11.1 The what and why of hybrid architectures

Up until now, we’ve used the adjective ”hybrid” to describe algorithms that rely on both classical and quantum processing; algorithms such as QAOA or VQE fit in this category, as well as the training of QSVMs and QNNs. When we talk about hybrid architectures or hybrid models, however, we refer to something more specific: we speak about models that combine classical models with other quantum-based models by joining them together and training them as a single unit. Of course, the training of hybrid models will itself be a hybrid algorithm. We know that the terminology might be confusing, but what can we do? Hybrid is too versatile a word to give it up.

In particular, we will combine quantum neural networks with classical neural networks, for they are the two models that fit more naturally together. The way we will go about doing this will be by taking a usual classical neural network and plugging in a quantum neural network...