计算机工程与应用2024，Vol.60Issue(5)：156-164,9.DOI:10.3778/j.issn.1002-8331.2211-0363
基于量子生成对抗网络的数据重构
Data Reconstruction Based on Quantum Generative Adversarial Networks
摘要
Abstract
Data reconstruction using neural networks is a very important research topic in the field of artificial intelli-gence.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 nonlineari-ties 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.关键词
量子计算/混合生成对抗网络/数据重构Key words
quantum computing/hybrid generative adversarial network/data reconstruction分类
信息技术与安全科学引用本文复制引用
江奕达,王明明..基于量子生成对抗网络的数据重构[J].计算机工程与应用,2024,60(5):156-164,9.基金项目
陕西省自然科学基础研究计划一般项目(面上)(2019JM-291). （面上）
