Quantum random-number generators (QRNGs) can offer a means to generate
information-theoretically provable random numbers, in principle. In practice,
unfortunately, the quantum randomness is inevitably mixed with classical
randomness due to classical noises. To distill this quantum randomness, one
needs to quantify the randomness of the source and apply a randomness
extractor. Here, we propose a generic framework for evaluating quantum
randomness of real-life QRNGs by min-entropy, and apply it to two different
existing quantum random-number systems in the literature. Moreover, we provide
a guideline of QRNG data postprocessing for which we implement two
information-theoretically provable randomness extractors: Toeplitz-hashing
extractor and Trevisan's extractor.Comment: 13 pages, 2 figure