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A quantum bagging algorithm with unsupervised base learners for label corrupted datasets
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AI
DOI：10.1016/j.jocs.2026.103011.png)
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Abstract
En 中文
Robustness to data corruption remains a significant challenge in the computational design of quantum machine learning algorithms. In this work, we propose a quantum bagging framework that uses QMeans clustering as the base learner to reduce prediction variance and enhance robustness to label noise. Unlike bagging frameworks built on supervised learners, our method leverages the unsupervised nature of QMeans, combined with a QRAM inspired bootstrapping formulation and bagging aggregation through majority voting. Through extensive simulations on both noisy classification and regression tasks, we demonstrate that the proposed quantum bagging algorithm performs comparably to its classical counterpart using K-Means while exhibiting greater resilience to label corruption than supervised bagging methods. This highlights the potential of unsupervised quantum bagging in learning from unreliable data.
Keywords:
Noise resilient learning
Quantum machine learning
Quantum bagging
QRAM based subsampling
Quantum bootstrapping
Quantum K-means clustering
Variance reduction
Journal
J
IF:
3.7
Papers:
205
Citations:
0