  Quantum embedding learning is an important step in the application of quantum
machine learning to classical data. In this paper we propose a quantum few-shot
embedding learning paradigm, which learns embeddings useful for training
downstream quantum machine learning tasks. Crucially, we identify the circuit
bypass problem in hybrid neural networks, where learned classical parameters do
not utilize the Hilbert space efficiently. We observe that the few-shot learned
embeddings generalize to unseen classes and suffer less from the circuit bypass
problem compared with other approaches.
