An Improved Quantum Anonymous Notification Protocol for Quantum-Augmented Networks

• URL: http://arxiv.org/abs/2511.12313v1
• Date: Sat, 15 Nov 2025 17:59:11 GMT
• Title: An Improved Quantum Anonymous Notification Protocol for Quantum-Augmented Networks
• Authors: Nitin Jha, Abhishek Parakh, Mahadevan Subramaniam,
• Abstract summary: Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security.<n>We propose an improved quantum anonymous notification (QAN) protocol that utilizes rotation operations on shared GHZ states.<n>We study the behavior of this modified QAN protocol under the dephasing noise model and observe stronger resilience to false notifications than earlier QAN approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the presence of several common channel noises. In this work, we propose an improved quantum anonymous notification (QAN) protocol that utilizes rotation operations on shared GHZ states to produce an anonymous notification in an n-user quantum-augmented network. We study the behavior of this modified QAN protocol under the dephasing noise model and observe stronger resilience to false notifications than earlier QAN approaches. The QAN framework is also proposed to be integrated with a machine-learning classifier, enhanced quantum-augmented network. Finally, we discuss how this notification layer integrates with QuANets so that receivers can allow switch-bypass handling of quantum payloads, reducing header-based information leakage and vulnerability to targeted interference at compromised switches.

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