Theory, Methods and Tools for Statistical Testing of Pseudo and Quantum Random Number Generators

Abstract: Statistical random number testing is a well studied field focusing on pseudo-random number generators, that is to say algorithms that produce random-looking sequences of numbers. These generators tend to have certain kinds of flaws, which have been exploited through rigorous testing. Such testing has led to advancements, and today pseudo random number generators are both very high-speed and produce seemingly random numbers. Recent advancements in quantum physics have opened up new doors, where products called quantum random number generators that produce acclaimed true randomness have emerged. Of course, scientists want to test such randomness, and turn to the old tests used for pseudo random number generators to do this. The main question this thesis seeks to answer is if publicly available such tests are good enough to evaluate a quantum random number generator. We also seek to compare sequences from such generators with those produced by state of the art pseudo random number generators, in an attempt to compare their quality. Another potential problem with quantum random number generators is the possibility of them breaking without the user knowing. Such a breakdown could have dire consequences. For example, if such a generator were to control the output of a slot machine, an malfunction could cause the machine to generate double earnings for a player compared to what was planned. Thus, we look at the possibilities to implement live tests to quantum random number generators, and propose such tests. Our study has covered six commonly available tools for random number testing, and we show that in particular one of these stands out in that it has a series of tests that fail our quantum random number generator as not random enough, despite passing an pseudo random number generator. This implies that the quantum random number generator behave differently from the pseudo random number ones, and that we need to think carefully about how we test, what we expect from an random sequence and what we want to use it for.