74 papers
This paper demonstrates that Boltzmann Generators can effectively sample the liquid-gas critical point of a Lennard-Jones fluid and capture critical signatures, though their current utility is constrained by small system sizes that suppress large-scale fluctuations.
Although power-counting suggests that spatially inhomogeneous diffusion is irrelevant in the contact process, large-scale simulations reveal that such disorder destabilizes the standard directed-percolation critical point by generating effective random-mass disorder, thereby driving the transition into an infinite-randomness universality class.
This paper presents a tractable model for strongly clustered random graphs based on triadic closure, providing exact analytical expressions for their local clustering spectrum and degree correlations while demonstrating that high transitivity leads to positive degree assortativity.
This paper introduces a combined theoretical and data-driven framework to predict how structural complexity and delayed feedback jointly trigger oscillations in complex networks, a theory validated by electronic circuit experiments and enhanced by reservoir computing for accurate onset prediction from time-series data.
This paper establishes a principled dynamical theory for sequential retrieval in input-driven Hopfield networks by deriving explicit mathematical conditions for self-sustained memory transitions within a two-timescale architecture, thereby bridging classical associative memory models with modern reasoning systems.
This paper attributes the performance degradation of GNN-based SAT solvers on difficult instances to inherent negative graph Ricci curvature in formula representations, which causes oversquashing by creating local connectivity bottlenecks that hinder the compression of long-range dependencies.
This paper investigates the localization transition of interacting particles in one-dimensional correlated disorder with vanishing backward scattering, using renormalization group methods and numerical simulations to demonstrate that the transition point shifts to the non-interacting limit and that the localization length scaling deviates from conventional behavior.
This paper analytically and numerically investigates the non-equilibrium dynamics of a tracer particle driven through a disordered, confined lattice, revealing how confinement induces dimensional crossovers, qualitatively alters force-dependent diffusion, and sustains superdiffusive fluctuations even under external driving.
This paper proposes that circularly polarized laser light can drive monolayer amorphous carbon into topological phases characterized by regular and anomalous edge modes, demonstrating that local atomic coordination and the spectral localizer marker are key to engineering topological states in disordered, non-crystalline materials.
This paper extends the strong disorder renormalization group method to analyze excited states and finite temperature properties of bond-disordered antiferromagnetic quantum spin chains with both short-range and long-range power-law interactions, deriving key thermodynamic and entanglement characteristics while characterizing the distribution of coupling signs and amplitudes.
This paper demonstrates that while stochastic Ising machines exhibit longer autocorrelation times for sampling neural-network quantum states of Heisenberg models, their massive parallelism projects a significant speed-up of 100 to 10,000 times over standard Metropolis-Hastings sampling, enabling efficient large-scale quantum simulations without requiring direct hardware deployment.
This paper demonstrates that a graph neural network trained on the Strong Disorder Renormalization Group (SDRG) method can accurately infer the entanglement structure and pairing hierarchy of disordered long-range interacting quantum spin chains, achieving quantitative agreement with SDRG predictions across various interaction exponents and temperatures without retraining.
This paper introduces Extreme Quantum Cognition Machines, a noise-tolerant quantum learning architecture that combines fixed quantum dynamics with a dynamical attention mechanism to perform deliberative decision-making, validated on linguistic tasks and applicable to fields like cybersecurity and biology.
The paper introduces the "worm decoder," a Markov-Chain Monte-Carlo-based algorithm that achieves optimal decoding for various qLDPC codes by approximating logical error probabilities, offering rigorous efficiency guarantees for typical errors and demonstrating superior performance over existing methods even under depolarizing noise through the use of soft information.
This study investigates the non-equilibrium dynamics of the disordered Power-of-Two model, revealing that while strong disorder induces localization and unique non-monotonic scrambling signatures, the system ultimately remains ergodic in the thermodynamic limit for any finite disorder strength.
This paper establishes a strict hierarchy where graph dynamical models are a special case of focal hypergraph models, which in turn are a subset of general hypergraph models, demonstrating that the choice between these formalisms should be determined by the specific nature of interactions (focal versus non-focal) rather than a preference for one framework.
This paper employs the random first-order transition theory and a generalized Master equation to characterize the non-Markovian, aging dynamics of thermal avalanches in driven amorphous systems, utilizing full counting statistics to derive the complete distributions of avalanche magnitudes and counts under various driving protocols.
This paper introduces a spectral approach to the 3D Edwards-Anderson spin glass, demonstrating that the eigenvalue statistics of overlap matrices exhibit a q-Gaussian crossover from a Wigner semicircle to a Gaussian distribution at the critical temperature, thereby offering a robust and computationally efficient indicator of spin-glass criticality.
Using extensive parallel-tempering Monte Carlo simulations, this study reveals that in the three-dimensional random-bond Ising model, a secondary percolation transition involving two equal-density clusters occurs above the thermodynamic ordering points, with the subsequent divergence of these cluster densities serving as a distinct percolation signature for the ferromagnetic and spin-glass phase transitions.
This paper demonstrates that algorithmic thresholds for solving random -Sat problems using Simulated Annealing are not fixed but vary depending on the time scaling of the algorithm relative to the system size, revealing distinct performance limits for linear, quadratic, and higher-order polynomial regimes.