# An efficient combination strategy for hybird quantum ensemble classifier

arXiv: https://arxiv.org/abs/2210.06785 · HTML: https://mlsanity.com/paper/2210.06785/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Quantum Deep Learning](https://mlsanity.com/paper/1412.3489/) (2014)
- [A divide-and-conquer algorithm for quantum state preparation](https://mlsanity.com/paper/2008.01511/) (2020)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [The Expressive Power of Parameterized Quantum Circuits](https://mlsanity.com/paper/1810.11922/) (2018)
- [Quantum Ensemble for Classification](https://mlsanity.com/paper/2007.01028/) (2020)
- [Quantum ensemble of trained classifiers](https://mlsanity.com/paper/2007.09293/) (2020)
- [Structured learning of metric ensembles with application to person re-identification](https://mlsanity.com/paper/1511.08531/) (2015)

## Built on by (most cited first, 1 of 1)

- [A weighted quantum ensemble of homogeneous quantum classifiers](https://mlsanity.com/paper/2506.07810/) (2025)

## Abstract (arXiv)

Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability from a limited feature space. Combining multiple models as base learners, ensemble learning (EL) can effectively improve the accuracy, generalization ability, and robustness of the final model. The key to EL lies in two aspects, the performance of base learners and the choice of the combination strategy. Recently, quantum EL (QEL) has been studied. However, existing combination strategies in QEL are inadequate in considering the accuracy and variance among base learners. This paper presents a hybrid EL framework that combines quantum and classical advantages. More importantly, we propose an efficient combination strategy for improving the accuracy of classification in the framework. We verify the feasibility and efficiency of our framework and strategy by using the MNIST dataset. Simulation results show that the hybrid EL framework with our combination strategy not only has a higher accuracy and lower variance than the single model without the ensemble, but also has a better accuracy than the majority voting and the weighted voting strategies in most cases.