1 citations · 1 across the 17 of their papers we have counts for
18 papers
Variational Quantum Attention for Molecular Graph Learning
Yu-Cheng Lin, Yu-Chao Hsu, Tai-Yue Li +2
Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments during message passing. Yet it…
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 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
Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updates often require repeated circ…
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…
Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +3
Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when predict…
An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction
Jen-Yu Chang, Yi-Chun Chang, Yu-Jui Lin +6
Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configurati…
: Operator-based Mixture Ensemble for Generative Assimilation
Pouria Behnoudfar, Nan Chen
Characterizing non-Gaussian posterior distributions in partially observed high-dimensional nonlinear systems remains a fundamental challenge in data assimilation. Ensemble Kalman f…
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…
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…
Scalable Quantum Molecular Generation via GPU-Accelerated Tensor-Network Simulation
Yu-Cheng Xiao, Jen-Yu Chang, Tzu-Ling Kuo +7
We propose Scalable Quantum Molecular Generation (SQMG), a variational quantum-circuit for sampling molecular graphs using chemical priors on atoms and bonds. SQMG assigns a fixed…
MPM-QIR: Measurement-Probability Matching for Quantum Image Representation and Compression via Variational Quantum Circuit
Chong-Wei Wang, Mei Ian Sam, Tzu-Ling Kuo +2
We present MPM-QIR, a variational-quantum-circuit (VQC) framework for classical image compression and representation whose core objective is to achieve equal or better reconstructi…
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 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 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…
Validating Large-Scale Quantum Machine Learning: Efficient Simulation of Quantum Support Vector Machines Using Tensor Networks
Kuan-Cheng Chen, Tai-Yue Li, Yun-Yuan Wang +6
We present an efficient tensor-network-based approach for simulating large-scale quantum circuits, demonstrated using Quantum Support Vector Machines (QSVMs). Our method effectivel…