Designing large quantum key distribution networks via medoid-based algorithms

• Published in: Future generation computer systems Vol. 115; pp. 814 - 824
• Main Authors: García-Cobo, Iván, Menéndez, Héctor D.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2021
• Subjects: Medoids; Partition Around Medoids; Quantum key distribution; Quantum network; Trusted repeater QKD; Quantum network; Quantum key distribution; Medoids; Partition Around Medoids; Trusted repeater QKD
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2020.09.037

The current development of quantum mechanics and its applications suppose a threat to modern cryptography as it was conceived. The abilities of quantum computers for solving complex mathematical problems, as a strong computational novelty, is the root of that risk. However, quantum technologies can also prevent this threat by leveraging quantum methods to distribute keys. This field, called Quantum Key Distribution (QKD) is growing, although it still needs more physical basics to become a reality as popular as the Internet. This work proposes a novel methodology that leverages medoid-based clustering techniques to design quantum key distribution networks on commercial fiber optics systems. Our methodology focuses on the current limitations of these communication systems, their error loss and how trusted repeaters can lead to achieve a proper communication with the current technology. We adapt our model to the current data on a wide territory covering an area of almost 100,000 km2, and prove that considering physical limitations of around 45 km with 3.1 error loss, our design can provide service to the whole area. This technique is the first to extend the state of the art network’s design, that is focused on up to 10 nodes, to networks dealing with more than 200 nodes. •We design large Quantum Key Distribution networks, making classical designs 20 times larger.•We leverage repeaters models on fiber optic to optimally cover a given populated area.•The optimization and selection process is based on graph theory and clustering techniques.•We adapt our model to a wide territory, covering 100,000 km2, and providing service to the whole area.