Perspectives

Quantum sensing exploits properties of quantum systems to go beyond what is possible with traditional measurement techniques, hence opening exciting opportunities in both low-energy and high-energy particle physics experiments.

Machine learning techniques may appear ill-suited for application in fields that prioritize rigor and deep understanding; however, they have recently found unexpected uses in theoretical physics and pure mathematics. In this Perspective, Gukov, Halverson and Ruehle have discussed rigorous applications of machine learning to theoretical physics and pure mathematics.

Neural operators learn mappings between functions on continuous domains, such as spatiotemporal processes and partial differential equations, offering a fast, data-driven surrogate model solution for otherwise intractable numerical simulations of complex real-world problems.

Generative machine learning models seek to approximate and then sample the probability distribution of the data sets on which they are trained. This Perspective article connects these methods to historical studies of information processing and attractor geometry in nonlinear systems.

Understanding the W boson as accurately as possible, including knowing its mass, has been a priority in particle physics for decades. This Perspective article gives an overview of the role of the W boson mass in the Standard Model and its extensions and compares techniques for measuring it.

Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. This Perspective surveys the progress in this field, highlighting a number of exciting open questions.

Klaus Hasselmann’s viewpoint has had enormous influence in climate science, both in its theoretical and practical aspects. This Perspective provides a review of Hasselmann’s scientific programme and proposes ways forward for advancing our knowledge on the multiscale behaviour of the climate system, and on the relationship between its forced and free variability.

Optical computing has the potential to be faster and more energy-efficient than conventional digital-electronic computing for certain applications. This Perspective article surveys the differences between optics and electronics that could be exploited, and explores the physics and engineering challenges in realizing useful optical computers.

Supersolidity is an intriguing state of matter that combines superfluid and crystal features. Theoretically predicted in the 1960s, it has only recently been observed in atomic gases that exhibit typical supersolid properties such as spontaneous density modulations combined with coherence effects and the occurrence of new Goldstone modes.

Across thermodynamics, systems exchange quantities such as energy and particles. What if the quantities are represented by operators that fail to commute with each other? This Perspective surveys the implications for quantum thermodynamics and adjacent fields.

The geometry of mechanical metamaterials is designed to achieve specific functions, often taking inspiration from biological structures. This Perspective article argues that disorder, commonly observed in nature but traditionally avoided in metamaterials, provides interesting and unexplored design opportunities.

Experiments in fluids have enabled the simulation of several aspects of black holes and quantum field theory in curved spacetime. This Perspective article discusses possible hydrodynamic simulators of quantum gravitational effects, ranging from the resolution of curvature singularities to the emergence of spacetime geometry from quantum degrees of freedom.

Visual anemometry measures winds using observations of associated environmental flow–structure interactions such as swaying trees and flapping flags. This Perspective article outlines opportunities for physics and data science to further develop visual anemometry for renewable energy, urban sustainability and environmental science.

Recent advances in machine learning are enabling progress in several aspects of experimental fluid mechanics. This Perspective article focuses on augmenting the quality of measurement techniques, improving experimental design and enabling real-time estimation and control.

Machine-learning-based sampling strategies have been recently developed for lattice quantum chromodynamics applications. These methods are in their early stages, but have the potential to enable currently intractable first-principles calculations in particle, nuclear and condensed matter physics and also to advance machine learning.

Topological lattice defects in topological materials offer a platform to explore a diverse range of phenomena, including fractional charges, chiral and gravitational anomalies, topological lasers and non-Hermitian skin effects. This Perspective article surveys the impact of these phenomena on condensed matter physics, photonics, acoustics and materials science.

Quantum simulations of the fundamental particles and forces of nature have a central role in understanding key static and dynamic quantum properties of matter. Motivations, techniques and future challenges for simulations of quantum fields are discussed, highlighting examples of early progress towards the dynamics of high-density, non-equilibrium systems of quarks, gluons and neutrinos.

Active matter locally dissipates energy to produce systematic motion. This Perspective highlights proliferation as a special type of activity that breaks particle number conservation and thereby gives rise to a unique set of collective phenomena characteristic of life.

Atomic gases, usually confined in flat geometries, are now experimentally realized also in curved settings. This Perspective article analyses their many-body physics in the available geometries of rings and shells and discusses the open research questions.

Superconductivity and cascades of correlated phases have been discovered in Bernal bilayer and rhombohedral trilayer graphene. This Perspective reviews the recent experimental findings and provides an analysis of the theoretical models that aim to explain them.