85 citations · 92 across the 29 of their papers we have counts for
32 papers
Efficient Neural Field Learning via Adaptive Coverage and Focused Sampling
Guang Zhao, Xihaier Luo, Huan-Hsin Tseng +4
Implicit neural representations (INRs) provide a flexible framework for modeling high-dimensional continuous fields, but their training is often inefficient due to uniform subsampl…
Quasi-polar Decomposition of Quantum Neural Networks via Adaptive Non-local Observables
Shih-Hao Ho, Yan Li, Huan-Hsin Tseng +3
We use Diagonal Adaptive Non-local Observables (DANO) as a canonical decomposition for studying Variational Quantum Circuit model evolution. Separating each learned observable into…
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…
Multivariate Time Series Forecasting with Adaptive Non-Local Observables
Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen
Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this…
Observable Geometry for Effective Quantum Circuits
Huan-Hsin Tseng, Hsin-Yi Lin, Samuel Yen-Chi Chen +3
We study redundancy and effectiveness of Variational Quantum Circuits via algebraic and geometric views of Lie groups. Considering unitary transformations acting on Hermitian obser…
Stable Self-Modulating Quantum Fast-Weight Programmers with Bounded Memory Gates
Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin +8
Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offeri…
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Samuel Yen-Chi Chen, Yifeng Peng, Kuo-Chung Peng +8
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmer…
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
Samuel Yen-Chi Chen, Yifeng Peng, Jiun-Cheng Jiang +8
Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing. In this paper, we propose a Recursive Qua…
Diagonal Adaptive Non-local Observables on Quantum Neural Networks
Huan-Hsin Tseng, Yan Li, Hsin-Yi Lin +1
Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Quantum Algorithms, partly shif…
Photonic Quantum-Enhanced Knowledge Distillation
Kuan-Cheng Chen, Shang Yu, Chen-Yu Liu +10
Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during…
Hybrid Quantum Temporal Convolutional Networks
Junghoon Justin Park, Maria Pak, Sebin Lee +4
Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (H…
Scalable Quantum Machine Learning via Multi-layer Fully-Connected Variational Quantum Circuits
Howard Su, Chen-Yu Liu, Samuel Yen-Chi Chen +2
Variational quantum circuits (VQCs) face an expressivity-trainability dilemma and scalability challenges. We propose Multi-Layer Fully-Connected Variational Quantum Circuits (FC-VQ…
Quantum Super-resolution by Adaptive Non-local Observables
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but requ…
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…