195 citations · 650 across the 44 of their papers we have counts for
8 papers · 1 filter
Compressive multi-beam scanning transmission electron microscopy
Akira Yasuhara, Takumi Sannomiya, Ryoichi Horisaki
We demonstrate a multi-beam scanning transmission electron microscopy (STEM) imaging that integrates down-sampling with super-resolution image reconstruction via a compressive sens…
Time-multiplexed layer reuse for physical neural networks
Kohei Tsuchiyama, Andre Roehm, Takatomo Mihana +1
Physical neural networks (PNNs) are promising candidates for next-generation computing, but existing demonstrations remain several orders of magnitude smaller than modern digital n…
Photonic decision making using optical frequency difference detection in mutually-coupled semiconductor lasers
Hidetoshi Taira, Takatomo Mihana, Shun Kotoku +4
As electronic computing approaches its performance limits, photonic accelerators have emerged as promising alternatives. Photonic accelerators exploiting semiconductor-laser synchr…
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…
Phase retrieval via Zernike phase contrast microscopy with an untrained neural network
Zinan Zhou, Keiichiro Toda, Rikimaru Kurata +3
Zernike's phase contrast microscopy (PCM) is among the most widely used techniques for observing phase objects, but it lacks quantitative nature, as it cannot directly provide phas…