Dual Discriminators Quantum Generation Adversarial Network Based on Quantum Convolutional Neural Network

Abstract

Abstract As a crucial component in quantum machine learning, quantum generative adversarial networks play a significant role in generating discrete distributions. However, due to issues such as the vanishing gradient and mode collapse, the results generated by quantum generative adversarial models are of suboptimal quality sometimes. Given the robust image feature extraction capabilities of quantum convolutional neural networks, a hybrid quantum convolutional neural network model is incorporated into the quantum generative adversarial network as a discriminator. Different from the traditional multi‐layer linear structure, this discriminator adopts a parallel structure. This parallel structure can analyze both the local and the global features of an image simultaneously. It can promptly detect the defects in the global distribution of generated data, prompting the generator to explore more data patterns and avoid falling into mode collapse. The feasibility of this solution is verified through generation experiments on the handwritten dataset, Fashion‐MNIST dataset, and CIFAR‐100 dataset. The experimental results show that the FID (Fréchet Inception Distance) scores of the generated results on these three datasets reach 14, 20, and 17 respectively, fully demonstrating the performance of this image generation algorithm.