Quantum random number characterization via quantum inspired machine learning

The generation of good random numbers impacts basic research and applications beyond pure academic interests: random numbers are required for countless applications, such as cryptography and simulations. For most applications, it is of outmost importance to know if a set of numbers is truly random, pseudo-random or contains some residual correlations. Tensor networks are powerful data structures that spring from quantum many-body physics and are now increasingly applied to machine learning applications. This thesis plans to explore the intersection between these two fields, applying quantum-inspired machine learning to random number generation. We aim to perform and characterise novel statistical checks comparing and characterising the statistical quality of different number sets: correlated, pseudo-random, and quantum-random.