Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users' quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 83% in users' end-to-end fidelities. The proposed framework also achieves a 64% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence.

翻译：依赖自由空间光量子通道的量子网络能够在难以铺设光纤基础设施且成本高昂的环境中支持量子应用。然而，基于自由空间光的量子网络要求用户间存在清晰的视距链路，而这因遮挡物和自然障碍物而极具挑战性。本文提出一种基于可重构智能表面辅助的自由空间光量子网络框架，通过为用户间纠缠分发提供虚拟视距链路，实现成本高效的解决方案。论文推导了量子态在自由空间光通道中经历大气衰减、大气湍流和指向误差时受到量子噪声和损耗的新型建模方法。随后，在异构纠缠速率与保真度约束下，构建了纠缠分发与可重构智能表面部署的联合优化问题，并采用模拟退火元启发式算法求解。仿真结果表明，该框架能有效满足所有用户量子应用的最低保真度要求，这与导致用户端到端保真度下降至少83%的基线算法形成鲜明对比。此外，与基线速率最大化框架相比，所提框架的用户间公平性提升了64%。最后，研究发现降雨等天气条件的影响比指向误差和大气湍流更为显著。