  Classification is one of the main applications of supervised learning. Recent
advancement in developing quantum computers has opened a new possibility for
machine learning on such machines. Due to the noisy performance of near-term
quantum computers, error mitigation techniques are essential for extracting
meaningful data from noisy raw experimental measurements. Here, we propose two
ensemble-learning error mitigation methods, namely bootstrap aggregating and
adaptive boosting, which can significantly enhance the performance of
variational quantum classifiers for both classical and quantum datasets. The
idea is to combine several weak classifiers, each implemented on a shallow
noisy quantum circuit, to make a strong one with high accuracy. While both of
our protocols substantially outperform error-mitigated primitive classifiers,
the adaptive boosting shows better performance than the bootstrap aggregating.
The protocols have been exemplified for classical handwriting digits as well as
quantum phase discrimination of a symmetry-protected topological Hamiltonian,
in which we observe a significant improvement in accuracy. Our
ensemble-learning methods provide a systematic way of utilising shallow
circuits to solve complex classification problems.
