[12a-S101-1~9] FS.1 Focused Session "AI Electronics"

△ [12a-S101-9] Solving N-armed bandit problem with entangled orbital angular momentum.

〇(M1)Hiroaki Shinkawa¹, Nicolas Chauvet¹, Guillaume Bachelier², Serge Huant², Andre Roehm¹, Ryoichi Horisaki¹, Makoto Naruse¹ (1.Univ. Tokyo, 2.Univ. Grenoble Alpes)

Keywords:entanglement, reinforcement learning, bandit problem

Amakasu et al. proposed a stochastic decision making method that uses orbital angular momentum entangled photons in order to solve competitive multi-armed bandit problem. Although this allows us to build a system for any number of machines, there is a problem in robustness to environmental changes when the number of machines is greater than 3. In this research, we propose two decision making methods, in which we can improve the robustness significantly without sacrificing the average reward.