Embedding Learning in Hybrid Quantum-Classical Neural Networks

Conference · 01 January 2022

DOI:https://doi.org/10.1109/QCE53715.2022.00026· OSTI ID:1923243

Liu, Minzhao; Liu, Junyu; Liu, Rui; Makhanov, Henry; Lykov, Danylo; Apte, Anuj; Alekseev, Yuri

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 are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.

• Research Organization: Argonne National Lab. (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Science - Office of Advanced Scientific Computing Research (ASCR)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1923243
• Resource Relation: Conference: 2022 IEEE International Conference on Quantum Computing and Engineering, 09/18/22 - 09/23/22, Broomfield, CO, US
• Country of Publication: United States
• Language: English