Trustworthy and Secure Quantum Computing and Quantum AI: Developed quantum differential privacy (QDP) mechanisms to protect sensitive information in quantum computing and quantum machine learning (QML) through privacy-preserving quantum data encoding, circuit operations, and measurement strategies. Investigated adversarial attacks and defense mechanisms to enhance the robustness, security, privacy, and trustworthiness of quantum computing and quantum AI systems [1]–[3].
AI-Driven Optimization for Near-Term Quantum Computing and Quantum AI: Developed AI-driven approaches to enhance the performance, reliability, and efficiency of near-term quantum computing and quantum machine learning (QML), leveraging reinforcement learning (RL) for Zero-Noise Extrapolation (ZNE) to mitigate quantum hardware noise and large language models (LLMs) for quantum circuit optimization to reduce circuit complexity and improve execution efficiency [4-5].
Distributed QML over Networks: Developed distributed and quantum federated learning (QFL) frameworks to enable collaborative quantum model training across geographically distributed quantum devices, with a particular focus on wireless communication design and resource allocation, including communication-efficient learning, wireless bandwidth and power allocation, and qubit resource management to support scalable, efficient, and reliable QFL under heterogeneous network and hardware constraints [6-8].
References:
[1] P. Atit and D. C. Nguyen, “Federated quantum learning with adaptive differential privacy via quantum noise,” IEEE Transactions on Information Forensics and Security, 2026.
[2] A. Pokharel, R. Rahman, S. Shaon, T. Morris, and D. C. Nguyen, “Differentially private federated quantum learning via quantum noise,” in 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), vol. 1, 2025, pp. 1559–1565.
[3] A. Pokharel, S. Shaon, T. Morris, and D. C. Nguyen, “Distributed quantum learning over near-term devices: Convergence analysis and security design,” IEEE Journal on Selected Areas in Communications, 2026.
[4] S. Shaon, C. G. Brinton, and D. C. Nguyen, “Communication-efficient quantum federated learning over large-scale wireless networks,” IEEE Transactions on Networking, 2026.
[5] R. Rahman and D. C. Nguyen, “Quantum noise mitigation with adaptive zero-noise extrapolation: A contextual multi-armed bandits approach,” IEEE Journal on Selected Areas in Communications, 2026.
[6] R. Rahman, S. Shaon, and D. C. Nguyen, “Tackling heterogeneity in quantum federated learning: An integrated sporadic-personalized approach,” IEEE Transactions on Computers, 2026.
[7] A. Pokharel, R. Rahman, T. Morris, and D. C. Nguyen, “Quantum federated learning for multimodal data: A modality-agnostic approach,” in 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2025, pp. 545–554.
[8] S. Shaon, A. Pokharel, A. Coutras, A. Sahoo, and D. C. Nguyen, “Resource-efficient distributed quantum learning over wireless networks with qubit reuse,” in 2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC). IEEE, 2026, pp. 1–6.