  Even if the output of a Random Number Generator (RNG) is perfectly uniformly
distributed, it may be correlated to pre-existing information and therefore be
predictable. Statistical tests are thus not sufficient to guarantee that an RNG
is usable for applications, e.g., in cryptography or gambling, where
unpredictability is important. To enable such applications a stronger notion of
randomness, termed "true randomness", is required, which includes independence
from prior information.
  Quantum systems are particularly suitable for true randomness generation, as
their unpredictability can be proved based on physical principles. Practical
implementations of Quantum RNGs (QRNGs) are however always subject to noise,
i.e., influences which are not fully controlled. This reduces the quality of
the raw randomness generated by the device, making it necessary to post-process
it. Here we provide a framework to analyse realistic QRNGs and to determine the
post-processing that is necessary to turn their raw output into true
randomness.

Quantum Physics;Cryptography and Security