Speaker
Description
The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. In this talk, I will present a methodology that leverages machine-learned normalizing flows to reduce the variance of arbitrary N-point correlation functions of bosonic operators in lattice gauge theory calculations by encoding a representation of the generating functional. I will show that this framework makes it possible to systematically approach noiseless estimators of correlation functions. I will demonstrate the methodology with applications to glueball correlation functions and Wilson loops in Quantum Chromodynamics and Yang-Mills theory, where we observe up to three orders of magnitude variance reduction.