From the 1 of 11 linked papers with an AI index.
11 papers
Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu +7
Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federa…
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +14
The paper proposes Complementary Matrix Gating, a coordinate‑wise gating scheme for fast‑weight programmers built on quantum‑inspired Kolmogorov‑Arnold networks, and shows it impro…
Rethinking Quantum Continual Learning with Quantum Fisher Information
Yu-Chao Hsu, Yu-Cheng Lin, Tai-Yue Li +2
Quantum continual learning aims to train quantum models on sequential tasks without losing previously learned knowledge. However, variational quantum classifiers (VQCs) are prone t…
Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang +16
Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with v…
Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
Yu-Cheng Lin, Yu-Chao Hsu, I-Shan Tsai +9
High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum circuit simulation, and selecte…
QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memory
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin +5
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 telecommunicat…