Postprocessing for quantum random number generators: entropy evaluation and randomness extraction
This work addresses the practical challenge of ensuring provable randomness in QRNGs for applications in cryptography and security, though it is incremental as it builds on existing methods.
The authors tackled the problem of classical noise contaminating quantum randomness in quantum random-number generators (QRNGs) by proposing a framework to evaluate quantum randomness using min-entropy and applying it to two existing systems, while also providing guidelines for postprocessing with two provable randomness extractors.
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.