In regards to "Classification with Quantum Neural Networks on near term processors" (which you can find here) , there are still a few things that do not make entirely sense to me.
First of all, why is the architecture called a "neural network"? As far as my knowledge goes, in classical ML, the fundamental unit of Neural Networks is the perceptron. However, in Farhi and Neven's paper, they never mention any form of Quantum Perceptron.
Secondly, I do not understand the figure describing the architecture (it's Figure 1 in the paper). If you look closely, there is some cross wiring, etc. (I have to admit, the figure looks like a Neural Network, but it doesn't make sense to me). I was able to implement the Subset Parity Problem using Qiskit (because nonetheless, I understand the math behind the model), and the circuit I got doesn't look like the one in the figure.
I hope you can clarify this for me.
Thank you in advance!