 The power of quantum machine learning algorithms based on parametrized quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalization error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data. This article was authored by Matias Sikaro, Hinyuan Huang, M. Sirezo, and others.