Improved Quantum Boosting
This work addresses the need for more efficient quantum machine learning algorithms, representing an incremental improvement over prior quantum boosting approaches.
The paper tackles the problem of improving quantum boosting algorithms by proposing a new quantum boosting algorithm based on Servedio's SmoothBoost, which is faster and simpler than previous quantum methods.
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.