7 citations · 17 across the 19 of their papers we have counts for
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Quantum spatial best-arm identification via quantum walks
Tomoki Yamagami, Etsuo Segawa, Takatomo Mihana +3
Quantum reinforcement learning has emerged as a framework combining quantum computation with sequential decision-making, and applications to the multi-armed bandit (MAB) problem ha…
Multi-player conflict avoidance through entangled quantum walks
Honoka Shiratori, Tomoki Yamagami, Etsuo Segawa +3
Quantum computing has the potential to solve complex problems faster and more efficiently than classical computing. It can achieve speedups by leveraging quantum phenomena like sup…
Scalable Conflict-free Decision Making with Photons
Kohei Konaka, André Röhm, Takatomo Mihana +1
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…
Normal variance mixture with arcsine law of an interpolating walk between persistent random walk and quantum walk
Saori Yoshino, Honoka Shiratori, Tomoki Yamagami +2
We propose a model that interpolates between quantum walks and persistent (correlated) random walks using one parameter on the one-dimensional lattice. We show that the limit distr…
Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk
Tomoki Yamagami, Etsuo Segawa, Takatomo Mihana +3
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 imp…
Asymmetric quantum decision-making
Honoka Shiratori, Hiroaki Shinkawa, André Röhm +7
Collective decision-making is crucial to information and communication systems. Decision conflicts among agents hinder the maximization of potential utilities of the entire system.…