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.
By Javad Komijani
arXiv:2510.06848v3 Announce Type: replace-cross
Abstract: Bell sampling is a simple yet powerful tool based on measuring two copies of a quantum state in the Bell basis, and has found applications in...
By Jonathan Allcock, Joao F. Doriguello, G\'abor Ivanyos, Miklos Santha
arXiv:2605. 27770v2 Announce Type: cross Abstract: We introduce `dualGNN', an autoregressive message-passing GNN for sampling fine, regular triangulations (FRTs) of convex polytopes.
By Nate MacFadden
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.
By Claudio Bonanno, Andrea Bulgarelli, Elia Cellini, Alessandro Nada, Dario Panfalone, Davide Vadacchino, Lorenzo Verzichelli
arXiv:2609.08873v1 Announce Type: cross
Abstract: Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over th...
By Syamantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu
arXiv:2606. 03003v1 Announce Type: cross Abstract: A latent world model built from an equivariant encoder $E$ and an equivariant predictor $f$ inherits a provable symmetry of its training loss: when the world's dynamics genuinely carries a group $G$ acting on latents by an orthogonal representation $\rho(g)$, the one-step prediction relMSE is exactly invariant across the whole group, so fitting the dynamics on a restricted slice of orientations mathematically determines it on the entire orbit (j\v{u} y\=i f\v{a}n s\=an).
By Hongbo Wang (Stony Brook University)
arXiv:2609. 17926v1 Announce Type: new Abstract: The standard geometric derivation of neural scaling exponents takes the intrinsic dimension of a data manifold as its input.
By Chon-Fai Kam, Miloud Bessafi, Fr\'ed\'eric Cadet
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.
By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet
arXiv:2607. 22889v1 Announce Type: new Abstract: Learning the natural parameters $z \in \mathbb{R}^n$ of discrete distributions $\mu_z$ from independent samples constrained to a subset $S \subseteq \{0,1\}^n$ is a foundational challenge in high-dimensional statistics.
By Rohan Chauhan, Ioannis Panageas
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.
By Octavio Vega, Aida X. El-Khadra
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.
By Ryan Abbott, Denis Boyda, Yang Fu, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-L\'opez, Phiala E. Shanahan, Julian M. Urban
arXiv:2409. 15600v3 Announce Type: replace Abstract: A representation of a molecule or material should be invariant to the symmetries of physics, unique, continuous, efficient and general.
By Rahul Khorana, Marcus Noack, Jin Qian