Routing Design for Enhancement of Capacity Utilisation and Weighted Throughput on Quantum Networks Using Genetic Algorithm

Quantum networks facilitate communication among quantum devices, playing a crucial role in enabling applications beyond the reach of classical networks. However, creating a quantum network that supports reliable long-distance entanglement distribution remains a significant challenge. Unlike classical networks, connections in quantum networks are established through entanglement generation. Effective communication between quantum devices requires a routing strategy that efficiently manages the establishment of entanglement. In addition to fidelity, the quality of communication is also determined by the capacity of the generated entanglement. Therefore, we proposed a capacity allocation scheduling scheme based on a metaheuristic approach, namely a Genetic Algorithm (GA), to ensure efficient capacity allocation for multiple communication paths within each communication request. Simulations were conducted in four different scenarios, and the results were compared to an existing capacity allocation scheduling algorithm, Progressive Filling (PF), to verify the effectiveness of the proposed scheme. The average capacity utilisation, U and weighted throughput, F results indicate that PF outperforms GA in a scenario with four communication requests, while GA performs better in scenarios involving six, eight, and ten communication requests. Specifically, the improvement in average capacity utilisation ranges from 8% to 46%, and the improvement in weighted throughput ranges from 24% to 94%. These results suggest that GA can serve as a valuable reference for designing optimal routing and capacity allocation schemes in more complex networks with multiple communication requests.