Postprocessing for quantum random-number generators: Entropy evaluation and randomness extraction

摘要

Quantum random-number generators (QRNGs) can offer a means to generate information-theoreticallynprovable random numbers, in principle. In practice, unfortunately, the quantum randomness is inevitably mixednwith classical randomness due to classical noises. To distill this quantum randomness, one needs to quantifynthe randomness of the source and apply a randomness extractor. Here, we propose a generic framework fornevaluating quantum randomness of real-lifeQRNGs by min-entropy, and apply it to two different existing quantumnrandom-number systems in the literature. Moreover, we provide a guideline of QRNG data postprocessing fornwhich we implement two information-theoretically provable randomness extractors: Toeplitz-hashing extractornand Trevisan’s extractor.