论文标题
改进的量子增强
Improved Quantum Boosting
论文作者
论文摘要
提升是一种将弱学习者转化（生成比随机稍好的假设）转换为强大学习者（产生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算法与量子算法相结合，进行了第一个量子改进，以进行近似计数。他们的助推器比弱者假设类别的VC尺寸的函数要快，比经典的提升更快，但随着弱学习者质量的函数而更糟糕。在本文中，我们基于Spoothio的SmoothBoost算法提供了更快，更简单的量子增强算法。
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.
