Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
This work addresses scalable decision-making in photonic accelerators, though it appears incremental by extending existing laser-based methods.
The researchers tackled the competitive multi-armed bandit problem using a laser network for collective decision-making, demonstrating asymmetric player preferences and analyzing network structures for stability.
Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically interconnected lasers to address the competitive multi-armed bandit (CMAB) problem, is a highly compelling approach due to its scalability and experimental feasibility. We investigated essential network structures for collective decision-making through quantitative stability analysis. Moreover, we demonstrated the asymmetric preferences of players in the CMAB problem, extending its functionality to more practical applications. Our study highlights the capability and significance of machine learning built upon chaotic lasers and photonic devices.