  Generalization is the ability of machine learning models to make accurate
predictions on new data by learning from training data. However, understanding
generalization of quantum machine learning models has been a major challenge.
Here, we introduce the data quantum Fisher information metric (DQFIM). It
describes the capacity of variational quantum algorithms depending on
variational ansatz, training data and their symmetries. We apply the DQFIM to
quantify circuit parameters and training data needed to successfully train and
generalize. Using the dynamical Lie algebra, we explain how to generalize using
a low number of training states. Counter-intuitively, breaking symmetries of
the training data can help to improve generalization. Finally, we find that
out-of-distribution generalization, where training and testing data are drawn
from different data distributions, can be better than using the same
distribution. Our work provides a useful framework to explore the power of
quantum machine learning models.
