1536 papers
Statistical mechanics explores how the chaotic motion of countless tiny particles gives rise to the predictable laws governing heat, pressure, and phase transitions. This field bridges the gap between the microscopic world of atoms and the macroscopic reality we experience daily, offering deep insights into why materials behave the way they do.
On Gist.Science, we process every new preprint in this category as it appears on arXiv to make these complex findings accessible to everyone. For each paper, we provide both a plain-language explanation for the curious reader and a detailed technical summary for specialists, ensuring that groundbreaking research is never lost behind a wall of jargon.
Below are the latest papers in statistical mechanics, freshly curated and summarized to help you understand the cutting edge of this fascinating discipline.
This paper investigates the stability of the two-dimensional toric code under coherent noise using a doubled-Hilbert-space formalism, establishing a connection to non-Hermitian statistical mechanics to reveal remarkable topological order stability near the Y-axis and identifying phase boundaries that define the intrinsic error thresholds for quantum error correction.
This paper resolves the challenge of negative probabilities in quantum mechanics by introducing an exact representation of quantum spin chain dynamics as classical continuous-time Markov chains that model the creation, annihilation, and propagation of particle-antiparticle pairs, with quantum behavior emerging from the statistical average of these classical processes.
This paper demonstrates that mixed-state subsystem symmetry-protected topological order in cluster states remains robust up to maximal decoherence rates when noise respects strong subsystem symmetry, and proposes "spurious topological entanglement negativity" as a constant correction to area-law scaling for detecting this order while highlighting the non-invariance of standard topological entanglement negativity under finite-depth quantum channels.
This paper introduces spectral decimation as a systematic, computationally efficient framework to quantify emergent symmetries in quantum many-body systems by defining a characteristic symmetry sector that distinguishes between chaotic dynamics, statistical mixtures, and emergent integrability in fragmented and disordered Hamiltonians.
This paper presents numerical simulations using Euler's Method to analyze the spread of Schramm-Loewner Evolution (SLE) and Multiple SLE dynamics from their mean behavior, revealing that the distribution of deviations is bimodal or bell-shaped depending on the initial position and parameter in standard SLE, while remaining consistently bell-shaped for Multiple SLE driven by Dyson Brownian Motion across varying parameters.
This paper investigates the nonequilibrium dynamics of a one-dimensional system transitioning from a charge-density-wave to a symmetry-protected topological phase, demonstrating that while both sudden quenches and slow ramps melt the initial order, only slow ramps successfully establish topological order by suppressing excitation production, whereas quenches fail due to a finite density of defects.
This paper establishes a unified framework demonstrating that the critical behavior of ideal Bose-Einstein condensation falls into three distinct classes determined solely by the low-energy scaling of the density of states, which is governed by dimensionality and confinement.
This paper introduces the Compressed Minimum-Purity Time Evolution (CoMPuTE) method, which maintains accurate long-time quantum dynamics by evolving reduced local density matrices under a minimum-purity principle, thereby achieving computational efficiency and enabling the study of late-time phenomena like energy diffusion in higher-dimensional systems.
This paper presents a novel stochastic model based on compounded Chapman-Kolmogorov equations to analyze elastoplastic contact of rough surfaces by incorporating scale-dependent hardness, thereby predicting contact status evolution across scales and offering new insights for multidisciplinary fields involving multiscale roughness.
This paper introduces a controlled tensor-network algorithm that combines Zolotarev's rational approximation with a variational DMRG-like approach to efficiently and accurately estimate trace norms of matrix product operators in many-body systems, overcoming the computational bottlenecks of full diagonalization and enabling practical studies of mixed-state quantum information quantities like entanglement negativity and quantum fidelity.