Quantum Exploration Algorithms for Multi-Armed Bandits

被引：0
|
作者：
Wang, Daochen [1 ,2 ]
You, Xuchen [1 ,3 ,4 ]
Li, Tongyang [1 ,3 ,4 ,5 ]
Childs, Andrew M. [1 ,3 ,4 ]
机构：
[1] Univ Maryland, Joint Ctr Quantum Informat & Comp Sci, College Pk, MD 20742 USA
[2] Univ Maryland, Dept Math, College Pk, MD 20742 USA
[3] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA
[4] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA
[5] MIT, Ctr Theoret Phys, Cambridge, MA 02139 USA
来源：
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2021年 / 35卷
基金：
美国国家科学基金会;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization. We study a quantum computational version of this problem with coherent oracle access to states encoding the reward probabilities of each arm as quantum amplitudes. Specifically, we provide an algorithm to find the best arm with fixed confidence based on variable-time amplitude amplification and estimation. This algorithm gives a quadratic speedup compared to the best possible classical result in terms of query complexity. We also prove a matching quantum lower bound (up to poly-logarithmic factors).
引用
收藏
页码：10102 / 10110
页数：9
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