Chen-Yu Liu — accepted AI-conference papers

2025

A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning

Chen-Yu Liu, Chao-Han Huck Yang, Hsi-Sheng Goan, Min-Hsiu Hsieh

ICLR 2025poster

Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in the input stage and the reliance on quantum resou…

Cited by 5 Source ↗PDF ↗Scholar ↗

2025

Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz

Chen-Yu Liu, Chu-Hsuan Abraham Lin, Kuan-Cheng Chen

ICASSP 2025accepted

In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but i…

Cited by 0 Source ↗Scholar ↗