We study multipartite entanglement and genuine tripartite entanglement based on general symmetric informationally complete positive operator valued measurements (GSIC-POVMs). By representing the bipartite density matrices in terms of GSIC-POVMs, we obtain the lower bound of the sum of squares of the corresponding probability. We then construct a special matrix with the correlation probability of GSIC-POVMs to derive useful and operational criteria to detect genuine tripartite entanglement. We also generalize the results to obtain a sufficient criterion to detect entanglement for multipartite quantum states in arbitrary dimensions. Detailed examples show that the new method can detect more entangled and genuine entangled states than previous criteria.

A detailed numerical study reveals that the asymptotic values of the standard-deviation-to-mean ratio of the out-of-time-ordered correlator in energy eigenstates can be successfully used as a measure of the quantum chaoticity of the system. We employ a finite-size fully connected quantum system with two degrees of freedom, namely, the algebraic $\mathrm{u}\left(3\right)$ model, and demonstrate a clear correspondence between the energy-smoothed relative oscillations of the correlators and the ratio of the chaotic part of the volume of phase space in the classical limit of the system. We also show how the relative oscillations scale with the system size and conjecture that the scaling exponent can also serve as a chaos indicator.

Quantization of a toy model of a pseudointegrable Hamiltonian impact system is introduced, including Einstein-Brillouin-Keller quantization conditions, a verification of Weyl's law, the study of their wave functions, and a study of their energy levels properties. It is demonstrated that the energy level statistics are similar to those of pseudointegrable billiards. Yet, here, the density of wave functions which concentrate on projections of classical level sets to the configuration space does not disappear at large energies, suggesting that there is no equidistribution in the configuration space in the large energy limit; this is shown analytically for some limit symmetric cases and is demonstrated numerically for some nonsymmetric cases.

Packings of beads confined in slowly tilted containers with a top free surface are commonly used in laboratory experiments to model natural grain avalanches and better understand and predict critical events from optical measurements of the surface activity. To that aim, after reproducible packing preparations, the present paper focuses on the effects of the surface fabrication, which can be scraped or soft leveled, on both the avalanche stability angle and the dynamic of precursory events for glass beads of 2-mm diameter. A depth effect of a scraping operation is highlighted by considering different packing heights and inclination speeds.

Fluctuations play an important role in the dynamics of stochastic systems. In particular, for small systems, the most probable thermodynamic quantities differ from their averages because of the fluctuations. Using the Onsager Machlup variational formalism we analyze the most probable paths for nonequilibrium systems, in particular, active Ornstein-Uhlenbeck particles, and investigate how the entropy production along these paths differs from the average entropy production. We investigate how much information about their nonequilibrium nature can be obtained from their extremum paths and how these paths depend on the persistence time and their swim velocities. We also look at how the entropy production along the most probable paths varies with the active noise and how it differs from the average entropy production. This study would be useful to design artificial active systems with certain target trajectories.

We consider the totally asymmetric simple exclusion processes on quenched random energy landscapes. We show that the current and the diffusion coefficient differ from those for homogeneous environments. Using the mean-field approximation, we analytically obtain the site density when the particle density is low or high. As a result, the current and the diffusion coefficient are described by the dilute limit of particles or holes, respectively. However, in the intermediate regime, due to the many-body effect, the current and the diffusion coefficient differ from those for single-particle dynamics. The current is almost constant and becomes the maximal value in the intermediate regime. Moreover, the diffusion coefficient decreases with the particle density in the intermediate regime. We obtain analytical expressions for the maximal current and the diffusion coefficient based on the renewal theory. The deepest energy depth plays a central role in determining the maximal current and the diffusion coefficient. As a result, the maximal current and the diffusion coefficient depend crucially on the disorder, i.e., non-self-averaging. Based on the extreme value theory, we find that sample-to-sample fluctuations of the maximal current and diffusion coefficient are characterized by the Weibull distribution. We show that the disorder averages of the maximal current and the diffusion coefficient converge to zero as the system size is increased and quantify the degree of the non-self-averaging effect for the maximal current and the diffusion coefficient.

The averaged steady-state surprisal links a driven stochastic system's information processing to its nonequilibrium thermodynamic response. By explicitly accounting for the effects of nonequilibrium steady states, a decomposition of the surprisal results in an information processing first law that extends and tightens—to strict equalities—various information processing second laws. Applying stochastic thermodynamics' integral fluctuation theorems then shows that the decomposition reduces to the second laws under appropriate limits. In unifying them, the first law paves the way to identifying the mechanisms by which nonequilibrium steady-state systems leverage information-bearing degrees of freedom to extract heat. To illustrate, we analyze an autonomous Maxwellian information ratchet that tunably violates detailed balance in its effective dynamics. This demonstrates how the presence of nonequilibrium steady states qualitatively alters an information engine's allowed functionality.

Notwithstanding various attempts to construct a partial information decomposition (PID) for multiple variables by defining synergistic, redundant, and unique information, there is no consensus on how one ought to precisely define either of these quantities. One aim here is to illustrate how that ambiguity—or, more positively, freedom of choice—may arise. Using the basic idea that information equals the average reduction in uncertainty when going from an initial to a final probability distribution, synergistic information will likewise be defined as a difference between two entropies. One term is uncontroversial and characterizes “the whole” information that source variables carry jointly about a target variable $\mathcal{T}$. The other term then is meant to characterize the information carried by the “sum of its parts.” Here we interpret that concept as needing a suitable probability distribution aggregated (“pooled”) from multiple marginal distributions (the parts). Ambiguity arises in the definition of the optimum way to pool two (or more) probability distributions. Independent of the exact definition of optimum pooling, the concept of pooling leads to a lattice that differs from the often-used redundancy-based lattice. One can associate not just a number (an average entropy) with each node of the lattice, but (pooled) probability distributions. As an example, one simple and reasonable approach to pooling is presented, which naturally gives rise to the overlap between different probability distributions as being a crucial quantity that characterizes both synergistic and unique information.

In this paper, we study a variant of the voter model on adaptive networks in which nodes can flip their spin, create new connections, or break existing connections. We first perform an analysis based on the mean-field approximation to compute asymptotic values for macroscopic estimates of the system, namely, the total mass of present edges in the system and the average spin. However, numerical results show that this approximation is not very suitable for such a system, for which it does not capture key features such as the network breaking into two disjoint and opposing (in spin) communities. Therefore, we propose another approximation based on an alternate coordinate system to improve accuracy and validate this model through simulations. Finally, we state a conjecture dealing with the qualitative properties of the system, corroborated by numerous numerical simulations.

In this work, we study the recently developed parametrized partition function formulation and show how we can infer the thermodynamic properties of fermions based on numerical simulation of bosons and distinguishable particles at various temperatures. In particular, we show that in the three-dimensional space defined by energy, temperature, and the parameter characterizing parametrized partition function, we can map the energies of bosons and distinguishable particles to fermionic energies through constant-energy contours. We apply this idea to both noninteracting and interacting Fermi systems and show it is possible to infer the fermionic energies at all temperatures, thus providing a practical and efficient approach to obtain thermodynamic properties of Fermi systems with numerical simulation. As an example, we present energies and heat capacities for 10 noninteracting fermions and 10 interacting fermions and show good agreement with the analytical result for the noninteracting case.

We investigate the current properties in the totally asymmetric simple exclusion process (TASEP) on a quenched random energy landscape. In low- and high-density regimes, the properties are characterized by single-particle dynamics. In the intermediate one, the current becomes constant and is maximized. Based on the renewal theory, we derive accurate results for the maximum current. The maximum current significantly depends on a disorder realization, i.e., non-self-averaging (SA). We demonstrate that the disorder average of the maximum current decreases with the system size, and the sample-to-sample fluctuations of the maximum current exceed those of current in the low- and high-density regimes. We find a significant difference between single-particle dynamics and the TASEP. In particular, the non-SA behavior of the maximum current is always observed, whereas the transition from non-SA to SA for current in single-particle dynamics exists.

We apply the hierarchical autoregressive neural network sampling algorithm to the two-dimensional $Q$-state Potts model and perform simulations around the phase transition at $Q=12$. We quantify the performance of the approach in the vicinity of the first-order phase transition and compare it with that of the Wolff cluster algorithm. We find a significant improvement as far as the statistical uncertainty is concerned at a similar numerical effort. In order to efficiently train large neural networks we introduce the technique of pretraining. It allows us to train some neural networks using smaller system sizes and then employ them as starting configurations for larger system sizes. This is possible due to the recursive construction of our hierarchical approach. Our results serve as a demonstration of the performance of the hierarchical approach for systems exhibiting bimodal distributions. Additionally, we provide estimates of the free energy and entropy in the vicinity of the phase transition with statistical uncertainties of the order of ${10}^{-7}$ for the former and ${10}^{-3}$ for the latter based on a statistics of ${10}^6$ configurations.

Embedded random matrix ensembles with $k$-body interactions are well established to be appropriate for many quantum systems. For these ensembles the two point correlation function is not yet derived, though these ensembles are introduced 50 years back. Two-point correlation function in eigenvalues of a random matrix ensemble is the ensemble average of the product of the density of eigenvalues at two eigenvalues, say $E$ and $E^{\prime }$. Fluctuation measures such as the number variance and Dyson-Mehta ${\mathrm{\Delta }}_3$ statistic are defined by the two-point function and so also the variance of the level motion in the ensemble. Recently, it is recognized that for the embedded ensembles with $k$-body interactions the one-point function (ensemble averaged density of eigenvalues) follows the so called $q$-normal distribution. With this, the eigenvalue density can be expanded by starting with the $q$-normal form and using the associated $q$-Hermite polynomials $H{e}_{\zeta }\left(x\right|q)$. Covariances $\overline{S_{\zeta }{S}_{{\zeta }^{\prime }}}$ (overline representing ensemble average) of the expansion coefficients $S_{\zeta }$ with $\zeta \ge 1$ here determine the two-point function as they are a linear combination of the bivariate moments ${\mathrm{\Sigma }}_{PQ}$ of the two-point function. Besides describing all these, in this paper formulas are derived for the bivariate moments ${\mathrm{\Sigma }}_{PQ}$ with $P+Q\le 8$, of the two-point correlation function, for the embedded Gaussian unitary ensembles with $k$-body interactions [EGUE($k$)] as appropriate for systems with $m$ fermions in $N$ single particle states. Used for obtaining the formulas is the $SU\left(N\right)$ Wigner-Racah algebra. These formulas with finite $N$ corrections are used to derive formulas for the covariances $\overline{S_{\zeta }{S}_{{\zeta }^{\prime }}}$ in the asymptotic limit. These show that the present work extends to all $k$ values, the results known in the past in the two extreme limits with $k/m\to 0$ (same as $q\to 1$) and $k=m$ (same as $q=0$).

The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standard Brownian motion. It also tends to a drift towards its mean function, and such a process is called mean reverting. Two examples of the generalized Ornstein-Uhlenbeck process are considered. In the first one, we study the Ornstein-Uhlenbeck process on a comb model, as an example of the harmonically bounded random motion in the topologically constrained geometry. The main dynamical characteristics (as the first and the second moments) and the probability density function are studied in the framework of both the Langevin stochastic equation and the Fokker-Planck equation. The second example is devoted to the study of the effects of stochastic resetting on the Ornstein-Uhlenbeck process, including stochastic resetting in the comb geometry. Here the nonequilibrium stationary state is the main question in task, where the two divergent forces, namely, the resetting and the drift towards the mean, lead to compelling results in the cases of both the Ornstein-Uhlenbeck process with resetting and its generalization on the two-dimensional comb structure.

The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of such metrics. The framework consists in dividing the network into sectors of influence, and then selecting the most influential nodes within these sectors. We explore three different methodologies to find sectors in a network: graph partitioning, graph hyperbolic embedding, and community structure. The framework is validated with a systematic analysis of real and synthetic networks. We show that the gain in performance generated by dividing a network into sectors before selecting the influential spreaders increases as the modularity and heterogeneity of the network increase. Also, we show that the division of the network into sectors can be efficiently performed in a time that scales linearly with the network size, thus making the framework applicable to large-scale influence maximization problems.

Elongated colloidal rods at sufficient packing conditions are known to form stable lamellar or smectic phases. Using a simplified volume-exclusion model, we propose a generic equation of state for hard-rod smectics that is robust against simulation results and is independent of the rod aspect ratio. We then extend our theory by exploring the elastic properties of a hard-rod smectic, including the layer compressibility ($B$) and bending modulus ($K_1$). By introducing weak backbone flexibility we are able to compare our predictions with experimental results on smectics of filamentous virus rods ($\mathit{fd}$) and find quantitative agreement between the smectic layer spacing, the out-of-plane fluctuation strength, as well as the smectic penetration length $\lambda =\sqrt{K_1/B}$. We demonstrate that the layer bending modulus is dominated by director splay and depends sensitively on lamellar out-of-plane fluctuations that we account for on the single-rod level. We find that the ratio between the smectic penetration length and the lamellar spacing is about two orders of magnitude smaller than typical values reported for thermotropic smectics. We attribute this to the fact that colloidal smectics are considerably softer in terms of layer compression than their thermotropic counterparts while the cost of layer bending is of comparable magnitude.

A Brownian shell model describing the random rotational motion of a spherical shell of uniform particle density is presented and validated by molecular dynamics simulations. The model is applied to proton spin rotation in aqueous paramagnetic ion complexes to yield an expression for the Larmor-frequency-dependent nuclear magnetic resonance spin-lattice relaxation rate $T_1^{-1}\left(\omega \right)$ describing the dipolar coupling of the nuclear spin of the proton with the electronic spin of the ion. The Brownian shell model provides a significant enhancement to existing particle-particle dipolar models without added complexity, allowing fits to experimental $T_1^{-1}\left(\omega \right)$ dispersion curves without arbitrary scaling parameters. The model is successfully applied to measurements of $T_1^{-1}\left(\omega \right)$ from aqueous manganese(II), iron(III), and copper(II) systems where the scalar coupling contribution is known to be small. Appropriate combinations of Brownian shell and translational diffusion models, representing the inner and outer sphere relaxation contributions, respectively, are shown to provide excellent fits. Quantitative fits are obtained to the full dispersion curve of each aquoion with just five fit parameters, with the distance and time parameters each taking a physically justifiable numerical value.

The formation of correlated structures is of importance in many diverse contexts such as strongly coupled plasmas, soft matter, and even biological mediums. In all these contexts the dynamics are mainly governed by electrostatic interactions and result in the formation of a variety of structures. In this study, the process of formation of structures is investigated with the help of molecular dynamics (MD) simulations in two and three dimensions. The overall medium has been modeled with an equal number of positive and negatively charged particles interacting via long-range pair Coulomb potential. A repulsive short-range Lennard-Jones (LJ) potential is added to take care of the blowing up of attractive Coulomb interaction between unlike charges. In the strongly coupled regime, a variety of classical bound states form. However, complete crystallization of the system, as typically observed in the context of one-component strongly coupled plasmas, does not occur. The influence of localized perturbation in the system has also been studied. The formation of a crystalline pattern of shielding clouds around this disturbance is observed. The spatial properties of the shielding structure have been analyzed using the radial distribution function and Voronoi diagram. The process of accumulation of oppositely charged particles around the disturbance triggers a lot of dynamic activity in the bulk of the medium. As a result of this, close encounters are possible even between those particles/clusters which were initially and/or at some point of time widely separated. This leads to the formation of a larger number of bigger clusters. There are, however, also instances when bound pairs break up and the electrons from bound pairs contribute to the shielding cloud, whereas ions bounce back into the bulk. A detailed discussion of these features has been provided in the manuscript.