Quantum Random Number Generator Probability Analysis and Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum random number generators (QRNGs) offer a fundamentally different approach to random number generation compared to classical methods, leveraging the inherent randomness of quantum processes. However, their practical implementation and analysis often face challenges, particularly concerning the accurate characterization of the underlying probability distribution and the optimization of generation algorithms. This paper presents a comprehensive probability analysis of QRNG output, focusing on identifying and quantifying sources of bias and uncertainty. We introduce a novel framework combining quantum information theory and statistical analysis to rigorously evaluate QRNG performance. Furthermore, we propose an optimization strategy based on adaptive sampling techniques and error correction, aiming to enhance both the security and efficiency of QRNGs. Our approach provides a detailed understanding of QRNG behavior and offers actionable insights for designing and deploying robust QRNG systems. The core of our analysis lies in the precise determination of the output probability distribution, denoted as P(s1, s2, ..., sK), where K represents the number of bits generated. We demonstrate how this distribution can be refined through iterative statistical estimation and how this information can be used to improve the generation process.

DOI: 10.5281/zenodo.22245658

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