Flow-based sampling for fermionic lattice field theories
This work enables flow-based sampling for theories with dynamical fermions, addressing a key bottleneck for particle physics and condensed matter simulations, though it is incremental as it builds on prior proof-of-principle studies.
The paper tackled the problem of sampling field configurations in fermionic lattice field theories, which is necessary for applying flow-based methods to the Standard Model and condensed matter systems, and demonstrated the approach on a two-dimensional theory of massless staggered fermions coupled to a scalar field via a Yukawa interaction.
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact. In the context of lattice field theory, proof-of-principle studies have demonstrated the effectiveness of this approach for scalar theories, gauge theories, and statistical systems. This work develops approaches that enable flow-based sampling of theories with dynamical fermions, which is necessary for the technique to be applied to lattice field theory studies of the Standard Model of particle physics and many condensed matter systems. As a practical demonstration, these methods are applied to the sampling of field configurations for a two-dimensional theory of massless staggered fermions coupled to a scalar field via a Yukawa interaction.