Diffusion-warm sampling of the XY model enables fast thermalization at scale
We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach to the XY model, a fundamental continuous-spin model in condensed matter physics, we show that our technique addresses the shortfalls of the Markov chain Monte Carlo (MCMC) in generalization to varying system sizes. More specifically, we show that training a temperature-conditioned diffusion model on smaller-size XY model lattices enables the generation of accurate samples in larger lattice sizes. By tracking physically important observables of the model, such as spin correlations, our experiments demonstrate that diffusion sampling followe...
| Authors | Sehmimul Hoque, Roger Melko, Pooya Ronagh |
|---|---|
| Primary category | quant-ph |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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