We propose a broadband, femtosecond two-photon excitation scheme for efficient population transfer to the ultranarrow linewidth $1s2s{}^1{S}_0$ metastable state in helium. Using 120 nm vacuum ultraviolet femtosecond laser pulses, we theoretically demonstrate that a direct two-photon excitation process can achieve a population transfer efficiency of 25%–30%, even when photoionization losses are included. The use of broadband pulses enables multiple excitation pathways to populate the excited state, in addition to compensating for significant ac Stark shifts occurring within the pulse duration. Furthermore, we introduce a two-color two-photon extreme-ultraviolet-near-infrared (XUV-IR) excitation scheme that will further reduce ionization losses and can achieve significantly higher transfer efficiencies of $\sim 70\%$. These results demonstrate that high excitation probability of ultranarrow linewidth ($\sim 50\mathrm{Hz}$) excited states can be achieved using experimentally accessible femtosecond laser sources with bandwidth in the terahertz range.

mRNAs and proteins are believed to degrade in non-Markovian fashions, challenging the common assumption of exponential mRNA and protein lifetimes. Here, we use the queue theory to derive a generalized master equation for a three-stage model of gene expression with nonexponential mRNA and protein degradations. Analytical and numerical results show that the protein degradation memory enlarges the bimodal region and increases the protein noise, whereas the mRNA degradation memory enhances bimodality but reduces the protein noise by lowering the translational burst variance. While these results provide insight into the roles of degradation memories in shaping gene-expression patterns, the generalized master equation is a useful tool for studying gene-expression dynamics in complex cellular environments.

Despite the absence of long-range structural order, amorphous solids retain an atomic-scale short-range ordered environment. However, the mechanisms through which short-range order influences vibrational modes and its subsequent effects on thermal conductivity remain elusive. In this work, we employ molecular dynamics and lattice dynamics simulations to investigate how short-range order modulates vibrational properties and thermal transport in amorphous silicon. Our simulations reveal that rapid cooling rates during the melt-quench process result in amorphous silicon structures with diminished short-range order. This reduction primarily intensifies the scattering of propagative vibrational modes for both transverse and longitudinal polarizations, particularly near the Ioffe-Regel crossover. Consequently, enhanced scattering weakens the mode correlation of propagative modes, further suppressing thermal conductivity. Additionally, our spatial coherence analysis demonstrates that reduced short-range order diminishes the wavelike behavior of propagative modes. Our findings provide a fundamental interpretation of the prevailing concept of mode correlation with the spatial coherence of thermal vibrations in amorphous systems, as well as physical insights into the role of short-range order in the wavelike nature of vibrational modes and the engineering of thermal transport in amorphous materials.

The search for exactly solvable models is an evergreen topic in theoretical physics. In the context of multistate Landau-Zener models—$N$-state quantum systems with linearly time-dependent Hamiltonians—the theory of integrability provides a framework for identifying solvable cases. In particular, it was proved that the integrability of a specific class known as the multitime Landau-Zener (MTLZ) models guarantees their exact solvability. A key finding was that an $N$-state MTLZ model can be represented by data defined on an $N$-vertex graph. While known host graphs for MTLZ models include hypercubes, fans, and their Cartesian products, no other families have been discovered, leading to the conjecture that these are the only possibilities. In this work, we conduct a systematic graph-theoretical search for integrable models within the MTLZ class. By first identifying minimal structures that a graph must contain to host an MTLZ model, we formulate an efficient algorithm to systematically search for candidate graphs for MTLZ models. Implementing this algorithm using computational software, we enumerate all candidate graphs with up to $N=13$ vertices and perform an in-depth analysis of those with $N\le 11$. Our results corroborate the aforementioned conjecture for graphs up to 11 vertices. For even larger graphs, we propose a specific family, termed descendants of “(0, 2)-graphs,” as promising candidates that may violate the conjecture above. Our work can serve as a guideline to identify previously unknown exactly solvable multistate Landau-Zener models in the future.

Long-wavelength collective shear dynamics are profoundly different in solids and liquids. According to the theoretical framework developed by Maxwell and Frenkel, collective shear waves vanish upon melting by acquiring a characteristic wave-vector gap, known as the $k$ gap. While this prediction has been supported by numerous simulations, experimental validation remains limited. Moreover, this phenomenon has never beentested across a continuous glass transition between a liquid phase and a glassy state with large but finite viscosity. In this work, we track the dispersion relation of collective shear modes in a two-dimensional colloidal system and provide direct experimental evidence for the emergence of a $k$ gap. This gap opens continuously at an effective temperature consistent with the onset of the glass transition and the vanishing of the static shear modulus. By extracting the instantaneous shear velocity from the experimental data, we uncover a shear relaxation time exhibiting a super-Arrhenius temperature dependence characteristic of glass-forming materials, accurately described by the Vogel-Fulcher-Tammann relation. Our results confirm the predictions of the Maxwell-Frenkel framework and highlight their relevance across continuous melting processes originating from low-temperature amorphous solid phases.

Optimal control of stochastic systems plays a central role in nonequilibrium physics, with applications in the study of biological molecular motors and the design of single-molecule experiments. While exact analytic solutions to optimization problems are rare, under slow driving conditions, the problem can be reformulated geometrically solely in terms of equilibrium properties. In this framework, minimum-work protocols are geodesics on a thermodynamic manifold whose metric is a generalized friction tensor. Here, we introduce a foundation for this friction-tensor formalism for conservatively driven systems on either discrete- or continuous-state spaces. Under complete control of the potential energy, a global thermodynamic manifold (on which points are identified with instantaneous energy landscapes) has as its metric a full-control friction tensor. Arbitrary partial-control friction tensors arise naturally as inherited metrics on submanifolds of this global manifold. We leverage a simple mathematical relationship between system dynamics and the geometry of the global manifold to derive expressions for the generalized friction and linear-response excess work. We show that the friction tensor, usually defined as an infinite-time integral, may be expressed as a product of straightforwardly computed matrices. We furthermore show that the linear-response excess power is decomposable into relaxation-time-scaled projections of the control velocity onto eigenmodes of the rate matrix or Fokker-Planck operator. This intuitive result provides a systematic means of interpreting otherwise-mysterious efficient control strategies, unveiling how multiparameter optimal control takes advantage of multiexponential relaxation dynamics to reduce dissipation relative to single-parameter control. We demonstrate the computational and conceptual advances in three illustrative examples.

Collective motion is typically modeled by local velocity-alignment rules. However, these models do not fully capture recent evidence for complex decision-making in biological swarms and are fragile to structured, adversarial perturbations. Here, we introduce a neurodynamic swarm model where collective behavior emerges not from alignment, but from nonlinear competition within Hopfield-type ring attractors. We show that this mechanism naturally gives rise to emergent decision bifurcations that reproduce sophisticated animal behaviors. Crucially, the inherent stability of the attractors provides the swarm with remarkable resilience, allowing it to maintain coherence against adversarial perturbations that shatter alignment-based models.

Resonance fluorescence from a two-level emitter is a standard route to single-photon generation, where the emitter produces only one photon at a time, and multiphoton emission or scattering is inherently suppressed. In this study, we challenge this paradigm in the weak-excitation limit, where coherent scattering dominates. Using a single quantum dot in a microcavity, we show that the incoherent component exhibits pronounced superbunching, revealing simultaneous multiphoton scattering from a single emitter. In contrast, the total reflected field—comprising the reflected driving field and incoherent scattering—displays perfect antibunching, arising from quantum interference that cancels two-photon probability amplitudes. By spectrally filtering the reflected field, we tune the relative contributions of coherent and incoherent components, observing transitions in photon statistics from antibunching to superbunching, or to Poissonian light. Supported by master-equation simulations and an interference-based model, these results uncover the hidden but fundamental role of multiphoton dynamics in resonance fluorescence, and open avenues for generating nonclassical light in cavity and waveguide quantum electrodynamics.

Anomalous diffusion often arises in complex environments where viscoelastic or crowded conditions influence particle motion. In many biological and soft-matter systems, distinct components of the medium exhibit unique viscoelastic responses, resulting in time-dependent changes in the observed diffusion exponents. Here, we develop a theoretical model of two particles, each embedded in a distinct viscoelastic medium, and coupled via a harmonic potential. By formulating and solving a system of coupled fractional Langevin equations (FLEs) with memory exponents $0<\alpha <\beta \le 1$, we uncover rich transient anomalous diffusion phenomena arising from the interplay of memory kernels and bilinear coupling. Notably, we identify “recovery dynamics,” where a subdiffusive particle $\left(\alpha \right)$ transiently accelerates and eventually regains its intrinsic long-time mobility. This recovery emerges only when memory exponents differ $(\alpha <\beta )$, whereas identical exponents $(\alpha =\beta )$ suppress recovery. Our FLE models are further validated in comparison with Langevin dynamics simulations of polymer-based real systems. Our theoretical predictions offer insight into experimentally observed transient anomalous diffusions, such as polymer-protein complexes and cross-linked cytoskeletal networks, highlighting the critical role of memory heterogeneity and mechanical interactions in biological anomalous diffusion.

We implement in situ midcircuit measurement and reset (MCMR) operations on a full-scale trapped-ion quantum computing system by using metastable qubit states in ${}^{171}\mathrm{Yb}^{+}$ ions. We compare two methods for isolating data qubits from measured qubits: one shelves the data qubits into the metastable state and the other drives the measured qubit to the metastable state without disturbing the other qubits. We experimentally demonstrate both methods on a crystal of two ${}^{171}\mathrm{Yb}^{+}$ ions using both the $S_{1/2}$ ground-state hyperfine clock qubit and the $S_{1/2}\text{−}{D}_{3/2}$ optical qubit. These MCMR methods result in errors on the data qubit of about $2\%$ without degrading the measurement fidelity. With straightforward reductions in laser noise, these errors can be suppressed to less than $0.1\%$. The demonstrated methods allow MCMR to be performed in a single-species ion chain without shuttling or additional qubit-addressing optics, greatly simplifying the system architecture and allowing straightforward integration with existing trapped-ion quantum computers.

We find nonmonotonic equilibrium energy distributions, qualitatively different from the Fermi-Dirac and Bose-Einstein forms, in strongly interacting many-body chaotic systems. The effect emerges in systems with finite energy spectra, supporting both positive and negative temperatures, in the regime of quantum ergodicity. The results are supported by exact diagonalization calculations for chaotic Fermi-Hubbard and Bose-Hubbard models, when they have Wigner-Dyson statistics of energy spectra and demonstrate eigenstate thermalization. The proposed effects may be observed in experiments with cold atoms in optical lattices.

We analyze the localization behavior in a non-Hermitian system subject to a quasiperiodic on-site potential. We characterize localization transitions using multiple quantitative indicators, including inverse participation ratio (IPR), eigenstate fractal dimension (EFD), extended eigenstate ratio (EER), and spectral survival ratio. Despite the breaking of self-dual symmetry due to non-Hermiticity, our results reveal the existence of a critical potential strength, with its value increasing linearly with the nearest-neighbor antisymmetric hopping term. On the other hand, the inclusion of longer-range hopping not only enriches the topological properties but also gives rise to localization phenomena. In particular, it induces the emergence of mobility edges, as evidenced by both IPR and EFD, along with distinct features in the spectrum fractal dimension, which we extract using the box-counting method applied to the complex energy spectrum. Additionally, we uncover self-similar structures in various quantities, such as EER and complex eigenvalue ratio, as the potential strength varies. These findings highlight important aspects of localization and fractal phenomena in non-Hermitian quasiperiodic systems.

During decision-making processes, many real-world social systems exhibit phase flipping, a relatively unexplored phenomenon consisting in sudden macroscopic switches in the majority opinion of the system. Existing studies mostly treat consensus formation as an isolated process. However, in the human society, opinion dynamics is almost always influenced by external factors, such as the diffusion of news on social media. Here, we construct a noisy threshold voter-susceptible-infected-recovered coupled model with multiple timescales and study the synergistic coevolution dynamics of opinion formation and message diffusion. We show that the model has two mechanisms that account for macroscopic changes in the consensus state and demonstrate how, in small enough systems, one of the two mechanisms can cause the occurrence of phase flipping. Our work not only improves the understanding of phase flipping phenomena in real-world opinion systems, but also provides potential strategies to stabilize the collective opinion in a positive state.