Predicting atmospheric turbulence for secure quantum communications in free space

TL;DR

This paper presents TAROCCO, a Recurrent Neural Network trained on weather data to predict atmospheric turbulence, enabling optimized timing for secure free-space quantum communication links.

Contribution

The paper introduces TAROCCO, a novel RNN model trained on real weather data to forecast turbulence strength for quantum communication channels.

Findings

TAROCCO accurately predicts turbulence over a 9-month period.

Predictions improve quantum key distribution protocol performance.

The model enables optimal routing for secure quantum links.

Abstract

Atmospheric turbulence is the main barrier to large-scale free-space quantum communication networks. Aberrations distort optical information carriers, thus limiting or preventing the possibility of establishing a secure link between two parties. For this reason, forecasting the turbulence strength within an optical channel is highly desirable, as it allows for knowing the optimal timing to establish a secure link in advance. Here, we train a Recurrent Neural Network, TAROCCO, to predict the turbulence strength within a free-space channel. The training is based on weather and turbulence data collected over 9 months for a 5.4 km intra-city free-space link across the City of Ottawa. The implications of accurate predictions from our network are demonstrated in a simulated high-dimensional Quantum Key Distribution protocol based on orbital angular momentum states of light across different turbulence regimes. TAROCCO will be crucial in validating a free-space channel to optimally route the key exchange for secure communications in real experimental scenarios.