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.

中文翻译：

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改进的量子提升

Boosting 是一种将弱学习器（生成比随机稍好一点的假设）转换为强学习器（生成比随机好得多的假设）的通用方法。最近，Arunachalam 和 Maity 通过将 Freund 和 Schapire 的 AdaBoost 算法与用于近似计数的量子算法相结合，对提升进行了第一个量子改进。作为弱学习器假设类的 VC 维度的函数，他们的 booster 比经典 boosting 更快，但作为弱学习器质量的函数更差。在本文中，我们基于 Servedio 的 SmoothBoost 算法给出了一种更快更简单的量子提升算法。