量子辅助显存高效训练用于Wi-Fi人体活动识别
原标题：Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition
AI 摘要
本文提出了一种量子辅助的显存高效训练框架 Q-MET，用于基于 Wi-Fi 的人体活动识别（HAR）。该框架通过混合量子-经典神经网络间接生成模型参数，将可训练参数减少 90%-95%，同时保持或超过传统分类精度。结合结构化剪枝，模型稀疏度达 75%-85%，精度损失小于 2%。这是首个同时解决 HAR 系统训练和推理阶段显存低效问题的量子辅助方法。
正文节选
Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition Abstract 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 deploy