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).
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).
Topicality. Reliable random number generation is one of the key requirements for modern cryptographic systems and cybersecurity applications. Traditional pseudo-random number generators are based on deterministic algorithms and may exhibit limited entropy and predictability, which can negatively affect information security. The subject of study in the article is methods of quantum random number generation and approaches to evaluating the quality of generated random sequences. The purpose of the article is to analyze modern methods of quantum random number generation, develop a software tool for obtaining quantum random numbers based on the ANU Quantum Random Number Generator service, perform statistical testing of the generated sequences, and analyze the obtained results. The following results were obtained. Modern quantum random number generation systems were analyzed and compared. A software tool was implemented in Python for automatic retrieval of quantum random numbers through the ANU QRNG API. The generated binary sequences were evaluated using Frequency Test and Block Frequency Test from the NIST SP 800-22 statistical test suite. The obtained p-values were 0.977181 and 0.200659 respectively, confirming successful completion of both tests. Additionally, visual analysis of the generated sequences revealed no noticeable patterns or repetitive structures. Conclusion. The obtained results confirm the high quality of quantum random sequences generated using ANU QRNG and demonstrate the applicability of the developed software tool for cryptographic and cybersecurity applications requiring high-quality randomness.
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.
Random numbers are essential for cryptography and scientific simulation. Generating truly random numbers for cryptography can be a slow and expensive process. Quantum physics offers a variety of promising solutions to this challenge, proposing sources of entropy that may be genuinely unpredictable, based on the inherent randomness of certain physical phenomena. These properties have been employed to design Quantum Random Number Generators (QRNGs), some of which are commercially available. In this work, we present the first published analysis of the Quantis family of QRNGs (excluding AIS-31 models), designed and manufactured by ID Quantique (IDQ). Our study also includes Comscire’s PQ32MU QRNG, and two online services: the Australian National University’s (ANU) QRNG, and the Humboldt Physik generator. Each QRNG is analysed using five batteries of statistical tests: Dieharder, National Institute of Standards and Technology (NIST) SP800-22, Ent, Tuftests and TestU01, as part of our thorough examination of their output. Our analysis highlights issues with current certification schemes, which largely rely on NIST SP800-22 and Diehard tests of randomness. We find that more recent tests of randomness identify issues in the output of QRNG, highlighting the need for mandatory post-processing even for low-security usage of random numbers sourced from QRNGs.
Generation of random numbers is a central problem for many applications in the field of information processing, including, e.g., cryptography, in classical and quantum regime, but also mathematical modeling, Monte Carlo methods, gambling and many others. Both, the quality of the randomness and efficiency of the random numbers generation process are crucial for the most of these applications. Software produced pseudorandom bit sequences, though sufficiently quick, do not fulfill required randomness quality demands. Hence, the physical hardware methods are intensively developed to generate truly random number sequences for information processing and electronic security application. In the present paper we discuss the idea of the quantum random number generators. We also present a variety of tests utilized to assess the quality of randomness of generated bit sequences. In the experimental part we apply such tests to assess and compare two quantum random number generators, PQ4000KSI (of company ComScire US) and JUR01 (constructed in Wroclaw University of Science and Technology upon the project of The National Center for Research and Development) as well as a pseudorandom generator from the Mathematica Wolfram package. Finally, we present our new prototype of fully operative miniaturized quantum random generator JUR02 producing a random bit sequence with velocity of 1 Mb/s, which successfully passed standard tests of randomness quality (like NIST and Dieharder tests). We also shortly discuss our former concept of an entanglement-based quantum random number generator protocol with unconditionally secure public randomness verification.
Cybersecurity contains three basic features such as confidentiality, integrity and availability. Data confidentiality ensuring by encryption has faced quantum computing threat, but this problem can be solved by using post-quantum cryptography and / or quantum cryptography (quantum key distribution). For cryptographic applications statistical properties of the generated pseudorandom sequences should not be differ from random sequences. To determine such properties a number of statistical testing methods are used, but they all are focused on the evaluation of binary sequences. In quantum cryptography there are some protocols with Q-trits (qutrits), but these sequences cannot be assessed effectively by binary statistic methods. From this position in the paper quality assessment approach for Q-trits pseudo random number generators and formed sequences was proposed. Also the experimental study of proposed approach (in comparison with NIST STS) was carried out. Given results show that NIST STS technique cannot be used to evaluate the quality of the Q-trit sequences and generators, but developed method is suitable to use in practice for evaluating Q-trit sequences (generators) quality.
Random number generators (RNGs) are notoriously challenging to build and test, especially for cryptographic applications. While statistical tests cannot definitively guarantee an RNG's output quality, they are a powerful verification tool and the only universally applicable testing method. In this work, we design, implement, and present various post-processing methods, using randomness extractors, to improve the RNG output quality and compare them through statistical testing. We begin by performing intensive tests on three RNGs-the 32-bit linear feedback shift register (LFSR), Intel's 'RDSEED,' and IDQuantique's 'Quantis'-and compare their performance. Next, we apply the different post-processing methods to each RNG and conduct further intensive testing on the processed output. To facilitate this, we introduce a comprehensive statistical testing environment, based on existing test suites, that can be parametrised for lightweight (fast) to intensive testing.