Adaptive Techniques in Practical Quantum Key Distribution

TL;DR

This paper develops adaptive, machine learning-based techniques to improve the performance of practical quantum key distribution systems over free-space and fiber channels, addressing real-world imperfections.

Contribution

It introduces innovative protocols and algorithms, including machine learning methods, to mitigate channel imperfections and optimize parameters in real time for QKD.

Findings

Enhanced robustness against atmospheric turbulence in free-space QKD

Improved handling of asymmetric losses in QKD networks

Real-time parameter optimization techniques for practical deployment

Abstract

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.