  For a practical quantum key distribution (QKD) system, parameter optimization
- the choice of intensities and probabilities of sending them - is a crucial
step in gaining optimal performance, especially when one realistically
considers finite communication time. With the increasing interest in the field
to implement QKD over free-space on moving platforms, such as drones, handheld
systems, and even satellites, one needs to perform parameter optimization with
low latency and with very limited computing power. Moreover, with the advent of
the Internet of Things (IoT), a highly attractive direction of QKD could be a
quantum network with multiple devices and numerous connections, which provides
a huge computational challenge for the controller that optimizes parameters for
a large-scale network. Traditionally, such an optimization relies on
brute-force search, or local search algorithms, which are computationally
intensive, and will be slow on low-power platforms (which increases latency in
the system) or infeasible for even moderately large networks. In this work we
present a new method that uses a neural network to directly predict the optimal
parameters for QKD systems. We test our machine learning algorithm on hardware
devices including a Raspberry Pi 3 single-board-computer (similar devices are
commonly used on drones) and a mobile phone, both of which have a power
consumption of less than 5 watts, and we find a speedup of up to 100-1000 times
when compared to standard local search algorithms. The predicted parameters are
highly accurate and can preserve over 95-99% of the optimal secure key rate.
Moreover, our approach is highly general and not limited to any specific QKD
protocol.
