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

arXiv: https://arxiv.org/abs/2509.07040 · HTML: https://mlsanity.com/paper/2509.07040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [q-means: A quantum algorithm for unsupervised machine learning](https://mlsanity.com/paper/1812.03584/) (2018)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [Quantum State Tomography with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2008.03240/) (2020)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)
- [Classification and reconstruction of optical quantum states with deep neural networks](https://mlsanity.com/paper/2012.02185/) (2020)
- [Quantum Ensemble for Classification](https://mlsanity.com/paper/2007.01028/) (2020)
- [Quantum ensembles of quantum classifiers](https://mlsanity.com/paper/1704.02146/) (2017)
- [A kernel-based quantum random forest for improved classification](https://mlsanity.com/paper/2210.02355/) (2022)
- [Improving the dynamics of quantum sensors with reinforcement learning](https://mlsanity.com/paper/1908.08416/) (2019)
- [Quantum ensemble of trained classifiers](https://mlsanity.com/paper/2007.09293/) (2020)
- [Data-Centric Machine Learning in Quantum Information Science](https://mlsanity.com/paper/2201.09134/) (2022)
- [A Quantum-Inspired Ensemble Method and Quantum-Inspired Forest Regressors](https://mlsanity.com/paper/1711.08117/) (2017)
- [QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest](https://mlsanity.com/paper/2406.12008/) (2024)

## Abstract (arXiv)

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.