Machine Learning Validation of a Physical Prime Random Number Generator

Random prime number generators are essential for encryption and security protocols, but their output can become flawed and needs constant checking. This paper describes a machine learning approach that can validate quantum random number generators (QRNGs, devices that use quantum physics to create truly random numbers) by learning patterns in the prime numbers they produce and detecting when the output becomes biased (skewed toward certain values). The researchers tested their framework on both a quantum-based prime generator and a classical electronic noise generator, successfully identifying flawed configurations.