Machine Learning Validation of a Physical Prime Random Number Generator

Summary

Researchers present a method for statistically validating physical prime random number generators, which existing test suites cannot assess because they target uniform pseudorandom sources. They extend an equiprobable binning framework of the prime distribution with a predictive machine learning model that learns correlations in prime output. The framework identifies flawed configurations of a prime QRNG based on vacuum-state quadrature measurements for output lengths up to 128 bits, and shows that a classical electronic-noise source produces a conclusively biased prime distribution, while the QRNG remains resilient to this cryptanalysis.