Reviews & Analysis

The Maximum Clique Problem (MCP) is a fundamental challenge in combinatorial optimization with applications across diverse fields, yet recent advancements in AI and quantum algorithms remain underexplored. Here, the authors provide a comprehensive review of classical and modern approaches to MCP, highlighting significant developments and potential impacts on computational problem-solving.

Disordered solids are ubiquitous yet still lack a complete theoretical description. This perspective proposes the Euclidean random matrix model, combining disorder via random positions with harmonic interactions, as a minimal framework for vibrational anomalies in glasses.

Ecosystems in the Anthropocene exhibit complex dynamics that traditional ecological networks with pairwise interactions only partially capture. Here, the authors review hypergraphs as a framework for modeling higher-order interactions, showing how they clarify how ecological structures form and reorganize, with implications for conservation strategies including rewilding and habitat restoration aimed at sustaining biodiversity under rapid environmental change.

Describing molecular photoionization in the XUV/X-ray range requires methods that approximate the complexity from nuclear motion, anisotropy, and continuum states, often creating a maze of specialised approaches. This tutorial reviews key concepts, highlights common modelling challenges, and guides the readers in selecting effective computational strategies.

Quantum neural networks (QNNs), implemented via parameterized quantum circuits (PQCs), promise a path to quantum advantage in learning by addressing the limitations of classical deep neural networks. Here, the authors explore the Fourier structure of PQCs to mitigate spectral bias, enhancing applications like quantum ODE solvers and quantum reinforcement learning, with implications for designing quantum-native architectures.

Machine learning is transforming scientific research. In this perspective, the authors highlight the challenges associated with it, including data quality, algorithmic bias, model explainability, and the fundamental question of whether machine learning makes true discoveries or merely reshuffles existing knowledge.

Direct detection experiments have set stringent limits on interactions between dark matter candidates and Standard Model particles, necessitating new approaches to surpass these constraints. The authors discuss the upcoming CRESST experiment upgrade, which aims to enhance sensitivity to sub-GeV dark matter through innovative detector strategies, addressing low-energy background challenges and outlining a decade-long research roadmap.

The authors review advancements in underground dark matter experiments, highlighting the need for enhanced detector sensitivity to unravel dark matter’s fundamental properties, with implications for astrophysics and cosmology.

In orbitronics, a fundamental debate exists on whether orbital angular momentum can travel long distances. This perspective reviews the experimental evidence and theoretical models on whether orbital momentum propagates or is generated locally, outlining pathways to resolve this critical question.

The growing computational demands of AI are significantly increasing carbon emissions from the ICT sector, while traditional CMOS-based computing faces scaling and sustainability limits. This perspective explores photonic computing as a promising, energy-efficient alternative for enabling carbon-sustainable next-generation AI hardware.

Quantum Information Science has made great strides in recent years setting the stage for further dramatic advances in many scientific fields of fundamental importance. This Perspective highlights why gauge theories, with their foundational role and computational complexity, present an especially fertile field for near-term demonstrations of quantum advantage.

Scalar energy can drive formation of non-equilibrium steady-states or power directional processes such as chemotaxis, ion transport across a membrane, or walking along a polar track. This work focuses on the key symmetry breaking element, kinetic asymmetry, that determines which way these processes are driven.

Tensor networks are a powerful complementary approach to Monte Carlo methods for simulating lattice gauge theories, enabling access to challenging regimes such as real-time dynamics and finite-density systems. This work reviews the state of the art, outlines a roadmap for algorithmic developments, and provides resource estimates to guide large-scale applications in high-energy physics.

Quantum simulators study important models of condensed matter and high-energy physics. Research on synthetic dimensions has paved the way for studying exotic phenomena, such as curved space-times, topological phases of matter, lattice gauge theories, twistronics without a twist, and more

Multiple parameter estimation techniques are employed to empirically validate theoretical propositions regarding complex systems by discerning relevant free parameters from often scarce experimental data. In this tutorial, the authors provide a beginner’s guide to parameter estimation via adjoint optimization, and show its efficiency in prototypical problems across different fields of physics.

The WavemiX network has made significant progress in bringing together theoreticians, experimentalists, and machine scientists. At the third edition of its flagship workshop, different communities collaborated to discuss the practical challenges of achieving superior spatial and time resolution and devise useful applications in ultrafast science.

The authors present a report on the Quantum Matter 2023 international conference and expo that took place in Madrid, on June 2023.

Surface wetting describes the interaction between a solid surface and a liquid droplet, the dynamics of which govern the performance of functional surfaces for nanoscience and industrial applications. Here, the authors review how a combination of new surface characterization techniques allows the probing of surface wettability in greater detail compared to conventional methods (e.g., contact angle measurement).

X-ray science at modern Synchrotrons and X-ray Free Electron Laser facilities are enabling the study of subtle structural changes of matter from the single atom to the macroscopic scale. This paper reviews new concepts in synchrotron storage rings design and reports on the successful commissioning and operation of the new X-ray storage ring of the European Synchrotron Radiation Facility (ESRF) in Grenoble.