  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.
