3 citations · 4 across the 12 of their papers we have counts for
13 papers
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…
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…
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…
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…
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…
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…