Distributed Quantum Learning: Convergence Analysis and Adaptive Post-Quantum Security Framework
By
[Submitted on 21 May 2026]
Summary
This paper presents a comprehensive study on Distributed Quantum Learning (DQL), focusing on two key aspects: convergence analysis under practical conditions (partial device participation, non-convex loss functions, heterogeneous data distributions) and security design using a multi-layered post-quantum cryptographic architecture with an adaptive quantum neural network-powered mechanism. The research reveals a fundamental trade-off between convergence rate, measurement shots, and participating device subset size. Hardware experiments demonstrate that the dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines while maintaining over 91% threat detection accuracy.
Source
bskyDistributed Quantum Learning: Convergence Analysis and Adaptive Post-Quantum Security Frameworkarxiv.orgKey quotes
· 5 pulledDistributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices.
The derived convergence bound uncovers a fundamental trade-off between convergence rate, measurement shots, and the size of the participating device subset.
Our dynamic security mechanism reduces total security execution time by approximately 49% relative to static high-security baselines, while maintaining a threat detection accuracy of over 91%.
Findings from our evaluations on a physical testbed modeling quantum control architectures expose the performance limitations of static post-quantum security.
Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems.
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