In evolutionary theory, Fisher’s fundamental theorem of natural selection establishes a simple relation between the variance of the growth rate and the temporal increase in the average growth rate. Here, the authors extend the theorem based on statistical physics and information theory and show that the speed in dynamical systems describing nonlinear population dynamics is bounded by Fisher information with universal limit exponents only depending on the kind of bifurcation and not on the specific systems.

With the continuous development of metropolitan broadband and network, the need of secure and faster transmission also increases. The authors demonstrate a single-carrier four-state continuous-variable quantum key distribution (CVQKD) with sub-Gbps key rate within metropolitan area and secure transmissions up to 25 km

Direct observation of light-induced topological Floquet states can be challenging due to a number of obstacles such as laser-assisted photoemission which can complicate photoemission spectra. Here, the authors report a theoretical approach to the identification of topological Floquet states using circular dichroism in angle resolved photoemission spectroscopy.

Discrete time crystals are a new state of matter emerging via spontaneous discrete translational symmetry breaking in time. The authors demonstrate that a dissipative discrete time crystal appears in an optical microcavity pumped by a phase-modulated continuous wave laser, offering a new route to study this exotic crystal phase.

Nonlinear dynamical systems are ubiquitous in nature and play an essential role in science, from providing models for the weather forecast to describing the chaotic behavior of plasma in nuclear reactors. This paper introduces an artificial intelligence framework that can learn the correct equations of motion for nonlinear systems from incomplete data, and opens up the door to applying interpretable machine learning techniques on a wide range of applications in the field of nonlinear dynamics.

In quantum isolated system, operator growth can be quantified by Krylov complexity. Here, the authors establish a rigorous bound on the Krylov complexity growth rate based on the uncertainty principle and show that the presence of quantum chaos is not strictly necessary to saturation of the bound.

Chimera states provide an intriguing physical platform to explore synchronization effects in neural networks and brain activity. Here, a recurrent neural network with embedded Chimera states is demonstrated, suggesting the generality and robustness of such states.

Topological nodal line semimetals are characterised by band crossings along a line, or closed loop inside, of the Brillouin zone and belong to a larger family of topological semimetals. Here, using time-resolved angle resolved photoemission spectroscopy, the authors investigate the ultrafast relaxation dynamics in the bulk nodal line state of one such material, ZrSiS, elucidating the role of optical and acoustic phonon cooling.

Resonances are ubiquitous in physics and hold important functionalities in engineering wave propagation and interference effects. This work proposes an approach for computing sensitivities, i.e., partial derivatives, of complex eigenfrequencies in any resonance problem, which here is applied to efficiently optimize nanophotonic resonators and to obtain an improved quality factor.

The Hofstadter–Hubbard model on 2D square lattices is a paradigmatic model to study the interplay of electron correlations and external magnetic field. The authors use quantum Monte Carlo to study the thermodynamic properties of the Hofstadter Hamiltonian at intermediate to strong coupling, finding that a strong orbital magnetic field delocalizes electrons and reduces the effective Hubbard interaction.

Quantum phase transitions, occurring at zero temperature for a given system, can be induced by the application of physical or chemical pressure, and can help elucidate the underlying mechanisms of unconventional superconductivity. Here, using Raman spectroscopy, the authors report scaling properties indicative of a marginal Fermi liquid for an Fe-based superconductor tuned through a quantum critical point by chemical substitution.

The variational quantum eigensolver is a quantum-classical algorithm used to solve optimisation problems in machine learning but demonstrates limitations when applied to simulations of large molecules. Here, the authors explore the use of adaptive variational algorithms and demonstrate how they can be used to improve performance when simulating molecules participating in carbon monoxide processes.

Flat bands are dispersionless electronic structures with increased electron–electron correlations and can coexist with the non-trivial topological features of a Kagome lattice. Here, the authors theoretically explore the interplay between band topology and electronic correlations in a multiorbital model on a Kagome lattice demonstrating that fractional filling can give rise to fractional Chern insulating states.