Machine Learning in Photonics II (MLP)

Abstract Due: 5 December 2025
Author Notification: 26 January 2026
Manuscript Due: 25 March 2026
Photonics, which encompasses the study and application of light, plays a pivotal role in various disciplines, including telecommunications, imaging, sensing, and data processing. Recently, machine learning has emerged as a transformative tool for the field of photonics. At the same time, photonics offers unique groundbreaking opportunities for machine-learning algorithms and computing. Machine learning is revolutionizing how we analyze, model, and design photonic devices and systems, employing advanced algorithms that are the critical enablers to drastically accelerate the design process (e.g., design exploration and optimization) and find novel solutions that cannot be obtained by conventional design methods. Furthermore, photonic hardware has the potential to revolutionize machine-learning algorithms and computing at large. The goal of photonic computing is not replacing conventional computers, but enabling applications that can be unreachable at present by conventional computing technology, namely those requiring low latency, high bandwidth and low power consumption.