A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets
This work addresses noise resilience in quantum machine learning for unreliable datasets, but it is incremental as it adapts classical bagging concepts to a quantum context.
The paper tackled the problem of label noise in quantum machine learning by proposing a quantum bagging algorithm with unsupervised base learners, demonstrating comparable performance to classical methods and greater resilience to label corruption in simulations.
The development of noise-resilient quantum machine learning (QML) algorithms is critical in the noisy intermediate-scale quantum (NISQ) era. 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 quantum bootstrapping via QRAM-based sampling 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 KMeans while exhibiting greater resilience to label corruption than supervised bagging methods. This highlights the potential of unsupervised quantum bagging in learning from unreliable data.