study introduces an adaptive LASSO based approach to infer the interaction type-"including pairwise and higher-order interactions-" from time-series data, demonstrating its effectiveness on synthetic datasets and human brain network data.

Systems across logistics, economics, ecology, and computing, where agents aim to maximize gain or minimize cost in resource allocation, exhibit behavior consistent with constrained optimization. This study combines optimal transport theory and maximum-entropy models to introduce a class of bipartite random graphs that enables systematic characterization of how dense, entropic graphs evolve into sparse, efficient structures, revealing the most probable network configurations under partial optimization.

Liquid crystal skyrmions are quasiparticle structures whose interactions at large separations can be understood in terms of elastic multipoles. Here, the authors derive these multipole interactions analytically and show how they drive the formation of chains and clusters under oscillating electric fields.

Stabilizing the ferroelectric phase in HfO₂ is a critical challenge for its application in next-generation nanoelectronics. Here, through a combination of first-principles calculations and experimental results the authors identify a critical extra-hole concentration for the stabilisation of the ferroelectric phase in HfO₂ thin films.

The authors develop a microwave interferometer method to measure how electric and acoustic waves mix inside piezoelectric materials. Using this approach, they detect and quantified electric-acoustic heterodyning in lead zirconium titanate, showing the mixed signal scales linearly with both input powers.

Ranking items based on pairwise comparisons, such as using match outcomes to rank sports teams, is a common task that becomes challenging when data is limited or noisy. Here, the authors introduce an efficient nonparametric Bayesian method for learning partial rankings that breaks ties among item ranks only when supported by sufficient statistical evidence in the data.

The structure of ties in social networks determines who receives information first, and those early opportunities often confer a competitive advantage. The authors develop the H3 hypergraph model, which reveals how hyperedge homophily drives inequality in information access and suggests that targeted interventions informed by higher-order dynamics can help close those gaps.

Magnetic skyrmions are chiral spin structures that can form two-dimensional lattices. Here, the authors show how lattice domains grow under the influence of pinning effects in a non-flat energy landscape and that magnetic field oscillations help to stabilize order.

High-energy electron beams interacting with targets produce electromagnetic showers, a pathway for producing electronpositron pair plasma in the laboratory. Here, the authors solve kinetic equations to derive spectra and pair numbers for varying target thicknesses, with results validated by Geant4 simulations, an explicit formula for the density of outgoing pairs is then obtain.