标题： 基于随机自动微分的量子中继网络优化 显示英文标题

标题： Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation

摘要： 量子中继器被设想用于实现远距离的纠缠分发。 对量子中继器网络的分析可以加快其实现，通过提供设计决策和研究重点的信息。 确定网络属性的导数对于这一目标至关重要，有助于优化并揭示参数敏感性。 然而，这样做很困难，因为这些网络是离散随机的。 在这里，我们使用一种最近开发的技术，随机自动微分，来从量子中继器网络的离散蒙特卡洛模拟中自动提取导数。 利用这些导数，我们优化了中继链的速率-保真度权衡，确定了链相对于不同节点的相干时间的敏感性，并最终选择了二维平面上量子中继器的位置，以优化四个终端节点之间的保证服务质量。 特别是，该技术使我们能够发现最佳可达到的服务质量、改善网络所需的最少中继器数量以及饱和网络所需的中继器数量如何随着网络的物理尺寸而变化。 显示英文摘要

摘要： Quantum repeaters are envisioned to enable long-distance entanglement distribution. Analysis of quantum-repeater networks could hasten their realization by informing design decisions and research priorities. Determining derivatives of network properties is crucial towards that end, facilitating optimizations and revealing parameter sensitivity. Doing so, however, is difficult because the networks are discretely random. Here we use a recently developed technique, stochastic automatic differentiation, to automatically extract derivatives from discrete Monte Carlo simulations of repeater networks. With these derivatives, we optimize rate-fidelity tradeoffs in a repeater chain, determine the chain's sensitivity with respect to the coherence times of different nodes, and finally choose the locations of quantum repeaters in a two-dimensional plane to optimize the guaranteed quality of service between four end nodes. In particular, the technique enabled us to discover how the best achievable quality of service, the minimal number of repeaters required to improve a network, and the number of repeaters required to saturate the network scale with the physical size of the network.