QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate. However, conventional LSTMs suffer from high parameter redundancy and limited nonlinear expressivity. In this work, we propose the Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory (QKAN-LSTM), which integrates Data Re-Uploading Activation (DARUAN) modules into the gating structure of LSTMs. Each DARUAN acts as a quantum variational activation function (QVAF), enhancing frequency adaptability and enabling an exponentially enriched spectral representation without multi-qubit entanglement. The resulting architecture preserves quantum-level expressivity while remaining fully executable on classical hardware. Empirical evaluations on three datasets, Damped Simple Harmonic Motion, Bessel Function, and Urban Telecommunication, demonstrate that QKAN-LSTM achieves superior predictive accuracy and generalization with a 79% reduction in trainable parameters compared to classical LSTMs. We extend the framework to the Jiang-Huang-Chen-Goan Network (JHCG Net), which generalizes KAN to encoder-decoder structures, and then further use QKAN to realize the latent KAN, thereby creating a Hybrid QKAN (HQKAN) for hierarchical representation learning. The proposed HQKAN-LSTM thus provides a scalable and interpretable pathway toward quantum-inspired sequential modeling in real-world data environments.
693272316d1060ca587a278f
Yu-Chao Hsu
693272316d1060ca587a2790
Jiun-Cheng Jiang
claimed_verified
2025-12-05T08:29:09.419Z
68b93b3a6c86c127a199ad90
/avatars/f370d99b240ce5b9e1bfdbd3130d9ee4.svg
Jiun-Cheng Jiang
Jim137
user
Jim137
693272316d1060ca587a2791
Chun-Hua Lin
693272316d1060ca587a2792
Kuo-Chung Peng
693272316d1060ca587a2793
Nan-Yow Chen
693272316d1060ca587a2794
Samuel Yen-Chi Chen
693272316d1060ca587a2795
En-Jui Kuo
693272316d1060ca587a2796
Hsi-Sheng Goan
QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
Long short-term memory (LSTM) models are a particular type of recurrent neural networks (RNNs) that are central to sequential modeling tasks in domains such as urban telecommunication forecasting, where temporal correlations and nonlinear dependencies dominate. However, conventional LSTMs suffer from high parameter redundancy and limited nonlinear expressivity. In this work, we propose the Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory (QKAN-LSTM), which integrates Data Re-Uploading Activation (DARUAN) modules into the gating structure of LSTMs. Each DARUAN acts as a quantum variational activation function (QVAF), enhancing frequency adaptability and enabling an exponentially enriched spectral representation without multi-qubit entanglement. The resulting architecture preserves quantum-level expressivity while remaining fully executable on classical hardware. Empirical evaluations on three datasets, Damped Simple Harmonic Motion, Bessel Function, and Urban Telecommunication, demonstrate that QKAN-LSTM achieves superior predictive accuracy and generalization with a 79% reduction in trainable parameters compared to classical LSTMs. We extend the framework to the Jiang-Huang-Chen-Goan Network (JHCG Net), which generalizes KAN to encoder-decoder structures, and then further use QKAN to realize the latent KAN, thereby creating a Hybrid QKAN (HQKAN) for hierarchical representation learning. The proposed HQKAN-LSTM thus provides a scalable and interpretable pathway toward quantum-inspired sequential modeling in real-world data environments.
A quantum-inspired LSTM model with Data Re-Uploading Activation modules achieves superior predictive accuracy and parameter efficiency in sequential modeling tasks.
LSTM recurrent neural networks Quantum-inspired Kolmogorov-Arnold Long Short-Term Memory QKAN-LSTM Data Re-Uploading Activation DARUAN quantum variational activation function QVAF Jiang-Huang-Chen-Goan Network JHCG Net latent KAN Hybrid QKAN HQKAN-LSTM