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Simulating lattice gauge theories on quantum computers presents unique challenges that drive the development of novel theoretical frameworks. The orbifold lattice approach offers a scalable method for simulating SU($N$) gauge theories in…

High Energy Physics - Lattice · Physics 2026-04-07 Emanuele Mendicelli , Georg Bergner , Masanori Hanada

Sampling from high-dimensional and structured probability distributions is a fundamental challenge in computational physics, particularly in the context of lattice field theory (LFT), where generating field configurations efficiently is…

Quantum Physics · Physics 2026-02-10 Jehu Martinez , Andrea Delgado

This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of…

Data Analysis, Statistics and Probability · Physics 2024-05-16 Rowan Kelleher , Anselm Vossen

The latent space of normalizing flows must be of the same dimensionality as their output space. This constraint presents a problem if we want to learn low-dimensional, semantically meaningful representations. Recent work has provided…

Machine Learning · Statistics 2020-06-17 Artur Bekasov , Iain Murray

We construct two-dimensional ${\cal N} = (2, 2)$ supersymmetric gauge theories on a Euclidean spacetime lattice with matter in the two-index symmetric and anti-symmetric representations of SU($N_c$) color group. These lattice theories…

High Energy Physics - Lattice · Physics 2015-06-19 Anosh Joseph

The Lie point symmetries and corresponding invariant solutions are obtained for a Gaussian, irrotational, compressible fluid flow. A supersymmetric extension of this model is then formulated through the use of a superspace and superfield…

Mathematical Physics · Physics 2009-11-13 A. M. Grundland , A. J. Hariton

We study a multigrid method for nonabelian lattice gauge theory, the time slice blocking, in two and four dimensions. For SU(2) gauge fields in two dimensions, critical slowing down is almost completely eliminated by this method. This…

High Energy Physics - Lattice · Physics 2014-11-17 M. Grabenstein , K. Pinn

Many modern applications of Bayesian inference, such as in cosmology, are based on complicated forward models with high-dimensional parameter spaces. This considerably limits the sampling of posterior distributions conditioned on observed…

Instrumentation and Methods for Astrophysics · Physics 2024-09-17 Marco Raveri , Cyrille Doux , Shivam Pandey

We investigate the dynamics of three-dimensional lattice gauge theories by means of an external Abelian magnetic field. For the SU(2) lattice gauge theory we find evidence of the unstable modes.

High Energy Physics - Lattice · Physics 2009-10-22 P. Cea , L. Cosmai

We present a quantum computational framework for SU(2) lattice gauge theory, leveraging continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as…

High Energy Physics - Lattice · Physics 2025-06-24 Victor Ale , Nora M. Bauer , Raghav G. Jha , Felix Ringer , George Siopsis

We study the renormalization group flow of $\mathbb{Z}_2$-invariant supersymmetric and non-supersymmetric scalar models in the local potential approximation using functional renormalization group methods. We focus our attention to the fixed…

High Energy Physics - Theory · Physics 2015-10-28 Tobias Hellwig , Andreas Wipf , Omar Zanusso

A new block spin renormalization group transformation for SU(N) gauge models is proposed near the non-trivial fixed point in perturbation theory and thereby the expectation values of various Wilson loops on the renormalized trajectory near…

High Energy Physics - Lattice · Physics 2011-12-01 Y. Iwasaki

Real-world data with underlying structure, such as pictures of faces, are hypothesized to lie on a low-dimensional manifold. This manifold hypothesis has motivated state-of-the-art generative algorithms that learn low-dimensional data…

Machine Learning · Statistics 2022-04-28 Edmond Cunningham , Renos Zabounidis , Abhinav Agrawal , Madalina Fiterau , Daniel Sheldon

We introduce in this work the normalizing field flows (NFF) for learning random fields from scattered measurements. More precisely, we construct a bijective transformation (a normalizing flow characterizing by neural networks) between a…

Machine Learning · Computer Science 2022-05-11 Ling Guo , Hao Wu , Tao Zhou

The Hamiltonian dynamics of a compressible inviscid fluid is formulated as a gauge theory. The idea of gauge equivalence is exploited to unify the study of apparantly distinct physical problems and solutions of new models can be generated…

High Energy Physics - Theory · Physics 2007-05-23 Subir Ghosh

An algorithm is proposed for the simulation of pure SU(N) lattice gauge theories based on Genetic Algorithms(GAs). We apply GAs to SU(2) pure gauge theory on a 2 dimensional lattice and show the results, the action per plaquette and Wilson…

High Energy Physics - Lattice · Physics 2009-10-31 A. Yamaguchi

Gauge-fixing as a sampling procedure of gauge copies provides a possibility to construct well-defined gauges also beyond perturbation theory. The implementation of such sampling strategies in lattice gauge theory is briefly outlined, and…

High Energy Physics - Theory · Physics 2013-03-15 Axel Maas

The renormalization functions involved in the determination of the topological susceptibility in the SU(2) lattice gauge theory are extracted by direct measurements, without relying on perturbation theory. The determination exploits the…

High Energy Physics - Lattice · Physics 2009-10-22 Bartomeu Alles , Massimo Campostrini , Adriano Di Giacomo , Yigit Gunduc , Ettore Vicari

The notion of the flow introduced by Kitaev is a manifestly topological formulation of the winding number on a real lattice. First, we show in this paper that the flow is quite useful for practical numerical computations for systems without…

Chaotic Dynamics · Physics 2024-08-01 F. Hamano , T. Fukui

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables. We propose a new type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast to…

Machine Learning · Computer Science 2017-02-01 Diederik P. Kingma , Tim Salimans , Rafal Jozefowicz , Xi Chen , Ilya Sutskever , Max Welling
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