Quantum bandits

doi:10.1007/s42484-020-00024-8

2020-08-06

PRE

DOI：10.1007/s42484-020-00024-8

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摘要

En 中文

We consider the quantum version of the bandit problem known as best arm identification (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both the learning agent and the environment are quantum; we then propose an algorithm based on quantum amplitude amplification to solve BAI. We formally analyze the behavior of the algorithm on all instances of the problem and we show, in particular, that it is able to get the optimal solution quadratically faster than what is known to hold in the classical case.

Keyword:

Bandits

Best arm identification

Quantum amplitude amplification

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Quantum Machine Intelligence

IF:

4.4

论文数:

440

被引数:

796

机构

Keio University

学者数:

2.2W

论文数: 1.6W

被引数: 13

centre national de la recherche scientifique (cnrs)

学者数:

24.5W

论文数: 18.2W

被引数: 279

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