Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection

Photonic computing is attracting increasing interest to accelerate information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor lasers is useful for solving the multi-armed bandit problem in reinforcement learning for computing applications. In this study, we numerically evaluate the chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection. We observe the chaotic mode-competition dynamics among the longitudinal modes and control them by injecting an external optical signal into one of the longitudinal modes. We define the dominant mode as the mode with the maximum intensity; the dominant-mode ratio for the injected mode increases as the optical injection strength increases. We find that the characteristics of the dominant mode ratio in terms of the optical injection strength are different among the modes owing to the different optical feedback phases. We propose a control technique for the characteristics of the dominant mode ratio by precisely tuning the initial optical frequency detuning between the optical injection signal and injected mode. We also evaluate the relationship between the region for the large dominant mode ratio and injection locking range. The region for the large dominant mode ratio does not correspond to the injection-locking range. This discrepancy results from the complex mode-competition dynamics in multimode semiconductor lasers with both optical feedback and injection. This control technique of chaotic mode-competition dynamics in multimode lasers is promising for applications in reinforcement learning and reservoir computing as photonic artificial intelligence.Comment: 17 pages, 12 figures, 1 tabl

Solving multi-armed bandit problems using a chaotic microresonator comb

The Multi-Armed Bandit (MAB) problem, foundational to reinforcement learning-based decision-making, addresses the challenge of maximizing rewards amidst multiple uncertain choices. While algorithmic solutions are effective, their computational efficiency diminishes with increasing problem complexity. Photonic accelerators, leveraging temporal and spatial-temporal chaos, have emerged as promising alternatives. However, despite these advancements, current approaches either compromise computation speed or amplify system complexity. In this paper, we introduce a chaotic microresonator frequency comb (chaos comb) to tackle the MAB problem, where each comb mode is assigned to a slot machine. Through a proof-of-concept experiment, we employ 44 comb modes to address an MAB with 44 slot machines, demonstrating performance competitive with both conventional software algorithms and other photonic methods. Further, the scalability of decision making is explored with up to 512 slot machines using experimentally obtained temporal chaos in different time slots. Power-law scalability is achieved with an exponent of 0.96, outperforming conventional software-based algorithms. Moreover, we find that a numerically calculated chaos comb accurately reproduces experimental results, paving the way for discussions on strategies to increase the number of slot machines