Postprocessing for quantum random number generators: entropy evaluation and randomness extraction

TL;DR

This paper presents a framework for evaluating quantum randomness in QRNGs using min-entropy and demonstrates postprocessing methods with provable extractors to improve randomness quality.

Contribution

It introduces a generic min-entropy based evaluation framework and applies it to real QRNGs, along with implementing two provable randomness extractors.

Findings

Quantifies quantum randomness in real QRNGs using min-entropy.

Provides guidelines for QRNG data postprocessing.

Implements two provable randomness extractors: Toeplitz-hashing and Trevisan's.

Abstract

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.