An efficient combination strategy for hybird quantum ensemble classifier

TL;DR

This paper introduces a hybrid quantum-classical ensemble learning framework with an innovative combination strategy that enhances classification accuracy and robustness, demonstrated through experiments on the MNIST dataset.

Contribution

It proposes a novel efficient combination strategy for quantum ensemble classifiers, improving accuracy and variance handling over existing methods.

Findings

Higher accuracy than single models and traditional voting strategies

Lower variance compared to non-ensemble models

Effective on the MNIST dataset

Abstract

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.