Condensed Matter · Physics & Astronomy
Disordered Systems & Neural Networks
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135 papers, from 4 September 2026 to 5 November 2026: 135 arXiv preprints.
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Newest papers
Quantifying the remaining search space in analogue optimisation
Benchmarking analogue optimisers requires certifying not only how close a returned energy is to the optimum, but also how many better configurations may remain.Disorder-blind Mpemba acceleration of quantum state preparation
Using the strong Mpemba effect to accelerate relaxation by initial-state design requires suppressing the slowest mode, which typically needs prior knowledge of the system.Finite-width shift of the edge of chaos in deep neural networks
Deep neural networks train best when they are initialised at the edge of chaos, the critical line in the plane of weight and bias variances on which the distance between two inputs neither grows nor…Nonequilibrium Spin-Glass Order in a Long-Range Quantum Ising Glass and Its Sensitivity to Noise
We study spin-glass order after a transverse-field quench in a long-range quantum Ising glass.Bulk and edge states in Fibonacci-Bose-Hubbard clusters
We study the one- and two-boson energy subspaces of finite open Bose--Hubbard chains with Fibonacci-modulated hopping and zero on-site potential.Dynamical Signatures of Selective Gap Closing in Multi-Dimensional Monotone Quasiperiodic Operators
We translate the spectral gap-closing criterion for multi-dimensional monotone quasiperiodic Schrödinger operators into a concrete dynamical protocol and verify it by large-scale numerics.Generative diffusion in long-range correlated landscapes: Speciation cascade and the foam structure of the score
While state of the art for image and video generation, the fundamental reasons for the effectiveness of generative diffusion remain unclear.How Does Chain-of-Thought Supervision Teach Models to Reason? A Theoretical Analysis
Training with chain-of-thought (CoT) supervision is an effective approach for teaching models multi-step reasoning procedures.Solvable dynamics model of in-context learning
Pretrained Transformer architectures display a surprising in-context learning ability without additional training.Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates…Characterization of Phase Transitions with the Annealed Importance Sampling Algorithm
We We explore the use of the Annealed Importance Sampling (AIS) algorithm as a numerical tool to study phase transitions and critical phenomena.A Spectral Renormalization-Group View of Learning
We study how the layers in deep neural networks (NNs) converge, using Wilsonian Renormalization-Group (RG) ideas to organize the spectral picture.Stochastic learning flow: moment equations for stochastic gradient descent at finite step size
Analytical theories of learning in deep networks often assume gradient flow, discarding the finite step size and batch noise of stochastic gradient descent (SGD).Non-Markovian quantum-emitter dynamics and bound-state formation in an Aubry--André--Harper photonic lattice
In non-Markovian light-matter interactions, a spectrally structured bath can alter the evolution of a quantum emitter by modifying the vacuum fluctuations experienced by the emitter.Going deep, reaching further: What can Boltzmann machines efficiently represent?
While universal approximation theorems theoretically underpin the representation capabilities of machine learning models, they have two shortcomings: They do not focus on efficient representations…Single-Temperature Ranked Greedy Cluster Exchange for Max-Cut on Two-Dimensional Tori
Ground-state search in two-dimensional Ising spin glasses on large toroidal lattices remains challenging for local-update heuristics because rugged energy barriers impede relaxation across…On Zeros and Algorithms for Disordered Systems II: The Antiferromagnetic Ising Model up to Kesten-Stigum
We give the first polynomial-time algorithm to estimate the partition function of the antiferromagnetic Ising model on the uniformly random $d$-regular graph to arbitrarily high accuracy for all…Quantum manifestations of converse symmetry breaking
Quantum synchronization holds promise in advancing quantum technologies such as sensing, communication, and computation.Time evolution in the multi-component dynamical Keldysh models
We construct a time evolution operator for a dynamical quantum models with a multi-component random Gaussian field characterized by a long correlation time.Capacity of the Hopfield-Hebbian dynamics with correlated patterns
Allowing for small fraction of errors in retrieved stored binary patterns, Hopfield-Hebbian neural network (NN) associative memory dynamics [14] achieves linearly scaled capacity.