Can machine learning fix the lattice errors that plague quantum simulations?

Lattice simulations approximate continuum physics but introduce systematic errors in how operators are defined. The authors trained machine learning models to identify better lattice representations of key operators in the Ising and Potts models, dramatically improving the accuracy of extracted scaling dimensions. The method generalizes across different critical systems and comes with public code.