5 papers
Meta-Learning for Quantum Optimization via Quantum Sequence Model
Yu-Cheng Lin, Yu-Chao Hsu, Samuel Yen-Chi Chen
The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for solving combinatorial optimization problems on near-term quantum processors. However, finding good v…
Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin +5
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into…
Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention
Yu-Chao Hsu, Kuan-Cheng Chen, Tai-Yue Li +1
In this work, we introduce the Quantum Adaptive Excitation Network (QAE-Net), a hybrid quantum-classical framework designed to enhance channel attention mechanisms in Convolutional…
Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
Yu-Chao Hsu, Nan-Yow Chen, Tai-Yu Li +3
We present the Quantum Kernel-Based Long short-memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy an…
Quantum Kernel-Based Long Short-term Memory
Yu-Chao Hsu, Tai-Yu Li, Kuan-Cheng Chen
The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In th…