A striking feature of three-dimensional (3D) topological insulators (TIs) is the theoretically expected topological magnetoelectric (TME) effect, which gives rise to additional terms in Maxwell's laws of electromagnetism with an universal quantized coefficient proportional to half-integer multiples of the fine-structure constant $\alpha$. In an ideal scenario one therefore expects also quantized contributions in the magnetooptical response of TIs. We review this premise by taking into account the trivial dielectric background of the TI bulk and potential host substrates, and the often present contribution of itinerant bulk carriers. We show (i) that one obtains a nonuniversal magnetooptical response whenever there is impedance mismatch between different layers and (ii) that the detectable signals due to the TME rapidly approach vanishingly small values as the impedance mismatch is detuned from zero. We demonstrate that it is methodologically impossible to deduce the existence of a TME exclusively from an optical experiment in the thin film limit of 3D TIs at high magnetic fields.

Though robustness of networks to random attacks has been widely studied, intentional destruction by an intelligent agent is not tractable with previous methods. Here we devise a single-player game on a lattice that mimics the logic of an attacker attempting to destroy a network. The objective of the game is to disable all nodes in the fewest number of steps. We develop a reinforcement learning approach using deep $Q$-learning that is capable of learning to play this game successfully, and in so doing, to optimally attack a network. Because the learning algorithm is universal, we train agents on different definitions of robustness and compare the learned strategies. We find that superficially similar definitions of robustness induce different strategies in the trained agent, implying that optimally attacking or defending a network is sensitive to the particular objective. Our method provides an approach to understand network robustness, with potential applications to other discrete processes in disordered systems.

Sum-frequency generation (SFG) spectroscopy provides a versatile method for the investigation of noncentrosymmetric media and interfaces. Here, using tunable picosecond infrared (IR) pulses from a free-electron laser, the nonlinear optical response of 4H-SiC, a common polytype of silicon carbide, has been probed in the frequency and time domain by infrared-visible vibrational SFG spectroscopy. In the SFG spectra we observe a sharp resonance near the longitudinal optical phonon frequency, arising from linear optical effects due to the epsilon-near-zero regime of the IR permittivity. In the time domain, the buildup of the SFG intensity is linked to the free-induction decay of the induced coherent IR polarization. When approaching the frequency of the phonon resonance, a slower polarization dephasing is observed as compared to off-resonant IR excitation. Thus, by introducing a temporal delay between the IR and the visible up-conversion pulse we are able to demonstrate spectral narrowing of the phonon SFG resonance, as corroborated by model calculations.

States obtained from multimode twin beams by symmetric as well as asymmetric photon subtraction are experimentally investigated. Using the joint photocount distributions measured by an intensified CCD camera, their joint photon-number distributions are reconstructed. Mean photon numbers, Fano factors, and noise-reduction parameters are determined for the group of states reached by subtracting up to four photocounts in each beam. Photon subtraction is monitored using the nonclassicality depths and joint quasidistributions of integrated intensities of the emerging states. The non-Gaussianity of the obtained states is also determined. The states reached by coherent photon subtraction are also analyzed. The investigations represent an important step towards the use of photon-subtracted twin beams in sub-shot-noise metrology and imaging.

The critical mass effect is a prevailing topic in the study of complex systems. Recent research indicates that a committed minority of cooperators, unwavering in their beliefs and consistently maintaining cooperation, can effectively foster widespread cooperation in social dilemma games. However, achieving a critical mass of cooperation in the one-shot prisoner's dilemma requires stricter conditions. The underlying mechanism behind this effect remains unclear, particularly in the context of repeated interactions. This work aims to investigate the influence of a committed minority on cooperation in the iterated prisoner's dilemma game, a widely studied model of repeated interactions between individuals confronting a social dilemma. In contrast to previous findings, we identify tipping points for both well-mixed and structured populations. Our findings demonstrate that a committed minority of unconditional cooperators can induce full cooperation under weak imitation conditions. Conversely, a committed minority of conditional cooperators, who employ extortion strategy, can promote widespread cooperation under strong imitation conditions. These results are consistent across various network topologies and imitation rules, suggesting that critical mass effects may be a universal principle in social dilemma games. Moreover, we discovered that an excessive density of committed extortioners can hinder cooperation in structured populations. This research advances our understanding of the role of committed minorities in shaping social behavior and provides valuable insights into cooperation dynamics.

Granular media near jamming exhibit fascinating properties, which can be harnessed to create jammed-granulate metamaterials: materials whose characteristics arise not only from the shape and material properties of the particles at the microscale but also from the geometric features of the packing. For the case of a bending beam made from jammed-granulate metamaterial, we study the impact of the particles' properties on the metamaterial's macroscopic mechanical characteristics. We find that the metamaterial's stiffness emerges from its volume fraction, in turn originating from its creation protocol; its ultimate strength corresponds to yielding of the force network. In contrast to many traditional materials, we find that macroscopic deformation occurs mostly through affine motion within the packing, aided by stress relief through local plastic events, surprisingly homogeneously spread and persistent throughout bending.

We develop an orbital-free functional framework to compute one-body quasiprobabilities for both fermionic and bosonic systems. Since the key variable is a quasidensity, this theory circumvents the problems of finding the Pauli potential or approximating the kinetic energy that are known to limit the applicability of standard orbital-free density functional theory. We present a set of strategies to (a) compute the one-body Wigner quasiprobability in an orbital-free manner from the knowledge of the universal functional and (b) obtain those functionals from the functionals of the one-body reduced density matrix (1-RDM). We find that the universal functional of optical lattices results from a translation, a contraction, and a rotation of the corresponding functional of the 1-RDM, revealing the strong connection between these two functional theories. Furthermore, we relate the key concepts of Wigner negativity and $v$ representability.

Nonlinear transport has been observed in many real systems, such as international trade, business transactions, human movement, and urban traffic. To understand the fundamental properties of transport in such systems, several models have been proposed in the past few years. These models exhibit complex behavior due to the interplay between nonlinearity and the underlying network topology. We studied a transport equation on three-body network motifs both numerically and analytically, and we found bifurcation on two motifs and hysteresis of a model on one motif. These three motifs show different bifurcation points. The first two motifs have a branching node, i.e., a node with outdegree 2, and the third motif is a fully connected bidirectional triangle. In particular, the hysteresis on the bidirectional triangle is not predictable from the linear stability analysis on the uniform stationary solution. The analysis based on the self-consistency equation of the parameter $u$ [see Eq. (6) for the precise definition] measuring the ratio between sizes on two nodes reveals the following: The bifurcation on the first two motifs is due to supercritical pitchfork bifurcation. The difference between these two motifs comes from the existence of bidirectional edges. The hysteresis on the third motif is due to the combination of the saddle-node and transcritical bifurcation. Our results imply that the nonlinear transport model exhibits nontrivial behavior even in minimal settings and that the bifurcation on large-scale networks becomes far more complex and unpredictable from the underlying network structure.

In the standard mean-field treatment of superconductors, the electron-electron interactions are assumed to be written in terms of local density operators. However, more general interactions, such as pair-hopping interactions, may exist or may be generated in a low-energy effective Hamiltonian. In this work, we study the effect of correlated hopping interactions toward the electromagnetic response of superconductors. When only the Hamiltonian after the mean-field approximation is provided, one cannot unambiguously determine its electromagnetic response whenever such interactions are allowed. This work demonstrates that such interactions induce additional terms in the current operator, leading to modifications in the Meissner weight and optical conductivities that deviate from conventional expectations. These results underscore the need for caution when incorporating gauge fields into the Bogoliubov–de Gennes Hamiltonian.

Holonomic quantum computing functions by transporting an adiabatically degenerate manifold of computational states around a closed loop in a control-parameter space; this cyclic evolution results in a non-Abelian geometric phase which may couple states within the manifold. Realizing the required degeneracy is challenging and typically requires auxiliary levels or intermediate-level couplings. One potential way to circumvent this is through Floquet engineering, where the periodic driving of a nondegenerate Hamiltonian leads to degenerate Floquet bands, and subsequently non-Abelian gauge structures may emerge. Here we present an experiment in ultracold ${}^{87}\mathrm{Rb}$ atoms where atomic spin states are dressed by modulated RF fields to induce periodic driving of a family of Hamiltonians linked through a fully tuneable parameter space. The adiabatic motion through this parameter space leads to the holonomic evolution of the degenerate spin states in $SU\left(2\right)$, characterized by a non-Abelian connection. We study the holonomic transformations of spin eigenstates in the presence of a background magnetic field, characterizing the fidelity of these single-qubit gate operations. Results indicate that while the Floquet engineering technique removes the need for explicit degeneracies, it inherits many of the same limitations present in degenerate systems.

We present experimental results which show a laser polarization-dependent contribution to electron beam pointing jitter in laser wakefield accelerators (LWFAs). We develop a theoretical model for the polarization dependence in terms of the transverse dynamics of trapped electrons, resonantly driven by bubble centroid oscillations. The latter are generated by the carrier wave phase evolution at the self-steepened laser pulse front. In the model, the polarization-dependent jitter originates from shot-to-shot fluctuations of the laser carrier envelope phase (CEP). The model is verified by particle-in-cell simulations and suggests that CEP stabilization of the driving lasers might be necessary to achieve ultimate electron pointing stability in LWFAs.

Electron spin states of solid-state defects such as nitrogen- and silicon-vacancy color centers in diamond are a leading quantum-memory candidate for quantum communications and computing. Via open-quantum-systems modeling of spin-phonon coupling—the major contributor of decoherence—at a given temperature, we derive the time dynamics of the density operator of an electron-spin qubit. We use our model to corroborate experimentally measured decoherence rates. We further derive the temporal decay of distillable entanglement in spin-spin entangled states heralded via photonic Bell-state measurements. Extensions of our model to include other decoherence mechanisms, e.g., undesired hyperfine couplings to the neighboring nuclear-spin environment, will pave the way to a rigorous predictive model for engineering artificial-atom qubits with desirable properties.

Heterogeneous dynamics commonly emerges in anomalous diffusion with intermittent transitions of diffusion states but proves challenging to identify using conventional statistical methods. To effectively capture these transient changes of diffusion states, we propose a deep learning model (U-AnDi) for the semantic segmentation of anomalous diffusion trajectories. This model is developed with the dilated causal convolution (DCC), gated activation unit (GAU), and U-Net architecture. The study addresses two key subtasks related to trajectory segmentation and changepoint detection, concentrating on variations in diffusion exponents and dynamic models. Additionally, extended analyses are conducted on the segmentation of single-model trajectories, multistate biological trajectories, and anomalous diffusion with added correlation functions. By rationally designing comparative models and evaluating the performance of U-AnDi against these models, we discover that U-AnDi consistently outperforms other models across all segmentation tasks, thereby affirming its superiority in the field. This performance edge also sheds light on the interpretability of U-AnDi's core components: DCC, GAU, and U-Net. The clarity with which these components contribute to U-AnDi's success underscores their congruence with the intrinsic physics underlying anomalous diffusion. Furthermore, our model is examined using real-world anomalous diffusion data: the diffusion of transmembrane proteins on cell membrane surfaces, and the segmentation results are highly consistent with experimental observations. Our findings could offer a heuristic deep learning solution for the detection of heterogeneous dynamics in single-molecule/particle tracking experiments, and have the potential to be generalized as a universal scheme for time-series segmentation.