  Although variational quantum algorithms based on parameterized quantum
circuits promise to achieve quantum advantages, in the noisy intermediate-scale
quantum (NISQ) era, their capabilities are greatly constrained due to limited
number of qubits and depth of quantum circuits. Therefore, we may view these
variational quantum algorithms as weak learners in supervised learning.
Ensemble methods are a general technique in machine learning for combining weak
learners to construct a more accurate one. In this paper, we theoretically
prove and numerically verify a learning guarantee for variational quantum
adaptive boosting (AdaBoost). To be specific, we theoretically depict how the
prediction error of variational quantum AdaBoost on binary classification
decreases with the increase of the number of boosting rounds and sample size.
By employing quantum convolutional neural networks, we further demonstrate that
variational quantum AdaBoost can not only achieve much higher accuracy in
prediction, but also help mitigate the impact of noise. Our work indicates that
in the current NISQ era, introducing appropriate ensemble methods is
particularly valuable in improving the performance of quantum machine learning
algorithms.
