In recent years, hybrid semiconductor–superconductor qubits have emerged as a compelling alternative to more traditional qubit platforms, owing to their unique combination of material properties and device-level tunability. A key feature of these systems is their gate-tunable Josephson coupling, which enables the realization of superconducting qubit architectures with full electric field control and which offers a promising route toward scalable and low-crosstalk quantum processors. One of the central motivations behind this approach is the possibility of unifying the advantages of superconducting and spin-based qubits. This approach to creating hybrid qubits can be achieved, for example, by encoding quantum information in the spin degree of freedom of a quasiparticle occupying an Andreev bound state, thereby combining long coherence times with fast and flexible control. Recent progress has been further accelerated by the bottom-up engineering of Andreev bound states into coupled quantum dot arrays, leading to the realization of novel device architectures such as minimal Kitaev chains hosting (poor man’s) Majorana states, precursors of topologically protected Majorana zero modes, and, very recently, to the first coherent control of a Majorana parity qubit. In parallel, alternative strategies have focused on the design of Hamiltonian-protected hybrid qubits, including Cooper pair parity qubits and fluxon parity qubits, which aim to enhance robustness against local noise and decoherence. These approaches leverage the interplay between superconducting phase dynamics and the discrete charge or flux degrees of freedom in hybrid junctions. In this article, we review recent theoretical and experimental advances in the development of semiconductor–superconductor hybrid qubits. We provide a comprehensive overview of the underlying physical mechanisms, device implementations, and emerging architectures, with particular emphasis on their potential to enable (topologically) protected quantum information processing. While many of these qubit designs remain at the proof-of-concept stage, the rapid pace of progress suggests that practical demonstrations may be within reach in the near future.
This is a short review of the current status of the Standard Model calculation of the Muon g−2, based on the recently published White Paper. After a brief summary of the theoretically very well determined QED and electroweak contributions, the review focuses on the two main hadronic contributions, the vacuum polarization (HVP) and the light-by-light (HLbL), both for what concerns the lattice as well as the dispersive, data-driven determinations. In view of discrepancies in the data sets used for the dispersive determination of the HVP, the final theoretical value of the Muon g−2 in the SM is based on the lattice value for the HVP contribution and an average of the lattice and dispersive determinations for the HLbL contribution. The review ends with a brief outlook on how to address two urgent challenges: (i) reduce the theory uncertainty by a factor four to match the experimental one; (ii) resolve the current puzzles affecting data-driven determinations of the HVP contribution.
The top quark, the heaviest elementary particle known, has a profound influence on electroweak physics. Its mass and interactions place it at the intersection of precision tests of the Standard Model (SM) and searches for physics beyond it (BSM). In this work, we provide a comprehensive review of the role of the top-quark mass in electroweak interactions, highlighting theoretical developments, latest experimental measurements, and their implications for Higgs physics, vacuum stability, and possible new physics scenarios.
Network science has become a central methodological framework for the study of complex systems, with applications across social, biological, and technological domains. Its strength lies in formalizing such systems as collections of interacting units, whose collective behavior depends not only on the properties of the individual components, but also on the structure, dynamics, and evolution of their interactions. In this Review, we analyze how this framework, rooted in statistical physics, is being applied to the study of sports systems. Sports provide a particularly suitable domain for network modeling, as performance emerges from interactions among agents, whose actions are constrained by spatial embedding and temporal ordering, and evolve under competitive, adaptive, and out-of-equilibrium conditions. We review the main network representations used in sports, from classical graph-based descriptions to more general frameworks, including spatial, temporal, multilayer, and higher-order networks. Particular attention is devoted to soccer, where network science has had its deepest methodological impact, while applications to other sports such as basketball, volleyball, American football, or badminton serve to illustrate the generality of the underlying principles. Network science provides sports analytics with a rigorous language for quantifying coordination, emergence, adaptation, and collective performance. At the same time, the availability of high-resolution event, tracking, and pose-estimation data makes sports an ideal testbed for validating and refining network models in realistic settings. As a result, the relationship between network science and sports, currently driven primarily by the application of network methods to sport, has the potential to become genuinely bidirectional. In this Review, we not only organize the literature on network methodologies and their applications to sports, but also discuss complementary concepts needed to capture the full picture, including entropy and complexity measures, as well as key ideas from statistical physics and nonlinear dynamics. Finally, despite the substantial progress made so far, the field remains under active development, and we highlight the main open questions that should be addressed in future research.
