# Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition

arXiv: https://arxiv.org/abs/2609.04271 · HTML: https://mlsanity.com/paper/2609.04271/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 99 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 30 of 34)

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- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://mlsanity.com/paper/1707.06342/) (2017)
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- [A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1804.03294/) (2018)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
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- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [When to Prune? A Policy towards Early Structural Pruning](https://mlsanity.com/paper/2110.12007/) (2021)
- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
- [SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing](https://mlsanity.com/paper/2207.07859/) (2022)
- [AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi](https://mlsanity.com/paper/2309.16964/) (2023)
- [Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection](https://mlsanity.com/paper/2410.09250/) (2024)
- [An Overview on IEEE 802.11bf: WLAN Sensing](https://mlsanity.com/paper/2310.17661/) (2023)
- [FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning](https://mlsanity.com/paper/2203.02014/) (2022)
- [Optimal preprocessing of WiFi CSI for sensing applications](https://mlsanity.com/paper/2307.12126/) (2023)
- [Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks](https://mlsanity.com/paper/2212.13930/) (2022)

## Abstract (arXiv)

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.