# A weighted quantum ensemble of homogeneous quantum classifiers

arXiv: https://arxiv.org/abs/2506.07810 · HTML: https://mlsanity.com/paper/2506.07810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Quantum Ensemble for Classification](https://mlsanity.com/paper/2007.01028/) (2020)
- [Quantum ensembles of quantum classifiers](https://mlsanity.com/paper/1704.02146/) (2017)
- [Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning](https://mlsanity.com/paper/2210.01656/) (2022)
- [An efficient combination strategy for hybird quantum ensemble classifier](https://mlsanity.com/paper/2210.06785/) (2022)

## Abstract (arXiv)

Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifiers with indexing registers for data encoding. This approach leverages instance-based quantum classifiers, enabling feature and training point subsampling through superposition and controlled unitaries, and allowing for a quantum-parallel execution of diverse internal classifiers with different data compositions in superposition. The method integrates a learning process involving circuit execution and classical weight optimization, for a trained ensemble execution with weights encoded in the circuit at test-time. Empirical evaluation demonstrate the effectiveness of the proposed method, offering insights into its performance.