Papers

Conventional finite-size theories describe first-order coexistence through free-energy competition and interfacial tunneling, but the distinct spectral roles of sector balance and inter-sector connectivity are not usually explicit. We show that pseudo-transitions and thermodynamic phase coexistence are governed by two spectral coordinates: sector imbalance locates balance, while connectivity determines whether that balance remains avoided. At balance, finite connectivity produces a unique dominant state with equal spectral weights in the symmetrized two-sector representation. For short-range systems with positive interface tension, interfacial costs suppress the connectivity exponentially, close the coexistence splitting, and asymptotically restore reducibility. Thus, genuine phase coexistence emerges as the singular limit of finite-size avoided coexistence, whereas one-dimensional pseudo-transitions retain finite connectivity. In a decorated bilayer Ising model, the coexistence gap at independently est

Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structural coherence but suffer from high computational costs, while local models are efficient but often fail to reproduce long-range correlations and global coherence. The renormalization group (RG) bridges this gap by seamlessly connecting spatial structures across different length scales, retaining quasi-local descriptions at each step while preserving long-range correlations. We introduce renormalization group flow matching (RGFM), a generative framework that systematically structures data generation across different spatial scales. By using an exact RG flow as the probability path, RGFM progressively generates data from long- to short-wavelength structures. To reconcile scalability with global structure, we exploit two key properties of the RG: quasi-locality and scale separation. We rigorously show that the RGFM probability flow can be accurately approximated by local velocity fields acting over a spatial range $O(\Lambda^{-1}[\ln L+\ln(1/\varepsilon)])$ for RG wavenumber scale $\Lambda$, linear system size $L$, and prescribed error tolerance $\varepsilon$. This property enables local generative modeling with patches of size $O(\ln L)$ and a computational cost that scales nearly linearly with the system volume. We numerically demonstrate that local RGFM reproduces long-range correlations far beyond its receptive field in representative one-dimensional distributions, while conventional local flow matching exhibits substantial errors at long distances. On FFHQ images, RGFM yields far more coherent and higher-quality samples than local flow matching at 64x64 and 256x256. Our results establish RG-guided probability flows as a promising route toward scalable generative modeling that captures long-range structure using only local computation.

Conventional full counting statistics assigns commuting phases to integrated stochastic currents and therefore resolves net transport but not, in general, the temporal ordering of events associated with different cycles. We introduce a non-Abelian counting construction in which crossings of two fundamental cycles rotate an auxiliary spin about different axes. The resulting ordered trajectory statistic has an exact finite-dimensional evolution equation for its first moment. The two rotation angles form a counting torus, and whenever the mean spin is nonzero its normalized direction defines a map $\mathbb T^2\to\mathbb S^2$, equivalently a complex eigenline bundle with a Chern number. Our main analytical result is a Chern–parity correspondence. Reflection symmetry equips this eigenline with a real structure and expresses $C_T\bmod2$ through first Stiefel–Whitney classes on the circles fixed by reflection. When the transverse polarization has no additional zeros along the reflection-fixed circles, these classes reduce to ordinary current-parity statistics at the four high-symmetry counting points. For a five-state nonequilibrium figure-eight network, varying only the observation time produces four polarization-gap closings and the reentrant sequence $C_T=0\to-1\to0\to-1\to0$. Every transition occurs at $(\pi,\pi)$ and coincides with a sign reversal of $\mathbb E[(-1)^{Q_1+Q_2}]$, while the full integer Chern number is obtained independently from the two-dimensional spin texture. Finite observation time can therefore organize a fixed stochastic process into distinct topological sectors of its ordered path ensemble.

Run-and-tumble particles (RTPs) are canonically modeled with uniform reorientation probabilities, an assumption that breaks down for many biological microswimmers. In this work, we investigate the dynamics of RTPs with arbitrary non-uniform tumble distributions. By deriving an exact Doi-Peliti field theory, we explicitly calculate a wide array of spatial and orientational observables. Notably, we demonstrate that the spatial dynamics exhibit an effective persistence and chirality governed entirely by the first Fourier modes of the tumble distribution, establishing a formal mapping to the dynamics of chiral active Brownian particles. Furthermore, our field-theoretic framework provides a systematic method to compute spatial moments to arbitrary order, allowing for the complete characterization and identification of complex tumbling dynamics. We illustrate the framework with wrapped Gaussian and bimodal Gaussian distributions, demonstrating explicit control over persistence and chirality. We further extend the field theory to $d$ dimensions, recovering the mean squared displacement in terms of a single effective tumble rate. Our results establish a direct link between the shape of the tumble distribution and the emergent dynamics, and provide a foundation for the study of interacting RTPs with non-uniform reorientation.

Equilibrium steady states are fully characterized by the Boltzmann distribution, which depends only on the energy and the temperature. In contrast, nonequilibrium steady states, which describe a wide range of physical and biological phenomena, are generically much more complex, and their stationary distributions can depend on a zoo of kinetic parameters. A central challenge of nonequilibrium statistical mechanics is the identification of universal relations that hold for nonequilibrium steady states despite this complexity. We show here that a form of the Cramer-Rao inequality applies to arbitrary nonequilibrium steady-state distributions and contains no explicit dependence on kinetic parameters. A main original feature in our observation is that this inequality becomes a well-known fluctuation-response relation in equilibrium. The inequality involves three well-defined quantities: the fluctuations of the energy, the response given by the derivative of the average energy with respect to the inverse temperature, and the Fisher information with respect to the inverse temperature. We illustrate this inequality using three representative models: an active particle in a harmonic potential, a simple scheme for kinetic proofreading, and a one-dimensional model for heat conduction between two baths.

Equilibrium free-energy differences can be measured from nonequilibrium work fluctuations using fluctuation theorems, such as the Jarzynski equality and the Crooks fluctuation theorem. However, these approaches become statistically inefficient in high-dissipation regimes because their convergence requires trajectories with negative entropy production, events that occur exponentially rarely. Here, we demonstrate that repeated measurements of trajectory fluctuations are sufficient to determine entropy production without relying on such rare events, or to determine a lower bound on it when only partial measurements are available. This information then yields exact estimates of free-energy differences, or rigorous bounds. We validate this approach, named EquiNET, with numerical simulations, including one of biomolecular folding and unfolding, showing that it recovers accurate free-energy estimates even in regimes where conventional approaches fail.

We investigate the critical exponents $(\gamma,\nu)$ at the current-driven tricritical point (TCP) associated with chiral symmetry breaking in a nonequilibrium steady state described by the D3/D7 model. In the symmetry-broken phase, we find that, unlike in the conventional Landau theory, both $\gamma$ and $\nu$ can take values different from those predicted by the Landau theory and depend on the path along which the TCP is approached in the phase diagram. However, when the TCP is approached with the ratio of the effective temperature to the heat bath temperature, $T_{*}/T$, held fixed, the critical exponents agree with those of the Landau theory. These results suggest that the critical phenomena at the current-driven TCP may be described by the Landau theory along the $T_{*}/T$-fixed line, whereas a nontrivial extension of the Landau theory is required for a more general description of the critical phenomena along arbitrary paths.

This paper addresses the structural breakdown of the Edgeworth expansion in the finite-blocklength (FBL) regime, where conventional asymptotic approximations yield unphysical negative probabilities in the deep-tail region. We propose a $q$-deformed framework that resolves this inconsistency by replacing additive polynomial perturbations with a geometric deformation of the information density space. Motivated by the linearization of nonlinear dynamics, we prove that dynamically scaling the $q$-logarithmic parameter exactly absorbs the third-order skewness while preserving global nonnegativity. We establish a universal asymptotic matching, demonstrating that the framework encapsulates higher-order asymptotic scales. Numerical results confirm that the proposed method matches the state-of-the-art precision of the Cornish-Fisher bound without the risk of negative probabilities. The framework offers a robust and computationally stable foundation for evaluating operational limits in ultra-reliable communications such as 6G and URLLC.

Quantum Monte Carlo (QMC) methods are among the central numerical tools for studying strongly correlated quantum many-body systems, particularly in higher dimensions. As quantum information has introduced new information-theoretic perspectives and diagnostics into many-body physics, QMC methods have accordingly been extended beyond the measurement of conventional linear observables. This review summarizes recent progress in adapting QMC to many-body quantum-information, focusing on qubit or spin-$1/2$ systems as a concrete setting while keeping the discussion broadly applicable to qudit and bosonic systems. We present a unified perspective on the extraction of nonlinear diagnostics, including entanglement entropies and entanglement spectra, Rényi negativities for mixed-state entanglement, stabilizer entropies for quantum magic, and decoherence-driven phenomena such as the interplay between imaginary-time evolution and decoherence and strong-to-weak spontaneous symmetry breaking.

Random walks with long-range jumps can drive superdiffusive transport, replacing ordinary diffusion with an effective long-range kinetic operator. Such superdiffusive kinetics is also central to critical phenomena, notably the self-avoiding walk with long-range jump statistics, or Lévy-SAW. This work investigates how the critical behavior is affected when the long-range connectivity itself becomes random. We study self-avoiding walks (SAWs) on a one-dimensional long-range random ring graph, where bonds are independently generated with Bernoulli probability $\sim|i-j|^{-(1+\sigma)}$. We term this walk Sparse-SAW. The same random bonds are responsible for both long-range superdiffusive transport and quenched disorder, with both simultaneously controlled by the single parameter $\sigma$, placing the problem beyond the conventional Harris and Weinrib-Halperin frameworks. Through large-scale Monte Carlo simulations and a Gaussian-truncated field theory, we show that Sparse-SAW belongs to the same universality class as the clean superdiffusive Lévy-SAW. The random bonds generate short-range uncorrelated and long-range correlated mass disorder while simultaneously producing the long-range kinetic operator. Under coarse-graining, the latter dominates, restoring the clean critical behavior. Our study suggests that the full non-Gaussian Bernoulli statistics may lead to disorder physics beyond the conventional theory of quenched disorder, while establishing random graphs as an efficient platform for extracting the critical exponents of the clean superdiffusive Lévy-SAW universality class.

We study the class of transversal field models with pair interactions, including the quantum Curie-Weiss and the transversal field Ising model, as well as quantum spin glasses based on the Sherrington-Kirkpatrick (SK) and Edwards-Anderson models. Our main result is a general correlation inequality based on a Gaussian convolution estimate, which is derived from an extension of the Ding-Song-Sun inequality and an application of Brascamp-Lieb convexity techniques. The results apply for sufficiently strong transversal fields and, for quantum spin glasses, yield a high-field regime without replica order. Combined with the previously established low-field replica-symmetry-breaking regime, this gives distinct low- and high-field overlap phases. As a corollary of the logarithmic Sobolev inequalities for the underlying functional integrals derived here, we show the concentration of the self- and replica-overlap, and determine the high-field asymptotics of the pressure in the quantum SK model.

Training large language models is costly. How low a loss the same compute can ultimately reach depends on how each step's gradient is converted into a weight update; the rule that performs this conversion is the optimizer. From SGD and AdamW to the recent Muon, effective update rules have mostly been shaped by engineering intuition and then selected on benchmarks. Muon semi-orthogonalizes the momentum matrix before applying the update and has kept breaking records on public training benchmarks; yet why the semi-orthogonalized direction works, and over how long a history the momentum should average, are two questions at present answered mainly by experience. Here we treat the weight matrix during training as a responsive medium with memory and build a physical model for it, in which both questions find answers: the semi-orthogonalized direction is the maximally dissipative response under an output-side safety budget, which explains why it works; momentum is the internal stress accumulated by the medium; how long it should average is set by the relaxation of this stress, and a real medium relaxes on more than one timescale, the simplest form being one fast and one slow. On this basis we propose the Bi-Maxwell optimizer. The framework further yields a testable consequence: gradient directions change fast early in training and more slowly later, so the optimal memory length should grow with training stage; step-by-step measurements of a proxy for it by a read-only probe across 8 independent training trajectories are consistent with this consequence. Replacing the memory kernel alone, from a single timescale to two, brings training to the target loss in noticeably fewer steps on a public large-language-model optimizer benchmark.

At a deeper level the principle of least action is interpreted as the law of least entropy increase consistent with Prigogine's principle of minimum entropy production, and the implications of quasistatic information quantization rule (Proc. R. Soc. A 480: 20240024), are explored for the conjectured second law of quantum complexity. It is thus shown that the conjectured second law of complexity is derivable from the information quantization rule such that long after heat-death the quantum state complexity evolves as $C(t)=C_{\rm max}\exp(-1/t)$, increasing with time to a saturation value $C_{\rm max}$ that is exponential in the equilibrium entropy $S_{\rm max}$. For the out-of-equilibrium circumstances, however, the quantum complexity can decrease with the time, asymptotically tending to a minimum determined by the distance from equilibrium.

Thermalization conventionally describes local properties of isolated many-body systems at equilibrium. Magic, or nonstabilizerness $\unicode{x2013}$ the resource enabling universal quantum computation $\unicode{x2013}$ is by contrast encoded in the global structure of the many-body wavefunction. We show that, despite its global nature, the magic of equilibrium pure states of chaotic many-body systems, including late-time evolved states and energy eigenstates, is universal: it is captured by the thermal Scrooge ensemble, the minimally informative ensemble of pure states consistent with the Gibbs state at the same effective temperature. Therefore, for systems with no conserved quantities other than the total energy, equilibrium magic is a function of temperature alone, independent of the initial state and other microscopic features of the equilibrium state. This yields concrete universal predictions for the stabilizer Rényi entropies (SREs). At infinite temperature, the SRE is set by Haar-like fluctuations of the Pauli spectrum, while at finite temperature energy conservation induces a volume-law thermodynamic correction controlled by the thermal Pauli spectrum. We support these predictions with analytical arguments and extensive numerical simulations. We further show that chaotic many-body systems at high temperatures possess long-range magic and entanglement that cannot be removed by finite-depth local quantum circuits. Our results establish magic as a thermodynamic property of chaotic many-body systems and suggest that Scrooge ensembles may provide a unified framework for quantum many-body resources.

Thermodynamic bounds on the spectrum of a Markov cycle are determined by quantities such as the cycle affinity and maximal escape rate, but do not distinguish cycles with different local rate profiles. In this work, we show that kinetic heterogeneity, through the geometric means of forward ($G_+$) and backward ($G_-$) rates, tightens the bound on oscillatory relaxation. Specifically, we prove that, for each winding sector of the eigenmodes, the oscillation frequency cannot exceed that of the uniform comparison cycle with rates $G_+$ and $G_-$. Combining with existing affinity-winding bounds, we also demonstrate a contracted region for the eigenvalues and shows that the boundary of Uhl-Seifert ellipse can be saturated if and only if cycles are uniform. Our proof provides a complex-analytic framework that unifies the kinetic and thermodynamic constraint.

The capillary rise of simple passive fluids in a tube is given by Jurin's law of capillary action, which balances surface tension and gravity. For fluids composed of active particles interacting via pairwise forces, a capillary rise was reported despite a negative mechanical surface tension, a phenomenon which remains unexplained. We establish the active form of Jurin's law from the microscopic dynamics. It includes a drag emerging from particle currents that we find responsible for capillary rise. These active currents, alongside negative surface tension, lead to complex and counterintuitive capillary action phenomena that are impossible in equilibrium. In particular the capillary rise of active fluids depends on the shape of the tube, not solely on the tube diameter.

The Boltzmann H-theorem states that entropy of an ensemble of thermodynamic states increases until all states become equally probable. The evolution randomly picks one of the states in the ensemble and brings every other state to have the same probability as the selected state. Different realizations of evolution yield a distribution of selected states. We study this distribution numerically and discuss its relevance for equilibrium fluctuations in thermal as well as non thermal systems.

Cooperation is ubiquitous in both natural and human societies, yet its evolutionary basis remains a major challenge. A long-standing puzzle is whether having more information leads to better decision-making and thus a higher level of cooperation. To address this question, we adopt a recently developed reinforcement learning framework in which individuals learn through trial and error to maximize cumulative rewards - a paradigm that has successfully explained diverse emergent patterns in human behaviors. Specifically, we equip a structured population with the Q-learning algorithm and systematically vary the size of the interactive neighborhood, which serves as a proxy for perceived information. Interestingly, we observe a non-monotonic relationship between cooperation prevalence and neighborhood size in both two-dimensional square lattices and Barabasi-Albert scale-free networks. This inverted U-shaped dependence reveals that an optimal amount of information exists, yielding the highest level of cooperation. Mechanistic analyses show that a moderate neighborhood size enables individuals to strike an optimal balance between information sufficiency and decision-making tractability. This balance allows them to detect reciprocal opportunities while avoiding the deterioration of decision quality due to information overload. Our findings challenge everyday intuition, suggesting that a proper amount of information - not more - is optimal for the emergence of cooperation.

We harness two established ingredients for collective demixing: differential speed and curvature to create a self-sorting device. In binary mixtures, motility differences drive spontaneous spatial segregation, while confinement geometry determines how rapidly and strongly this demixing develops. Using particle based simulations, we systematically identify the geometrical conditions that promote efficient segregation and use these results to guide the design of a finite sorting architecture. We then translate these physical mechanisms into a sequence of curved microfluidic units that progressively amplify the separation of the two species and direct them toward distinct collection regions. Experiments with binary Quincke-roller mixtures confirm that an initially mixed suspension progressively demixes as it propagates through the device, leading to strong enrichment downstream. Our results demonstrate how collective active demixing can be converted into a functional continuous sorting strategy, providing a route toward autonomous microfluidic separation based on particle motility and confinement geometry.

The Boltzmann-Loschmidt dispute in 1876-1877 discussed the problem of time reversal of ther- malization from time reversible classical equations of motion. Here, 150 years later, we highlight this problem in the frame of quantum computing. A quantum protocol is proposed that allows to perform a time reversal of complex evolution in the regime of many-body quantum chaos with many qubits. The system represents an evolution of qubits on a square lattice with inter-qubit next-nearest static couplings with a driven pulsed magnetic field. The system entropy grows rapidly to maximal values but returns to initial small values after time reversal. This time reversal is shown to be stable with respect to quantum gate imperfections. However, similar to the Lorenz butterfly effect, there is the butterfly effect of qubit when inversion of only one qubit breaks time reversibility of the whole system. It is argued that this protocol is accessible to nowadays quantum computers and annealers with hundreds of qubits.

Finite datasets can share the same size and low-order statistics while differing strongly in structural complexity. We connect this dataset complexity to loss-landscape geometry by pairing local label mixing across neighborhood scales with local entropy around trained neural-network solutions. Adapted from the Franz–Parisi construction in spin-glass theory, local entropy measures the effective volume of low-loss, solution-like parameter configurations at each distance from a reference. We estimate it in finite networks using adaptive sequential Monte Carlo. In a controlled synthetic sweep, greater dataset complexity produces a larger decrease in local entropy near the reference. Farther away, its radial derivative becomes weak and nearly common across conditions. Dataset complexity therefore changes where the effective solution volume contracts, rather than making it decrease uniformly faster. Experiments on real image data show the same qualitative trend, with label randomization further amplifying the effect. These results show that dataset structure shapes how low-loss neighborhoods are organized across finite distances from trained solutions.

The Minimum Matching Problem consists of finding an independent edge set of minimum weight $M_{\star}(G)$ in a given edge-weighted graph $G$. When $G$ is bipartite, this reduces to the Assignment Problem. We consider a variant of this problem defined by taking the union of optimal matchings across various slightly modified versions of the base graph: $H_{\mathcal{J}}(G)=\bigcup_{U \in \mathcal{J}} M_{\star}(G_{U})$. We establish two families of results: (1) In two distinct settings for the Assignment Problem, we prove that the resulting graphs $H_{\mathcal{J}}$, as well as certain associated graphs $\bar{H}_{\mathcal{J}}$, are spanning trees on the relevant base graphs $G$ and $\bar{G}$. (2) In these same settings, assuming the edge weights are given by the $p$-th power of Euclidean distances for point configurations in the plane, we show that for $p=1$ the tree $H_{\mathcal{J}}$ is non-crossing (i.e., its planar embedding has no crossing edges), whereas, remarkably, for $p=2$ the associated tree $\bar{H}_{\mathcal{J}}$ is non-crossing. Finally, we introduce novel conjectures in Statistical Mechanics, to be explored in future work: in the Random Euclidean Assignment Problem (where points are i.i.d.\ on a planar domain), we conjecture that for $p=2$ the trees $\bar{H}_{\mathcal{J}}$ are asymptotically distributed as Uniform Spanning Trees with free and wired boundary conditions in the two respective settings. In particular, suitable paths on the tree in the second setting, and on its planar dual in the first setting, are asymptotically distributed as $\text{SLE}_{\kappa}$ with $\kappa=2$.

Hilbert-space ergodicity (HSE) provides a new framework for studying thermalization in driven quantum systems, complementing the eigenstate thermalization hypothesis, which is restricted to static systems. This ergodicity is hierarchical: by quantifying how randomly the dynamics explores the Hilbert space, one obtains a family of levels termed $k$-HSE. While HSE has been observed at the lowest and highest levels, finite-order HSE dynamics remains largely unexplored due to the difficulty of constructing such drives. Here, we explore this intermediate regime and uncover its distinctive physics. We first propose and prove that a family of $m$-tone drives on qubits realizes $k$-HSE up to $k = 2m{-}3$, with drive parameters determined at $O(k)$ cost. Using a single nitrogen-vacancy center in diamond, we verify this design by showing that a 3-tone drive realizes 3-HSE, with fourth-order statistics depending on the initial state. Further in-depth theoretical analysis shows that this initial-state dependence is generic across drives, demonstrating the possibility of recovering the initial state from higher-order statistics even when the dynamics is ergodic. Our work broadens the study of quantum ergodicity and reveals intriguing physics within its hierarchy.

Topological states and criticality have long been regarded as incompatible ingredients: the former requires a finite spectral gap, whereas the latter demands its closure. Guided by this view, topological photonics has focused almost exclusively on gapped phases, treating gap-closing transitions as mere phase boundaries. In this work, we propose a class of topological states in which topology coexists with criticality in experimentally accessible synthetic-frequency photonic platforms. In a one-dimensional (1D) synthetic lattice, we identify such critical topological photonic states through midgap degeneracies in the single-particle entanglement spectrum, and uncover a topology-enforced multicritical point that reorganizes the topology of neighboring critical states. We further extend this framework to two dimensions (2D). Our work provides an experimentally accessible route to critical topological photonics, and may also inspire novel applications such as critical topological sensing.

We study one-dimensional run-and-tumble particles confined by a harmonic potential and coupled to an equilibrium thermal bath. Exploiting the coupling of active and thermal degrees of freedom through the Ornstein-Uhlenbeck propagator, we show that the position distribution factorizes, as a convolution, into the Ornstein-Uhlenbeck distribution and the distribution of the athermal run-and-tumble problem. This yields closed-form results, correcting expressions from earlier treatments by identifying the Ornstein-Uhlenbeck propagator, rather than the free-diffusion one, as the correct kernel. Results are confirmed both in Fourier space and by direct Langevin simulation. We further focus our analysis on the stationary regime, characterized by two dimensionless parameters–the ratio of trapping to persistence length, and the ratio of thermal to active diffusion–which control the crossover from a non-Gaussian, boundary-peaked distribution to the Gaussian, equilibrium-like limit. Energy fluctuations and the Kullback-Leibler divergence from the equilibrium distribution quantify the resulting non-equilibrium character of the confined active particle.

Conventional probabilistic Ising machines often suffer from inefficient exploration of configuration space, while replica-based quantum Monte Carlo methods reduce sampling bottlenecks at the cost of large hardware overhead. Here we propose a time-dimensional exchange coupling (TEC) that replaces spatial replica coupling with a temporal exchange interaction between successive spin configurations of a single p-bit network. This TEC improves sampling efficiency without duplicating replica hardware. At low temperatures, antiferromagnetic TEC expands sampling range; at high temperatures, ferromagnetic TEC stabilizes the optimal state. For MaxCut problems with up to 2000 vertices, TEC significantly accelerates convergence speed. SPICE simulations confirm hardware feasibility. TEC offers a scalable, hardware-efficient strategy to enhance combinatorial optimization on existing Ising machines.

We study the superdiffusive behavior of random walks on the randomly oriented Manhattan lattice, i.e., the $d$-dimensional integer lattice $\mathbb{Z}^d$ where each axis-aligned line is independently assigned a random direction (forward or backward) with equal probability. The walker takes nearest-neighbor steps, choosing an axis randomly and moving along the assigned direction of that axis's line, with equal probabilities for each axis. We show that, in the critical dimension $d=3$, the diffusion coefficient of the random walk diverges in the Tauberian sense as $\sqrt{\log t}$ with a multiplicative correction $(\log\log t)^{\pm(2+\varepsilon)}$ as time $t\to\infty$. This gives an answer to a conjecture by Ledger, Tóth and Valkó (2018).

We investigate the impact of charge regulation (CR) on the non-uniform swelling behavior of polyelectrolyte hydrogels. The Poisson-Boltzmann theory with electro-elastic coupling between the local polymer density and elastic deformation is considered. We investigate the spatial distributions of the elastic displacement and polymer density under different salt concentrations and compare charge-regulated gels with fixed-charge (non-CR) gels of the same net charge. Our results show that the CR induces spatially varying charge fractions, which strengthen the electro-elastic response and lead to stronger non-uniform swelling compared with non-CR gels. These findings provide a theoretical basis for understanding and controlling non-uniform swelling in responsive polyelectrolyte hydrogels.

Shannon–Rényi entropies probe the full measurement distribution of a quantum many-body state, but their microscopic evaluation remains difficult even in free-fermion systems. We show that the Born distribution of the critical transverse-field Ising chain is exactly the parity boundary of a weak inverse-square classical Ising model, a structure revealed by the Cauchy form of its probability kernel. The construction is exact at finite size for every real $n>0$ and isolates the long-distance sectors controlling the $n>1$ thermodynamics. Cutting a finite interval generates the universal $c\,n/[8(n-1)]$ logarithm directly from the missing $1/r^2$ interactions, with $c=1/2$ the Ising central charge. On the ring, the explicit finite-size factor has no constant term, while any additional noninteger singularity is confined to a residual cycle-only free energy. All explicit long-distance contributions become marginal only at $n=1$.

We provide a perspective into measuring the quantum information spreading by introducing N-operator correlators. Leveraging the idea from N-point correlation function from cosmology, we formulate a generalized Out-of-Time-Ordered Correlator (OTOC), called N-Operator Correlator (NOC). Such formulation can reduce the effect of restricted analysis associated with selecting only two operators for OTOC. NOC can take into account a larger set of measurement probes in measuring the degree of quantum information spreading. We demonstrate these concepts with two analytically tractable toy systems with computing thermal averages of operator time evolutions at a finite temperature: a single qubit in longitudinal field and XYZ two-qubit system. Our perspective serves as a basic introduction to generalized operator correlators in quantum mechanics.