85 citations · 113 across the 27 of their papers we have counts for
27 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…
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…
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…
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 Machine Learning Architecture Search via Deep Reinforcement Learning
Xin Dai, Tzu-Chieh Wei, Shinjae Yoo +1
The rapid advancement of quantum computing (QC) and machine learning (ML) has given rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the stre…
Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks
Xihaier Luo, Wei Xu, Yihui Ren +2
Reliably reconstructing physical fields from sparse sensor data is a challenge that frequently arises in many scientific domains. In practice, the process generating the data often…
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…
Federated Quantum Long Short-term Memory (FedQLSTM)
Mahdi Chehimi, Samuel Yen-Chi Chen, Walid Saad +1
Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although…
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…
Quantum Federated Learning With Quantum Networks
Tyler Wang, Huan-Hsin Tseng, Shinjae Yoo
A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable info…
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
Yi Huang, Yihui Ren, Shinjae Yoo +1
High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high-energy physics. Dev…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…