85 citations · 92 across the 29 of their papers we have counts for
26 papers · 1 filter
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…
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…
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-…
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…
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…
Quantum Gradient Class Activation Map for Model Interpretability
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not bee…
Quantum Privacy Aggregation of Teacher Ensembles (QPATE) for Privacy-preserving Quantum Machine Learning
William Watkins, Heehwan Wang, Sangyoon Bae +4
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge. Papernot et. al. developed a technique, known as Private Aggregation…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…