Towards a Quantum-classical Augmented Network
This work addresses the problem of improving network security and efficiency for quantum communication systems, but it is incremental as it builds on existing quantum and classical network technologies.
The paper tackles the challenge of deploying large-scale quantum networks by proposing to augment classical networks with quantum technologies, specifically modifying the HTTP protocol to carry both quantum and classical payloads based on privacy needs, and implements machine learning models to classify privacy labels, enabling reduced quantum resource utilization.
In the past decade, several small-scale quantum key distribution networks have been established. However, the deployment of large-scale quantum networks depends on the development of quantum repeaters, quantum channels, quantum memories, and quantum network protocols. To improve the security of existing networks and adopt currently feasible quantum technologies, the next step is to augment classical networks with quantum devices, properties, and phenomena. To achieve this, we propose a change in the structure of the HTTP protocol such that it can carry both quantum and classical payload. This work lays the foundation for dividing one single network packet into classical and quantum payloads depending on the privacy needs. We implement logistic regression, CNN, LSTM, and BiLSTM models to classify the privacy label for outgoing communications. This enables reduced utilization of quantum resources allowing for a more efficient secure quantum network design. Experimental results using the proposed methods are presented.