LAUSR

Quantum communication networks enable advanced quantum applications through remote entanglement distribution among source-destination pairs. Despite efforts to optimize entanglement distribution, fairness in multi-request scenarios has been neglected, potentially causing issues like “request starvation”. To address such issue, this paper concentrates on the unique properties of entangled systems and introduces a process-oriented fairness metric, i.e., expected throughput, departing from conventional approaches used in classical networks. Furthermore, we propose an entanglement distribution scheme named Fair-EAS, which prioritizes entanglement allocation and selection for batching multiple requests to maximize overall throughput while maintaining max-min fairness. To facilitate a convenient solution, we transform the nonlinearity of the problem into an equivalent linear programming formulation and decouple the solution into offline and online phases. In the offline phase, we design a multi-round water-filling-like optimization algorithm to determine the optimal path set for predicting entanglement allocation. In the online phase, we introduce an adaptive compensation algorithm and an entanglement “fragment” exhaustion algorithm to dynamically adjust the path set based on successfully generated entangled pairs. Comprehensive simulations show that Fair-EAS outperforms the existing schemes in terms of fairness by significantly enhancing the minimum throughput and throughput deviation among multiple requests while maintaining an overall throughput close to the optimal level.