使用随机自动微分优化量子中继器网络
Optimization of Quantum-Repeater Networks using Stochastic Automatic Differentiation
AI总结：
本文使用随机自动微分技术从离散蒙特卡洛模拟中提取导数，优化了量子中继器网络的速率-保真度权衡、节点敏感性和中继器布局。
摘要：
量子中继器被设想用于实现长距离纠缠分发。量子中继器网络的分析可以通过为设计决策和研究重点提供信息，从而加速其实现。确定网络属性的导数对此目标至关重要，有助于促进优化并揭示参数敏感性。然而，由于网络是离散随机的，这样做很困难。在这里，我们使用一种最近开发的技术，随机自动微分，从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，我们优化了中继器链中的速率-保真度权衡，确定了该链对不同节点相干时间的敏感性，最后在二维平面中选择量子中继器的位置，以优化四个端节点之间有保证的服务质量。具体而言，该技术使我们能够发现最佳可实现的服务质量、改善网络所需的最少中继器数量，以及使网络规模随网络物理大小饱和所需的中继器数量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
