The SecQ Lab advances the foundations of security and scalability in complex systems through the integration of quantum intelligence (quantum optimization and quantum machine learning) and graph machine learning. Our research spans critical infrastructures, social and technological networks, and blockchain systems, with a focus on developing trustworthy, resilient, and scalable solutions.
"Quantum Approximate Optimization and Machine Learning for Real-World Applications" received support from IonQ Research (Google/IonQ Cloud credits); see IonQ for program details.
Our proposal on securing CPS using quantum computing was funded by the Commonwealth Cyber Initiative – Central Virginia Node (CCI CVN); see CCI.
Our paper "RELIC: Reinforcement Learning Based Ising Optimization via Graph Compression" was accepted to the QCRL Workshop Track at IEEE Quantum Week (QCE'25); conference info: IEEE QCE.
Our paper "Noise-aware Quantum Annealing for State Estimation in Power Systems" was accepted to the QAPP Track at IEEE Quantum Week (QCE'25); conference info: IEEE QCE.
Our paper "Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression" was accepted (oral) at AAAI 2025; paper: arXiv:2412.18571, conference: AAAI'25.
Our proposal "Privacy Preserving Federated IoT Learning for Smart Public Health Surveillance" was funded by the CCI Hub; see CCI.
Our proposal "Weakly-supervised Federated Graph Learning for Cyber-Physical Systems" was funded by CCI – Central Virginia Node (Aug 1, 2023 – Jul 31, 2024); see CCI.
Our proposal "Collaborative Research: AMPS: Rethinking State Estimation for Power Distribution Systems in the Quantum Era" was funded by the National Science Foundation (NSF); agency: NSF.
Our paper "FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing" was accepted to the 2022 IEEE International Conference on Quantum Computing and Engineering (QCE'22); paper: arXiv:2205.05004, conference: IEEE QCE'22.
ERB 2321, College of Engineering,
Virginia Commonwealth University,
Richmond, VA, 23284
Email: tndinh [at] vcu.edu
Full List of Publications
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Develops a novel quantum-inspired framework for power state estimation, addressing cyber risks and operational challenges in decentralized grids.
Harness the power of near-term quantum devices for addressing the most pressing human health challenges, including timely drug discovery, a crucial capability in preparation for future pandemics.
Develops a principled and systematic federated learning framework that offers protection against threats from both malicious users and servers.
Scalable analysis for billion-scale social networks to unveil social dynamic patterns