I am a Ph.D. student in the Department of Computer & Information Sciences at University of Delaware, where I work with Prof. Ilya Safro in the Algorithms, AI & Computational Science Lab. I am currently a Research Intern at Fujitsu Research Quantum Lab.

My current research involves Machine Learning for Quantum Computing, specifically on Quadratic Unconstrained Combinatorial Optimization, Qubit Routing Problem and Unit Commitment Problem. Before working on Quantum, I focused on Safety-aware and Robust AI modeling. (I made the switch in Jan 2025).

I obtained my B.S. at Texas Christian University.

News

• [Oct 2025] Rejoined Fujitsu Research of America Quantum Lab as Research Intern
• [Jul 2025] Paper accepted to QCE’25
• [Jun 2025] Joined Fujitsu Research of America Quantum Lab as Research Intern
• [Dec 2024] Two papers accepted to AAAI’25
• [Sep 2024] Paper accepted to NeurIPS’24
• [Jul 2024] Participated in UD ATOM Hackathon
• [Jul 2024] Paper accepted to CIKM’24
• [Jul 2023] Participated in UD DS + AI Hackathon
• [Jul 2023] Paper accepted to CVIU’23, Volume 235

What I’m Working On

My current research focuses on developing machine learning techniques to improve the quantum computing stack.

• Parameter transferability in the Quantum Approximate Optimization Algorithm (QAOA). A key challenge in scaling QAOA lies in finding good variational parameters, especially for large problem instances where quantum resources are limited. My goal is to build machine learning models that can learn transferable patterns from small problem instances and generalize them to significantly larger ones, effectively minimizing expensive optimization and circuit evaluations.
• Qubit Routing. To be updated…
• Unit Commitment. To be updated…

TLDR; I aim to bridge machine learning with quantum algorithm design to make quantum optimization more scalable, robust, and ultimately impactful for real-world applications.

Selected Publications

Quantum Era

• Cross-Problem Parameter Transferability in Quantum Approximate Optimization Algorithm: A Machine Learning Approach. Kien X. Nguyen, Bao Bach, and Ilya Safro. In Proceedings of the IEEE Quantum Computing and Engineering, 2025.

Pre-Quantum Era

• Interpretable Failure Detection with Human-Level Concepts. Kien X. Nguyen, Tang Li, and Xi Peng. In Proceedings of the AAAI Conference on Artificial Intelligence, 2025 (Oral).

• Adaptive Cascading Network for Continual Test-time Adaptation. Kien X. Nguyen, Fengchun Qiao, and Xi Peng. In the 33rd ACM International Conference on Information and Knowledge Management, 2024.