Quantum random-number generators (QRNGs) can offer a means to generateinformation-theoretically provable random numbers, in principle. In practice,unfortunately, the quantum randomness is inevitably mixed with classicalrandomness due to classical noises. To distill this quantum randomness, oneneeds to quantify the randomness of the source and apply a randomnessextractor. Here, we propose a generic framework for evaluating quantumrandomness of real-life QRNGs by min-entropy, and apply it to two differentexisting quantum random-number systems in the literature. Moreover, we providea guideline of QRNG data postprocessing for which we implement twoinformation-theoretically provable randomness extractors: Toeplitz-hashingextractor and Trevisan's extractor.
展开▼
