mlsanityData to 7 Oct 2026

Aspects of scaling and scalability for flow-based sampling of lattice QCD

arXiv 2211.07541 ↗First posted 14 Nov 2022hep-lat cond-mat.stat-mech cs.LGcited by 26

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Abstract (arXiv)

Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-based approaches. We conclude that flow-based approaches to sampling are better thought of as a broad family of algorithms with different scaling properties, and that scalability must be assessed experimentally.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 44 of its 123 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

44 of 123 references.

References · 44

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32 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 10; 2025: 5; 2026: 2.

26 papers.

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  1. 2023Rigid Body Flows for Sampling Molecular Crystal Structurescited by 27
  2. 2023Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theoriescited by 25
  3. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theorycited by 24
  4. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  5. 2023On Sampling with Approximate Transport Mapscited by 20
  6. 2024Efficient mapping of phase diagrams with conditional Boltzmann Generatorscited by 18
  7. 2023Generative Diffusion Models for Lattice Field Theorycited by 13
  8. 2024Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defectscited by 13
  9. 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
  10. 2024Applications of flow models to the generation of correlated lattice QCD ensemblescited by 10
  11. 2024Scaling of Stochastic Normalizing Flows in SU(3) lattice gauge theorycited by 10
  12. 2024Bayesian RG Flow in Neural Network Field Theoriescited by 9
14 more
  1. 2025Physics-Conditioned Diffusion Models for Lattice Gauge Theorycited by 9
  2. 2024Practical applications of machine-learned flows on gauge fieldscited by 6
  3. 2024Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flowscited by 6
  4. 2025Scaling flow-based approaches for topology sampling in SU(3) gauge theorycited by 6
  5. 2023Gauge-equivariant pooling layers for preconditioners in lattice QCDcited by 5
  6. 2024Non-Perturbative Trivializing Flows for Lattice Gauge Theoriescited by 3
  7. 2025Group-Equivariant Diffusion Models for Lattice Field Theorycited by 3
  8. 2025Neural Triangular Transport Maps: A New Approach Towards Sampling in Lattice QCDcited by 2
  9. 2024Empirical evaluation of normalizing flows in Markov Chain Monte Carlocited by 1
  10. 2023Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulationscited by 0
  11. 2024A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equationcited by 0
  12. 2025Particle Monte Carlo methods for Lattice Field Theorycited by 0
  13. 2026Machine learning for four-dimensional SU(3) lattice gauge theoriescited by 0
  14. 2026Accurate Sampling from Diffusion Modelscited by 0