发表机构
University of Liverpool; University of Science and Technology Beijing; Memorial University（利物浦大学; 北京科技大学; 纪念大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对Wi-Fi人体活动识别系统训练与推理阶段的内存低效问题，提出量子辅助内存高效训练框架Q-MET，通过混合量子经典神经网络与结构化剪枝，大幅减少可训练参数，同时保持较高分类准确率，为HAR系统部署提供了新方案。
AI 中文摘要
基于Wi-Fi的人体活动识别（HAR）已成为集成感知与通信的重要组成部分，为一系列上下文感知服务铺平了道路。然而，大多数现有的基于Wi-Fi的HAR系统依赖深度学习（DL）模型，这些模型在训练和推理阶段均存在计算与内存密集的问题，给实际部署带来了重大挑战。传统训练需要同时更新数百万个参数，导致内存消耗过高。本文提出了一种新型量子辅助内存高效训练框架（Q-MET），旨在提升训练与推理阶段的效率。Q-MET利用混合量子经典神经网络间接生成HAR模型的参数，与直接优化相比，大幅减少了可训练参数数量。为进一步支持在资源受限设备上的部署，我们在训练阶段集成结构化剪枝。实验结果表明，与传统基于反向传播的DL训练相比，Q-MET实现了90%至95%的可训练参数减少，同时保持甚至超过了经典分类准确率。此外，Q-MET通过结构化剪枝支持轻量级推理，实现了75%至85%的模型稀疏度，且分类准确率损失小于2%。据我们所知，本研究是首个同时解决HAR系统训练与推理阶段内存低效问题的量子辅助方法。
英文摘要
Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models that are computationally and memory intensive in both training and inference, which poses significant challenges for real-world deployment. Conventional training requires simultaneous updates of millions of parameters, leading to prohibitive memory consumption. In this paper, we propose a novel quantum-assisted memory-efficient training framework (Q-MET) designed to improve efficiency in both training and inference. Q-MET utilizes a hybrid quantum classical neural network to indirectly generate parameters for HAR models, significantly reducing the trainable parameter count compared to direct optimization. To further support the deployment on resource-constrained devices, we integrate structured pruning during the training phase. Experimental results demonstrate that Q-MET achieves a 90% to 95% reduction in trainable parameters compared with conventional backpropagation-based DL training while maintaining or even exceeding classical classification accuracy. Additionally, Q-MET supports lightweight inference through structured pruning, achieving 75% to 85% model sparsity with less than 2% loss in classification accuracy. To the best of our knowledge, this work represents the first quantum-assisted approach to simultaneously tackle memory inefficiencies in both the training and inference stages of HAR systems.
Comments18 pages, 9 figures
Journal refIEEE Trans. Netw. Sci. Eng., pp. 1-18, 2026
