新疆师范大学学报（自然科学版）2026，Vol.45Issue(3)：66-75,82,11.
面向人类活动识别的量子生成对抗网络数据增强与分类优化
Quantum Generative Adversarial Networks for Human Activity Recognition:Data Augmentation and Classification Optimization
摘要
Abstract
To address the problem of classifier performance degradation caused by data imbalance in human activity recognition,traditional methods such as oversampling and cost-sensitive learning can alleviate the class bias,but they have drawbacks such as overfitting,information loss,and poor adaptability to extreme imbalance scenarios.This paper combines quantum computing and generative adversarial networks to design a joint optimization framework.Its generator uses a Variational Quantum Circuit(VQC),which implements implicit modeling of high-dimensional sensor data through parameterized quantum gate stacking with low parameter quantity;The discriminator uses a classical neural network and introduces the Quantum Wasserstein Generative Adversarial Network-Gradient Penalty(WGAN-GP)to constrain the discriminator,in order to enhance the diversity of generated samples and the stability of training.Experimental results based on the CASAS household activity recognition dataset show that the quantum hybrid generator only requires about 13%of the parameters of the classical model to achieve better generation effects.The improved quantum generative adversarial network not only improves the convergence speed but also enhances the accuracy of the final results.关键词
量子生成器/经典判别器/变分量子电路/WGAN-GPKey words
Quantum generator/Classical discriminator/Variational Quantum Circuit/WGAN-GP分类
信息技术与安全科学引用本文复制引用
李文慧,阮越,薛希玲..面向人类活动识别的量子生成对抗网络数据增强与分类优化[J].新疆师范大学学报（自然科学版）,2026,45(3):66-75,82,11.基金项目
国家自然科学基金项目(61802002) （61802002）
安徽省教育厅自然科学重点项目(KJ2020A0233). （KJ2020A0233）
