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Electronics

Distinction for Quantum Random Number Generators Based on Machine Learning

Yu Han, Tao Pei, Feng Zhang

Published Feb 27, 2026• 1 citation
Publisher

Abstract

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.

DOI
10.3390/electronics15050971

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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.
Abstract In this study, we compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomized NNs on dimensionality-reduced data, and applying CNNs to full image data. We evaluate these approaches on three fully-classical data sets of increasing complexity: an artificial hypercube data set, MNIST handwritten digits and industrial images of practical relevance. Our study’s central goal is to shed more light on how quantum and classical models perform for various binary classification tasks and on what defines a good quantum model. To this end, our study involves a correlation analysis between classification accuracy and quantum model hyperparameters, and an analysis on the role of entanglement in quantum models, as well as on the impact of initial training parameters. We find classical and quantum-classical hybrid models achieve statistically-equivalent classification accuracies across most data sets with no approach consistently outperforming the other. Interestingly, we observe that quantum NNs show lower variance with respect to initial training parameters and that the role of entanglement is nuanced. While incorporating entangling gates seems to be generally advantageous, we also observe that their (optimizable) entangling power is not correlated with model performance. We also observe an inverse proportionality between the number of entangling gates and the average gate entangling power. Our study provides an industry perspective on quantum machine learning for practical binary image classification tasks, highlighting both current limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
Machine learning (ML) has proven its efficacy in acquiring information from previous data and lever-aging that understanding to handle practical problems. The recent development of quantum computing, based on the principles of quantum physics, has made the integration of quantum computing and machine learning a prominent area of research. Nonetheless, the practical utilization of quantum computing in machine learning continues to present considerable obstacles, especially regarding implementation and the management of real-world tasks. This research presents a hybrid quantum-classical convolutional neural network model (HQ-CNN) and evaluates its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets. The study also presents an in-depth discussion of the challenges that arise from integrating quantum computing into machine learning, emphasizing the training procedure, the results obtained post-training, and the number of qubits used in the research. The experimental findings indicate that increasing the number of qubits generally enhances image classification efficacy. However, an increased number of qubits does not inherently ensure enhanced overall model performance.
The unprecedented progress in the domain of quantum computing in the last few years has influenced researchers around the globe to solve multitudes of problems in this promising computing technology. This power of the quantum computer has allowed multitudes of computationally hard problems to be sped up exponentially over their classical counterparts. Along with such power, another promising application of quantum computing has been found in image processing and machine learning. Researches in both quantum image processing and quantum machine learning are still in their infancy but promise exceptional power over its classical counterparts. In this thesis, neural networks will be trained to determine parameters for various parametric quantum circuits to perform important classification tasks, such as image classification. But for image classification, features from the images must also be extracted and epresented in terms of qubits, requiring convolutional layers tailored for quantum techniques. This thesis aims to find good quantum convolutional neural network architectures for image classification with higher accuracy. This is still challenging due to increased cost and error with a higher number of qubits within a system. This thesis is expected to be important in the future direction of the research of quantum CNN.
Randomness is widely used in various fields, including encryption, statistical analysis, and numerical simulations. Randomness is also a fundamental resource in science and engineering. For such applications, we usually need to provide unbiased and independent random bits. This raises the issue of where to get these supposed random bits. Quantum random number generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. In practice, unfortunately, quantum randomness is inevitably mixed with classical randomness due to classical noise. Also, randomness is often correlated and biased. It is necessary to process the resulting raw bits sequence and convert them to good-quality output values that are as close to uniform distribution as possible. Random extractors are required for this. We analyze the randomness obtained by quantum random number generators as well as various examples of post-processing. We discuss the types of randomness extractors. Also, we use information-theoretically secure extractors and present improved novel randomness extraction.
This paper explores a novel randomness evaluation method for data produced by quantum random number generators (QRNGs), leveraging quantum mechanics to achieve highly random data. We employ neural networks and machine learning techniques to analyze the operational principles of QRNGs, enabling the test suites to assess the generators based on their predictability scores. Our findings demonstrate that our model’s ability to predict outcomes surpasses that of comparable approaches. Additionally, we discuss the optimal timing for conducting these tests—specifically, analyzing the raw output from QRNGs before processing through an extractor yields the best performance.