This paper presents a novel hybrid quantum generative model, the VAE-QWGAN, which combines the strengths of a classical Variational AutoEncoder (VAE) with a hybrid Quantum Wasserstein Generative Adversarial Network (QWGAN). The VAE-QWGAN integrates the VAE decoder and QGAN generator into a single quantum model with shared parameters, utilizing the VAE's encoder for latent vector sampling during training. To generate new data from the trained model at inference, input latent vectors are sampled from a Gaussian Mixture Model (GMM), learnt on the training latent vectors. This, in turn, enhances the diversity and quality of generated images. We evaluate the model's performance on MNIST/Fashion-MNIST datasets, and demonstrate improved quality and diversity of generated images compared to existing approaches.

翻译：本文提出了一种新颖的混合量子生成模型——VAE-QWGAN，它结合了经典变分自编码器与混合量子Wasserstein生成对抗网络的优势。VAE-QWGAN将VAE解码器与QGAN生成器集成到一个具有共享参数的单一量子模型中，并利用VAE编码器在训练期间进行隐向量采样。在推理阶段，为了从训练好的模型中生成新数据，输入隐向量从高斯混合模型中采样，该模型是在训练隐向量上学习得到的。这进而提升了生成图像的多样性与质量。我们在MNIST/Fashion-MNIST数据集上评估了模型的性能，并证明了与现有方法相比，生成图像的质量和多样性均得到改善。