A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets

• URL: http://arxiv.org/abs/2509.07040v1
• Date: Mon, 08 Sep 2025 09:34:33 GMT
• Title: A Quantum Bagging Algorithm with Unsupervised Base Learners for Label Corrupted Datasets
• Authors: Neeshu Rathi, Sanjeev Kumar,
• Abstract summary: We propose a quantum bagging framework that uses QMeans clustering as the base learner to reduce prediction variance and enhance robustness to label noise.<n>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.
• Score: 2.850389822151216
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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