Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations.This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data.Howeve