Distributing entanglement over long distances is one of the central tasks in quantum networks. An important problem, especially for near-term quantum networks, is to develop optimal entanglement distribution protocols that take into account the limitations of current and near-term hardware, such as quantum memories with limited coherence time. We address this problem by initiating the study of quantum network protocols for entanglement distribution using the theory of decision processes, such that optimal protocols (referred to as $policies$ in the context of decision processes) can be found using dynamic programming or reinforcement learning algorithms. As a first step, in this work we focus exclusively on the elementary link level. We start by defining a quantum decision process for elementary links, along with figures of merit for evaluating policies. We then provide two algorithms for determining policies, one of which we prove to be optimal (with respect to fidelity and success probability) among all policies. Then we show that the previously-studied memory-cutoff protocol can be phrased as a policy within our decision process framework, allowing us to obtain several new fundamental results about it. The conceptual developments and results of this work pave the way for the systematic study of the fundamental limitations of near-term quantum networks, and the requirements for physically realizing them.

中文翻译：

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量子网络中基本链接的策略

长距离分布纠缠是量子网络的核心任务之一。一个重要的问题，特别是对于近期量子网络，是开发考虑当前和近期硬件限制的最优纠缠分布协议，例如相干时间有限的量子存储器。我们通过使用决策过程理论开始研究纠缠分布的量子网络协议来解决这个问题，这样可以使用动态编程或强化学习算法找到最佳协议（在决策过程中称为 $policies$） . 作为第一步，在这项工作中，我们只关注基本链路级别。我们首先定义基本链接的量子决策过程，以及用于评估政策的品质因数。然后我们提供了两种用于确定策略的算法，我们证明其中一种算法在所有策略中是最优的（关于保真度和成功概率）。然后我们展示了先前研究的记忆切断协议可以在我们的决策过程框架中表述为一种策略，使我们能够获得一些关于它的新的基本结果。这项工作的概念发展和结果为系统研究近期量子网络的基本局限性以及物理实现它们的要求铺平了道路。然后我们展示了先前研究的记忆切断协议可以在我们的决策过程框架中表述为一种策略，使我们能够获得一些关于它的新的基本结果。这项工作的概念发展和结果为系统研究近期量子网络的基本局限性以及物理实现它们的要求铺平了道路。然后我们展示了先前研究的记忆切断协议可以在我们的决策过程框架中表述为一种策略，使我们能够获得一些关于它的新的基本结果。这项工作的概念发展和结果为系统研究近期量子网络的基本局限性以及物理实现它们的要求铺平了道路。