  Boosting is a general method to convert a weak learner (which generates
hypotheses that are just slightly better than random) into a strong learner
(which generates hypotheses that are much better than random). Recently,
Arunachalam and Maity gave the first quantum improvement for boosting, by
combining Freund and Schapire's AdaBoost algorithm with a quantum algorithm for
approximate counting. Their booster is faster than classical boosting as a
function of the VC-dimension of the weak learner's hypothesis class, but worse
as a function of the quality of the weak learner. In this paper we give a
substantially faster and simpler quantum boosting algorithm, based on
Servedio's SmoothBoost algorithm.
