CyberSec Research

Understanding the behavior of quantum many-body systems under decoherence is essential for developing robust quantum technologies. Here, we examine the fate of weak ergodicity breaking in systems hosting quantum many-body scars when subject to local pure dephasing -- an experimentally relevant form of environmental noise. Focusing on a large class of models with an approximate su(2)-structured scar subspace, we show that scarred eigenmodes of the Liouvillean exhibit a transition reminiscent of spontaneous $\mathbb{PT}$-symmetry breaking as the dephasing strength increases. Unlike previously studied non-Hermitian mechanisms, this transition arises from a distinct quantum jump effect. Remarkably, in platforms such as the XY spin ladder and PXP model of Rydberg atom arrays, the critical dephasing rate shows only weak dependence on system size, revealing an unexpected robustness of scarred dynamics in noisy quantum simulators.

Spontaneous symmetry breaking (SSB) is the cornerstone of our understanding of quantum phases of matter. Recent works have generalized this concept to the domain of mixed states in open quantum systems, where symmetries can be realized in two distinct ways dubbed strong and weak. Novel intrinsically mixed phases of quantum matter can then be defined by the spontaneous breaking of strong symmetry down to weak symmetry. However, proposed order parameters for strong-to-weak SSB (based on mixed-state fidelities or purities) seem to require exponentially many copies of the state, raising the question: is it possible to efficiently detect strong-to-weak SSB in general? Here we answer this question negatively in the paradigmatic cases of $Z_2$ and $U(1)$ symmetries. We construct ensembles of pseudorandom mixed states that do not break the strong symmetry, yet are computationally indistinguishable from states that do. This rules out the existence of efficient state-agnostic protocols to detect strong-to-weak SSB.

Information processing in the brain is coordinated by the dynamic activity of neurons and neural populations at a range of spatiotemporal scales. These dynamics, captured in the form of electrophysiological recordings and neuroimaging, show evidence of time-irreversibility and broken detailed balance suggesting that the brain operates in a nonequilibrium stationary state. Furthermore, the level of nonequilibrium, measured by entropy production or irreversibility appears to be a crucial signature of cognitive complexity and consciousness. The subsequent study of neural dynamics from the perspective of nonequilibrium statistical physics is an emergent field that challenges the assumptions of symmetry and maximum-entropy that are common in traditional models. In this review, we discuss the plethora of exciting results emerging at the interface of nonequilibrium dynamics and neuroscience. We begin with an introduction to the mathematical paradigms necessary to understand nonequilibrium dynamics in both continuous and discrete state-spaces. Next, we review both model-free and model-based approaches to analysing nonequilibrium dynamics in both continuous-state recordings and neural spike-trains, as well as the results of such analyses. We briefly consider the topic of nonequilibrium computation in neural systems, before concluding with a discussion and outlook on the field.

This article deals with the existence and scaling of an energy cascade in steady granular liquid flows between the scale at which the system is forced and the scale at which it dissipates energy. In particular, we examine the possible origins of a breaking of the Kolmogorov Universality class that applies to Newtonian liquids under similar conditions. In order to answer these questions, we build a generic field theory of granular liquid flows and, through a study of its symmetries, show that indeed the Kolmogorov scaling can be broken, although most of the symmetries of the Newtonian flows are preserved.

We reconsider the dynamics of localized states in the deterministic and stochastic discrete nonlinear Schr\"odinger equation. Localized initial conditions disperse if the strength of the nonlinear part drops below a threshold. Localized states are unstable in a noisy environment. As expected, an infinite temperature state emerges when multiplicative noise is applied, while additive noise yields unbounded dynamics since conservation of normalization is violated.

We introduce a non-Hermitian $\beta$-ensemble and determine its spectral density in the limit of large $\beta$ and large matrix size $n$. The ensemble is given by a general tridiagonal complex random matrix of normal and chi-distributed random variables, extending previous work of two of the authors. The joint distribution of eigenvalues contains a Vandermonde determinant to the power $\beta$ and a residual coupling to the eigenvectors. A tool in the computation of the limiting spectral density is a single characteristic polynomial for centred tridiagonal Jacobi matrices, for which we explicitly determine the coefficients in terms of its matrix elements. In the low temperature limit $\beta\gg1$ our ensemble reduces to such a centred matrix with vanishing diagonal. A general theorem from free probability based on the variance of the coefficients of the characteristic polynomial allows us to obtain the spectral density when additionally taking the large-$n$ limit. It is rotationally invariant on a compact disc, given by the logarithm of the radius plus a constant. The same density is obtained when starting form a tridiagonal complex symmetric ensemble, which thus plays a special role. Extensive numerical simulations confirm our analytical results and put this and the previously studied ensemble in the context of the pseudospectrum.

We explore the statistical nature of point defects in a two-dimensional hexagonal colloidal crystal from the perspective of stochastic dynamics. Starting from the experimentally recorded trajectories of time series, the underlying drifting forces along with the diffusion matrix from thermal fluctuations are extracted. We then employ a deposition in which the deterministic terms are split into diffusive and transverse components under a stochastic potential with the lattice periodicity to uncover the dynamic landscape as well as the transverse matrix, two key structures from limited ranges of measurements. The analysis elucidates some fundamental dichotomy between mono-point and di-point defects of paired vacancies or interstitials. Having large transverse magnitude, the second class of defects are likely to break the detailed balance, Such a scenario was attributed to the root cause of lattice melting by experimental observations. The constructed potential can in turn facilitate large-scale simulation for the ongoing research.

Computing free energy is a fundamental problem in statistical physics. Recently, two distinct methods have been developed and have demonstrated remarkable success: the tensor-network-based contraction method and the neural-network-based variational method. Tensor networks are accu?rate, but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems. The neural network method applies to systems with general topology. However, as a variational method, it is not as accurate as tensor networks. In this work, we propose an integrated approach, tensor-network-based variational autoregressive networks (TNVAN), that leverages the strengths of both tensor networks and neural networks: combining the variational autoregressive neural network's ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network's power to accurately compute the partition function for small sub-systems, resulting in a robust method for precisely estimating free energy. To evaluate the proposed approach, we conducted numerical experiments on spin glass systems with various topologies, including two-dimensional lattices, fully connected graphs, and random graphs. Our numerical results demonstrate the superior accuracy of our method compared to existing approaches. In particular, it effectively handles systems with long-range interactions and leverages GPU efficiency without requiring singular value decomposition, indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.

These lecture notes provide an introduction to Langevin processes and briefly discuss some interesting properties and simple applications. They compile material presented at the "School of Physics and Mathematics Without Frontiers" (ZigZag), held at La Havana, Cuba, in March 2024, the School "Information, Noise and Physics of Life" held at Ni\v{s}, Serbia, in June 2024, both sponsored by ICTP, the PEBBLE summer camp at Westlake University, China, in August 2024, the Barcelona school on "Non-equilibrium Statistical Physics", in April 2025, and the 2012-2016 course "Out of Equilibrium Dynamics of Complex Systems" for the Master 2 program "Physics of Complex Systems" in the Paris area.

A quantum master equation describing the stochastic dynamics of a quantum massive system interacting with a quantum gravitational field is useful for the investigation of quantum gravitational and quantum informational issues such as the quantum nature of gravity, gravity-induced entanglement and gravitational decoherence. Studies of the decoherence of quantum systems by an electromagnetic field shows that a lower temperature environment is more conducive to successful quantum information processing experiments. Likewise, the quantum nature of (perturbative) gravity is far better revealed at lower temperatures than high, minimizing the corruptive effects of thermal noise. In this work, generalizing earlier results of the Markovian ABH master equation [1,2] which is valid only for high temperatures, we derive a non-Markovian quantum master equation for the reduced density matrix, and the associated Fokker-Planck equation for the Wigner distribution function, for the stochastic dynamics of two masses following quantum trajectories, interacting with a graviton field, including the effects of graviton noise, valid for all temperatures. We follow the influence functional approach exemplified in the derivation of the non-Markovian Hu-Paz-Zhang master equation [62,64] for quantum Brownian motion. We find that in the low temperature limit, the off-diagonal elements of the reduced density matrix decrease in time logarithmically for the zero temperature part and quadratically in time for the temperature-dependent part, which is distinctly different from the Markovian case. We end with a summary of our findings and a discussion on how this problem studied here is related to the quantum stochastic equation derived in [77] for gravitational self force studies, and to quantum optomechanics where experimental observation of gravitational decoherence and entanglement may be implemented.

We propose an $f$-divergence extension of the Hasegawa-Nishiyama thermodynamic uncertainty relation. More precisely, we introduce the stochastic thermodynamic entropy production based on generalised $f$-divergences and derive corresponding uncertainty relations in connection with the symmetry entropy.

We consider two (off-lattice) varieties of out-of-equilibrium systems, viz., granular and active matter systems, that, in addition to displaying velocity ordering, exhibit fascinating pattern formation in the density field, similar to those during vapor-liquid phase transitions. In the granular system, velocity ordering occurs due to reduction in the normal components of velocities, arising from inelastic collisions. In the active matter case, on the other hand, velocity alignment occurs because of the inherent tendency of the active particles to follow each other. Inspite of this difference, the patterns, even during density-field evolutions, in these systems can be remarkably similar. This we have quantified via the calculations of the two-point equal time correlation functions and the structure factors. These results have been compared with the well studied case of kinetics of phase separation within the framework of the Ising model. Despite the order-parameter conservation constraint in all the cases, in the density field, the quantitative structural features in the Ising case is quite different from those for the granular and active matters. Interestingly, the correlation function for the latter varieties, particularly for an active matter model, quite accurately describes the structure in a real assembly of biologically active particles.

We study motility-induced phase separation~(MIPS) in active AB binary mixtures undergoing the chemical reaction $A \rightleftharpoons B$. Starting from the evolution equations for the density fields $\rho_i(\vec r, t)$ describing MIPS, we phenomenologically incorporate the effects of the reaction through the reaction rate $\Gamma$ into the equations. The steady-state domain morphologies depend on $\Gamma$ and the relative activity of the species, $\Delta$. For a sufficiently large $\Gamma$ and $\Delta\ne 1$, the more active component of the mixture forms a droplet morphology. We characterize the morphology of domains by calculating the equal-time correlation function $C(r, t)$ and the structure factor $S(k, t)$, exhibiting scaling violation. The average domain size, $L(t)$, follows a diffusive growth as $L(t)\sim t^{1/3}$ before reaching the steady state domain size, $L_{\rm ss}$. Additionally, $L_{\rm ss}$ shows the scaling relation $L_{\rm ss}\sim\Gamma^{-1/4}$, independent of $\Delta$.

We study the dynamics of an athermal inertial active particle moving in a shear-thinning medium in $d=1$. The viscosity of the medium is modeled using a Coulomb-tanh function, while the activity is represented by an asymmetric dichotomous noise with strengths $-\Delta$ and $\mu\Delta$, transitioning between these states at a rate $\lambda$. Starting from the Fokker-Planck~(FP) equation for the time-dependent probability distributions $P(v,-\Delta,t)$ and $P(v,\mu\Delta,t)$ of the particle's velocity $v$ at time $t$, moving under the influence of active forces $-\Delta$ and $\mu\Delta$ respectively, we analytically derive the steady-state velocity distribution function $P_s(v)$, explicitly dependent on $\mu$. Also, we obtain a quadrature expression for the effective diffusion coefficient $D_e$ for the symmetric active force case~($\mu=1$). For a given $\Delta$ and $\mu$, we show that $P_s(v)$ exhibits multiple transitions as $\lambda$ is varied. Subsequently, we numerically compute $P_s(v)$, the mean-squared velocity $\langle v^2\rangle(t)$, and the diffusion coefficient $D_e$ by solving the particle's equation of motion, all of which show excellent agreement with the analytical results in the steady-state. Finally, we examine the universal nature of the transitions in $P_s(v)$ by considering an alternative functional form of medium's viscosity that also capture the shear-thinning behavior.

Community detection, the unsupervised task of clustering nodes of a graph, finds applications across various fields. The common approaches for community detection involve optimizing an objective function to partition the nodes into communities at a single scale of granularity. However, the single-scale approaches often fall short of producing partitions that are robust and at a suitable scale. The existing algorithm, PyGenStability, returns multiple robust partitions for a network by optimizing the multi-scale Markov stability function. However, in cases where the suitable scale is not known or assumed by the user, there is no principled method to select a single robust partition at a suitable scale from the multiple partitions that PyGenStability produces. Our proposed method combines the Markov stability framework with a pre-trained machine learning model for scale selection to obtain one robust partition at a scale that is learned based on the graph structure. This automatic scale selection involves using a gradient boosting model pre-trained on hand-crafted and embedding-based network features from a labeled dataset of 10k benchmark networks. This model was trained to predicts the scale value that maximizes the similarity of the output partition to the planted partition of the benchmark network. Combining our scale selection algorithm with the PyGenStability algorithm results in PyGenStabilityOne (PO): a hyperparameter-free multi-scale community detection algorithm that returns one robust partition at a suitable scale without the need for any assumptions, input, or tweaking from the user. We compare the performance of PO against 29 algorithms and show that it outperforms 25 other algorithms by statistically meaningful margins. Our results facilitate choosing between community detection algorithms, among which PO stands out as the accurate, robust, and hyperparameter-free method.

In quenched disordered systems, the existence of ordering is generally believed to be only possible in the weak disorder regime (disregarding models of spin-glass type). In particular, sufficiently large random fields is expected to prohibit any finite temperature ordering. Here, we prove that this is not necessarily true, and show rigorously that for physically relevant systems in $\mathbb{Z}^d$ with $d\ge 3$, disorder can induce ordering that is \textit{infinitely stable}, in the sense that (1) there exists ordering at arbitrarily large disorder strength and (2) the transition temperature is asymptotically nonzero in the limit of infinite disorder. Analogous results can hold in 2 dimensions provided that the underlying graph is non-planar (e.g., $\mathbb{Z}^2$ sites with nearest and next-nearest neighbor interactions).

A prediction makes a claim about a system's future given knowledge of its past. A retrodiction makes a claim about its past given knowledge of its future. We introduce the ambidextrous hidden Markov chain that does both optimally -- the bidirectional machine whose state structure makes explicit all statistical correlations in a stochastic process. We introduce an informational taxonomy to profile these correlations via a suite of multivariate information measures. While prior results laid out the different kinds of information contained in isolated measurements, in addition to being limited to single measurements the associated informations were challenging to calculate explicitly. Overcoming these via bidirectional machine states, we expand that analysis to information embedded across sequential measurements. The result highlights fourteen new interpretable and calculable information measures that fully characterize a process' informational structure. Additionally, we introduce a labeling and indexing scheme that systematizes information-theoretic analyses of highly complex multivariate systems. Operationalizing this, we provide algorithms to directly calculate all of these quantities in closed form for finitely-modeled processes.

The apparent paradox of Maxwell's demon motivated the development of information thermodynamics and, more recently, engineering advances enabling the creation of nanoscale information engines. From these advances, it is now understood that nanoscale machines like the molecular motors within cells can in principle operate as Maxwell demons. This motivates the question: does information help power molecular motors? Answering this would seemingly require simultaneous measurement of all system degrees of freedom, which is generally intractable in single-molecule experiments. To overcome this limitation, we derive a statistical estimator to infer both the direction and magnitude of subsystem heat flows, and thus to determine whether -- and how strongly -- a motor operates as a Maxwell demon. The estimator uses only trajectory measurements for a single degree of freedom. We demonstrate the estimator by applying it to simulations of an experimental realization of an information engine and a kinesin molecular motor. Our results show that kinesin transitions to a Maxwell-demon mechanism in the presence of nonequilibrium noise, with a corresponding increase in velocity consistent with experiments. These findings suggest that molecular motors may have evolved to leverage active fluctuations within cells.