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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 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.
Parallel bandit architecture based on laser chaos for reinforcement learning
Q-learning is a well-known approach in reinforcement learning that can deal with many states.
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.
Theory of Acceleration of Decision Making by Correlated Time Sequences
In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence.
Conflict-free collective stochastic decision making by orbital angular momentum of photons through quantum interference
In recent cross-disciplinary studies involving both optics and computing, single-photon-based decision-making has been demonstrated by utilizing the wave-particle duality of light to solve multi-armed bandit problems.
Arm order recognition in multi-armed bandit problem with laser chaos time series
By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series.
Generative adversarial network based on chaotic time series
Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data.
Entangled-photon decision maker
The competitive multi-armed bandit (CMAB) problem is related to social issues such as maximizing total social benefits while preserving equality among individuals by overcoming conflicts between individual decisions, which could seriously decrease social benefits.
