Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks

2026-04-05

AI Summary

This work addresses the challenge of meeting heterogeneous user demands for entanglement rate and fidelity in dual-connectivity wireless quantum networks, while accounting for limited entanglement generation capabilities at base stations. To this end, the paper proposes a dual-connectivity architecture enabling coordinated service from multiple base stations and establishes the first joint optimization framework that integrates quality-of-service (QoS) constraints with base station capacity limits. The problem is formulated as a mixed-integer nonlinear program, and a low-complexity alternating optimization algorithm is developed to efficiently determine base station–user associations and entanglement rate allocations. Simulation results demonstrate that the proposed architecture significantly outperforms conventional single-connectivity schemes, achieving near-optimal overall entanglement distribution efficiency while satisfying diverse QoS requirements.

Abstract

The development of quantum networks (QNs) relies on efficient mechanisms for distributing entanglement among multiple quantum users (QUs) under practical system constraints. This paper investigates the problem of entanglement rate maximization in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (QBSs). Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity (SC) schemes. The joint QBS-QU association and entanglement generation rate allocation problem is formulated as a mixed-integer nonlinear programming problem that incorporates practical constraints, including limited QBS entanglement generation capacity as well as heterogeneous minimum entanglement rate demands and fidelity requirements for QUs. To efficiently solve this challenging problem, an alternating optimization (AO) algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems. Simulation results demonstrate that the proposed DC architecture significantly outperforms SC schemes, while the AO algorithm achieves near-optimal performance with substantially reduced computational complexity.

Problem

• entanglement rate maximization
• dual-connectivity
• quantum networks
• QBS-QU association
• resource allocation

Innovation

• Dual-Connectivity
• Entanglement Rate Maximization
• Quantum Networks
• Alternating Optimization
• QBS-QU Association