Transfer learning in hybrid classical-quantum neural networks

Transfer learning is a typical example of an artificial intelligence technique that has been originally inspired by biological intelligence. It originates from the simple observation that the knowledge acquired in a specific context can be transferred to a different area. For example, when we learn a second language we do not start from scratch, but we make use of our previous linguistic knowledge. Sometimes transfer learning is the only way to approach complex cognitive tasks, e.g., before learning quantum mechanics it is advisable to first study linear algebra. This general idea has been successfully applied also to design artificial neural networks [1-3]. It has been shown [4, 5] that in many situations, instead of training a full network from scratch, it is more efficient to start from a pre-trained deep network and then optimize only some of the final layers for a particular task and dataset of interest (see Figure 1).