Summary

This has been a long and intense chapter. We began by learning what hybrid neural networks actually are and in which use cases they can be useful. We then explored how to implement and train these hybrid networks in PennyLane and, along the way, we discussed a few good practices that apply to any machine learning project. In addition, we left our comfort zone and considered a new kind of QML problem: the training of multi-class classifiers.

Once we finished our study of PennyLane, we dived into Qiskit, and a big surprise was waiting for us there. Since Qiskit relied on an interface with the PyTorch ML package for the implementation of hybrid QNNs, we invested a good deal of effort in learning how to use PyTorch. In the process, we saw how PyTorch provided us with a level of flexibility that we simply couldn’t get using TensorFlow and Keras. At the point where we had a solid understanding of the PyTorch package, we got to work with Qiskit and its PyTorch connector and we...