Optimizing entanglement generation and distribution using genetic algorithms

Abstract

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path toward scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.

Quantum Networks: Emerging Research Areas, Challenges, and Opportunities

“…Quantum networks can provide secure communication channels through the use of quantum key distribution (QKD), which allows for the distribution of encryption keys that are inherently secure due to the principles of quantum mechanics. This can be used for secure messaging, online transactions, and other applications where data security is essential [9]. Another application is in the field of data transmission.…”

Quantum Communication Networks for Energy Applications: Review and Perspective

“…[289] Similarly, Francisco Ferriera da Silva et al used genetic algorithms to evaluate the performance of different quantum repeater chains for quantum communication across long distances. [290] Machine learning has also been applied to analyze imperfections in QKD protocols, [291] while Monte Carlo methods have been applied for evaluating QKD protocols [292] in real-world environments. [293] The use of computational simulation for the rational design of quantum networks has the potential to accelerate QIS development.…”

Tools for quantum network design

“…¼ 1gj for the case of jfl 2 f1; 2; …; kg : Capacities in this scenario are defined, similarly to Eqs. ( 3), (4), and ( 16)- (19). In particular, we just need to regard h log 2 d ðiÞ k l i k l corresponding to the size of jU d ðiÞ i AiBi or ĉAiBi d ðiÞ in the definitions of Eqs.…”

“…We review tools for evaluating the performance of quantum networks as the key element that will enable the design of quantum networks. We leave the actual design and optimization of quantum networks [7][8][9][10][11][12][13][14][15][16][17][18][19][20] beyond the scope of this review. We also omit the closely related topics of quantum networks for creating correlations [21][22][23][24][25] and of complex quantum networks.…”