Adaptive Techniques in Practical Quantum Key Distribution
- URL: http://arxiv.org/abs/2004.11003v1
- Date: Thu, 23 Apr 2020 07:03:20 GMT
- Title: Adaptive Techniques in Practical Quantum Key Distribution
- Authors: Wenyuan Wang
- Abstract summary: Quantum Key Distribution (QKD) can provide information-theoretically secure communications.
The performance of QKD is limited by "practical imperfections" in realistic sources, channels, and detectors.
We develop adaptive techniques with innovative protocol and algorithm design, as well as novel techniques such as machine learning.
- Score: 3.5027291542274357
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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