Data Reconstruction Based on Quantum Generative Adversarial Networks

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. © 2024 Editorial Department of Scientia Agricultura Sinica. All rights reserved.