H
Hoang-Quan Nguyen Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
5 h-index
42 pubs
111 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Hoang-Quan Nguyen's research investigates computational and theoretical approaches to materials science, with a focus on composite materials and concrete. His work includes developing micromechanical models to predict the relationship between the strength and porosity of pervious concrete, and numerical methods to analyze its flexural damage behavior. Nguyen also studies the behavior of highly-filled inclusion-matrix composites using refined morphological representative pattern approaches.
His research extends into quantum computing and artificial intelligence, with recent publications on quantum transformer-based frameworks for visual clustering and quantum visual feature encoding. He has also explored quantum control gates for functional MRI understanding and developed foundation models and large datasets for visual insect understanding. Nguyen collaborates with researchers at the University of Arkansas at Fayetteville, including Hoang Quan Nguyen and Xuan-Bac Nguyen.
Metrics
- h-index: 5
- Publications: 42
- Citations: 111
Selected Publications
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QUADRO: A Hybrid Quantum Optimization Framework for Drone Delivery (2025)
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QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks (2025)
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Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding (2025)
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Diffusion-inspired quantum noise mitigation in parameterized quantum circuits (2025)
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Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits (2024)
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Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding (2024)
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Quantum visual feature encoding revisited (2024)
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Quantum Visual Feature Encoding Revisited (2024)
Collaboration Network
Top Collaborators
Khoa Luu
18 shared publications
In database
- Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding
- Quantum visual feature encoding revisited
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
- Hierarchical Quantum Control Gates for Functional MRI Understanding
Showing 5 of 18 shared publications
Hugh Churchill
12 shared publications
In database
- Quantum visual feature encoding revisited
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Hierarchical Quantum Control Gates for Functional MRI Understanding
- Diffusion-inspired quantum noise mitigation in parameterized quantum circuits
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
Showing 5 of 12 shared publications
Xuan-Bac Nguyen
11 shared publications
- Quantum visual feature encoding revisited
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Hierarchical Quantum Control Gates for Functional MRI Understanding
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
Showing 5 of 11 shared publications
Xuan-Bac Nguyen
6 shared publications
In database
- Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding
- Diffusion-inspired quantum noise mitigation in parameterized quantum circuits
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
- Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
Showing 5 of 6 shared publications
Samee U. Khan
Mississippi State University (US)
6 shared publications
- Quantum visual feature encoding revisited
- Diffusion-inspired quantum noise mitigation in parameterized quantum circuits
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
- Hierarchical Quantum Control Gates for Functional MRI Understanding
Showing 5 of 6 shared publications
Nicholas J. Borys
6 shared publications
- Diffusion-inspired quantum noise mitigation in parameterized quantum circuits
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
- $φ$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Showing 5 of 6 shared publications
Thanh-Dat Truong
University of Arkansas at Fayetteville
5 shared publications
In database
- Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding
- Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
Samee U. Khan
5 shared publications
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Hierarchical Quantum Control Gates for Functional MRI Understanding
- Quantum Visual Feature Encoding Revisited
- Quantum Visual Feature Encoding Revisited
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
Samuel Yen-Chi Chen
Wells Fargo (United States) (US)
5 shared publications
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Diffusion-inspired quantum noise mitigation in parameterized quantum circuits
- QClusformer: A Quantum Transformer-based Framework for Unsupervised Visual Clustering
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
- Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
Ashley P. G. Dowling
University of Arkansas at Fayetteville
4 shared publications
In database
- Insect-Foundation: A Foundation Model and Large-Scale 1M Dataset for Visual Insect Understanding
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
S. D. Pandey
4 shared publications
In database
- $φ$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery
- QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Xin Li
University at Albany, State University of New York (US)
3 shared publications
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding
- Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding
T. Faltermeier
3 shared publications
- $φ$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Xuan-Bac Nguyen
2 shared publications
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Hugh Churchill
2 shared publications
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
- QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Similar Researchers
Based on overlapping research topics
Xuan-Bac Nguyen
University of Arkansas at Fayetteville
Quantum Computing Applications
Machine Learning Applications
Computer Graphics and Visualization Techniques
Nathaniel Fredricks
University of Arkansas at Fayetteville
Materials Science Research
Machine Learning Applications
Computer Graphics and Visualization Techniques
Justin Zhan
University of Arkansas
Materials Science Research
Machine Learning Applications
Computer Graphics and Visualization Techniques
Pierce Helton
University of Arkansas at Fayetteville
Machine Learning Applications
Computer Graphics and Visualization Techniques
Insect and Arachnid Ecology and Behavior
Kaicong Wu
University of Arkansas at Fayetteville
Materials Science Research
Machine Learning Applications
Computer Graphics and Visualization Techniques
Changgen Li
University of Arkansas at Fayetteville
Materials Science Research
Machine Learning Applications
Computer Graphics and Visualization Techniques