quantum neurons '' we could say that we have a kind of quantum perceptron.
Other point to be considered is the training stage. Basically, the rule given by equation (13) updates the parameters of the system. This is much more closer to a (classical) neural network approach than the expression (4).
Despite of these advantages, a doubt about this QNN is that its neural network approach is virtual in the sense that it is just a biased interpretation of an approximated quantum model of the system.
Although this argument may be consistent, it does not discard the model because a neural network should have three basic elements: (1) An operator to process the input signal(s) (equation (7) in the above model); (2) A test to decide if the results is the desired one (expression (11)); (3) A rule to adapt parameters if need (equation (12)). In the above model, these relations are not virtual ones in the sense that the final result is a system that reproduce a desired behavior (a gate, for example).