Toward Scalable Normalizing Flows for the Hubbard Model
🤖 AI Summary
This work investigates the effective scaling of normalizing flows to larger lattice sizes and lower temperatures in the Hubbard model for efficient learning of its Boltzmann distribution. Addressing the scalability bottleneck of normalizing flows in strongly correlated fermionic systems, we integrate stochastic normalizing flows with nonequilibrium Markov chain Monte Carlo (MCMC) methods, systematically analyzing their stability, computational efficiency, and resource requirements in the low-temperature, large-scale limit. Our study is the first to reveal the scaling behavior of normalizing flows under these challenging conditions and establishes a stable, scalable pathway for generative modeling of strongly correlated quantum systems.
Application Category
📝 Abstract
Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter physics. In this work, we investigate the steps required to extend such simulations to larger lattice sizes and lower temperatures, with a focus on enhancing stability and efficiency. Additionally, we present the scaling behavior of stochastic normalizing flows and non-equilibrium Markov chain Monte Carlo methods for this fermionic system.
Problem
Research questions and friction points this paper is trying to address.
normalizing flows
Hubbard model
scalability
low temperature
large lattice size
Innovation
Methods, ideas, or system contributions that make the work stand out.
normalizing flows
Hubbard model
scalability
non-equilibrium MCMC
stochastic normalizing flows
🔎 Similar Papers
No similar papers found.
