7 papers
A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability
Rushat Rai, Yun-Yuan Wang, Autsada Kakaen +10
We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling. A graph neural network (GNN)…
Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs
Rushat Rai, Pei-Jie Chang, Doan Viet Nguyen +8
Optimizing recessed-gate AlGaN/GaN MIS-HEMTs requires accurate multi-characteristic models, but experimental semiconductor datasets remain costly and encode process-induced variabi…
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…
Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning
An Ning, Tai Yue Li, Nan Yow Chen
We propose the Quantum Graph Attention Network (QGAT), a hybrid graph neural network that integrates variational quantum circuits into the attention mechanism. At its core, QGAT em…
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 Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement
An Ning, Tai-Yue Li, Nan-Yow Chen
In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach…