Nonlinear photonic neural network with frequency comb lines

We propose and demonstrate a proof-of-concept hybrid photonic-digital neural network that exploits optical frequency comb lines and their second-order nonlinear interactions for both classification and image generation. In this architecture, input features are encoded in the amplitudes of individual comb lines, while their relative phases serve as trainable parameters. For classification, it achieves ∼98% average accuracy on a 40-sample make-moons dataset, with the mean square error reduced from 0.7 to 0.05 post-training. For image generation, a conditional variational autoencoder is implemented using a small dataset of 18 MNIST digits over 3 categories, generating new digits with a reconstruction loss of 0.75. These results establish the feasibility of combining frequency comb lines and nonlinear optics for both discriminative and generative neural network tasks.