Artificial intelligence solutions for quantum communications

Tesi amb menció de Doctorat Internacional (English) This Ph.D. thesis focuses on the application of intelligent models to Discrete-Variable Quantum Key Distribution (DV-QKD) protocol. The first objective focuses on providing a method for AI-based polarization drift compensation for transmitting discrete photons in a quantum channel. In order to fully achieve this goal, we need to tackle two specific sub-goals. Firstly, AI based State of Polarization (SOP) tracking is designed to compensate polarization drift in quantum channels. The SOP trajectory is predicted ahead under different environmental events that causes SOP distortion. Here, we use SOP recognition procedure at the quantum receiver and evaluate different interpolation methods for planning the compensational rotation. On the other hand, a heuristic-based rotation manager adapted for BB84 protocol was proposed to minimize the number of rotations applied to the receiving photons in order to prevent the reduction in key rate generation. The second objective focuses on checking the feasibility of the proposed method for polarization compensation in DV-QKD systems. Here, issues mainly are: a) the strict requirements for quantum transmitters and receivers and, b) the need for carefully selecting the fibers supporting the quantum channel to minimize the environmental effects that could dramatically change the SOP of the photons. In order to fully achieve this goal, an experimental testbed which is being used in the polarization encoded QKD system has been set up. Also, software modules are needed for an intelligent QKD system to compensate for the uncalibrated testbed’s components. Finally, analysis of the experimental results and KPI measurements including testbed validation, fine tuning, and issue solving are evaluated. The final objective targets developing a Digital Twin (DT) that can address the shortcomings of the DV-QKD system, which cannot be achieved through the use of AI-based systems in the first goal. In order to fully achieve this goal, we need to address two specific sub-goals. On the one hand, the improvement of AI based SOP compensation using the DT. Specifically, DT helps to select, among different AI models, which one needs to be used in the receiver in order to take proper actions against different detected environmental events. On the other hand, DT targets at discerning eavesdropping actions from environmental events in quantum channel as both increase the quantum Bit Error Rate (qBE