Quantum-Safe Systems and Intelligent Learning
I study the design of quantum-safe communication systems that remain secure against both classical and quantum adversaries. My research focuses on end-to-end security architectures that integrate NIST transitional post-quantum cryptography, including ML-KEM (Kyber) and ML-DSA (Dilithium), with quantum-assisted key establishment. In particular, I am interested in entanglement-based QKD as a complementary trust anchor within hybrid protocols, and in how policy-driven negotiation, downgrade resistance, and performance trade-offs shape real-world deployment.
I investigate quantum machine learning under realistic hardware constraints, focusing on how model structure, optimization dynamics, and noise jointly influence learning behavior. Rather than treating quantum models as opaque optimizers, my work emphasizes interpretable and structure-aware approaches that connect complexity and information flow to training stability and generalization. In parallel, I explore training-efficient hybrid learning paradigms suitable for near-term quantum systems.