Conventional score-based diffusion models learn scores without representing normalized densities, whereas tractable normalized models support both sampling and direct likelihood evaluation.
A recent tensor-network approach provides such a representation but is largely restricted to low-dimensional lattices.
Here we introduce a discrete diffusion model that parameterizes normalized probability distributions using variational autoregressive networks.
Explicit Markov jump operators govern the forward noising and reverse denoising dynamics, extending discrete diffusion models with normalized distributions to spin systems on higher-dimensional lattices.
We apply this framework to the two- and three-dimensional Ising models across ordered, critical, and disordered regimes, accurately computing thermodynamic quantities including free energy, energy, and magnetization.
We further integrate the framework with Monte Carlo sampling, using adaptive diffusion steps to maintain high acceptance rates even at low temperatures while enhancing sample diversity.
These results establish a neural-network framework for the discrete diffusion model with normalized probability distributions.