15 papers
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…
Quantum Transformer BSDE Solver via Multi-Layer Fully-Connected Variational Quantum Circuits
Howard Su, Huan-Hsin Tseng, Chi-Sheng Chen +1
Solving high-dimensional parabolic partial differential equations (PDEs) is important in engineering, physics, and stochastic control. Deep BSDE methods reformulate semilinear PDEs…
Predicting Poets' Origins from Verse: A Computational Analysis of Regional Linguistic Fingerprints in the Complete Tang Poems
Chi-Sheng Chen, Hung-Yun Liu
We ask whether the geographic origin of Tang-dynasty poets leaves a detectable linguistic trace in their work. Aggregating every poem attributed to each author in the Complete Tang…
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…
Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Chi-Sheng Chen, En-Jui Kuo
Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accur…
Few-Shot Continual Learning for 3D Brain MRI with Frozen Foundation Models
Chi-Sheng Chen, Xinyu Zhang, Guan-Ying Chen +3
Foundation models pretrained on large-scale 3D medical imaging data face challenges when adapted to multiple downstream tasks under continual learning with limited labeled data. We…
FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
Chi-Sheng Chen, Xinyu Zhang, En-Jui Kuo +3
Time series forecasting models often lack interpretability, limiting their adoption in domains requiring explainable predictions. We propose \textsc{FreqLens}, an interpretable for…
A Unified SPD Token Transformer Framework for EEG Classification: Systematic Comparison of Geometric Embeddings
Chi-Sheng Chen, En-Jui Kuo, Guan-Ying Chen +2
Spatial covariance matrices of EEG signals are Symmetric Positive Definite (SPD) and lie on a Riemannian manifold, yet the theoretical connection between embedding geometry and opt…
Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures
Chi-Sheng Chen, En-Jui Kuo
Diffusion models typically employ static or heuristic classifier-free guidance (CFG) schedules, which often fail to adapt across timesteps and noise conditions. In this work, we in…
Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures
Chi-Sheng Chen, En-Jui Kuo
This paper presents a comprehensive evaluation of quantum text generation models against traditional Transformer/MLP architectures, addressing the growing interest in quantum compu…
Quantum-Enhanced Channel Mixing in RWKV Models for Time Series Forecasting
Chi-Sheng Chen, En-Jui Kuo
Recent advancements in neural sequence modeling have led to architectures such as RWKV, which combine recurrent-style time mixing with feedforward channel mixing to enable efficien…
Unraveling Quantum Environments: Transformer-Assisted Learning in Lindblad Dynamics
Chi-Sheng Chen, En-Jui Kuo
Understanding dissipation in open quantum systems is crucial for the development of robust quantum technologies. In this work, we introduce a Transformer-based machine learning fra…
Quantum Adaptive Self-Attention for Quantum Transformer Models
Chi-Sheng Chen, En-Jui Kuo
A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unc…
Accretion-Driven Squeezing of Fuzzy Dark Matter Halo Cores in the Schrödinger-Newton Framework
Chon-Fai Kam, Chi-Sheng Chen, En-Jui Kuo
We investigate the impact of accretion onto supermassive black holes on the density profiles of a fuzzy dark matter soliton core at the center of a dark matter halo. Treating the s…