In the last two decades, fluctuation theorems have been proved formally and demonstrated experimentally for several variables (such as entropy production, work, or flux) and different noises causing the fluctuations (of either thermal or other origin; Markovian or non-Markovian). Here, we report the observation of a detailed fluctuation relation in a statistical process outside thermodynamics and physics: the card game UNO. As the fluctuating variable, we consider the number of steps $W$ needed for one player's deck to change from $x$ to $y$ number of cards. The other players and the remaining cards play the role of a finite non-Markovian bath. Numerical simulations of runs of the game show that $W$ obeys a fluctuation relation analogous to Crooks' theorem. While the observed behavior shares some common features with infinite random walks, it also exhibits deviations that are clear signatures of non-Markovianity and the finiteness of the bath: Notably, the parameter corresponding to temperature depends strongly on the transition $x\to y$. Our paper contributes to extending the scope of fluctuation theorems beyond their usual thermodynamic setting.

We study $N$ fermionic impurities in a one-dimensional lattice bosonic bath at unit filling. Using DMRG and mixed boundary conditions—periodic for bosons, open for fermions—we find an $N$-polaron ground state replacing phase separation at high interspecies repulsion. This tightly bound state of clustered particles emerges due to strong impurity-bath correlations, which induce large impurity-impurity correlations that we quantify via the von Neumann entropy and bipartite mutual information, respectively. This system also reveals a fermionic self-localization effect from a Mott insulator background due to local correlations between the impurities and the bath. The growth of long-range correlations breaks this Mott phase, resulting in the transition to impurity clusters delocalized along the system. Finally, we show that there is a critical impurity number, which depends on intraspecies bosonic interaction, beyond which phase separation is recovered.

Recent advances in boundary critical phenomena have led to the discovery of a new surface universality class in the three-dimensional $O\left(N\right)$ model. The newly found “extraordinary-log” phase can be realized on a two-dimensional surface for $N<N_c$, with $N_c>3$, and on a plane defect embedded into a three-dimensional system, for any $N$. One of the key features of the extraordinary-log phase is the presence of logarithmic violations of standard finite-size scaling. In this work we study finite-size scaling in the extraordinary-log universality class by means of Monte Carlo simulations of an improved lattice model. We simulate the model with open boundary conditions, realizing the extraordinary-log phase on the surface for $N=2,3$, as well as with fully periodic boundary conditions and in the presence of a plane defect for $N=2,3,4$. In line with theory predictions, renormalization-group invariant observables studied here exhibit a logarithmic dependence on the size of the system. We numerically access not only the leading term in the $\beta$ function governing these logarithmic violations, but also the subleading term, which controls the evolution of the boundary phase diagram as a function of $N$.

The Andreev-Bashkin (AB) effect, corresponding to the dissipationless dragging of one superfluid by another, was predicted in the early 1970s but has so far eluded experimental detection. In this work, we theoretically introduce a detection paradigm and show that it enables the observation of the hitherto undetected AB effect for realistic parameters. We accomplish this by using the powerful techniques of cavity optomechanics, which were crucial to the observation of gravitational waves, on a spinor ring Bose-Einstein condensate. In contrast to established AB detection methods, our scheme allows for real-time, in situ, minimally destructive and three orders-of-magnitude-more-sensitive measurement of the AB effect. Our proposal, which considers persistent currents in weakly repulsive atomic condensates and amplifies the AB signal using a dynamic Bragg spectroscopy technique, is supported by numerical simulations of the stochastic Gross-Pitaevski equation, which agree very well with our analytic Bogoliubov–de Gennes calculations. Our work suggests a tool for sensitively and nondestructively probing the dynamics of rotationally interacting superfluids using cavities and has fundamental implications for ongoing studies of superfluid hydrodynamics, atomtronics, matter-wave interferometry, and cavity optomechanical sensing.

Polymerase dynamics (PD) is an important model for explaining transcriptional regulation in gene perturbation data. In this study, we conducted a detailed analysis of the dynamic behavior of stochastic gene transcription models with PD. We first derived an exact time-dependent formula of mRNA distribution for the classical telegraph model with PD, then revealed a different regulation mechanism whereby PD simultaneously suppresses the Fano factor and enhances the bimodal distribution. For deeper insights into PD regulation, we established optimal effective models without PD to approximate the steady-state mRNA distribution in models with PD. Optimized effective parameters reliably captured input gene initiation and mRNA production rates and reflected the parameter variations of complex systems with PD under biological perturbations. The effective models also revealed that PD introduced quantitatively distinct kurtosis values and dynamics of mRNA distribution. By fitting transcriptome-wide mRNA distribution data from mouse fibroblast and embryonic stem cells, we found that over 1000 data sets may be better captured by integrating PD into the telegraph model. By fitting synthetic mRNA distribution data, we showed that the combinations of cell sample size $N$ and number of time points, $n$, required for the reliable selection between telegraph models with and without PD are $N={10}^3$ and $n\ge 8$, $N={10}^4$ and $n\ge 2$, or $N={10}^5$ and steady-state data, whereas the parameter combinations required for the reliable estimation of polymerase recruitment and pause release rates are $N={10}^4$ and $n\ge 8$ or $N={10}^5$ and $n\ge 4$. Our proposed method can also be used to determine the regulatory roles of other biological compounds.

The effects of noise on memory in a linear recurrent network are theoretically investigated. Memory is characterized by its ability to store previous inputs in its instantaneous state of network, which receives a correlated or uncorrelated noise. Two major properties are revealed: First, the memory reduced by noise is uniquely determined by the noise's power spectral density (PSD). Second, the memory will not decrease regardless of noise intensity if the PSD is in a certain class of distribution (including power law). The results are verified using the human brain signals, showing good agreement.

We derive the equations of motion of arbitrary momentum-dependent observables for a fully first-quantized, driven two-level atom in a linear potential under the presence of a stochastic laser phase. In particular, we show that such momentum-dependent observables evolve under stochastic differential equations (SDEs) in the form of geometrical Brownian motion and find that these SDEs reduce to ordinary differential equations (ODEs) for the expectation values under white laser phase noise. As an example, we solve these ODEs numerically and determine the effect of laser phase noise on Rabi oscillations including quantized center-of-mass degrees of freedom of the atom. Moreover, the SDE formalism we derive holds for any phase noise source and can also be applied to extended noise models such as color noise.

Due to the presence of flat Chern bands, moiré transition-metal dichalcogenide (TMD) bilayers are a platform to realize strongly correlated topological phases of fermions such as fractional Chern insulators. TMDs are also known to host long-lived excitons, which inherit the topology of the underlying Chern bands. For the particular example of ${\mathrm{MoTe}}_2/{\mathrm{WSe}}_2$ heterobilayers we perform a time-dependent Hartree-Fock calculation to identify a regime in the phase diagram where the excitons themselves form a topological flat band. This paves a way towards realizing strongly correlated states of bosons in moiré TMDs.

The Leggett-Garg inequality (LGI) is a powerful tool for distinguishing between quantum and classical properties in studies of macroscopic systems. Applying the LGI to non-Hermitian systems with dissipation presents a fascinating opportunity, as competing mechanisms can either strengthen or weaken LGI violations. On one hand, dissipation-induced nonlinear interactions amplify LGI violations compared to Hermitian systems; on the other hand, dissipation leads to decoherence, which could weaken the LGI violation. In this paper, we investigate a non-Hermitian system of ultracold Fermi gas with dissipation. Our experiments reveal that as dissipation increases, the upper bound of the third-order LGI parameter $K_3$ initially rises, reaching its maximum at the exceptional point, where $K_3=C_{21}+C_{32}-C_{31}$, encompassing three two-time correlation functions. Beyond a certain dissipation threshold, the LGI violation weakens, approaching the classical limit, indicating a quantum-to-classical transition (QCT). Furthermore, we observe that the LGI violation decreases with increasing evolution time, reinforcing the QCT in the time domain. This study provides a crucial stepping stone for using the LGI to explore the QCT in many-body open quantum systems.

Using density-functional theory, we demonstrate that the formal layer charges of the metallic samarium nickelate electrode influence the spontaneous ferroelectric polarization of the barium titanate in ${\mathrm{SmNiO}}_3\text{/}{\mathrm{BaTiO}}_3$ capacitors. We find that, despite the metallic screening of ${\mathrm{SmNiO}}_3$, the spontaneous polarization of ${\mathrm{BaTiO}}_3$ always aligns with the layer polarization of the ${\mathrm{SmNiO}}_3$ formal charges. We also find zero critical thickness for the ferroelectricity in ${\mathrm{BaTiO}}_3$ in this orientation. The opposite polarization direction is highly disfavored for thin ${\mathrm{BaTiO}}_3$ slabs but becomes less unstable with increasing slab thickness. We construct a simple electrostatic model that allows us both to study the behavior for thicker ${\mathrm{BaTiO}}_3$ and ${\mathrm{SmNiO}}_3$ slabs and to extract the influence of various material parameters on the behavior. We mimic a metal-insulator transition in the ${\mathrm{SmNiO}}_3$ by varying the metallic screening length, which we find influences the stability of the ferroelectric polarization. Our results show that layer charges in the metal electrodes strongly influence the properties of ferroelectric capacitors and can even provide new ways to control them.

Deflection angles of charged particles interacting with materials play a critical role in various plasma applications. The development of a mathematically well-posed kinetic collision operator that accounts for deflection angles of strong Coulomb interactions remains a fundamental open problem. This paper presents a relativistic method for modifying the electromagnetic field in an anisotropic and adjustable manner to manipulate a system of charged particles, specifically by the transfer of angular momentum from a superluminal wave source to particles at specific times and locations. The method provides a mechanism to influence the scattering outcomes of strong interactions by manipulating the angular distribution of particles, and thus the deflection angles of their interactions with a solid surface, without requiring detailed knowledge of the kinetic collision operator. To this end, we demonstrate how a specific type of singularity, generated by Maxwell's equations for a superluminal wave source at the boundary of the plasma, can modify the electromagnetic field in a highly directional manner. The proposed method can lead to the development of novel approaches for controlling interactions of charged particles with a material in plasma systems.

In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The linear inverse model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. In this study, we introduce a new variant of LIM, called colored LIM, which models stochasticity using Ornstein-Uhlenbeck colored noise. Despite the nontrivial correlation between observable and colored noise, we show that colored LIM unveils the desired information merely from the correlation function of the observable. Therefore, this approach not only accounts for the memory effects of environmental noise, traditionally represented by time-uncorrelated white noise in the classical-LIM framework, but does so using the same observational dataset without requiring additional data. Furthermore, we show that colored LIM does not reduce to classical LIM in the white noise limit, underscoring the importance of temporal dependencies in stochastic systems. In this paper, we rigorously develop the colored LIM, explore its connections with the classical LIM, dynamic mode decomposition, and vector autoregressive moving average models, and validate its effectiveness on both ideal linear and nonlinear systems. In addition, we illustrate the potential applications and implications of colored LIM for real-world problems, including the El Niño–southern oscillation and the electricity network of Tohoku University.

We show theoretically that the noise due to laser induced backaction acting on a small nanosphere levitated in a standing-wave trap can be considerably reduced by utilizing a suitable reflective boundary. We examine the spherical mirror geometry as a case study of this backaction suppression effect, discussing the theoretical and experimental constraints. We study the effects of laser recoil directly, by analyzing optical force fluctuations acting on a dipolar particle trapped at the center of a spherical mirror. We also compute the corresponding measurement imprecision in an interferometric, shot-noise-limited position measurement, using the formalism of Fisher information flow. Our results show that the standing-wave trapping field is necessary for backaction suppression in three dimensions, and they satisfy the Heisenberg limit of detection.

This work presents an experimental, numerical, and theoretical investigation of slow drainage processes in porous media, focusing on systematic variations in pore-scale disorder. We examine a system comprising a monolayer of cylinders in a quasi-two-dimensional (2D) geometry, where the medium's disorder is controlled via a disorder parameter, $\epsilon$. By systematically altering $\epsilon$, we randomly shift the positions of the cylinders and analyze the resulting effects on the gravity stabilized invasion front. Our results show that increasing $\epsilon$ has a significant impact on the stable width of the invasion front, with this width scaling with the disorder parameter according to an exponent $\beta =0.57$, which closely matches the theoretical value predicted by percolation theory for 2D systems. Additionally, we derive a reciprocal relationship between the dimensionless fluctuation number and the disorder parameter $\epsilon$. Moreover, we find a correlation between the invasion front width and the size of trapped clusters in the system, and we explore how pore disorder affects the air saturation left behind the invasion front. Both our experimental and numerical findings exhibit exponent values that align with theoretical scaling predictions. This study advances the current understanding of multiphase flows in porous media by specifically addressing the influence of pore-scale disorder on the morphology of invasion fronts.

A fundamental longstanding problem in studying spin models is the efficient and accurate numerical simulation of the long-time behavior of larger systems. The exponential growth of the Hilbert space and the entanglement accumulation at long times pose major challenges for current methods. To address these issues, we employ the multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) framework to simulate the many-body spin dynamics of the Heisenberg model in various settings, including the Ising and XYZ limits with different interaction ranges and random couplings. Benchmarks with analytical and exact numerical approaches show that ML-MCTDH accurately captures the time evolution of one- and two-body observables in both one- and two-dimensional lattices. A comparison with the discrete truncated Wigner approximation (DTWA) highlights that ML-MCTDH is particularly well-suited for handling anisotropic models and provides more reliable results for two-point observables across all tested cases. The behavior of the corresponding entanglement dynamics is analyzed to reveal the complexity of the quantum states. Our findings indicate that the rate of entanglement growth strongly depends on the interaction range and the presence of disorder. This particular relationship is then used to examine the convergence behavior of ML-MCTDH. Our results indicate that the multilayer structure of ML-MCTDH is a promising numerical framework for handling the dynamics of generic many-body spin systems.

Bell measurements, entailing the projection onto one of the Bell states, play a key role in quantum information and communication, where the outcome of a variety of protocols crucially depends on the success probability of such measurements. Although in the case of qubit systems, Bell measurements can be implemented using only linear optical components, the same result is no longer true for qudits, where at least the use of ancillary photons is required. In order to circumvent this limitation, one possibility is to introduce nonlinear effects. In this work, we adopt the latter approach and propose a scalable Bell measurement scheme for high-dimensional states, exploiting multiple squeezer devices applied to a linear optical circuit for discriminating the different Bell states. Our approach does not require ancillary photons, is not limited by the dimension of the quantum states, and is experimentally scalable, thus paving the way toward the realization of an effective high-dimensional Bell measurement.

Data-driven model discovery of complex dynamical systems can be done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical functions. Examples where sparse optimization fails abound, including the classic Ikeda or optical-cavity map in nonlinear dynamics, as well as a wide variety of ecosystems. Another approach is based on machine learning, e.g., deep neural networks, which excels at capturing system behavior from data but functions as black boxes, offering little insight into how inputs influence the outputs. We propose a general model-discovery framework based on the Kolmogorov-Arnold networks (KANs) that are not constrained by the sparsity condition. The KAN framework with a simple structure is capable of accurately capturing the complex behavior of dynamical systems that do not meet the sparsity requirement while offering greater interpretability compared to conventional neural networks. This interpretability provides insights into the dynamics generating the data, which are typically inaccessible in traditional black-box function approximation methods. We demonstrate nonuniqueness in that a large number of approximate models of the system can be found that generate the same invariant set with the true statistics such as the Lyapunov exponents and Kullback–Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.