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Quantum Information Processing

Generalization analysis of quantum neural networks using dynamical Lie algebras

  • 01-11-2025
Published in:
Author
Hiroshi Ohno
Published in
Quantum Information Processing | Issue 11/2025

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Abstract

This article delves into the crucial aspect of generalization in quantum machine learning, focusing on quantum neural networks (QNNs). The study introduces a novel generalization bound for QNNs using covering numbers derived from dynamical Lie algebras (DLAs), offering a unique perspective on assessing model complexity. Through extensive numerical simulations, the authors validate their theoretical results, demonstrating the practical implications of their findings. The research also explores the relationship between DLAs and various phenomena in QNNs, such as barren plateaus and overparameterization, providing a comprehensive framework for understanding the learning properties of QNNs. The article concludes with a discussion on the desirable characteristics of parameterized unitaries in quantum machine learning, highlighting the importance of DLA dimensions in achieving better generalization capabilities. This insightful study offers valuable insights for professionals seeking to enhance the performance and reliability of quantum neural networks.
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previous article Quantum information parallel transfer utilizing multimode cat state encoding in a cavity-magnon system
next article A simple mechanism for unstable degeneracies in local Hamiltonians
Title
Generalization analysis of quantum neural networks using dynamical Lie algebras
Author
Hiroshi Ohno
Publication date
01-11-2025
Publisher
Springer US
Published in
Quantum Information Processing / Issue 11/2025
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-025-04990-5
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