Embedding Learning in Hybrid Quantum-Classical Neural Networks

• URL: http://arxiv.org/abs/2204.04550v2
• Date: Thu, 1 Dec 2022 11:58:46 GMT
• Title: Embedding Learning in Hybrid Quantum-Classical Neural Networks
• Authors: Minzhao Liu, Junyu Liu, Rui Liu, Henry Makhanov, Danylo Lykov, Anuj Apte and Yuri Alexeev
• Abstract summary: We propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. 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.
• Score: 8.029801398363261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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