no code implementations • 5 Dec 2023 • Shun Kotoku, Takatomo Mihana, André Röhm, Ryoichi Horisaki, Makoto Naruse
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing.
no code implementations • 28 Jul 2023 • Hisako Ito, Takatomo Mihana, Ryoichi Horisaki, Makoto Naruse
In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem.
no code implementations • 20 Apr 2023 • Tomoki Yamagami, Etsuo Segawa, Takatomo Mihana, André Röhm, Ryoichi Horisaki, Makoto Naruse
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.
no code implementations • 27 Jan 2023 • Kohei Tsuchiyama, André Röhm, Takatomo Mihana, Ryoichi Horisaki, Makoto Naruse
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.
no code implementations • 20 Dec 2022 • Hiroaki Shinkawa, Nicolas Chauvet, André Röhm, Takatomo Mihana, Ryoichi Horisaki, Guillaume Bachelier, Makoto Naruse
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.
no code implementations • 12 Oct 2022 • Kensei Morijiri, Kento Takehana, Takatomo Mihana, Kazutaka Kanno, Makoto Naruse, Atsushi Uchida
We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude.
no code implementations • 5 Aug 2022 • Hiroaki Shinkawa, Nicolas Chauvet, André Röhm, Takatomo Mihana, Ryoichi Horisaki, Guillaume Bachelier, Makoto Naruse
Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences.
no code implementations • 12 May 2022 • Ryugo Iwami, Takatomo Mihana, Kazutaka Kanno, Satoshi Sunada, Makoto Naruse, Atsushi Uchida
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.
no code implementations • 26 Mar 2018 • Makoto Naruse, Takatomo Mihana, Hirokazu Hori, Hayato Saigo, Kazuya Okamura, Mikio Hasegawa, Atsushi Uchida
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.