Chapter 11
The Best of Both Worlds: Hybrid Architectures
Unity makes strength.
— English aphorism
By now, we have a solid understanding of both classical and quantum neural networks. In this chapter, we will leverage this knowledge to explore an interesting kind of model: hybrid architectures of quantum neural networks.
In this chapter, we will discuss what these models are and how they can be useful, and we will also learn how to implement and train them with PennyLane and Qiskit. The whole chapter is going to be very hands-on, and we will also take the time to fill in some gaps regarding the actual practice of training models in real-world scenarios. In addition to this — and just to spice things up a bit — we will go beyond our usual binary classifiers and also consider other kinds of problems.
We’ll cover the following topics in this chapter:
The what and why of hybrid architectures
Hybrid architectures in PennyLane (with a brief overview of best practices...
