10 papers
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…