Summary
This has been a long journey, hasn’t it? In this chapter, we first introduced quantum neural networks as quantum analogs of classical neural networks. We have seen how the training of a quantum neural network is very similar to that of a classical one, and we’ve also explored the differentiation methods that make this possible.
With the theory out of the way, we got our keyboards ready to do some work. We learned how to implement and train a quantum neural network using PennyLane, and we also discussed some technicalities about this framework, such as details about the differentiation methods that it provides.
PennyLane comes with some wonderful simulators, but — as we already mentioned in Chapter 2, The Tools of the Trade in Quantum Computing — it’s also integrated with quantum hardware platforms such as Amazon Braket and IBM Quantum. Thus, your ability to train quantum neural networks on actual quantum computers is at your fingertips!
We concluded...
