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Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition
Not provided in the abstract
quantum computinghuman activity recognitionmemory efficiencystructured pruning
2609.04271
Builder Relevance
7h ago80%
Abstract
This paper presents a quantum-assisted framework to enhance memory efficiency in training Wi-Fi-based human activity recognition models.
Reality Card
Core Claim
Q-MET achieves a 90% to 95% reduction in trainable parameters compared to conventional deep learning training while maintaining or exceeding classification accuracy.
Method / Result
Achieves 75% to 85% model sparsity with less than 2% loss in classification accuracy.
Limitations
The abstract does not provide specific details on the reproducibility of the quantum-assisted approach.
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