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We introduce a new Monte Carlo method for pure gauge theories. It is not intended for use with dynamical fermions. It belongs to the class of Local Hybrid Monte Carlo (LHMC) algorithms, which make use of the locality of the action by…

高能物理 - 格点 · 物理学 2009-10-22 A. D. Kennedy , K. M. Bitar

Standard sampling algorithms for lattice QCD suffer from topology freezing (or critical slowing down) when approaching the continuum limit, thus leading to poor sampling of the distinct topological sectors. I will present a modified…

高能物理 - 格点 · 物理学 2021-11-11 David Albandea , Pilar Hernández , Alberto Ramos , Fernando Romero-López

We propose a new method for simulating lattice gauge theories in the presence of fermions. The method combines flow-based generative models for local gauge field updates and hierarchical updates of the factorized fermion determinant. The…

高能物理 - 格点 · 物理学 2022-01-10 Jacob Finkenrath

A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimized (trained) to produce samples from a distribution…

高能物理 - 格点 · 物理学 2019-09-10 M. S. Albergo , G. Kanwar , P. E. Shanahan

Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice volumes. We introduce a multiscale generative sampler,…

高能物理 - 格点 · 物理学 2026-04-14 A. Singha , J. Kauffmann , E. Cellini , K. Jansen , S. Nakajima

This review gives an overview on the research of algorithms for dynamical fermions used in large scale lattice QCD simulations. First a short overview on the state-of-the-art of ensemble generation at the physical point is given. Followed…

高能物理 - 格点 · 物理学 2024-02-20 Jacob Finkenrath

The recent introduction of machine learning techniques, especially normalizing flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional HMC algorithm. Naive use of…

高能物理 - 格点 · 物理学 2022-12-06 David Albandea , Luigi Del Debbio , Pilar Hernández , Richard Kenway , Joe Marsh Rossney , Alberto Ramos

In modern lattice simulations, conventional update algorithms do not allow for tunneling between topological sectors at fine lattice spacings. We compare the viability of multiple less commonly used algorithms (metadynamics, instanton…

高能物理 - 格点 · 物理学 2025-03-14 Timo Eichhorn , Christian Hoelbling

At fine lattice spacings, lattice simulations are plagued by slow (topological) modes that give rise to large autocorrelation times. These, in turn, lead to statistical and systematic errors that are difficult to estimate. We study the…

高能物理 - 格点 · 物理学 2025-03-14 Timo Eichhorn , Christian Hoelbling , Philip Rouenhoff , Lukas Varnhorst

Large N gauge theories with adjoint matter can be numerically studied using lattice techniques. Eguchi-Kawai reductions holds for this theory and one can reduce the lattice model to a single site. Hybrid Monte Carlo algorithm can be used to…

高能物理 - 格点 · 物理学 2015-03-19 R. Narayanan

At fine lattice spacings, Markov chain Monte Carlo simulations of QCD and other gauge theories with or without fermions are plagued by slow modes that give rise to large autocorrelation times. This can lead to simulation runs that are…

高能物理 - 格点 · 物理学 2024-06-12 Timo Eichhorn , Gianluca Fuwa , Christian Hoelbling , Lukas Varnhorst

The recent introduction of Machine Learning techniques, especially Normalizing Flows, for the sampling of lattice gauge theories has shed some hope on improving the sampling efficiency of the traditional Hybrid Monte Carlo (HMC) algorithm.…

高能物理 - 格点 · 物理学 2023-09-21 David Albandea , Luigi Del Debbio , Pilar Hernández , Richard Kenway , Joe Marsh Rossney , Alberto Ramos

We propose a formally valid machine-learning-assisted global proposal mechanism for Monte Carlo sampling in lattice gauge theory. The construction is based on a coupling-flow update on the SU(2) lattice-link manifold, in which active links…

高能物理 - 格点 · 物理学 2026-05-27 Seung-il Nam

This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant. This is the default approach in state-of-the-art…

The Hybrid Monte Carlo algorithm for the simulation of QCD with dynamical staggered fermions is compared with Kramers equation algorithm. We find substantially different autocorrelation times for local and nonlocal observables. The…

高能物理 - 格点 · 物理学 2009-10-28 M. E. Berbenni-Bitsch , A. P. Gottlob , S. Meyer , M. Puetz

In theories with topological sectors, such as lattice QCD and four-dimensional SU(N) gauge theories with periodic boundary conditions, conventional update algorithms suffer from topological freezing due to large action barriers separating…

高能物理 - 格点 · 物理学 2026-04-07 Timo Eichhorn , Gianluca Fuwa , Christian Hoelbling , Lukas Varnhorst

The simulation of real-time dynamics in lattice gauge theories is particularly hard for classical computing due to the exponential scaling of the required resources. On the other hand, quantum algorithms can potentially perform the same…

量子物理 · 物理学 2020-11-11 Simon V. Mathis , Guglielmo Mazzola , Ivano Tavernelli

The advantages of using Multi-Step corrections for simulations of lattice gauge theories with dynamical fermions will be discussed. This technique is suited for algorithms based on the Multi-Boson representation of the dynamical fermions as…

高能物理 - 格点 · 物理学 2008-11-26 Enno E. Scholz , Istvan Montvay

We consider two-flavor QCD in the lattice regularization with improved Wilson fermions. In this formulation chiral symmetry is explicitly broken at order a and hence the isovector axial currents require improvement as well as a finite…

高能物理 - 格点 · 物理学 2007-05-23 Roland Hoffmann

Gauge fixing is an essential step in lattice QCD calculations, particularly for studying gauge-dependent observables. Traditional iterative algorithms are computationally expensive and often suffer from critical slowing down and scaling…

高能物理 - 格点 · 物理学 2026-03-05 Ho Hsiao , Benjamin J. Choi , Hiroshi Ohno , Akio Tomiya
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