论文标题
带有本地操作和嘈杂的经典通信的分布式量子协议的量子机学习
Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications
论文作者
论文摘要
分布式量子信息处理协议，例如量子纠缠蒸馏和量子状态歧视依赖于本地操作和经典通信（LOCC）。现有的基于LOCC的协议通常假设理想，无声的通信渠道的可用性。在本文中，我们研究了在嘈杂的渠道上进行经典沟通的情况，我们建议通过使用量子机学习工具来解决此环境中LOCC协议的设计。我们特别关注量子纠缠蒸馏和量子状态歧视的重要任务，并通过参数化的量子电路（PQC）实施本地处理，这些量子电路（PQC）进行了优化，以最大程度地提高相应任务中的平均忠诚度和平均成功概率，同时考虑通信错误。引入的方法，即噪声意识 - 链球网（Na-loccnet），证明比为无噪声通信设计的现有协议具有显着优势。
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.
