Takatomo Mihana

Assistant professor

University of Tokyo

Japan

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About

Takatomo Mihana is an Assistant Professor at the University of Tokyo, Japan. His research focuses on advanced topics in computational systems, including remote training of reservoir computers and decentralized multiagent reinforcement learning. Recent works also explore the dynamics of chaotic multimode semiconductor lasers and applications of bandit algorithms in decision-making processes.

Articles (17)

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2025

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2023

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2023

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JAPAN

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Ryoichi Horisaki

Associate Professor

University of Tokyo

JAPAN

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André Röhm

JAPAN

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Atsushi Uchida

Saitama University

JAPAN

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Takatomo Mihana

Research Interests

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About

Articles (17)

Remote training of a reservoir computer via digital twins

Two-Dimensional Beam Selection by Multiarmed Bandit Algorithm Based on a Quantum Walk

Ultrafast Resource Allocation by Parallel Bandit Architecture Using Chaotic Lasers for Downlink NOMA Systems

Experimental control of mode-competition dynamics in a chaotic multimode semiconductor laser for decision making

Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network

Enlarged matrix method for stochastic decision making using orbital angular momenta

Decentralized multiagent reinforcement learning algorithm using a cluster-synchronized laser network

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement

Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing

Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk

Information-theoretical analysis of statistical measures for multiscale dynamics

Skeleton structure inherent in discrete-time quantum walks

Acceleration of Approximate Matrix Multiplications on GPUs

Parallel photonic accelerator for decision making using optical spatiotemporal chaos

Experimental Demonstration of Approximate Communication Based on Radio-Over- Fiber Systems

Bandit Approach to Conflict-Free Parallel Q-Learning in View of Photonic Implementation

Controlling chaotic itinerancy in laser dynamics for reinforcement learning

Collaborators (9)

Mikio Hasegawa

Etsuo Segawa

Ken'ichiro Tanaka

Tetsuya Kawanishi

Makoto Naruse

Kazutaka Kanno

Ryoichi Horisaki

André Röhm

Atsushi Uchida

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