Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

SN Computer Science

Verifying the Reliability of Quantum Random Number Generator: A Comprehensive Testing Approach

Rounak Biswas, Dhruv Roy Talukdar, Utpal Roy

Published Jan 3, 2024• 3 citations
Publisher
DOI
10.1007/s42979-023-02323-w

Related papers

PiRATe: A Comprehensive Set of Statistical Tests for Evaluation of Quantum Random Number Generators

Soheil Hajibaba, AmirHosein Dadahkhani, Hamid Asgari +2 more

As true random numbers are essential for many applications, high-quality Random Number Generators (RNGs) are highly demanded. Several different statistical test suites have been developed to evaluate the quality of random number generators. However, some programming skills are required to work with these statistical tests as they are not available as self-contained software and often are without GUI. In this work, we have developed PiRATe (Pi Randomness Assessment Test) an easy-to-use wrapper software for the assessment of random number generators using a comprehensive set of statistical tests, including DIEHARDER, NIST SP800-22, NIST SP800-90B, ENT, Borel Normality, and four Chaitin-Schwartz-Solovay-Strassen tests. We then used PiRATe to evaluate the quality of five different (Commercial and Experimental) Quantum Random Number Generators (QRNGs).

Engineering Science & Technology•May 22, 2024•2 citations

Study of quantum random number generators

Patrik Mäki

•May 15, 2021•0 citations

Single-Photon Quantum Random Number Generator: Evaluation and Application

Al-Sulaiti, Aisha Abdulaziz Th M.

Random Number Generators (RNGs) are used in various applications, including cryptography, scientific simulations, gaming, and virtual reality. The quality of these random numbers is crucial, especially for computational approaches that rely on unpredictability to produce accurate results. In this thesis, we perform a comprehensive assessment of the quality of a novel Quantum Random Number Generator (QRNG) based on single photon. QRNGs leverage quantum mechanical properties to produce truly random numbers. This ensures high entropy, unpredictability, and security, making QRNGs particularly useful in fields that require high-quality randomness.We evaluate the statistical properties of strings generated by our QRNG and then its practical application by comparing it to traditional Pseudo-Random Number Generators (PRNGs) in Monte Carlo Integration (MCI), a technique that estimates integrals using random sampling. In particular, we use two statistical randomness test suits, namely NIST and Dieharder, to assess the statistical properties of our QRNG. All tests within these tests were passed, confirming that the corresponding QRNG achieves true randomness with sufficient high entropy. Next, we use our QRNG-generated numbers to seed Monte Carlo Integration (MCI) and compare it against results obtained using classical PRNG. The QRNG-based MCI demonstrated more accurate and stable numerical estimates, showing lower error rates and improved convergence compared to PRNG-based simulations.These findings highlight the advantages of QRNGs in enhancing numerical accuracy and computational security. While QRNGs may have slower generation rates than PRNGs, their superior randomness quality allows for more reliable Monte Carlo-based simulations, suggesting that fewer samples may be needed to achieve the same level of accuracy compared to classical PRNGs. This research underscores the potential of QRNG-powered numerical methods, paving the way for their integration into more advanced applications, including scientific computing, financial modeling, and cryptographic security.

•Jan 1, 2025•0 citations

New concepts and construction of quantum random number generators

Piotr Paweł Jóźwiak, Janusz Edward Jacak, Witold Aleksander Jacak

Quantum Information Processing•Mar 28, 2024•9 citations

Experimentally probing the algorithmic randomness and incomputability of quantum randomness

Alastair A. Abbott, Cristian S Calude, Michael J Dinneen +1 more

Abstract The advantages of quantum random number generators (QRNGs) over pseudo-random number generators (PRNGs) are normally attributed to the nature of quantum measurements. This is often seen as implying the superiority of the sequences of bits themselves generated by QRNGs, despite the absence of empirical tests supporting this. Nonetheless, one may expect sequences of bits generated by QRNGs to have properties that pseudo-random sequences do not; indeed, pseudo-random sequences are necessarily computable, a highly nontypical property of sequences. In this paper, we discuss the differences between QRNGs and PRNGs and the challenges involved in certifying the quality of QRNGs theoretically and testing their output experimentally. While QRNGs are often tested with standard suites of statistical tests, such tests are designed for PRNGs and only verify statistical properties of a QRNG, but are insensitive to many supposed advantages of QRNGs. We discuss the ability to test the incomputability and algorithmic complexity of QRNGs. While such properties cannot be directly verified with certainty, we show how one can construct indirect tests that may provide evidence for the incomputability of QRNGs. We use these tests to compare various PRNGs to a QRNG, based on superconducting transmon qutrits and certified by the Kochen–Specker theorem, to see whether such evidence can be found in practice. While our tests fail to observe a strong advantage of the quantum random sequences due to algorithmic properties, the results are nonetheless informative: some of the test results are ambiguous and require further study, while others highlight difficulties that can guide the development of future tests of algorithmic randomness and incomputability.

arXiv:1806.08762•Physica Scripta•Nov 23, 2018•28 citations

Modeling, simulation and analysis of a high-speed quantum random number generator

Pablo Bolívar López

•Oct 19, 2020•0 citations
As true random numbers are essential for many applications, high-quality Random Number Generators (RNGs) are highly demanded. Several different statistical test suites have been developed to evaluate the quality of random number generators. However, some programming skills are required to work with these statistical tests as they are not available as self-contained software and often are without GUI. In this work, we have developed PiRATe (Pi Randomness Assessment Test) an easy-to-use wrapper software for the assessment of random number generators using a comprehensive set of statistical tests, including DIEHARDER, NIST SP800-22, NIST SP800-90B, ENT, Borel Normality, and four Chaitin-Schwartz-Solovay-Strassen tests. We then used PiRATe to evaluate the quality of five different (Commercial and Experimental) Quantum Random Number Generators (QRNGs).
Random Number Generators (RNGs) are used in various applications, including cryptography, scientific simulations, gaming, and virtual reality. The quality of these random numbers is crucial, especially for computational approaches that rely on unpredictability to produce accurate results. In this thesis, we perform a comprehensive assessment of the quality of a novel Quantum Random Number Generator (QRNG) based on single photon. QRNGs leverage quantum mechanical properties to produce truly random numbers. This ensures high entropy, unpredictability, and security, making QRNGs particularly useful in fields that require high-quality randomness.We evaluate the statistical properties of strings generated by our QRNG and then its practical application by comparing it to traditional Pseudo-Random Number Generators (PRNGs) in Monte Carlo Integration (MCI), a technique that estimates integrals using random sampling. In particular, we use two statistical randomness test suits, namely NIST and Dieharder, to assess the statistical properties of our QRNG. All tests within these tests were passed, confirming that the corresponding QRNG achieves true randomness with sufficient high entropy. Next, we use our QRNG-generated numbers to seed Monte Carlo Integration (MCI) and compare it against results obtained using classical PRNG. The QRNG-based MCI demonstrated more accurate and stable numerical estimates, showing lower error rates and improved convergence compared to PRNG-based simulations.These findings highlight the advantages of QRNGs in enhancing numerical accuracy and computational security. While QRNGs may have slower generation rates than PRNGs, their superior randomness quality allows for more reliable Monte Carlo-based simulations, suggesting that fewer samples may be needed to achieve the same level of accuracy compared to classical PRNGs. This research underscores the potential of QRNG-powered numerical methods, paving the way for their integration into more advanced applications, including scientific computing, financial modeling, and cryptographic security.
Abstract The advantages of quantum random number generators (QRNGs) over pseudo-random number generators (PRNGs) are normally attributed to the nature of quantum measurements. This is often seen as implying the superiority of the sequences of bits themselves generated by QRNGs, despite the absence of empirical tests supporting this. Nonetheless, one may expect sequences of bits generated by QRNGs to have properties that pseudo-random sequences do not; indeed, pseudo-random sequences are necessarily computable, a highly nontypical property of sequences. In this paper, we discuss the differences between QRNGs and PRNGs and the challenges involved in certifying the quality of QRNGs theoretically and testing their output experimentally. While QRNGs are often tested with standard suites of statistical tests, such tests are designed for PRNGs and only verify statistical properties of a QRNG, but are insensitive to many supposed advantages of QRNGs. We discuss the ability to test the incomputability and algorithmic complexity of QRNGs. While such properties cannot be directly verified with certainty, we show how one can construct indirect tests that may provide evidence for the incomputability of QRNGs. We use these tests to compare various PRNGs to a QRNG, based on superconducting transmon qutrits and certified by the Kochen–Specker theorem, to see whether such evidence can be found in practice. While our tests fail to observe a strong advantage of the quantum random sequences due to algorithmic properties, the results are nonetheless informative: some of the test results are ambiguous and require further study, while others highlight difficulties that can guide the development of future tests of algorithmic randomness and incomputability.