Optimization of MZI-Based Photonic Neural Networks via Pseudo-Real Number Theory and GridNet Architecture - Wang, Li; Wang, Tao (Informatica, 2026)
As information technology continues to evolve, the computing performance of existing electronic chips approaches its computational limit, and integrated photon technology has the advantages of fast computing speed and strong anti-interference ability. Therefore, a photon neural network algorithm based on pseudo real number theory is proposed in the study. This method employs a Mach-Zehnder interferometer array to construct a basic convolutional neural network, wherein the Mach-Zehnder interferometer splitter is replaced by a directional coupler to form the fundamental unit. The study employs singular value decomposition for matrix multiplication calculations, performs unitary matrix decomposition based on the rectangular decomposition principle, and utilizes pseudo-real number theory for weight training within the photonic neural network architecture. By extracting the real part of complex optical fields, this approach enables optical systems to compute arbitrary real-valued matrices. Experiments demonstrate that on the MNIST handwritten digit dataset, the photonic neural network converges to a loss function value of 0.08 after 20 iterations, achieving a minimum loss function value 0.02 and 0.09 lower than other algorithms respectively. Module U, housing the largest array of Mach-Zehnder interferometers, exhibits greater susceptibility to noise. The computational speed of the photonic neural network surpasses traditional methods by six orders of magnitude. Following optimization, its computational speed exceeds other approaches by 13 ns and 7 ns respectively. Ablation experiments reveals that the directional coupler module exerts the most significant influence on enhancing computational speed, while pseudo-real number theory has the greatest impact on recognition accuracy. From this, the approach proposed by the research can effectively improve the computing speed of neural networks and reduce the operating costs in actual production.