A Laplacian-based quantum graph neural networks for quantum semi-supervised learning

Abstract The Laplacian learning method has proven effective in classical graph-based semi-supervised learning, yet its quantum counterpart remains underexplored. This study systematically evaluates the Laplacian-based quantum semi-supervised learning (QSSL) approach across four benchmark datasets—Iris, Wine, Breast Cancer Wisconsin, and Heart Disease. By experimenting with varying qubit counts and entangling layers, we demonstrate that increased quantum resources do not necessarily lead to improved performance. Our findings reveal that the effectiveness of the method is highly sensitive to dataset characteristics, as well as the number of entangling layers. Optimal configurations, generally featuring moderate entanglement, strike a balance between model complexity and generalization. These results emphasize the importance of dataset-specific hyperparameter tuning in quantum semi-supervised learning frameworks.

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Publication Details

Journal
Quantum Information Processing
Published
2025-04-03
DOI
https://doi.org/10.1007/s11128-025-04725-6
Citations
2
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
4.96

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article

A Laplacian-based quantum graph neural networks for quantum semi-supervised learning

João C. Neves, Moharram Challenger, Farid Bozorgnia, André Sequeira et al.
2 citations
Quantum Information Processing
Quantum Computing Algorithms and Architecture
4.96
article

A Laplacian-based quantum graph neural networks for quantum semi-supervised learning

João C. Neves, Moharram Challenger, Farid Bozorgnia, André Sequeira, Hamed Gholipour, Kailash Hambarde, Hamzeh Mohammadigheymasi, Hugo Proença, Javier Mancilla
article en
2 citations

Abstract

Abstract The Laplacian learning method has proven effective in classical graph-based semi-supervised learning, yet its quantum counterpart remains underexplored. This study systematically evaluates the Laplacian-based quantum semi-supervised learning (QSSL) approach across four benchmark datasets—Iris, Wine, Breast Cancer Wisconsin, and Heart Disease. By experimenting with varying qubit counts and entangling layers, we demonstrate that increased quantum resources do not necessarily lead to improved performance. Our findings reveal that the effectiveness of the method is highly sensitive to dataset characteristics, as well as the number of entangling layers. Optimal configurations, generally featuring moderate entanglement, strike a balance between model complexity and generalization. These results emphasize the importance of dataset-specific hyperparameter tuning in quantum semi-supervised learning frameworks.

Quantum Information ProcessingVol. 24(4)
Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT), Planetary Science Institute (US), Harvard University (US), University of Lisbon (PT), University of Antwerp (BE), University of Beira Interior (PT), Flanders Make (Belgium) (BE), Instituto de Telecomunicações (PT), New Uzbekistan University (UZ), National University of Uzbekistan (UZ), Universidade de Vigo (ES), The University of Tokyo (JP)
Universidade da Beira Interior, Ministério da Ciência, Tecnologia e Ensino Superior, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 6%
Quantum Computing Algorithms and Architecture
4.96
DOIOpen Access PDFOpenAlex
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