Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.

翻译：分布式量子信息处理协议（如量子纠缠蒸馏和量子态区分）依赖于局部操作和经典通信（LOCC）。现有基于LOCC的协议通常假设理想无噪通信信道的可用性。本文研究了经典通信发生在噪声信道上的情形，并提出通过使用量子机器学习工具来设计此类场景下的LOCC协议。我们特别聚焦于量子纠缠蒸馏和量子态区分这两项重要任务，通过参数化量子电路（PQC）实现局部处理，并对其进行优化以在各自任务中最大化平均保真度和平均成功概率，同时考虑通信误差。所引入的方法——噪声感知LOCCNet（NA-LOCCNet）——被证明较之专为无噪声通信设计的现有协议具有显著优势。