[10p-N202-1~9] 3.9 Optical quantum physics and technologies

2:15 PM - 2:30 PM JST(5:15 AM - 5:30 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[10p-N202-6] Policy for Spatial Best-Arm Identification Problem via Quantum Walks

〇Tomoki Yamagami^1,2, Etsuo Segawa³, Takatomo Mihana², Andre Roehm², Atsushi Uchida¹, Ryoichi Horisaki² (1.Saitama Univ., 2.Univ. Tokyo, 3.Yokohama Nat. Univ.)

Keywords:

quantum walk,multi-armed bandit problem,best-arm identification

In recent years, research on quantum reinforcement learning, which incorporates quantum computation into reinforcement learning, has been actively conducted, and its application to the multi-armed bandit (MAB) problem, or a fundamental issue in reinforcement learning, has also been reported. As an extension of the MAB problem, the graph bandit problem formulates decision-making under spatial constraints. However, no quantum approach has yet been proposed for this problem. In this study, we propose a best-arm identification algorithm for the graph bandit problem using quantum walks.