74 papers
This paper analytically derives the free energy and phase diagram of a spherical Hopfield model with Gaussian-correlated pattern structures, revealing that a spin glass phase emerges at high temperatures while a glass phase containing both patterns and correlations appears at lower temperatures under low loading capacity.
This paper demonstrates that introducing a "dreaming" phase, where random patterns are unlearned, significantly enhances the memorization capacity of a clipped Hopfield model that avoids catastrophic forgetting by incorporating biologically plausible bounded synaptic strengths.
This paper presents a compact dynamical mean-field theory for large networks of coupled phase oscillators that preserves $2\pi$-periodicity and disorder averaging to derive a self-consistent single-oscillator stochastic equation, successfully bridging microscopic phase response data with macroscopic synchronization predictions for arbitrary phase-reducible systems.
This paper derives an expression for local Hall conductivity in disordered systems to demonstrate that non-magnetic disorder and the fragmentation of disordered patches can expand the parameter regimes for both Chern insulating states and topological Anderson insulators, thereby motivating new local experimental techniques to visualize these effects.
This paper introduces a self-consistent mean-field quantum optimization algorithm that decomposes classical Ising Hamiltonians into independent subproblems influenced by a variational common environment, enabling the solution of large-scale problems on current quantum hardware through extensive numerical simulations and experimental applications.
This paper empirically demonstrates that while the runtime of local greedy search for optimizing Sherrington-Kirkpatrick spin glass Hamiltonians is universal across various coupling distributions, the performance of Parisi's local reluctant search is surprisingly non-universal and sensitive to the specific entry distribution, particularly when couplings have discrete support.
This paper demonstrates that the infinite random Heisenberg XXZ spin- chain exhibits slow, logarithmic information propagation characteristic of many-body localization within any arbitrary fixed energy interval, provided the interaction and disorder parameters fall within a regime determined solely by that energy interval.
This paper proves that the infinite system-size free energy of quantum -spin glasses in a transverse magnetic field converges to that of the quantum random energy model as , achieved by combining non-commutative analytical techniques with the geometry of extreme negative deviations in classical -spin glasses.
This paper analyzes all 960 Chess960 starting positions using Stockfish evaluations and a novel information-based metric to reveal a universal White first-move advantage and a highly heterogeneous landscape of strategic complexity and decision asymmetry, demonstrating that the classical chess setup is merely one configuration among many rather than a unique extremum.
This paper reviews the limitations of static urban models and advocates for dynamic, empirically grounded frameworks based on partial differential equations to better capture the complex, non-equilibrium spatial dynamics of urban sprawl driven by population, infrastructure, and market factors.
This paper demonstrates that utilizing unconventionally large learning rates to induce transient chaotic dynamics during neural network training creates an optimal balance between exploration and exploitation, thereby accelerating convergence to high accuracy across various architectures and tasks.
This paper presents an extended non-affine deformation theory incorporating a time-dependent memory kernel within the Generalized Langevin Equation, which successfully predicts the viscoelastic response of poly(methyl methacrylate) across twenty frequency decades and validates these findings against diverse experimental and computational methods.
This paper extends the two-state, two-timescale (TS2) theory to provide a unified, parameter-free framework that quantitatively describes both the structural -relaxation and the recently observed slow Arrhenius process (SAP) in amorphous polymers by modeling the SAP as the high-temperature limit of cluster-scale relaxation, while also predicting its eventual transition to Vogel-Fulcher-Tammann-Hesse dynamics at lower temperatures.
This paper investigates how hierarchically patterned architected thin films in bi-layer composites can localize interfacial failure and enhance fracture toughness by creating a buffer region for diffuse damage dissipation, utilizing a novel network formalism that integrates discrete differential geometry and spectral graph theory to analyze load redistribution and deformation modes.
This study reveals that thermal cycling induces irreversible degradation in the van der Waals bonding and electronic transport of hBN/graphene heterostructures on metallic islands due to thermal-expansion-driven delamination and interfacial residue redistribution, while demonstrating that hot pressing can restore contact and highlighting the critical role of interfacial stability in 2D device applications.
This paper introduces and benchmarks two hybrid quantum-classical methods combining time-dependent Lanczos and matrix-product state approaches with the multi-trajectory Ehrenfest approximation to simulate electron-phonon systems, demonstrating that coupling strongly disordered interacting fermions to classical Einstein phonons induces delocalization and destabilizes many-body localization.
This paper introduces a novel mechanism called exceptional-bound (EB) band engineering that enables system-size-controlled topological transitions in non-Hermitian systems through unique critical scaling near exceptional points, distinct from the non-Hermitian skin effect and applicable to diverse experimental platforms.
This paper introduces a Lindbladian learning method that combines maximum-likelihood estimation on transient Pauli measurements with a neural differential equation framework to robustly infer open-system quantum dynamics, including dissipative mechanisms, across various hardware platforms and noise conditions with high efficiency.
This paper investigates non-Hermitian quasiperiodic localization transitions using semiclassical Husimi dynamics and finds that, unlike their Hermitian counterparts, the critical points do not universally align with classical phase-space predictions due to a sensitive dependence on the irrational parameter, though a faithful classical-quantum mimicry can still be achieved within specific parameter regimes and finite time windows.
This paper introduces new hard benchmarks for Constraint Satisfaction Problems derived from statistical physics to demonstrate that, contrary to some claims, classical heuristics currently outperform Graph Neural Networks on truly difficult instances.