Unrolled generative adversarial network for continuous distributions under hybrid quantum-classical model

Quantum generative adversarial networks (QGANs) can effectively enhance the performance and efficiency of classical GANs by utilizing the parallelism of quantum computation and quantum superposition. However, QGANs typically suffer from mode collapse during the training process of generative tasks. It would make the generator only be able to generate partially correct data approximately. To solve this problem, an unrolled QGAN model based on a hybrid quantum-classical framework is constructed. The unrolled QGAN can match the generator with a better discriminator by separately training the discriminator prior to the training on the generator. The model is applied to generate quantum and Gaussian distributions, and comparative experiments are performed between the QGAN and the proposed unrolled one. Mean value, KL divergence, and standard deviation are calculated and compared to evaluate the generative performance of the model. Numerical and experimental results show that the proposed unrolled QGAN can increase the diversity and coverage of generated data distributions, significantly enhancing the generative effect.