.png){kind=icon}
.png){kind=icon}
Return
Distinction for Quantum Random Number Generators Based on Machine Learning
.png){kind=icon}
OA
AI
DOI：10.3390/electronics15050971.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Randomness is crucial for our understanding of nature and indispensable in information processing tasks. In practical applications, assessing the quality of random numbers is crucial—particularly in cryptographic applications, where random numbers must exhibit statistical uniformity. Various statistical estimation methods have been developed to test the statistical characteristics of generated random numbers, enabling comprehensive evaluation of their statistical uniformity from multiple perspectives. Despite recent advances in quantum information providing physically well-characterized models for randomness quantification, distinction between different types of random numbers (including quantum random numbers) remains a challenging task, and statistical uniformity is rarely directly applicable to such discrimination scenarios. With the development of artificial intelligence technologies, the problem of random number discrimination is expected to draw on the paradigm of image classification in computer vision. This research proposes a machine learning-based randomness discrimination method, specifically addressing the challenge of quantum random number identification. Specifically, we design an image-based convolutional neural network (CNN) approach: one-dimensional random number sequences are converted into two-dimensional grayscale images, and binary classification of these images is achieved by capturing high-dimensional latent features that are undetectable via traditional statistical tests, thereby enabling effective random number discrimination. Experimental results demonstrate that, for the selected quantum random numbers, the proposed discrimination method successfully achieves two key distinctions: (1) between raw quantum random numbers and classical random numbers; and (2) between raw quantum random numbers and post-processed quantum random numbers—additionally revealing the role of statistical uniformity in these discrimination tasks. This achievement provides significant support for the design of randomness extraction protocols and the security assessment of quantum random number generators.
Keywords:
random number discrimination
quantum random number (QRNG)
machine learning (ML)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.6
Papers:
9.3K
Citations:
4.7W
View more organizations