Cover Image

Operator Spectroscopy of Trained Lattice Samplers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Qian, Moxian
Format:	Preprint
Published:	2026
Subjects:	High Energy Physics - Lattice Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variational Autoregressive Networks Applied to $ϕ^4$ Field Theory Systems
by: Qian, Moxian, et al.
Published: (2025)

Multilevel Generative Samplers for Investigating Critical Phenomena
by: Singha, Ankur, et al.
Published: (2025)

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
by: Sun, Yixuan, et al.
Published: (2025)

Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
by: Máté, Bálint, et al.
Published: (2024)

NETS: A Non-Equilibrium Transport Sampler
by: Albergo, Michael S., et al.
Published: (2024)

Physics-Conditioned Diffusion Models for Lattice Gauge Theory
by: Zhu, Qianteng, et al.
Published: (2025)

Diffusion Models as Stochastic Quantization in Lattice Field Theory
by: Wang, Lingxiao, et al.
Published: (2023)

Group-Equivariant Diffusion Models for Lattice Field Theory
by: Vega, Octavio, et al.
Published: (2025)

Particle Monte Carlo methods for Lattice Field Theory
by: Yallup, David
Published: (2025)

Generalizable Equivariant Diffusion Models for Non-Abelian Lattice Gauge Theory
by: Aarts, Gert, et al.
Published: (2026)

Lecture Notes on Normalizing Flows for Lattice Quantum Field Theories
by: Cheng, Miranda C. N., et al.
Published: (2025)

Efficient Learning of Lattice Gauge Theories with Fermions
by: Shukla, Shreya, et al.
Published: (2025)

Non-Perturbative Trivializing Flows for Lattice Gauge Theories
by: Gerdes, Mathis, et al.
Published: (2024)

Training neural control variates using correlated configurations
by: Oh, Hyunwoo
Published: (2025)

Variance reduction in lattice QCD observables via normalizing flows
by: Abbott, Ryan, et al.
Published: (2026)

Analytic Bijections for Smooth and Interpretable Normalizing Flows
by: Gerdes, Mathis, et al.
Published: (2026)

Diffusion model for SU(N) gauge theories
by: Komijani, Javad, et al.
Published: (2026)

Noise scheduling and linear dynamics in diffusion models on Lie groups
by: Komijani, Javad
Published: (2026)

Machine learning for four-dimensional SU(3) lattice gauge theories
by: Wenger, Urs
Published: (2026)

A Monte Carlo estimator of flow fields for sampling and noise problems
by: Albergo, Michael S., et al.
Published: (2026)

Exploring gauge-fixing conditions with gradient-based optimization
by: Detmold, William, et al.
Published: (2024)

Diffusion models learn distributions generated by complex Langevin dynamics
by: Habibi, Diaa E., et al.
Published: (2024)

Symmetry-preserving neural networks in lattice field theories
by: Favoni, Matteo
Published: (2025)

Normalizing flows for SU($N$) gauge theories employing singular value decomposition
by: Komijani, Javad, et al.
Published: (2025)

Applications of flow models to the generation of correlated lattice QCD ensembles
by: Abbott, Ryan, et al.
Published: (2024)

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
by: Rayat, Ali, et al.
Published: (2026)

Stochastic normalizing flows for Effective String Theory
by: Caselle, Michele, et al.
Published: (2024)

Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network
by: Holland, Kieran, et al.
Published: (2024)

Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows
by: Caselle, Michele, et al.
Published: (2023)

Fixed point actions from convolutional neural networks
by: Holland, Kieran, et al.
Published: (2023)

Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows
by: Caselle, Michele, et al.
Published: (2024)

Studying Effective String Theory using deep generative models
by: Caselle, Michele, et al.
Published: (2025)

Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
by: Rayat, Ali, et al.
Published: (2026)

Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers
by: Nicoli, Kim A., et al.
Published: (2025)

Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe
by: Qin, Hong
Published: (2019)

SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions
by: Kanaujia, Vikas, et al.
Published: (2025)

Spectroscopy with the tensor renormalization group method
by: Az-zahra, Fathiyya Izzatun, et al.
Published: (2024)

The Casimir Effect for Lattice Fermions
by: Mandlecha, Yash V.
Published: (2026)

Introduction to Lattice Field Theory
by: Briceño, Raúl A.
Published: (2025)

Optimized Distillation Profiles for Heavy-Light Spectroscopy
by: Neuendorf, Jan, et al.
Published: (2024)