Papers

When independent learners are trained on data from the same disordered system, finite and noisy samples can lead them to different descriptions of the underlying physics. We quantify the reproducibility of such descriptions through consensus, the degree of agreement among independently trained models. In a teacher-student framework on the two-dimensional random-bond Ising model, an ensemble of students each infers a single uniform effective coupling from a finite set of equilibrium configurations drawn from one quenched bond realization. Aggregating the inferred couplings across students and realizations yields the consensus distribution, whose width quantifies the agreement among learners. The variance of this distribution separates exactly into a finite-sampling contribution, bounded below by inverse of Fisher information, and a quenched-disorder contribution. Sum of these two variance is minimized near critical region, so the consensus width has a minimum near the pseudocritical temperature that sharpens with increasing disorder. The two minima arise from different mechanisms: the sampling term inherits the critical peak of the Fisher information, while the disorder term is temperature-independent at leading order and acquires its critical minimum only at fourth order in the disorder strength, through the covariance between the linear and cubic responses to the bond disorder. Because the students fit a uniform coupling to a heterogeneous lattice, the inferred coupling also carries a misspecification bias that survives in the infinite-data limit. This bias displaces the minimum of the mean-squared error above the consensus minimum, so the temperature at which independent learners agree most closely is not the temperature at which their shared answer is most faithful.

We construct Floquet codes from the Majorana XYZ subsystem code with local realizations both in qubits and directly in microscopic Majorana modes with lattice size $L\times L$. For a three-step cycle, odd $L$ supports one static logical qubit, whereas even $L$ supports two. For even $L$ with $L=4n+2$, both logical qubits admit time-independent Pauli representatives. For $L=4n$, by contrast, one Pauli of the second logical qubit remains fixed throughout the cycle, while every representative of its conjugate must evolve through the measurement cycle. This distinction follows from a parity-dependent algebraic obstruction and disappears when the cycle is reduced to two steps, which restores a fully static logical pair. Thus, the same encoded logical degree of freedom can be switched between static and partially dynamical forms by the measurement schedule. With one open direction, suitable protocols can be implemented using only local Majorana parity measurements. To our knowledge, this is the first Floquet-code family with both a local qubit representation and a direct microscopic Majorana realization.

We study the magnetic deformation of the $(2+1)$d Ising CFT, also known as Ising Field Theory (IFT), on a spatial sphere. We use the Fuzzy Sphere (FS) Ising setup and compare it to the Truncated Conformal Space Approach (TCSA). Universal, infinite volume, IFT quantities are extracted by modeling the effects of curvature, as expected from a local effective theory on the sphere. We find evidence that IFT contains a bound-state with a small binding energy. Locality also allows us to extract the same IFT quantities from different angular momentum sectors, providing additional consistency checks.

The Jarzynski relation enables the estimation of equilibrium free energy differences from non-equilibrium, finite-time switching simulations. These estimates usually converge poorly because rare trajectories dominate the exponential work average. Here, we numerically determined and explored the sequence of intermediate Hamiltonians connecting initial and final states that minimize the mean squared error (MSE) of the Jarzynski estimator and thereby enhance convergence. For discrete-time Markov models, an exact tilted-master-equation representation of the MSE in the large-sample limit, combined with automatic differentiation, enables efficient gradient-based minimization over all intermediate energies. We applied our approach to three model systems of increasing complexity: a two-state model, a double-well potential, and a shifted potential well. In all three systems, the optimal intermediate Hamiltonians jump at the initial and final times. Extensive Monte Carlo simulations show that optimal intermediates can reduce the MSE by more than an order of magnitude compared with linear and logarithmic interpolation, most strongly for large changes in the energy landscape. Remarkably, they need not dissipate less work than intermediates yielding larger errors. Our results suggest heuristics for more efficient non-equilibrium free energy calculations of realistic molecular systems: optimal intermediate Hamiltonians jump at the initial and final times; for barrier-crossing problems, the barrier should be lowered rapidly and raised again later; and minimizing dissipation does not guarantee faster convergence.

In a quantum walk, the first-detection return probability (FDRP) characterizes salient features, determining whether the quantum walk is transient or recurrent. We study the FDRP of quantum walks on a chain where the initial site is stroboscopically monitored by a detector and the walker performs long-range hopping between sites. We assume that the hopping strength decays with the distance $d$ as $d^{-\alpha}$ and $\alpha\geq 0$ and show that the power-law exponent $\alpha$ critically determines the behavior of the FDRP. The value $\alpha=1$ separates recurrent ($\alpha<1$) from transient ($\alpha>1$) quantum walks through a continuous phase transition in the total detection probability. For $\alpha<1$, strong long-range hopping induces localization, resulting in unit total detection probability. Instead, for $\alpha>1$ the long-range walk is transient and the return probability decays algebraically as a function of time as $t^{-\beta}$. The associated decay exponent $\beta$ features nonanalytic points as a function of $\alpha$. Such singularities are not exclusively determined by the low-energy spectrum, but are caused by the interference between infrared and ultraviolet energy modes induced by projective measurements, signalling the emergence of critical behavior intrinsic to the non-unitary dynamics. These dynamics are solely controlled by tuning the long-range exponent $\alpha$ and can thus be experimentally probed in atomic and molecular systems.

Understanding symmetry restoration in isolated quantum many-body systems is an important problem in nonequilibrium many-body quantum physics. Recent studies have shown that the quantum Mpemba effect can be characterized through entanglement asymmetry, where states with stronger initial symmetry breaking restore symmetry faster. However, it remains unclear whether conventional energy-based measures, such as the trace distance, capture the same phenomenon. We investigate this question in closed spin-$1/2$ quantum systems with different symmetries by analyzing the dynamics of symmetry-breaking initial states. Combining numerical simulations with an analytical decomposition of the trace distance into symmetry-coherence and residual contributions, we identify the conditions under which trace distance tracks entanglement asymmetry and reproduces the Mpemba–like behavior observed in it. For charge symmetry, the residual contribution is negligible, making the trace distance effectively governed by symmetry-sector coherences. In contrast, for permutation symmetry, a significant residual contribution leads to qualitatively different relaxation dynamics. Our results establish when conventional energy-based diagnostics reliably capture symmetry-restoration dynamics and clarify the distinct physical information encoded by entanglement asymmetry and trace distance.

Reciprocal contacts can favor cooperation by protecting a lineage until its descendants compete as a group. An individual is chosen to reproduce from the whole population, with a probability increasing with its averaged cooperation payoff. Its offspring replaces a neighbor through contacts weighted equally in both directions. We compare a single cooperator taking over defectors with the reverse invasion, using the same distribution of introduction sites. When payoffs weakly affect reproduction, every network with at least six sites and a path visiting them all admits weights favoring cooperation at any benefit-cost ratio $b/c>1$. This holds for mutants placed uniformly or arising in newborn offspring. Unequal contacts sustain aligned domains around two protected sites, converting individual reproduction into collective competition. Cooperation cannot have an advantage when its benefit does not exceed its cost, at any positive selection strength with positive reproduction rates. Where mutants arise changes which contact patterns can use this mechanism. On a five-site path, the weak-selection threshold has the sharp lower bound seven under uniform placement; newborn introduction never favors cooperation at this order. We classify the contact patterns with at most five sites that can favor cooperation under either rule; newborn introduction requires a four- or five-site cycle. Tracing copies backward to their ancestors separates domain competition from the probability of reaching it. Exact polynomial inequalities prove sharp threshold bounds for paths and the four- and five-site cycles. The same ancestry reconstructs evolution without selection on weighted paths and cycles and fixes the slowest relaxation rate on every connected reciprocal network. Cooperation thus depends on both the collective competitors created by contacts and the route by which a new lineage enters them.

We study factorizations of the single-particle spectrum of non-interacting quantum systems and their consequences for many-particle statistics. Representing the single-particle partition function by a spectral alphabet, tensor factorizations become multiplicative factorizations of the alphabet, which can be lifted to canonical Bose and Fermi partition functions using standard symmetric-function and $\lambda$-ring identities. We show how product spectra arise from different factorizations, how antisymmetrization can be assigned across an odd number of factors, and which tensor factorizations are compatible with a fixed spectrum. The paper therefore studies spectral factorization and provides a consolidated combinatorial framework for canonical Bose and Fermi partition functions.

Quantifying the degree of order/disorder in many-particle systems remains an outstanding problem in physics, materials science, and mathematics. To this end, we consider the translational order metric $\tau_T$, defined as the squared $L^2$ norm of the total correlation function $h(\mathbf{r})$, and introduce its bond-orientational analogue $\tau_O$, defined from the weighted total correlation function $h_{\mathbf{f}}(\mathbf{r})$ using local orientational weights (S. Torquato et al., Phys. Rev. X 16, 011042 (2026)). The pair $(\tau_T,\tau_O)$ places both forms of order on a common two-point statistical footing. We compute both metrics for two nonhyperuniform sphere-packing models in 2D and 3D as functions of packing fraction $\phi$: 1) equilibrium hard particles and 2) nonequilibrium random sequential addition (RSA) packings. For the nonhyperuniform systems, bond-orientational order remains subdominant along the equilibrium-fluid and RSA configurations; however, its magnitude relative to the translational metric increases near the upper end of the equilibrium-fluid branches, much more strongly in 2D than in 3D, and the two metrics become comparable along the sampled crystal branches. At common packing fractions, equilibrium fluids and RSA packings trace distinct $(\tau_T,\tau_O)$ trajectories, revealing preparation-dependent differences in structural order. As a representative hyperuniform family, we study 2D disordered stealthy hyperuniform (SHU) ground states for $0<\chi<1/2$, where $\chi$ is the stealthiness parameter. Within the disordered SHU phase, bond-orientational order remains subdominant, but its magnitude relative to the translational metric increases toward the disorder-to-order threshold. In all three models, $\tau_T$ and $\tau_O$ are positively correlated beyond the Poisson-reference regime: $\tau_O$ increases monotonically with $\tau_T$ across the sampled state points.

We numerically study the bulk correlation functions in the two-dimensional Abelian sandpile model, aiming both to compare with the predictions of logarithmic conformal field theory and to extend the analysis to lattices where analytical methods are difficult to apply. Wilson's algorithm efficiently generates large-scale uniform spanning trees in parallel, which can be mapped to independent recurrent configurations via the Majumdar–Dhar burning bijection, eliminating sample autocorrelations and yielding fast convergence. On the square (single-sublattice) and honeycomb (two-sublattice) lattices our results agree well with the analytical predictions. For the kagome lattice we provide the first systematic numerical study of the bulk correlation functions, and cross-check the bulk height-1 probability against a closed-form analytical expression that we also derive here via the lattice Green function.

We present a comprehensive description of the network of Italian Business-to-Business commercial relationships, based on the universe of electronic invoices collected by the Italian Tax Office. The firm-to-firm detail of the data is exploited to describe the distribution of the numbers of buyers (customers) and sellers (suppliers) per firm, the centrality of each firm in the production network, and the average distance between firms. We characterize for the first time the firm-to-firm network describing the Italian production system, and present its geographic and sectoral breakdown. The analysis reveals heavy tailed distributions for the numbers of buyers and sellers per firm, implying a scale-free structure of the network. The distributions of centrality values display heavy tails as well, indicating a strong concentration of importance in a relatively small number of firms. We estimate the tail exponents for all these distributions, finding in all cases lower exponents for downstream (buyer-side) distributions than for the upstream (seller-side) ones.

The cluster expansion is a widely used approach for predicting the finite-temperature thermodynamics of alloys from zero-kelvin first-principles calculations, but its conventional formulation becomes intractable for materials with more than three or four chemical species. High-entropy alloys have therefore remained largely out of reach. We present pyeCE, an open-source Python library that implements the embedded cluster expansion (eCE), in which machine learning maps many chemical species onto a smaller set of effective species and thereby limits the growth in the number of cluster functions. pyeCE provides the complete modeling workflow, including the construction of symmetry-adapted descriptors with a learnable per-sublattice chemical embedding, a neural-network energy model, ladder-based training, uncertainty quantification, and finite-temperature simulations through Monte Carlo sampling. Built on the PyTorch and pymatgen libraries, it supports systems with multiple species on multiple sublattices and runs on graphics processing units. We demonstrate the package on two material systems. In the first, a single model spanning the full composition space of a 9-component refractory alloy resolves short-range order and order–disorder behavior. This model enables rapid screening for compositions with strong Cr clustering, a feature linked to the formation of a continuous, corrosion-resistant oxide scale. In the second, a model of hydrogen dissolution in a Mo–Nb–W alloy reproduces the composition dependence of hydrogen uptake and resolves the interstitial environments that hydrogen occupies. The modular design of pyeCE allows it to be extended to problems beyond alloy thermodynamics, including kinetics, defect energetics, and the coupling of chemical order to magnetic and vibrational degrees of freedom.

The organization of multiple knots along a polymer contour can depend sensitively on the topology of the constituent knots. Using coarse-grained Brownian-dynamics simulations, we investigate topology-dependent mixing of two knots in a flexible polymer at controlled normalized wall separation. We compare homogeneous ($3_1\otimes 3_1$) and heterogeneous ($3_1\otimes 5_1$) knot pairs using a contour-based coordinate that distinguishes mixed and demixed configurations. Starting from partially overlapping configurations, the $3_1\otimes 5_1$ pair is observed in mixed configurations more frequently over the simulation time window than the $3_1\otimes 3_1$ pair at intermediate wall separation. We further find that, over the intermediate range of normalized wall separation, mixing of the $3_1\otimes5_1$ pair produces a composite knotted region that occupies substantially less polymer contour than the two constituent knots collectively occupy in demixed configurations. The corresponding reduction in contour occupancy is much smaller for the $3_1\otimes3_1$ pair. This indicates that the heterogeneous constituent topologies can be accommodated within a smaller portion of the polymer contour in the composite configuration. These results establish that constituent topology can strongly influence multiple-knot organization in flexible polymers.

We investigate a generalized inhomogeneous two-dimensional Ising model on the kagomé lattice with two alternating couplings $J_i$ and $J'_i$, $i=1,2,3$, along the three lattice directions. The local Boltzmann weights are mapped onto a non-symmetric eight-vertex $R$-matrix satisfying the free-fermion condition for arbitrary values of the six couplings. We analyze the Yang–Baxter structure for the cases $J'_i=J_i$ and $J'_i=-J_i$ and derive the corresponding one-dimensional quantum spin chains in the anisotropic limit. The first case yields a transverse-field Ising-type Hamiltonian, while the second leads to a modified chain with a graded permutation structure and shifted partition-function zeros. For a partially anisotropic model, we also obtain the free energy and specific heat.

We study additive observables of a non-confined Feller process characterized by a non- normalizable stationary probability density and return times with infinite mean. The process is equivalent, after a deterministic rescaling, to a squared Bessel process, but we focus here on a question: the spatial structure of its local-time field. We show that the local time admits a fac- torization in the long time limit. Its spatial profile is governed by the non-normalizable stationary density, whereas its temporal fluctuations are controlled by a single Mittag-Leffler random ampli- tude. As a consequence, normalized spatial correlations of the local time converge to a universal constant independent of the two observation levels. Occupation times of finite intervals follow as corollaries and display Darling-Kac fluctuations. In contrast, non-integrable power observables exhibit self-similar squared-Bessel-type limits rather than Mittag-Leffler statistics. This spatial- field perspective provides a unifying framework for infinite ergodic theory under state-dependent multiplicative noise.

We studied the 2-Temperature induced phase separation (2-TIPS) in a system of soft repulsive sphe rocylinders (SRS), confined on the surface of a sphere. We used two different anchoring conditions for the SRS on the spherical surface- free and tangential. For the free anchoring, increasing either the packing fraction or the rod aspect ratio suppresses phase separation between the active and passive subsystems, and the system corresponds to a dense-dilute phase co-existence. These phenomena can be explained via the trapping effect and are emergent due to the confinement. For the tangential anchoring, the system phase separates from its initial isotropic state into a locally ordered dense region and a disordered dilute region. Our work provides useful insights into phase separation in binary mixtures under spherical confinement.

The Mpemba effect is an anomalous phenomenon in which the time to thermalize does not depend monotonically on the distance from equilibrium, allowing systems with an initially larger temperature difference to thermalize before those with a smaller one. The rarity of the effect makes it hard to find parameters that produce it, complicating the design of experiments and the development of applications. Here, we find necessary and sufficient conditions for the Markovian Mpemba effect in three-level systems and explain its underlying physical mechanisms. Based on our understanding of the three-level case, we develop an efficient algorithm to determine the parameters for the effect in $N$-level systems. This protocol could open the door for the realization of Mpemba experiments and related applications in large systems.

Stochastic switching between gene expression states, coupled with production and degradation dynamics, governs the accumulation of mRNA and proteins in cells. The concentrations of these accumulated entities dictate the phenotypic distribution of genetically identical cells. The underlying accumulation dynamics are well-captured by a two-state promoter switching model, with statistical and thermodynamic properties quantified via the Fano factor and entropy production rates. However, how these measures correlate with concentration distributions and their shifts under varying kinetic parameters remains largely unexplored. To this end, we use chemical master equations to study a generalized model of mRNA accumulation dynamics in the presence of stochastic switching between two activity states and state-dependent production and degradation rates. We derive exact expressions for the steady-state probability distribution and analytically compute the mean concentration, Fano factor, and entropy production rate (EPR). Simplifying these expressions, we identify contributions arising from stochastic switching rates and relaxation dynamics toward equilibrium in each activity state. Next, using our theoretical results, we characterize the variation in the Fano factor and EPR as a function of mean expression during modality changes of the distributions mediated by the variation of switching rates. We also identify the conditions in kinetic parameters that achieve the highest Fano factor and entropy production rates. Our findings establish a generalized framework for examining stochastic accumulation dynamics, clarifying how kinetic parameters dictate molecular distributions, noise, and dissipation. These insights extend readily to broader contexts coupling stochastic switching with accumulation, including protein burst dynamics, phenotype-switching-mediated drug intake, and queuing theory.

Self-assembly of hard particles with diverse shapes gives rise to a rich variety of structures through excluded-volume constraints alone. Here we show that even a minimal system of three hard squares confined in a two-dimensional periodic box exhibits nontrivial configurational behavior relevant to structure selection. As the packing fraction increases, radial distribution functions obtained from Markov-chain Monte Carlo and uniform non-overlapping insertion sampling agree at low densities, deviate markedly over an intermediate range, and converge again at higher densities. Pressure measurements provide strong numerical evidence that the discrepancy originates from the separation of the allowed configurational space into two disconnected regions above a characteristic density. We identify the separation density as $\phi_{\rm sep}=3/5$, construct explicit overlap-free transition pathways connecting the two regions immediately below it, and quantify their relative configurational-space volumes. At higher packing fractions, an approximately L-shaped arrangement of the particle centers becomes strongly favored over a staggered one, revealing a structural motif characteristic of tetratic and square-lattice ordering in larger hard-square systems. These results show that excluded-volume geometry can govern both configurational connectivity and local structure selection even in a three-particle system, revealing how signatures of many-particle self-assembly can already emerge in the few-particle limit.

Controlling changes in cell shape are crucial for many biological processes, such as tissue development and wound healing. Tissues typically are heterogeneous with a variety of cell shapes and sizes. Of particular interest are local deviations from an average cell shape and size. These fluctuations may extend and transmit across tissues, potentially offering valuable insights into tissue characteristics such as variations in effective "stiffness" or rigidity. In this study, we present a theoretical framework that captures the dynamics of epithelial cell shapes within tissue, incorporating both their average behaviour and fluctuation patterns. We model cells as interacting soft ellipsoids of varying size and aspect ratio. Coarse-graining our model, we obtain a set of continuum stochastic differential equations from which we derive spatial-temporal correlation functions. These correlation functions fit with our experimental data from the developmental process of the Drosophila pupal wing. From the correlation functions, critical parameters representing active cell shape changes and effective tissue "stiffness" can be determined.

The nature of the orbital-selective Mott transition (OSMT) remains elusive. This study shows that, by doping an orbitally degenerate spin-1 antiferromagnetic insulator in the Kanamori-Hubbard model, electronic modes emerge in the band gap in an orbital-selective manner, exhibiting momentum-shifted spin-mode dispersion relations. Electronic modes originating from conventional spin excitations, including the low-energy one, appear in the doped orbital, while those from inter-orbital spin excitations appear in the undoped orbital. These emergent modes constitute an essential feature of the OSMT.

Stochastic resetting as a practical and efficient search strategy in complex and disordered environments has long been a topic of interest to researchers. Based on the competition between jumping and resetting, this article proposes and investigates intermittent continuous-time random walks (CTRWs) under stochastic resetting, using the smaller waiting time for jump and reset as the renewal time, where the waiting times for both jump and reset can have arbitrary distributions. After each renewal event, the system will proceed with new waiting times for jump and reset regardless of their previous histories. We study the governing equation and Montroll-Weiss equation with renewal resetting, as well as the Markovian resetting for intermittent CTRWs. We prove the existence of non-equilibrium stationary states within the renewal reset mechanism when the jump and reset waiting times follow any exponential and power law distributions. For exponential and Gaussian distributed jump lengths, we examine the mean square displacements (MSDs) of particles to determine their monotonicity and asymptotic stability. Moreover, we calculate the first-arrival time to quantify search efficiency, and validate the intermittent CTRWs under renewal resetting lead to a finite mean first-arrival time (MFAT) to any fixed position for exponential jump and reset waiting time distributions (WTDs), power-law jump and exponential reset WTDs, as well as exponential jump and power-law reset WTDs. However, the MFAT diverges for power-law jump and reset WTDs. The intermittent CTRW model, which is based on the competition mechanism, can be applied to many physical scenarios, such as the foraging strategy of animals that return to their nests after an unsuccessful foraging attempt, or the work planning of intelligent robots that return to energy replenishment points after prolonged operation.

The Navier-Stokes equations, which remain unsolved, are crucial equations in fluid mechanics. Discovering the solutions to the Navier-Stokes equations is one of the challenging Millennium problems. In 1900, Hilbert proposed a potential approach to tackle this problem by establishing the relationship between microscopic dynamics and the macroscopic continuum equations. The key bridge is the derivation of Boltzmann equation and the theory of probability. In this paper, we shall use the collision renewal process with arbitrarily distributed waiting times to derive the Boltzmann equation for the time evolution of the probability of the velocity and the displacement of the particle, based on which we prove that the renewal process with exponential collision waiting time is equivalent to the classical Navier-Stokes equations, and that with power-law waiting time is equivalent to the fractional Navier-Stokes equations. Since the collision renewal process with arbitrarily distributed waiting times is a random process and is easy to perform the stochastic simulations of trajectories to obtain the corresponding solution, we actually find a stochastic approach to solve the classical and fractional Navier-Stokes equations.

Although minimalist models for patchy attractive particles have revealed powerful design rules for how particle valence directs colloidal assembly, less work has focused on comparably simple models of reversible, network-forming star polymers. Here, we use relative-entropy coarse-graining and simulation results from finer-resolution bead–spring star polymers to generate 5-bead models of dynamic, end-associating four-armed poly(ethylene glycol) macromers. Our results comparing structural correlations, network connectivity, and phase behavior of the coarse- and fine-grained models provide insight into how the accuracy and transferability of the coarse-grained models depend on the state point chosen for coarse-graining. The results also reveal intrinsic trade-offs between reducing degrees of freedom and expanding the range of effective interactions in coarse-graining that impact the total number of pairwise interactions in the resulting model, with implications for its computational efficiency.

Nonreciprocal interactions fundamentally alter energy transport by breaking the symmetry between forward and backward responses. Here, we uncover an exact transport structure for a one-dimensional harmonic chain with asymmetric nearest-neighbor couplings. Using a Green-function approach, we demonstrate that the direction of heat transport is determined not solely by the temperature bias, but by its competition with an effective directional asymmetry. This competition can reverse the direction of heat flow, enabling cold-to-hot transport. We further show that nonreciprocity introduces an additional power channel associated with the antisymmetric sector of the interaction, leading to a generalized steady-state energy balance involving two reservoir heat currents and a nonreciprocal power current. At the fluctuation level, the heat exchanges with the two reservoirs constitute correlated yet distinct stochastic currents, whose joint scaled cumulant generating function obeys an exact Gallavotti-Cohen symmetry. Together, these results establish a unified energetic and fluctuation framework for nonreciprocal heat transport and demonstrate that directional interactions can serve as an active resource for controlling nonequilibrium energy flows.

Understanding how thermal fluctuations modify or suppress quantum-enhanced sensing is a central problem in quantum metrology. Closely related questions arise in quantum thermodynamics, particularly regarding the relation between the work induced by a signal and the information acquired by a sensor. Here, we establish general relations among the sensitivity of thermal quantum states, as quantified by different quantum Fisher information metrics, their temperature dependence, and the work induced by a unitary signal. We further show that, at high temperature, all monotone QFI metrics coincide to leading order with a universal quantity that can be expressed as the variance of a simple observable and itself defines a lesser-known QFI metric. This emergent uniqueness is reminiscent of the uniqueness of classical Fisher information in information geometry. In continuous-variable systems, the infinite-temperature limit is finite and generically non-zero for quadratic Gaussian signals, while it diverges for higher-degree signals. Remarkably, for any purely quadratic signal generator, the infinite-temperature QFI is at least twice the zero-temperature SLD QFI or, equivalently, at least eight times the ground-state variance of the generator. Consequently, the Cramér–Rao lower bound on the estimator variance is reduced by at least a factor of two. We illustrate these results using harmonic chains of bosonic modes relevant to trapped-ion platforms and quantum field-theoretic models.

The stabilizer Rényi entropy is a quantitative measure of non-stabilizerness, or magic, and has typically been found to scale extensively with system size $N$ (i.e., $\Theta(N)$) for a variety of many-body quantum states. In this note, we study the stabilizer Rényi entropy of the ground state of the spin-$\frac{1}{2}$ Dyck-Fredkin chain and its $t$-deformation, a local frustration-free model with unusual spectral-gap scaling. Exploiting the combinatorial structure, we carry out numerically exact finite-size calculations, which indicate asymptotic behavior depending on $t$: $\Theta(N)$ for $t<1$, $\Theta(\log N)$ at $t=1$, and $\Theta(1)$ for $t>1$. The scaling at $t=1$ could be another manifestation of the unconventional criticality of the model, while the contrast with the behavior of the entanglement entropy suggests that non-stabilizerness might provide a new window into quantum many-body systems.

Disordered hyperuniformity spans twenty-five orders of magnitude, from the microscopic world of soft and condensed matter to the distribution of matter in the universe. We introduce a free-energy functional with no adjustable coupling, a phase-field-crystal ordering term and a Newtonian tail, and show that its two kinetic limits generate two of these structures. Under conserved overdamped dynamics it arrests in a bicontinuous labyrinth of the kind found in confined fluids, block copolymers, active matter and vegetation patterns, which is a class III disordered hyperuniform state; under inertial dynamics in an expanding background it generates a cosmic web whose tidal skeleton matches that of gravity alone to within $0.03$ in every morphological class. In both limits the suppressed long-wavelength fluctuations are inherited from the initial condition rather than made by the dynamics: the large-scale exponent of the arrested state equals the primordial one, and gravity rescales the infrared spectrum by the linear growth factor to better than one per cent over a factor $1363$ of growth. The two sectors are continuously connected, and their distance is measurable: once the ordering modulation saturates, its mean-field back-reaction leaves the medium with a residual pressure proportional to $\lambda_0^2$ that suppresses the growth of the largest scales by a smooth factor and vanishes in the cold-matter limit $\lambda_0\to0$.

Deciphering the microscopic details of crystal nucleation remains a key open problem in chemical physics. For aqueous sodium chloride (NaCl), the extent to which nucleation involves amorphous ion aggregation, and the supersaturation regime in which two-step-like behavior emerges, remains actively debated. In this work, we examine NaCl nucleation at 12, 13, and 14 mol/kg ($S = 3.2, 3.5, 3.8$) using unbiased path sampling with $\infty$RETIS. Through a combination of reaction coordinate analysis, joint and conditional probability distributions, diffusion tensor analysis, and conditional free energy landscapes, we identify nucleation pathways without a priori mechanistic assumptions. While all concentrations are governed by the same reaction coordinate, indicating that nucleus growth and structural ordering are central to crossing the barrier, the nature of reactive trajectories changes with supersaturation. At lower concentrations, nucleation mainly occurs via tightly coupled increases in cluster size and ordering, compatible with a one-step-like mechanism. As concentration increases, amorphous aggregation becomes increasingly decoupled from crystallization, and pathways involving substantial amorphous growth prior to crystallization become progressively more probable. Importantly, these pathways coexist within a single broad reaction channel rather than proceeding through distinct metastable intermediate states. Together, these results reconcile earlier, contradictory reports on NaCl nucleation mechanisms and support a view of nucleation as an ensemble of competing reactive pathways whose relative probabilities vary continuously with supersaturation. More broadly, our findings illustrate that substantial mechanistic diversity can emerge within a classical nucleation framework, without requiring distinct non-classical descriptions.

We extend the KTHNY theory of defect-mediated melting in two dimensions to the case of active solids exhibiting both non-reciprocal (odd) elasticity and Cosserat (micropolar) coupling. For perturbatively small values of non-reciprocity, melting still proceeds via defect unbinding, but the melting temperature shifts due to activity. However, at a threshold value of non-reciprocity, we discover a qualitatively distinct mechanism for 2D melting, which we call topological instability. This melting occurs through the proliferation of defect pairs at any temperature. We use a combination of field theory and simulations to characterize this zero-temperature transition in terms of the defect-pair fugacity. Surprisingly, for higher activity values, we find reentrant crystallization, where the quasi-long-range order survives even at temperatures for which the equilibrium crystal would melt. Although active solids present one realization of this topological instability, we envision core-energy-driven defect proliferation as a generic and unexplored route for two-dimensional melting.