Embedding Learning in Hybrid Quantum-Classical Neural Networks [article]

Henry Liu, Junyu Liu, Rui Liu, Henry Makhanov, Danylo Lykov, Anuj Apte, Yuri Alexeev
2022 arXiv   pre-print
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
more » ... classes and suffer less from the circuit bypass problem compared with other approaches.
arXiv:2204.04550v1 fatcat:otncnu5bqndkfn2cy7n4bmyz2u