论文标题
实用量子密钥分布中的自适应技术
Adaptive Techniques in Practical Quantum Key Distribution
论文作者
论文摘要
量子密钥分布（QKD）可以从理论上提供信息安全的通信，并且是下一代密码学的有力候选者。但是，实际上，QKD的性能受到现实来源，频道和探测器中的“实际缺陷”的限制（例如，多光子组件或从通道中的来源，损失和未对准的编码不完美，或检测器中的黑暗计数）。解决这种实用的缺陷是实施QKD协议的关键部分，其现实情况良好。 QKD有两个非常重要的未来方向：（1）QKD在可用空间上可以允许移动平台之间的安全通信，例如手持式系统，无人机，飞机，甚至卫星，以及（2）基于纤维的QKD网络，可以同时向任意位置的众多用户提供QKD服务。这些方向都非常有前途，但是到目前为止，它们受到渠道和设备中实际缺陷的限制，这构成了巨大的挑战并限制了其性能。 In this thesis, we develop adaptive techniques with innovative protocol and algorithm design, as well as novel techniques such as machine learning, to address some of these key challenges, including (a) atmospheric turbulence in channels for free-space QKD, (b) asymmetric losses in channels for QKD network, and (c) efficient parameter optimization in real time, which is important for both free-space QKD and QKD networks.我们认为，这项工作将为未来的自由空间QKD和基于纤维的QKD网络的重要实施铺平道路。
Quantum Key Distribution (QKD) can provide information-theoretically secure communications and is a strong candidate for the next generation of cryptography. However, in practice, the performance of QKD is limited by "practical imperfections" in realistic sources, channels, and detectors (such as multi-photon components or imperfect encoding from the sources, losses and misalignment in the channels, or dark counts in detectors). Addressing such practical imperfections is a crucial part of implementing QKD protocols with good performance in reality. There are two highly important future directions for QKD: (1) QKD over free space, which can allow secure communications between mobile platforms such as handheld systems, drones, planes, and even satellites, and (2) fibre-based QKD networks, which can simultaneously provide QKD service to numerous users at arbitrary locations. These directions are both highly promising, but so far they are limited by practical imperfections in the channels and devices, which pose huge challenges and limit their performance. In this thesis, we develop adaptive techniques with innovative protocol and algorithm design, as well as novel techniques such as machine learning, to address some of these key challenges, including (a) atmospheric turbulence in channels for free-space QKD, (b) asymmetric losses in channels for QKD network, and (c) efficient parameter optimization in real time, which is important for both free-space QKD and QKD networks. We believe that this work will pave the way to important implementations of free-space QKD and fibre-based QKD networks in the future.
