  There is an increasing interest in Quantum Machine Learning (QML) models, how
they work and for which applications they could be useful. There have been many
different proposals on how classical data can be encoded and what circuit
ans\"atze and measurement operators should be used to process the encoded data
and measure the output state of an ansatz. The choice of the aforementioned
operators plays a determinant role in the expressive power of the QML model. In
this work we investigate how certain changes in the circuit structure change
this expressivity. We introduce both numerical and analytical tools to explore
the effect that these operators have in the overall performance of the QML
model. These tools are based on previous work on the teacher-student scheme,
the partial Fourier series and the averaged operator size. We focus our
analysis on simple QML models with two and three qubits and observe that
increasing the number of parameterized and entangling gates leads to a more
expressive model for certain circuit structures. Also, on which qubit the
measurement is performed affects the type of functions that QML models could
learn. This work sketches the determinant role that the processing and
measurement operators have on the expressive power of simple quantum circuits.
