The paper presents numerical evidence that topological freezing in SU(3) Yang‑Mills theory can be reduced using a flow‑based method. By combining open boundary conditions with Stochastic Normalizing Flows, the authors analyze how computational costs scale at lattice spacings as fine as 0.03 fm. They find that both the integrated autocorrelation time of the squared topological charge and the statistical uncertainty on the topological susceptibility stay under control across the explored parameter range with current computational resources.
arXiv:2606. 27481v1 Announce Type: cross Abstract: We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model.
The paper presents a method for sampling SU(N) gauge theory on a two‑dimensional lattice by independently generating plaquette holonomies and then reweighting configurations to satisfy consistency at lattice corners. By reformulating the Wilson action in terms of holonomy variables, the authors reduce the problem to sampling pairs of SU(N) matrices conditioned on their commutator, for which they derive closed‑form densities for SU(2) and SU(3). They demonstrate high per‑plaquette acceptance rates (>99%) and moderate effective sample sizes on both small (2×2) and large (32×32) lattices using normalizing flows and closed‑form samplers.
arXiv:2603. 02984v2 Announce Type: replace-cross Abstract: Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters.
In the Wilson formulation of lattice gauge theory, the fundamental degrees of freedom are group-valued link variables, while the action is a sum over the trace of the plaquettes, the smallest Wilson l...
arXiv:2606. 15986v1 Announce Type: cross Abstract: The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators.
arXiv:2606. 30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models.
arXiv:2608. 19331v1 Announce Type: cross Abstract: We modify the NN/QFT duality [1] to incorporate the layerwise permutation symmetry of the network, resulting in a $(0\!
arXiv:2510. 26081v2 Announce Type: replace-cross Abstract: Near the critical point, Markov Chain Monte Carlo (MCMC) simulations of lattice quantum field theories (LQFT) become increasingly inefficient due to critical slowing down.
arXiv:2606. 09806v1 Announce Type: cross Abstract: We introduce Topological Neural Operators (TNOs), a principled framework for operator learning on cell complexes that lifts neural operators (NOs) from functions on points and/or edges to topological domains.
The paper presents Monte Carlo simulations of a continuous-spin Blume‑Capel model and maps its phase diagram in the crystal‑field–temperature plane, varying the spin softness. It demonstrates that normalizing flows—generative models enabling direct, autocorrelation‑free sampling—serve as effective samplers throughout the diagram, including first‑order transitions and near the tricritical point. Finite‑size scaling of flow-generated samples accurately identifies first‑, second‑order transitions and the tricritical point, matching the precision of conventional Monte Carlo methods.
arXiv:2609.07591v1 Announce Type: cross Abstract: Foundation models for ground states in spin-1/2 systems are a promising method for problems ranging from quantum chemistry to identifying new phase d...
arXiv:2607. 28706v1 Announce Type: cross Abstract: We study the convergence properties of the random-sweep Gibbs sampler for Gaussian graphical models with a thin-membrane prior.