English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

An efficient combination strategy for hybird quantum ensemble classifier