Quantum speedup in adaptive boosting of binary classification

作者：XiMing Wang,YueChi Ma,Min-Hsiu Hsieh,Man-Hong Yung

摘要：In classical machine learning,a set of weak classifiers can be adaptively combined for improving the overall performance,a technique called adaptive boosting(or AdaBoost).However,constructing a combined classifier for a large data set is typically resource consuming.Here we propose a quantum extension of AdaBoost,demonstrating a quantum algorithm that can output the optimal strong classifier with a quadratic speedup in the number of queries of the weak classifiers.Our results also include a generalization of the standard AdaBoost to the cases where the output of each classifier may be probabilistic.We prove that the query complexity of the non-deterministic classifiers is the same as those of deterministic classifiers,which may be of independent interest to the classical machine-learning community.Additionally,once the optimal classifier is determined by our quantum algorithm,no quantum resources are further required.This fact may lead to applications on near term quantum devices.

发文机构：School of Physics and Mathmatical Science Department of Physics Shenzhen Institute for Quantum Science and Engineering Center for Quantum Information Center for Quantum Software and Information Guangdong Provincial Key Laboratory of Quantum Science and Engineering

关键词：ADABOOSTquantummachinelearningquantumalgorithm

分类号： TP3[自动化与计算机技术—计算机科学与技术]