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Random number generation is an enabling technology for fields as varied as Monte Carlo simulations and quantum information science. An important application is a secure quantum key distribution (QKD) system; here, we propose and demonstrate…

Quantum Physics · Physics 2018-10-05 Qiang Zhou , Raju Valivarthi , Caleb John , Wolfgang Tittel

Emergence of stochastic simulations as an extensively used computational tool for scientific purposes intensified the need for more accurate ways of generating sufficiently long sequences of uncorrelated random numbers. Even though several…

Mathematical Software · Computer Science 2014-08-14 Ayse Ferhan Yesil , M. Cemal Yalabik

Quantum technologies promise information processing and communication technology advancements, including random number generation (RNG). Using Bell inequalities, a user of a quantum RNG hardware can certify that the values provided by an…

Quantum Physics · Physics 2025-11-20 Robert Okuła , Piotr Mironowicz

As quantum technologies advance, the security of popular cryptographic protocols becomes more threatened by the capabilities of Cryptographically Relevant Quantum Computers (CRQCs). In this scenario, Post-Quantum Cryptography (PQC) has…

Capacity is an important tool in decision-making under risk and uncertainty and multi-criteria decision-making. When learning a capacity-based model, it is important to be able to generate uniformly a capacity. Due to the monotonicity…

Discrete Mathematics · Computer Science 2023-05-24 Peiqi Sun , Michel Grabisch , Christophe Labreuche

Quantum random number generation (QRNG) relies on the inherent unpredictability of quantum mechanical phenomena to efficiently generate high-quality random numbers that can be used in a wide range of cryptography and simulation…

Quantum Physics · Physics 2026-04-29 Conrad Strydom , Mark Tame

In the quantum Monte Carlo (QMC) method, the Pseudo-Random Number Generator (PRNG) plays a crucial role in determining the computation time. However, the hidden structure of the PRNG may lead to serious issues such as the breakdown of the…

Strongly Correlated Electrons · Physics 2024-03-12 Dong-Xu Liu , Wei Xu , Xue-Feng Zhang

Semi-quantum cryptography involves at least one user who is semi-quantum or "classical" in nature. Such a user can only interact with the quantum channel in a very restricted way. Many semi-quantum key distribution protocols have been…

Quantum Physics · Physics 2022-11-01 Julia Guskind , Walter O. Krawec

The fundamental principles of quantum mechanics, such as its probabilistic nature, allow for the theoretical ability of quantum computers to generate statistically random numbers, as opposed to classical computers which are only able to…

Quantum Physics · Physics 2024-01-24 Owen Root , Maria Becker

A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition…

Quantum Physics · Physics 2021-03-23 Kentaro Tamura , Yutaka Shikano

With a growing interest in securing user data within the internet-of-things (IoT), embedded encryption has become of paramount importance, requiring light-weight high-quality Random Number Generators (RNGs). Emerging stochastic device…

Emerging Technologies · Computer Science 2026-03-03 Furqan Zahoor , Ibrahim A. Albulushi , Saleh Bunaiyan , Anupam Chattopadhyay , Hesham ElSawy , Feras Al-Dirini

We present a high-quality, bias-free quantum random number generator (QRNG) using photon arrival time selectively in accordance with the number of photon detection events within a sampling time interval in attenuated light. It is well…

Quantum Physics · Physics 2015-05-21 Jian-min Wang , Tian-yu Xie , Hong-fei Zhang , Dong-xu Yang , Chao Xie , Jian Wang

True Random Number Generators (TRNGs) play a fundamental role in hardware security, cryptographic systems, and data protection. In the context of Deep NeuralNetworks (DNNs), safeguarding model parameters, particularly weights, is critical…

Pseudorandom number generators (PRNGs) are ubiquitous in stochastic simulations and machine learning (ML), where they drive sampling, parameter initialization, regularization, and data shuffling. While widely used, the potential impact of…

Performance · Computer Science 2025-10-30 Benjamin A. Antunes

Traditional quantum random number generators can produce only one type of random number, while the optimal distribution of random numbers for different applications is usually distinct. The typical solution to this challenge is either using…

Current prevailing designs of quantum random number generators (QRNGs) designs typically employ post-processing techniques to distill raw random data, followed by statistical verification with suites like NIST SP 800-22. This paper…

Quantum Physics · Physics 2025-09-03 Yi-Fan Chen , Dong Wang , Yi-Bo Zhao , Liang Cheng , Yi Zhang , Yang Zhang

Quantum random number generation exploits inherent randomness of quantum mechanical processes and measurements. Real-time generation rate of quantum random numbers is usually limited by electronic bandwidth and data processing rates. Here…

Quantum Physics · Physics 2020-01-08 Xiaomin Guo , Chen Cheng , Mingchuan Wu , Qingzhong Gao , Pu Li , Yanqiang Guo

Quantum random number generators (QRNGs) harness the inherent unpredictability of quantum mechanics to produce true randomness. Yet, in many optical implementations, the light source remains a potential vulnerability - susceptible to…

Quantum Physics · Physics 2025-11-07 KaiWei Qiu , Yu Cai , Nelly H. Y. Ng , Jing Yan Haw

True random number generators (RNGs) are desirable for applications ranging from cryptogra- phy to computer simulations. Quantum phenomena prove to be attractive for physical RNGs due to their fundamental randomness and immunity to attack…

In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These…

Machine Learning · Computer Science 2019-10-11 Jordan J. Bird , Anikó Ekárt , Diego R. Faria