# Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning

arXiv: https://arxiv.org/abs/2210.01656 · HTML: https://mlsanity.com/paper/2210.01656/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [Review of Ansatz Designing Techniques for Variational Quantum Algorithms](https://mlsanity.com/paper/2212.04913/) (2022)
- [Toward Automated Quantum Variational Machine Learning](https://mlsanity.com/paper/2312.01567/) (2023)
- [A weighted quantum ensemble of homogeneous quantum classifiers](https://mlsanity.com/paper/2506.07810/) (2025)

## Abstract (arXiv)

Due to the immense potential of quantum computers and the significant computing overhead required in machine learning applications, the variational quantum classifier (VQC) has received a lot of interest recently for image classification. The performance of VQC is jeopardized by the noise in Noisy Intermediate-Scale Quantum (NISQ) computers, which is a significant hurdle. It is crucial to remember that large error rates occur in quantum algorithms due to quantum decoherence and imprecision of quantum gates. Previous studies have looked towards using ensemble learning in conventional computing to reduce quantum noise. We also point out that the simple average aggregation in classical ensemble learning may not work well for NISQ computers due to the unbalanced confidence distribution in VQC. Therefore, in this study, we suggest that ensemble quantum classifiers be optimized with plurality voting. On the MNIST dataset and IBM quantum computers, experiments are carried out. The results show that the suggested method can outperform state-of-the-art on two- and four-class classifications by up to 16.0% and 6.1% , respectively.