In this chapter, we consider a quantum version of the classical boosting meta-algorithm – a family of machine learning algorithms that convert weak classifiers into strong ones. Classically, boosting consists of two main operations: i) adaptive (iterative) training of the weak classifiers, thus improving their individual performance, and ii) finding an optimal configuration of weights applied to the individual weak learners when combining them into a single strong one.
Adaptive learning consists of iterative re-weighting of the samples from the training dataset, forcing the model to improve its performance on the difficult-to-classify samples by giving them heavier weights. These weights are adjusted at each algorithm iteration. Arguably, the best-known and most successful example of such algorithms is the popular adaptive boosting (AdaBoost) model. It was first formulated in 1997 by Freund and Schapire , whose work has been recognised...