  The core of quantum machine learning is to devise quantum models with good
trainability and low generalization error bound than their classical
counterparts to ensure better reliability and interpretability. Recent studies
confirmed that quantum neural networks (QNNs) have the ability to achieve this
goal on specific datasets. With this regard, it is of great importance to
understand whether these advantages are still preserved on real-world tasks.
Through systematic numerical experiments, we empirically observe that current
QNNs fail to provide any benefit over classical learning models. Concretely,
our results deliver two key messages. First, QNNs suffer from the severely
limited effective model capacity, which incurs poor generalization on
real-world datasets. Second, the trainability of QNNs is insensitive to
regularization techniques, which sharply contrasts with the classical scenario.
These empirical results force us to rethink the role of current QNNs and to
design novel protocols for solving real-world problems with quantum advantages.
