Optimization of Quantum Communications

Many of the analytical results currently achieved in the theory of quantum communication and quantum cryptography are hard to extend to more complex scenarios, for instance to more realistic models of decoherence or more complex network architectures. In fact, there are today a number of open questions, ranging from the secret key capacity of important quantum communication channels (such as the thermal loss channel or the amplitude damping channel) to the development of robust point-to-point and network protocols for quantum key distribution (QKD) based on the so-called continuous variables (CVs). In such situations, it is advantageous to turn to numerical/advanced data-processing techniques in order to pursue further investigations.

This project focuses on the development of Monte Carlo and machine learning methods in order to establish optimal bounds for quantum/private communication. In particular, research into the construction of classical (and quantum) machine learning models to tackle quantum problems in this regime may provide valuable insight and improvements on the fundamental limits of quantum communications. Collaborative developments for this research have already begun which will hopefully lead to expedient improvements of the optimal bounds for important discrete variable (DV) quantum communication channels and more. The introduction of such numerical tools will provide new insight into otherwise analytically inaccessible problems, and open new avenues of potential investigation in the optimization of quantum communications and technologies.