论文标题
使用机器智能的自我调整发射器用于量子密钥分布
Self-Tuning Transmitter for Quantum Key Distribution Using Machine Intelligence
论文作者
论文摘要
量子技术的开发和性能在很大程度上依赖于量子状态的特性，量子状态通常需要仔细优化所有基础组件的驱动条件。在量子钥匙分布（QKD）中，最近已将脉冲激光器的光学注入锁定（油）作为一种有前途的技术，可以实现具有有效系统设计的高速量子发射器。但是，由于复杂的基础激光动力学，调整这种激光系统既是一项具有挑战性又耗时的任务。在这里，我们在实验上证明了一个油性QKD发射器，可以通过使用遗传算法来自动调整其最佳工作状态。从对激光工作参数的最小知识开始，系统相干性和系统的量子位错误率可自主优化，以匹配与技术状态相匹配的级别。
The development and performance of quantum technologies heavily relies on the properties of the quantum states, which often require careful optimization of the driving conditions of all underlying components. In quantum key distribution (QKD), optical injection locking (OIL) of pulsed lasers has recently been shown as a promising technique to realize high-speed quantum transmitters with efficient system design. However, due to the complex underlying laser dynamics, tuning such laser system is both a challenging and time-consuming task. Here, we experimentally demonstrate an OIL-based QKD transmitter that can be automatically tuned to its optimum operating state by employing a genetic algorithm. Starting with minimal knowledge of the laser operating parameters, the phase coherence and the quantum bit error rate of the system are optimized autonomously to a level matching the state of the art.
