[1]HAN Siyu,JIA Linhan,LI Yufeng.Robust data re-uploading quantum model[J].CAAI Transactions on Intelligent Systems,2026,21(3):617-626.[doi:10.11992/tis.202507029]
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Robust data re-uploading quantum model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 3 Page number: 617-626 Column: 学术论文—机器学习 Public date: 2026-05-05
Title:
Robust data re-uploading quantum model
Author(s):
HAN Siyu1; 2;  JIA Linhan1; 2;  LI Yufeng1; 2
1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;
2. School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
Keywords:
quantum machine learning; data re-uploading; regularization; overfitting; robustness; quantum computing; quantum neural network; variational quantum circuit
CLC:
TP18
DOI:
10.11992/tis.202507029
Abstract:
To address the severe overfitting and hyperparameter sensitivity problem of the data re-uploading model, we introduce the robust quantum re-uploading classifier, featuring an innovative loss function that combines quantum state fidelity with a strategically designed regularization term. This combination effectively reduces overfitting and considerably enhances model stability and generalization. Experimental results on benchmark datasets clearly demonstrate the classifier’s superiority over existing methods, particularly in mitigating overfitting and sustaining robust predictions. Consequently, this novel strategy not only opens new avenues for designing loss functions in QML but also provides empirical insights into generalization capabilities. It establishes a strong foundation for future quantum classification research and applications.
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