70 citations · 198 across the 22 of their papers we have counts for
23 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…
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…
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…
Stability of Optically Injected Two-State Quantum-Dot Lasers
Stefan Meinecke, Benjamin Lingnau, André Röhm +1
Simultaneous two-state lasing is a unique property of semiconductor quantum-dot (QD) lasers. This not only changes steady-state characteristics of the laser device but also its dyn…
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,…
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.…
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…
Skeleton structure inherent in discrete-time quantum walks
Tomoki Yamagami, Etsuo Segawa, Ken'ichiro Tanaka +4
In this paper, we claim that a common underlying structure--a skeleton structure--is present behind discrete-time quantum walks (QWs) on a one-dimensional lattice with a homogeneou…
Conflict-free joint sampling for preference satisfaction through quantum interference
Hiroaki Shinkawa, Nicolas Chauvet, André Röhm +4
Collective decision-making is vital for recent information and communications technologies. In our previous research, we mathematically derived conflict-free joint decision-making…
Analysis of temporal structure of laser chaos by Allan variance
Naoki Asuke, Nicolas Chauvet, André Röhm +6
Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long.…
Optimal preference satisfaction for conflict-free joint decisions
Hiroaki Shinkawa, Nicolas Chauvet, Guillaume Bachelier +3
We all have preferences when multiple choices are available. If we insist on satisfying our preferences only, we may suffer a loss due to conflicts with other people's identical se…
Efficient Pairing in Unknown Environments: Minimal Observations and TSP-based Optimization
Naoki Fujita, Nicolas Chauvet, Andre Roehm +4
Generating paired sequences with maximal compatibility from a given set is one of the most important challenges in various applications, including information and communication tec…
Directivity of quantum walk via its random walk replica
Tomoki Yamagami, Etsuo Segawa, Nicolas Chauvet +3
Quantum walks (QWs) exhibit different properties compared with classical random walks (RWs), most notably by linear spreading and localization. In the meantime, random walks that r…
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
André Röhm, Daniel J. Gauthier, Ingo Fischer
Reservoir computers are powerful tools for chaotic time series prediction. They can be trained to approximate phase space flows and can thus both predict future values to a high ac…
Performance boost of time-delay reservoir computing by non-resonant clock cycle
Florian Stelzer, André Röhm, Kathy Lüdge +1
The time-delay-based reservoir computing setup has seen tremendous success in both experiment and simulation. It allows for the construction of large neuromorphic computing systems…
Reservoir computing with simple oscillators: Virtual and real networks
André Röhm, Kathy Lüdge
The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occuring computational capabilities of dynamical systems. One important subset of system…
Bistability in Two Simple Symmetrically Coupled Oscillators with Symmetry-broken Amplitude- and Phase-Locking
André Röhm, Kathy Lüdge, Isabelle Schneider
In the model system of two instantaneously and symmetrically coupled identical Stuart-Landau oscillators we demonstrate that there exist stable solutions with symmetry-broken ampli…