Dev Gurung
Researcher in Quantum/Machine Learning, Federated Learning, Applied/AI, Wireless Systems
My research focuses on developing robust, privacy-preserving, and secure algorithms for distributed networks, bridging the gap between quantum computing, artificial intelligence, and cybersecurity.
Research Interests
Quantum/Machine Learning
Designing and analyzing quantum circuits and algorithms (VQC, QCNN) via Qiskit and PennyLane to enhance traditional machine learning models.
Quantum/ Federated Learning
Developing decentralized machine learning frameworks that optimize for communication efficiency, data privacy, and security across heterogeneous devices.
Distributed Systems
Architecture design and performance analysis for next-generation networks including vehicular networks (V2X) and Low Earth Orbit (LEO) satellite communications.
Post/Quantum Cryptography
Integrating post/quantum secure protocols and blockchain technology to ensure long-term, verifiable security in federated learning environments.
Applied AI
Leveraging artificial intelligence techniques to solve complex real-world problems, with a focus on practical implementation, optimization, and system integration.
Wireless Communication
Investigating next-generation communication protocols, network coding, and signal processing to ensure efficient and reliable data transmission in dynamic environments.
Selected Publications
View All Publications →Chained Continuous Quantum Federated Learning Framework
D. Gurung, et al.
Future Generation Computer Systems (2025) [Accepted]
Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
D. Gurung, et al.
IEEE Transactions on Network and Service Management (2025) [Accepted]
Performance analysis and evaluation of post-quantum secure blockchained federated learning
D. Gurung, et al.
Computer Networks (2024) [Accepted]