  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.
