Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition
This work represents the first quantum-assisted approach to simultaneously tackle memory inefficiencies in both the training and inference stages of HAR systems, and achieves a 90% to 95% reduction in trainable parameters compared with conventional backpropagation-based DL training.
T. An, Jie Zhang, Guo-Lin Yin et al.
· IEEE Transactions on Network... · 0 citations