Abstract: Data reconstruction using neural networks is a very important research topic in the field of artificial intelligence. Generative adversarial network (GAN), as a popular algorithm of artificial intelligence in recent years, has a good performance in completing data reconstruction tasks. As a new computing mode that can accelerate classical computing, quantum computing is constantly merging with classical artificial intelligence algorithms. Among them, pure quantum generative adversarial network (QGAN) has a good performance in image related tasks. However, since the fitting ability in the quantum model still needs to be improved, this paper proposes a hybrid generative confrontation network (Q-CGAN) based on the GAN framework to realize the data reconstruction task. The framework exploits classical nonlinearities to improve fitting performance and quantum properties to provide quantum speedups. Using the MNIST handwritten data set to compare and verify the reconstruction effect of the hybrid model in this network, the results show that Q-CGAN has better performance in the data reconstruction process than pure quantum generators. In addition, the effect of using different quantum encoding schemes and different parameterized quantum circuits in the hybrid model on the data reconstruction effect is also studied.

Key words: quantum computing, hybrid generative adversarial network, data reconstruction

摘要： 使用神经网络实现数据重构是人工智能领域一项十分重要的研究课题。生成对抗网络（generative adversarial network，GAN）作为近年来人工智能的热门算法，在完成数据重构任务中有较好的表现。量子计算作为一种能够加速经典计算的新型计算模式，正不断地与经典人工智能算法相融合。其中，量子生成对抗网络（quantum generative adversarial network，QGAN）在图像相关任务中具有良好的表现，但是量子模型的拟合能力还有待提高。故此，提出了一种基于GAN框架的量子-经典混合生成对抗网络（Q-CGAN）用于实现数据重构任务。该框架利用经典网络的非线性提高拟合效果，利用量子特性提供量子加速。使用MNIST手写数据集对比验证了量子模型和混合模型的重构效果，结果显示，Q-CGAN较纯量子生成器在数据重构过程中具有更好的表现。此外，还研究了混合模型中使用不同量子编码方案和不同参数化量子线路对数据重构效果的影响。

关键词: 量子计算, 混合生成对抗网络, 数据重构