Deep Learning-Based Security Analysis of Quantum Random Numbers Generated by Imperfect Devices

The quantum random number generator (QRNG) is theoretically capable of generating unpredictable random numbers based on the inherent uncertainty of quantum mechanics, which is of paramount importance for information security. However, the security of practical QRNG is susceptible to the influence of unknown classical noise introduced by imperfect measurement devices, leading to potential security threats. In this paper, we propose a deep learning-based prediction model for analyzing the bidirectional security of QRNG where the quantum source is contaminated by classical noise. Firstly, we train a deep learning model capable of evaluating the randomness of mixed entropy source composed of quantum source and classical source, which exhibits excellent performance and effectively avoids the limitations of Statistical tests in evaluating the randomness of the mixture of quantum and classical source results. Secondly, systematically analyzes the impact of non-ideal measurement devices on the practical security of the continuous variable QRNG, which provides an explicit basis for compensating the discrepancy between theory and experiment. Finally, we perform correlation detection on QRNG output sequences with a deep learning model and focus on both the forward and backward security of random numbers. Through bidirectional security detection, random number sequences that may be biased or manipulated can be more accurately and comprehensively evaluated, further preventing potential correlations from opening security holes for eavesdroppers.