Embedding Learning in Hybrid Quantum-Classical Neural Networks
Quantum Physics
2023-01-31 v2
Abstract
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.
Cite
@article{arxiv.2204.04550,
title = {Embedding Learning in Hybrid Quantum-Classical Neural Networks},
author = {Minzhao Liu and Junyu Liu and Rui Liu and Henry Makhanov and Danylo Lykov and Anuj Apte and Yuri Alexeev},
journal= {arXiv preprint arXiv:2204.04550},
year = {2023}
}
Comments
8 pages, 11 figures