Distributed Quantum Learning over Near-term Devices: Convergence Analysis and Security Design
Distributed quantum learning (DQL) has emerged as a promising paradigm to scale quantum-enhanced machine learning by interconnecting multiple quantum devices. However, for efficient real-world deployment, it is essential to characterize how DQL converges under practical scenarios while simultaneously safeguarding multi-device quantum infrastructures from evolving security threats. Addressing these aspects in an integrated manner is key to ensuring both performance and resilience in large-scale DQL systems. Therefore, this paper presents a new DQL study where our innovation lies in: (i) conducting a holistic convergence analysis for DQL under practical settings, i.e., partial device partic...
| Authors | Atit Pokharel, Shaba Shaon, Thomas Morris et al. |
|---|---|
| Primary category | quant-ph |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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