Hybrid Quantum-classical Compressed Generative Adversarial Networks Based on Matrix Product Operators

Neural networks play a pivotal role in artificial intelligence,particularly in image generation.As a popular algorithm in recent years,generative adversarial networks(GANs) have demonstrated superior performance in this area.Quantum computing,merging with traditional AI algorithms,accelerates processing speeds and enhances data security,making it especially suitable for managing high-dimensional data and optimization problems.Within this context,hybrid quantum-classical GANs show promising results.However,these models face challenges in generating high-dimensional images,and the inclusion of linear layers in generatorsresults in elevated parameter counts.Therefore,a hybrid quantum-classical compressed GAN model using matrix pro-duct operators is proposed.This model improves the structure of the block quantum generator,enabling the generation of multiple data blocks in a single call,which enhances efficiency.It integrates the nonlinear properties of classical networks with matrix product operators,ensuring high-quality image generation,speeding up model convergence,and reducing parameter counts.Expe-rimental results show that the optimized generator structure increases total runtime by approximately 92.88%,reduces model parameters by about 5.59%,and surpasses traditional and hybrid quantum-classical models in convergence speed on MNIST and FMNIST datasets,demonstrating its potential for high-dimensional image generation.