A quantum network distributes quantum entanglements between remote nodes, which is key to many quantum applications. However, unavoidable noise in quantum operations could lead to both low throughput and low quality of entanglement distribution. This paper aims to address the simultaneous exponential degradation in throughput and quality in a buffered multi-hop quantum network. Based on an end-to-end fidelity model with worst-case (isotropic) noise, we formulate the high-fidelity remote entanglement distribution problem for a single source-destination pair, and prove its NP-hardness. To address the problem, we develop a fully polynomial-time approximation scheme for the control plane of the quantum network, and a distributed data plane protocol that achieves the desired long-term throughput and worst-case fidelity based on control plane outputs. To evaluate our algorithm and protocol, we develop a discrete-time quantum network simulator. Simulation results show the superior performance of our approach compared to existing fidelity-agnostic and fidelity-aware solutions.
翻译:量子网络在远程节点之间分配量子纠缠器,这是许多量子应用的关键。 但是, 量子操作中不可避免的噪音可能导致量子操作量子分布的低量和低质质。 本文旨在解决缓冲多霍量子网络中吞吐量和质量的同步指数降解问题。 基于以最坏( 异性) 噪音为主的端到端忠度模型, 我们为单一来源- 目的地双对制定高纤维远程离心分解分布问题, 并证明其NP- 硬性。 为了解决这个问题, 我们为量子网络的控制平面开发了完全的多元时近似方案, 以及基于控制平面输出实现预期的长期吞吐量和最坏情况对称的分布式数据平面协议。 为了评估我们的算法和协议, 我们开发了一个离子时量子网络模拟器。 模拟结果显示我们的方法相对于现有的对忠诚- 和对正性认知性解决方案的优异性表现。