Machine Learning for Lattice QCD

In this review, we explore the application of machine learning (ML) to lattice quantum chromodynamics (QCD), a key tool in studying nonperturbative phenomena in particle physics. By integrating ML techniques such as neural networks, lattice QCD simulations are significantly enhanced, enabling challenges like critical slowing down and topological charge to be addressed. These methods reduce computational costs and improve accuracy in configuration generation and physical measurements. Despite concerns over the black-box nature of ML, its application shows great promise in advancing lattice QCD research beyond traditional methods.