Direct tensor processing with coherent light

Abstract Tensor processing is a cornerstone of many modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here we report parallel optical matrix–matrix multiplication (POMMM), which enables fully parallel tensor processing through a single coherent light propagation. This approach addresses key limitations of current optical methods, scaling the performance with data dimension, while improving theoretical computational power and efficiency. We demonstrate its high consistency with GPU-based matrix–matrix multiplication across both real-valued and complex-valued domains. Moreover, we showcase its adaptability, scalability and versatility in tensor processing applications such as convolutional and vision transformer neural networks. Furthermore, we analyse the theoretical compatibility and efficiency of POMMM in relation to existing optical computing paradigms, highlighting its potential to outperform current state-of-the-art methods. By enabling a variety of computational tasks and supporting multi-wavelength and large-scale expansion, POMMM provides a scalable, high-efficiency foundation for advancing next-generation optical computing.