Since the analogy between laser oscillation and second-order phase transition was indicated in the 1970s, dynamical fluctuations on lasing threshold inherent in critical phenomena have gained significant interest. Here, we numerically and experimentally demonstrate that a semiconductor laser subject to delayed optical feedback can exhibit unusual large intensity fluctuations characterized by power-law distributions. Such an intensity fluctuation consists of distinct intermittent bursts of light intensity, whose peak values attain tens of times the intensity of the maximum gain mode. This burst behavior emerges when a laser with a long time delay (over $100\mathrm{ns}$) and an optimal feedback strength operates around the lasing threshold. The intensity and waiting time statistics follow power-law-like distributions. We also report on the experimental results that suggested power-law intensity dynamics in a semiconductor laser with delayed feedback. The reason for the emergence of power-law behavior in the laser system is discussed from the perspective of nonequilibrium-critical behavior in a slowly driven-dissipative system known as self-organized criticality.

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete mean-variance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.

Calculations combining first-principles electron-phonon ($e$-ph) interactions with the Boltzmann equation enable studies of ultrafast carrier and phonon dynamics. However, in materials with weak Coulomb screening, electrons and holes form bound excitons so their scattering processes become correlated, posing additional challenges for modeling nonequilibrium physics. Here we show calculations of ultrafast exciton dynamics and related time-domain spectroscopies using ab initio exciton-phonon (ex-ph) interactions together with an excitonic Boltzmann equation. Starting from the nonequilibrium exciton populations, we develop simulations of time-domain absorption and photoemission spectra that take into account electron-hole correlations. We use this method to study monolayer ${\mathrm{WSe}}_2$, where our calculations predict subpicosecond timescales for exciton relaxation and valley depolarization and reveal the key role of intermediate dark excitons. The approach introduced in this paper enables a quantitative description of nonequilibrium dynamics and ultrafast spectroscopies in materials with strongly bound excitons.

Few simulations exist for microswimmers near deformable interfaces. Here, we present numerical simulations of the hydrodynamic flows associated with a single microswimmer embedded in a binary fluid mixture. The two fluids demix, separated by a penetrable and deformable interface that we assume to be initially prepared in its planar ground state. We find that the microswimmer can either penetrate the interface, move parallel to it, or bounce back off it. We analyze how the trajectory depends on the swimmer type (pusher/puller) and the angle of incidence with respect to the interface. Our simulations are performed in a system with periodic boundary conditions, corresponding to an infinite array of fluid interfaces. A puller reaches a steady state in which it either swims parallel to the interface or selects a perpendicular orientation, repeatedly penetrating through the interface. In contrast, a pusher follows a bouncing trajectory between two interfaces. We discuss several examples in biology in which swimmers penetrate soft interfaces. Our paper can be seen as a highly simplified model of such processes.

We investigate the momentum-resolved spin and charge susceptibilities, as well as the chemical potential and double occupancy in the two-dimensional Hubbard model as functions of doping, temperature, and interaction strength. Through these quantities, we identify a weak-coupling regime, a strong-coupling regime with short-range correlations and an intermediate-coupling regime with long magnetic correlation lengths. In the spin channel, we observe an additional crossover from commensurate to incommensurate correlations. In contrast, we find charge correlations to be only short ranged for all studied temperatures, which suggests that the spin and charge responses are decoupled. These findings were obtained by a connected determinant diagrammatic Monte Carlo algorithm for the computation of double expansions, which we introduce in this paper. This permits us to obtain numerically exact results at temperatures as low as $T=0.067$ and interactions up to $U=7$, while working on arbitrarily large lattices. Our method also allows us to gain physical insights from investigating the analytic structure of perturbative series. We connect to previous work by studying smaller lattice geometries and report substantial finite-size effects.

t-stochastic neighbor embedding (t-SNE) is a nonparametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship or similarities between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. To verify our proposed method, we visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.

The use of intense tailored light fields is the perfect tool to achieve ultrafast control of electronic properties in quantum materials. Among them, Mott insulators are materials in which strong electron-electron interactions drive the material into an insulating phase. When shining a Mott insulator with a strong laser pulse, the electric field may induce the creation of doublon-hole pairs, triggering a photoinduced transition into a metallic state. In this paper, we take advantage of the threshold character of this photoinduced transition and we propose a setup that consists of a midinfrared laser pulse and a train of short pulses separated by a half period of the midinfrared with alternating phases. By varying the time delay between the two pulses and the internal carrier envelope phase of the short pulses, we achieve control of the phase transition, which leaves its fingerprint at its high harmonic spectrum.

We provide a perturbative framework to calculate extreme events of non-Markovian processes, by mapping the stochastic process to a two-species reaction-diffusion process in a Doi-Peliti field theory combined with the Martin-Siggia-Rose formalism. This field theory treats interactions and the effect of external, possibly self-correlated, noise in a perturbation about a Markovian process, thereby providing a systematic, diagrammatic approach to extreme events. We apply the formalism to Brownian motion and calculate its survival probability distribution subject to self-correlated noise.

Time-series analysis is fundamental for modeling and predicting dynamical behaviors from time-ordered data, with applications in many disciplines such as physics, biology, finance, and engineering. Measured time-series data, however, are often low dimensional or even univariate, thus requiring embedding methods to reconstruct the original system's state space. The observability of a system establishes fundamental conditions under which such reconstruction is possible. However, complete observability is too restrictive in applications where reconstructing the entire state space is not necessary and only a specific subspace is relevant. Here, we establish the theoretic condition to reconstruct a nonlinear functional of state variables from measurement processes, generalizing the concept of functional observability to nonlinear systems. When the functional observability condition holds, we show how to construct a map from the embedding space to the desired functional of state variables, characterizing the quality of such reconstruction. The theoretical results are then illustrated numerically using chaotic systems with contrasting observability properties. By exploring the presence of functionally unobservable regions in embedded attractors, we also apply our theory for the early warning of seizure-like events in simulated and empirical data. The studies demonstrate that the proposed functional observability condition can be assessed a priori to guide time-series analysis and experimental design for the dynamical characterization of complex systems.

We present a general theory for the coherence of Fano lasers based on a bound state in the continuum. We find that such lasers enable an orders-of-magnitude reduction in the quantum-limited linewidth and, by introducing mirror symmetry breaking, the linewidth can be further reduced. In contrast to ordinary macroscopic lasers, though, the linewidth may rebroaden due to optical nonlinearities enhanced by the strong light localization. This leads to the identification of optimal material systems. We also show that the coherence of this new type of microscopic laser can be understood intuitively using a simple, effective potential model. Based on this model, we examine the laser stability and deduce the dependence of the laser linewidth on the general Fano line shape. Our model facilitates the incorporation of other degrees of design freedom and can be applied to a general class of lasers with strongly dispersive mirrors.

Quantum computing has demonstrated the potential to revolutionize our understanding of nuclear, atomic, and molecular structure by obtaining forefront solutions in nonrelativistic quantum many-body theory. In this work, we show that quantum computing can be used to solve for the structure of hadrons, governed by strongly interacting relativistic quantum field theory. Following our previous work on light unflavored mesons as a relativistic bound-state problem within the nonperturbative Hamiltonian formalism, we present the numerical calculations on simulated quantum devices using the basis light-front quantization approach. We implement and compare the variational quantum eigensolver and the subspace-search variational quantum eigensolver to find the low-lying mass spectrum of the light meson system and its corresponding light-front wave functions as quantum states from ideal simulators, noisy simulators, and IBM quantum computers. Based on obtained quantum states, we evaluate the meson decay constants and parton distribution functions directly on the quantum circuits. Our calculations on the quantum computers and simulators are in reasonable agreement with accurate numerical solutions solved on classical computers when noises are moderately small, and our overall results are comparable with the available experimental data.

The current fluctuations due to a temperature bias, i.e., the delta-$T$ noise, allow one to access properties of strongly interacting systems which cannot be addressed by the usual voltage-induced noise. In this work, we study the full delta-$T$ noise between two different fractional quantum Hall edge states, with filling factors $({\nu }_L,{\nu }_R)$ in the Laughlin sequence, coupled through a quantum point contact and connected to two reservoirs at different temperatures. We are able to solve exactly the problem for all couplings and for any set of temperatures in the specific case of a hybrid junction $(1/3,1)$. Moreover, we derive a universal analytical expression which connects the delta-$T$ noise to the equilibrium one valid for all generic pairs $({\nu }_L,{\nu }_R)$ up to the first order in the temperature mismatch. We expect that this linear term can be accessible in today's experimental setups. We describe the two opposite-coupling regimes focusing on the strong one, which corresponds to a nontrivial situation. Our analysis on delta-$T$ noise allows us to better understand the transport properties of strongly interacting systems and to move toward more involved investigations concerning the statistics and scaling dimension of their emergent excitations.

The motion of pedestrians is a paradigmatic phenomenon to study collective human behavior. We propose a model-free approach to analyze the movement of pedestrians in experiments and get a quantitative understanding of crowd dynamics. Using concepts from control and analysis of dynamical systems, we set up a scheme which allows us to identify dynamical unstable signatures in pedestrian flows. These signatures are the building blocks for crowd control and soft management of people and thus result in a fundamental understanding of collective human behavior. Our approach is entirely data driven, and we provide a proof of concept using field and laboratory experiments. In addition, this methodology provides, based on experimental observations, quantitative benchmarks to judge the quality of mathematical models for pedestrian motion.

Utilizing optimal control to simulate a model Hamiltonian is an emerging strategy that leverages the intrinsic physics of a device with digital quantum simulation methods. Here we evaluate optimal control for probing the nonequilibrium properties of symmetry-protected topological (SPT) states simulated with superconducting hardware. Assuming a tunable transmon architecture, we cast the evolution of these SPT states as a series of one- and two-site pulse optimization problems that are solved in the presence of leakage constraints. From the generated pulses, we classically simulate the time-dependent melting of the perturbed SPT string order across a six-site model with an average state infidelity of ${10}^{-3}$. The feasibility of these pulses as well as their efficient application indicate that high-fidelity simulations of string order melting are within reach of current quantum computing systems.

Most of the spiral magnetoelectric multiferroics investigated in recent years are geometrically or exchange-frustrated magnets, where the presence of triangular or other frustrated spin networks produce low magnetic transition temperatures. This critically limits their potential uses. The exceptional stability of the spiral magnetic order (at $T_S$) in the layered structure of the ${\mathrm{YBaCuFeO}}_5$ double perovskite involves a nonconventional mechanism: spiral order by disorder. The model has been theoretically developed by Scaramucci et al. [Phys. Rev. Res. 2, 013273 (2020)] after the discovery of a huge impact of cation disorder on $T_S$ [M. Morin et al., Nat. Commun. 7, 13758 (2016)]. In this work the influence of disorder (and only disorder) on the magnetic phase diagram is studied on a quantitative basis extending the range of previous studies. We thoroughly investigate the impact of frustration due to $B$-site disorder ($n_d$) on the magnetic spirals in the reference composition ${\mathrm{YBaCuFeO}}_5$. The interplay between disorder, stability, and the detailed features of the incommensurate spiral magnetic orders were systematic, quantitative, and methodically investigated in samples of identical composition, spanning a wide range of $n_d$ values. Three different regimes are distinguished in the ${\mathrm{YBaCuFeO}}_5$ phase diagram versus disorder. A triple point is found in ${\mathrm{YBaCuFeO}}_5$ driven by Fe/Cu disorder that sets limits to $T_S$ and the cycloidal component of the helicoidal order. These layered materials appear as a very efficient realization of the avenue “spiral order by disorder” to supply functional helimagnets at normal working temperatures.