Diffusion model for SU(N) gauge theories
Implicit score matching provides a computationally efficient approach for training diffusion models and generating high-quality samples from complex distributions. In this work, we develop a score-matching framework for SU(N) lattice gauge theories, which can be extended to other Lie groups. We apply the method to SU(3) gauge configurations with the Wilson gauge action in two and four dimensions and assess the quality of the generated samples by comparison with Hybrid Monte Carlo (HMC) simulations. We show that the diffusion models can be successfully trained and applied for sampling the Wilson gauge action. For large values of inverse coupling, accurate reverse-time integration requires ...
| Authors | Javad Komijani, Marina K. Marinkovic, Lara Turgut |
|---|---|
| Primary category | hep-lat |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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