3 citations · 7 across the 12 of their papers we have counts for
5 papers · 1 filter
A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
Chen-Yu Liu, Chao-Han Huck Yang, Hsi-Sheng Goan +1
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits i…
QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train
Chen-Yu Liu, Chu-Hsuan Abraham Lin, Chao-Han Huck Yang +2
Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face cha…
Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in da…
Oracle Separation between Noisy Quantum Polynomial Time and the Polynomial Hierarchy
Nai-Hui Chia, Min-Hsiu Hsieh, Shih-Han Hung +1
This work investigates the oracle separation between the physically motivated complexity class of noisy quantum circuits, inspired by definitions such as those presented by Chen, C…
Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become cr…