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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Due to the immense potential of quantum computers and the significantcomputing overhead required in machine learning applications, the variationalquantum classifier (VQC) has received a lot of interest recently for imageclassification. The performance of VQC is jeopardized by the noise in NoisyIntermediate-Scale Quantum (NISQ) computers, which is a significant hurdle. Itis crucial to remember that large error rates occur in quantum algorithms dueto quantum decoherence and imprecision of quantum gates. Previous studies havelooked towards using ensemble learning in conventional computing to reducequantum noise. We also point out that the simple average aggregation inclassical ensemble learning may not work well for NISQ computers due to theunbalanced confidence distribution in VQC. Therefore, in this study, we suggestthat ensemble quantum classifiers be optimized with plurality voting. On theMNIST dataset and IBM quantum computers, experiments are carried out. Theresults 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.

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