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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Single-qubit graph classifier with classical feature aggregation.

Shaochun Li1, Junzhi Cui2, Jingli Ren1

  • 1School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, 450001, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 6, 2026
PubMed
Summary

This study introduces a novel single-qubit graph classifier merging classical and quantum computing for efficient graph data processing. The quantum graph neural network shows competitive performance and robustness, enhancing machine learning applications.

Keywords:
Graph classificationHybrid classical-quantum networkQuantum graph neural networksSingle qubit classifier

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Graph Neural Networks

Background:

  • Graph data processing is crucial for many AI tasks.
  • Classical graph neural networks face scalability challenges.
  • Quantum computing offers potential for enhanced computational power.

Purpose of the Study:

  • To propose a novel single-qubit graph classifier.
  • To combine classical graph representation with quantum computing.
  • To achieve efficient and robust graph data processing.

Main Methods:

  • Developed a lightweight architecture for graph data processing.
  • Utilized a classical subroutine for node feature aggregation.
  • Optimized weight training using quantum programs for a single-qubit classifier.
  • Implemented a parallel training scheme for multi-classification tasks.

Main Results:

  • The single-qubit classifier demonstrated competitive performance in binary classification tasks.
  • The model exhibited strong robustness across different quantum noise simulations.
  • Parallel training enhanced performance and robustness in multi-classification tasks.

Conclusions:

  • The proposed single-qubit graph classifier offers an efficient and robust approach.
  • This model can be flexibly integrated with classical graph neural networks.
  • It paves the way for broader applications of quantum graph neural networks.