Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks

• URL: http://arxiv.org/abs/2604.04143v1
• Date: Sun, 05 Apr 2026 15:07:41 GMT
• Title: Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks
• Abstract summary: This paper investigates the problem of entanglement rate in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (Ss)<n>Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity schemes.<n>An alternating optimization algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems.
• Score: 9.619596495371793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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