85 citations · 92 across the 29 of their papers we have counts for
13 papers · 1 filter
Neural Architecture Search for Quantum Autoencoders
Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-laye…
It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
Junghoon Justin Park, Huan-Hsin Tseng, Shinjae Yoo +2
Quantum reinforcement learning (QRL) promises compact function approximators with access to vast Hilbert spaces, but its practical progress is slowed by NISQ-era constraints such a…
Resting-state fMRI Analysis using Quantum Time-series Transformer
Junghoon Justin Park, Jungwoo Seo, Sangyoon Bae +4
Resting-state functional magnetic resonance imaging (fMRI) has emerged as a pivotal tool for revealing intrinsic brain network connectivity and identifying neural biomarkers of neu…
Quantum Reinforcement Learning by Adaptive Non-local Observables
Hsin-Yi Lin, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1
Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We…
Special-Unitary Parameterization for Trainable Variational Quantum Circuits
Kuan-Cheng Chen, Huan-Hsin Tseng, Samuel Yen-Chi Chen +2
We propose SUN-VQC, a variational-circuit architecture whose elementary layers are single exponentials of a symmetry-restricted Lie subgroup, $\mathrm{SU}(2^{k}) \subset \mathrm{SU…
Entanglement Certification by Measuring Nonlocality
Xuan Du Trinh, Zhengyu Wu, Junlin Bai +3
Reliable verification of entanglement is a central requirement for quantum networks. This paper presents a practical verification approach based on violations of the Clauser-Horne-…
On Quantum BSDE Solver for High-Dimensional Parabolic PDEs
Howard Su, Huan-Hsin Tseng
We propose a quantum machine learning framework for approximating solutions to high-dimensional parabolic partial differential equations (PDEs) that can be reformulated as backward…
Learning to Program Quantum Measurements for Machine Learning
Samuel Yen-Chi Chen, Huan-Hsin Tseng, Hsin-Yi Lin +1
The rapid advancements in quantum computing (QC) and machine learning (ML) have sparked significant interest, driving extensive exploration of quantum machine learning (QML) algori…
Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
Junghoon Justin Park, Jiook Cha, Samuel Yen-Chi Chen +2
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a…
Adaptive Non-local Observable on Quantum Neural Networks
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Conventional Variational Quantum Circuits (VQCs) for Quantum Machine Learning typically rely on a fixed Hermitian observable, often built from Pauli operators. Inspired by the Heis…
Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning
Chi-Sheng Chen, Samuel Yen-Chi Chen, Huan-Hsin Tseng
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in deco…
Learning to Measure Quantum Neural Networks
Samuel Yen-Chi Chen, Huan-Hsin Tseng, Hsin-Yi Lin +1
The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into quantum machine learning (QML) algorithms…
Transfer Learning Analysis of Variational Quantum Circuits
Huan-Hsin Tseng, Hsin-Yi Lin, Samuel Yen-Chi Chen +1
This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates the transition of…