  Near-term quantum devices can be used to build quantum machine learning
models, such as quantum kernel methods and quantum neural networks (QNN) to
perform classification tasks. There have been many proposals how to use
variational quantum circuits as quantum perceptrons or as QNNs. The aim of this
work is to systematically compare different QNN architectures and to evaluate
their relative expressive power with a teacher-student scheme. Specifically,
the teacher model generates the datasets mapping random inputs to outputs which
then have to be learned by the student models. This way, we avoid training on
arbitrary data sets and allow to compare the learning capacity of different
models directly via the loss, the prediction map, the accuracy and the relative
entropy between the prediction maps. We focus particularly on a quantum
perceptron model inspired by the recent work of Tacchino et. al.
\cite{Tacchino1} and compare it to the data re-uploading scheme that was
originally introduced by P\'erez-Salinas et. al. \cite{data_re-uploading}. We
discuss alterations of the perceptron model and the formation of deep QNN to
better understand the role of hidden units and non-linearities in these
architectures.
