  Ridge functions are used to describe and study the lower bound of the
approximation done by the neural networks which can be written as a linear
combination of activation functions. If the activation functions are also ridge
functions, these networks are called explainable neural networks.
  In this paper, we first show that quantum neural networks which are based on
variational quantum circuits can be written as a linear combination of ridge
functions. Consequently, we show that the interpretability and explainability
of such quantum neural networks can be directly considered and studied as an
approximation with the linear combination of ridge functions.
