Despite requiring particularly challenging and technically complex efforts, lattice simulations of gauge theories are a vital tool to derive ab initio predictions in the strongly coupled regime. Within particle physics, lattice QCD is now well-established as a leading method to obtain fully systematically controlled predictions of several low-energy QCD quantities, including hadron masses, parton distribution functions, and hadronic form factors. Continued lattice gauge theory efforts will however require further algorithmic improvements to overcome computational bottlenecks, such as critical slowing down of Monte Carlo importance sampling. I will discuss recently emerging ideas for applying machine learning methods towards this end. If the potential of these methods is fully realized, they stand to accelerate lattice gauge theory calculations by orders of magnitude while maintaining systematic exactness.
Ankita Budhraja, Juraj Klaric, Johannes Michel, Maria Laura Piscopo