论文标题

使用遗传算法优化纠缠产生和分布

Optimizing Entanglement Generation and Distribution Using Genetic Algorithms

论文作者

da Silva, Francisco Ferreira, Torres-Knoop, Ariana, Coopmans, Tim, Maier, David, Wehner, Stephanie

论文摘要

通过纠缠分布的长距离量子通信对于量子互联网至关重要。但是,由于光子的损失,扩大到如此长的距离已被证明是具有挑战性的,后者随着覆盖的距离而呈指数增长。从理论上讲,量子中继器可以用来扩展可以分布纠缠的距离,但实际上仍然缺乏硬件质量。此外,通常不清楚某个中继器参数的改进(例如内存质量或尝试率)如何影响整体网络性能,从而使可伸缩量子的中继器的道路不清楚。在这项工作中,我们提出了一种基于遗传算法和量子中继器链的模拟的方法,以优化纠缠产生和分布。通过将其应用于几个不同的中继器链(包括现实世界纤维拓扑)的模拟中,我们证明它可以用于回答诸如满足网络性能基准测试的最小可行量子中继器等问题。这种方法构成了为泛欧量子互联网开发蓝图的宝贵工具。我们以NetSquid模拟的形式和Smart-Stopos优化工具制作了代码,可以在本地或高性能计算中心自由使用。

Long-distance quantum communication via entanglement distribution is of great importance for the quantum internet. However, scaling up to such long distances has proved challenging due to the loss of photons, which grows exponentially with the distance covered. Quantum repeaters could in theory be used to extend the distances over which entanglement can be distributed, but in practice hardware quality is still lacking. Furthermore, it is generally not clear how an improvement in a certain repeater parameter, such as memory quality or attempt rate, impacts the overall network performance, rendering the path towards scalable quantum repeaters unclear. In this work we propose a methodology based on genetic algorithms and simulations of quantum repeater chains for optimization of entanglement generation and distribution. By applying it to simulations of several different repeater chains, including real-world fiber topology, we demonstrate that it can be used to answer questions such as what are the minimum viable quantum repeaters satisfying given network performance benchmarks. This methodology constitutes an invaluable tool for the development of a blueprint for a pan-European quantum internet. We have made our code, in the form of NetSquid simulations and the smart-stopos optimization tool, freely available for use either locally or on high-performance computing centers.

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