Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 38 references that are in the map. First posted on arXiv 5 Dec 2023.
Builds on
6 of 38 references.
References · 6
- 2016Neuromorphic Silicon Photonic Networkscited by 68
- 2017Reinforcement Learning in a large scale photonic Recurrent Neural Networkcited by 30
- 2017Ultrafast photonic reinforcement learning based on laser chaoscited by 13
- 2022Controlling chaotic itinerancy in laser dynamics for reinforcement learningcited by 7
- 2022Parallel photonic accelerator for decision making using optical spatiotemporal chaoscited by 6
- 2023Conflict-free joint decision by lag and zero-lag synchronization in laser networkcited by 2
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.