Universal convolution from wave dynamics: photonic processing and encryption in synthetic dimension

Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the complex-valued kernel. Leveraging this universal principle, we develop a convolutional architecture of minimal complexity through wave evolution in programmable photonic synthetic lattices, delivering high-throughput, multifunctional capabilities at a rate of 13.5 tera-operations per second (TOPS) for image processing. Beyond convolution acceleration, the kernel’s complex nature facilitates the photonic simulation of both irreversible diffusion and reversible unitary quantum dynamics under classical incoherent excitation. Capitalizing on the physics-based reversibility and undetectable phase information, we demonstrate a convolution-driven optical encryption strategy. This work establishes a unified perspective for photonic computing by grounding convolution in wave dynamics, opening avenues toward scalable, multifunctional photonic processors with high integration potential. Convolution is a key operation in image processing, artificial intelligence and signal analysis. Here, the authors demonstrate that wave evolution in synthetic photonic lattices naturally performs convolution, enabling high-throughput photonic processing and optical encryption.