Harnessing artificial intelligence for coherent beam combination

Abstract

Coherent beam combining (CBC) is an effective scheme to surpass the physical power limits of single fiber lasers, achieving higher power and superior beam quality, with phase control being the critical factor. Active phase control compensates for phase noise-induced coherence degradation by directly or indirectly detecting phase differences among sub-beams. Traditional phase control algorithms face challenges in large-scale CBC systems due to low control bandwidth. With the rapid development of artificial intelligence (AI) technologies, integrating advanced intelligent algorithms into active phase control systems holds promise for enhancing the performance of CBC systems. This paper begins with a brief introduction to the principles of traditional phase control algorithms, such as Stochastic Parallel Gradient Descent (SPGD) and locking of optical coherence by single-detector electronic-frequency tagging (LOCSET), elucidating why AI can assist in active phase control systems. Subsequently, we review recent advancements in phase control based on deep learning and reinforcement learning, concluding with a summary and future outlook. As phase control technology advances, the integration of AI and traditional algorithms will play a pivotal role in achieving high-bandwidth and accurate phase control in large-scale CBC systems.