  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.
