Scalable conflict-free bandit algorithm using a quantum optical setup

Abstract Quantum optics utilizes the unique properties of light for computation or communication. In this work, we explore its ability to solve certain reinforcement learning tasks, with a particular view towards the scalability of the approach. Our method utilizes the Orbital Angular Momentum (OAM) of photons to solve the Competitive Multi-Armed Bandit (CMAB) problem while maximizing rewards. In particular, we encode each player’s preferences in the OAM amplitudes, while the phases are optimize