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Found 12 papers, 1 papers with code
Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing.
Asymmetric quantum decision-making
Quantum processes can realize conflict-free joint decisions among two agents using the entanglement of photons or quantum interference of orbital angular momentum (OAM).
Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk
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 implement various kinds of applications.
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing
In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification.
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation
In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents.
Conflict-free joint sampling for preference satisfaction through quantum interference
Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences.
Learning unseen coexisting attractors
Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system.
Optimal preference satisfaction for conflict-free joint decisions
Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players.
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing
Reservoir computers are powerful tools for chaotic time series prediction.
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops.
Performance boost of time-delay reservoir computing by non-resonant clock cycle
Here we show that the case of equal or resonant time-delay and clock cycle could be actively detrimental and leads to an increase of the approximation error of the reservoir.
Reservoir computing with simple oscillators: Virtual and real networks
The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occuring computational capabilities of dynamical systems.
