Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network

Photonic accelerators [1] have been gaining attention in recent years, and a variety of implementations and applications have now been explored [2-9]. These advancements can be attributed to a growing awareness of the saturating speed of performance improvements in conventional computational systems [10], despite the soaring demands for information processing in an extensive range of applications, especially in machine learning. Reinforcement learning [11] is a subfield of machine learning that involves optimizing computer outputs or actions to maximize the reward function. Its applications are now essential to our daily lives, ranging from self-driving vehicles [12] and targeted advertising [13] to wireless networking [14], and there is now a strong demand for computational acceleration. Specifically, what we focus on here is decision-making.