 The study provides rigorous bounds on the generalization error in variational quantum machine learning algorithms based on parametrized quantum circuits, showing that known implementable models generalize well from an efficient amount of training data. This article was authored by Matias C. Karo, Hinyuan Huang, M. Sarazzo, and others.