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Related papers: Lecture Notes on Normalizing Flows for Lattice Qua…

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Topological defects play a fundamental role in the investigation of symmetries in quantum field theories. For conformal field theories in two space-time dimensions, it is possible to construct these defects using lattice models allowing…

High Energy Physics - Theory · Physics 2026-04-30 Madhav Sinha , Thiago Silva Tavares , Hubert Saleur , Ananda Roy

We propose a renormalisation group inspired normalising flow that combines benefits from traditional Markov chain Monte Carlo methods and standard normalising flows to sample lattice field theories. Specifically, we use samples from a…

High Energy Physics - Lattice · Physics 2024-12-18 Marc Bauer , Renzo Kapust , Jan M. Pawlowski , Finn L. Temmen

Quantum fluid (or hydrodynamic) models provide an attractive alternative for the modeling and simulation of the electron dynamics in nano-scale objects. Compared to more standard approaches, such as density functional theory or phase-space…

Quantum Physics · Physics 2021-10-12 Giovanni Manfredi , Paul-Antoine Hervieux , Jérôme Hurst

After many years of development of the basic tools, quantum simulation with ultracold atoms has now reached the level of maturity where it can be used to investigate complex quantum processes. Planning of new experiments and upgrading…

Quantum Gases · Physics 2020-07-13 Florian Schäfer , Takeshi Fukuhara , Seiji Sugawa , Yosuke Takasu , Yoshiro Takahashi

These lecture notes provide an elementary introduction, within the framework of finite quantum systems, to recent developments in the theory of entropic fluctuations.

Mathematical Physics · Physics 2011-06-21 Vojkan Jaksic , Yoshiko Ogata , Yan Pautrat , Claude-Alain Pillet

We provide a non-technical overview of recent extensions of renormalization methods and techniques to Group Field Theories (GFTs), a class of combinatorially non-local quantum field theories which generalize matrix models to dimension $d…

General Relativity and Quantum Cosmology · Physics 2016-07-19 Sylvain Carrozza

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…

High Energy Physics - Lattice · Physics 2022-12-06 David Albandea , Luigi Del Debbio , Pilar Hernández , Richard Kenway , Joe Marsh Rossney , Alberto Ramos

These lectures discuss field theoretic techniques that might allow for realistic simulations of lattice QCD on small computers.

High Energy Physics - Lattice · Physics 2016-10-26 G. P. Lepage

Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow…

Stochastic normalizing flows are a class of deep generative models that combine normalizing flows with Monte Carlo updates and can be used in lattice field theory to sample from Boltzmann distributions. In this proceeding, we outline the…

High Energy Physics - Lattice · Physics 2022-10-10 Michele Caselle , Elia Cellini , Alessandro Nada , Marco Panero

The lecture notes cover the basics of quantum computing methods for quantum field theory applications. No detailed knowledge of either quantum computing or quantum field theory is assumed and we have attempted to keep the material at a…

Quantum Physics · Physics 2025-12-03 Aninda Sinha , Ujjwal Basumatary

The applications and impact of high fidelity simulation of fluid flows are far-reaching. They include settling some long-standing and fundamental questions in turbulence. However, the computational resources required for such efforts are…

Quantum Physics · Physics 2025-03-25 Sachin S. Bharadwaj , Katepalli R. Sreenivasan

We propose a unifying approach that starts from the perturbative construction of trivializing maps by L\"uscher and then improves on it by learning. The resulting continuous normalizing flow model can be implemented using common tools of…

High Energy Physics - Lattice · Physics 2023-03-29 Simone Bacchio , Pan Kessel , Stefan Schaefer , Lorenz Vaitl

Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact. The goal of this survey article is to give a coherent and comprehensive review of the…

Machine Learning · Statistics 2020-06-09 Ivan Kobyzev , Simon J. D. Prince , Marcus A. Brubaker

We investigate a lattice scalar field theory in the presence of a bias favouring the establishment of an energy current, as a model for stationary nonequilibrium processes at low temperature in a non-integrable system. There is a transition…

Statistical Mechanics · Physics 2009-10-31 John Cardy , Peter Suranyi

Normalizing flows are a powerful tool to create flexible probability distributions with a wide range of potential applications in cosmology. Here we are studying normalizing flows which represent cosmological observables at field level,…

Cosmology and Nongalactic Astrophysics · Physics 2021-05-26 Adam Rouhiainen , Utkarsh Giri , Moritz Münchmeyer

Supersymmetry plays prominent roles in the study of quantum field theory and in many proposals for potential new physics beyond the standard model, while lattice field theory provides a non-perturbative regularization suitable for strongly…

High Energy Physics - Lattice · Physics 2022-09-21 David Schaich

We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD and emphasize the need for new methods to deal with finite-density and real-time evolution. We show that these…

High Energy Physics - Lattice · Physics 2022-09-21 Yannick Meurice , Ryo Sakai , Judah Unmuth-Yockey

We discuss some higher-loop studies of renormalization-group flows and fixed points in various quantum field theories.

High Energy Physics - Theory · Physics 2022-07-05 Robert Shrock

We consider the problem of sampling lattice field configurations on a lattice from the Boltzmann distribution corresponding to some action. Since such densities arise as approximationw of an underlying functional density, we frame the task…

Machine Learning · Computer Science 2024-11-27 Bálint Máté , François Fleuret