195 citations · 650 across the 44 of their papers we have counts for
46 papers
Context-dependent time-series prediction via HyperReservoirs
Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki +1
Time series prediction is a common application of reservoir computing. When the training and testing time series data contains multiple dynamical regimes, because an underlying par…
Reconstructing the information processing capacity of physical systems from noisy observations
Shun Kotoku, Rodrigo Martínez-Peña, Takatomo Mihana +5
Driven dynamical systems can compute when their transient states encode complex transformations of past inputs. The information processing capacity (IPC) framework allows for a det…
EDoF-NeRF: extended depth-of-field neural radiance fields using a coded aperture camera
Yoshiyuki Shirasaki, Ryoichi Horisaki
We propose a method for extending the depth-of-field (DoF) to construct high-fidelity neural radiance fields (NeRF) -- an emerging technique for rendering photorealistic novel view…
Cathodoluminescence Wavefront Retrieval
Izzah Machfuudzoh, Ryoichi Horisaki, Takumi Sannomiya
Free-electron-based nanoscopy enables the study of optical excitations in materials with deep-subwavelength spatial resolution, with cathodoluminescence (CL) being one of the most…
Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems
Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana +2
Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optic…
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…
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network
Shun Kotoku, Takatomo Mihana, André Röhm +1
Multi-agent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. We propose a…
Diffraction casting
Ryosuke Mashiko, Makoto Naruse, Ryoichi Horisaki
Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields, requiring high integration and high speed computa…
Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
Shun Kotoku, Takatomo Mihana, André Röhm +2
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network,…
Conflict-free joint decision by lag and zero-lag synchronization in laser network
Hisako Ito, Takatomo Mihana, Ryoichi Horisaki +1
With the end of Moore's Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of ligh…
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…
Quantitative phase retrieval for Zernike phase-contrast microscopy
Rikimaru Kurata, Keiichiro Toda, Genki Ishigane +3
We present a single-image numerical phase retrieval method for Zernike phase-contrast microscopy (ZPM) that addresses halo and shade-off artifacts, as well as the weak phase condit…
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.…
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing
Kohei Tsuchiyama, André Röhm, Takatomo Mihana +2
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing ha…
Information-theoretical analysis of statistical measures for multiscale dynamics
Naoki Asuke, Tomoki Yamagami, Takatomo Mihana +3
Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has b…
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation
Hiroaki Shinkawa, Nicolas Chauvet, André Röhm +4
Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous stu…
Pairing optimization via statistics: Algebraic structure in pairing problems and its application to performance enhancement
Naoki Fujita, André Röhm, Takatomo Mihana +4
Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situat…