Photonic Neural Network

Photonic neural networks (PNNs) leverage the speed and energy efficiency of light to perform computations, aiming to surpass electronic counterparts in artificial intelligence applications. Current research focuses on optimizing PNN architectures, including diffractive and interference-based networks, and developing efficient training algorithms like asymmetrical training and dual adaptive training to mitigate systematic errors inherent in photonic implementations. These advancements address challenges in feature representation, scalability, and noise tolerance, paving the way for more compact, energy-efficient, and accurate PNNs for various machine learning tasks, such as image classification and molecular property prediction.