Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design

This paper presents a comprehensive study on Distributed Quantum Learning (DQL), focusing on its convergence under practical conditions such as partial device participation and heterogeneous data distributions. It introduces a multi-layered post-quantum cryptographic architecture that integrates a quantum neural network for adaptive security monitoring, achieving a 49% reduction in security execution time compared to static methods, while maintaining over 91% threat detection accuracy. These advancements are crucial for practitioners aiming to deploy efficient and secure DQL systems in real-world applications.