AI-driven free space quantum communications in the third telecom window

We propose a new feedback correction system driven by artificial intelligence (AI), in particular reinforcement learning (RL), able to learn from the turbulence pattern how to correct the distortions. Indeed, RL is utilized to solve difficult tasks in chaotic problems making predictions based on the environment responses. We apply this novel approach in a Quantum Key Distribution (QKD) free space horizontal link field-trial test within the metropolitan area of Florence operating the Quantum Communication in the third telecommunication window (1550 nm) with time-bins states. We use the combination of a fast-steering mirror (FSM), a four-quadrant detector (QD), and a closed-loop to correct the turbulence-induced beam-wandering effect. Our closed-loop architecture is composed of a core Proportion-Integrative-Derivative (PID) controller and an auxiliary RL algorithm to find the optimal P, I, and D parameters. We demonstrate the robustness and effectiveness of using the RL approach to smooth the turbulence effects in communication.