{"slug": "quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based", "title": "Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition", "summary": "Researchers proposed Q-MET, a quantum-assisted memory-efficient training framework for Wi-Fi-based human activity recognition, achieving a 90% to 95% reduction in trainable parameters compared to conventional deep learning training while maintaining or exceeding classification accuracy. The framework also enables lightweight inference via structured pruning, achieving 75% to 85% model sparsity with less than 2% accuracy loss, marking the first quantum-assisted approach to address memory inefficiencies in both training and inference for HAR systems.", "body_md": "arXiv:2609.04271v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based", "canonical_source": "https://arxiv.org/abs/2609.04271", "published_at": "2026-09-07 04:00:00+00:00", "updated_at": "2026-09-07 04:26:54.637140+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning"], "entities": ["Q-MET", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based", "markdown": "https://wpnews.pro/news/quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based.md", "text": "https://wpnews.pro/news/quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based.txt", "jsonld": "https://wpnews.pro/news/quantum-assisted-memory-efficient-training-for-parameter-intensive-wi-fi-based.jsonld"}}