Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing.
In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem.
Quantum walks (QWs) have a property that classical random walks (RWs) do not possess -- the coexistence of linear spreading and localization -- and this property is utilized to implement various kinds of applications.
In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification.
In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents.
We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude.
Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences.
In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning.
In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series.