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Journal articles on the topic 'Quantum Random Number Generators'

Author: Grafiati
Published: 6 September 2023

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1

Iavich, Maksim, Tamari Kuchukhidze, Giorgi Iashvili, and Sergiy Gnatyuk. "Hybrid quantum random number generator for cryptographic algorithms." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (November 29, 2021): 103–18. http://dx.doi.org/10.32620/reks.2021.4.09.

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The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.
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Manli Xu, Manli Xu, Jingzheng Huang Jingzheng Huang, Wenye Liang Wenye Liang, Chunmei Zhang Chunmei Zhang, Shuang Wang Shuang Wang, Zhenqiang Yin Zhenqiang Yin, Wei Chen Wei Chen, and Zhengfu Han Zhengfu Han. "Adjustable unbalanced quantum random-number generator." Chinese Optics Letters 13, no. 2 (2015): 021405–21409. http://dx.doi.org/10.3788/col201513.021405.

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Martínez, Aldo, Aldo Solis, Rafael Díaz Hernández Rojas, Alfred U'Ren, Jorge Hirsch, and Isaac Pérez Castillo. "Advanced Statistical Testing of Quantum Random Number Generators." Entropy 20, no. 11 (November 17, 2018): 886. http://dx.doi.org/10.3390/e20110886.

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Pseudo-random number generators are widely used in many branches of science, mainly in applications related to Monte Carlo methods, although they are deterministic in design and, therefore, unsuitable for tackling fundamental problems in security and cryptography. The natural laws of the microscopic realm provide a fairly simple method to generate non-deterministic sequences of random numbers, based on measurements of quantum states. In practice, however, the experimental devices on which quantum random number generators are based are often unable to pass some tests of randomness. In this review, we briefly discuss two such tests, point out the challenges that we have encountered in experimental implementations and finally present a fairly simple method that successfully generates non-deterministic maximally random sequences.
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Roussille, Hugo, Lionel Djadaojee, and Frédéric Chevy. "A simple quantum generator of random numbers." Emergent Scientist 1 (2017): 7. http://dx.doi.org/10.1051/emsci/2017009.

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Cryptography techniques rely on chains of random numbers used to generate safe encryption keys. Since random number generator algorithms are in fact pseudo-random their behavior can be predicted if the generation method is known and as such they cannot be used for perfectly safe communications. In this article, we present a perfectly random generator based on quantum measurement processes. The main advantage of such a generator is that using quantum mechanics, its behavior cannot be predicted in any way. We verify the randomness of our generator and compare it to commonly used pseudo-random generators.
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Iavich, Maksim, Tamari Kuchukhidze, Sergiy Gnatyuk, and Andriy Fesenko. "Novel Certification Method for Quantum Random Number Generators." International Journal of Computer Network and Information Security 13, no. 3 (June 8, 2021): 28–38. http://dx.doi.org/10.5815/ijcnis.2021.03.03.

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Random numbers have many uses, but finding true randomness is incredibly difficult. Therefore, quantum mechanics is used, using the essentially unpredictable behavior of a photon, to generate truly random numbers that form the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the performance of our device and the quality of the random bits generated. Self-testing as well as device independent quantum random number generation methods are analyzed in the paper. The advantages and disadvantages of both methods are identified. The model of a novel semi self-testing certification method for quantum random number generators is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. The method is very important for computer science, because it combines the best features from selftesting and device independent methods. It can be used, when the random numbers’ entropy depends on the device and when it does not. In the related researches, these approaches are offered to be used separately, depending on the random number generator. The offered novel certification technology can be properly used, when the device is compromised or spoiled. The technology can successfully detect unintended irregularities, operational problems, abnormalities and problems in the randomization process. The offered mythology assists to eliminate problems related to physical devices. The offered system has the higher certification randomness security and is faster than self-testing approaches. The method is rather efficient because it implements the different certification approaches in the parallel threads. The offered techniques make the offered research must more efficient than the other existing approaches. The corresponding programming simulation is implemented by means of the simulation techniques.
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6

Márton, Botond L., Dóra Istenes, and László Bacsárdi. "Enhancing the operational efficiency of quantum random number generators." Infocommunications journal 13, no. 2 (2021): 10–18. http://dx.doi.org/10.36244/icj.2021.2.2.

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Random numbers are of vital importance in today’s world and used for example in many cryptographical protocols to secure the communication over the internet. The generators producing these numbers are Pseudo Random Number Generators (PRNGs) or True Random Number Generators (TRNGs). A subclass of TRNGs are the Quantum based Random Number Generators (QRNGs) whose generation processes are based on quantum phenomena. However, the achievable quality of the numbers generated from a practical implementation can differ from the theoretically possible. To ease this negative effect post-processing can be used, which contains the use of extractors. They extract as much entropy as possible from the original source and produce a new output with better properties. The quality and the different properties of a given output can be measured with the help of statistical tests. In our work we examined the effect of different extractors on two QRNG outputs and found that witg the right extractor we can improve their quality.
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Marangon, Davide G., Giuseppe Vallone, Ugo Zanforlin, and Paolo Villoresi. "Enhanced security for multi-detector quantum random number generators." Quantum Science and Technology 1, no. 1 (November 1, 2016): 015005. http://dx.doi.org/10.1088/2058-9565/1/1/015005.

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8

Hongo, Kenta, Ryo Maezono, and Kenichi Miura. "Random number generators tested on quantum Monte Carlo simulations." Journal of Computational Chemistry 31, no. 11 (March 24, 2010): 2186–94. http://dx.doi.org/10.1002/jcc.21509.

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9

Miszczak, Jarosław Adam. "Employing online quantum random number generators for generating truly random quantum states in Mathematica." Computer Physics Communications 184, no. 1 (January 2013): 257–58. http://dx.doi.org/10.1016/j.cpc.2012.08.012.

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10

Huang, Min, Ziyang Chen, Yichen Zhang, and Hong Guo. "A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise." Entropy 22, no. 6 (June 2, 2020): 618. http://dx.doi.org/10.3390/e22060618.

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Among all the methods of extracting randomness, quantum random number generators are promising for their genuine randomness. However, existing quantum random number generator schemes aim at generating sequences with a uniform distribution, which may not meet the requirements of specific applications such as a continuous-variable quantum key distribution system. In this paper, we demonstrate a practical quantum random number generation scheme directly generating Gaussian distributed random sequences based on measuring vacuum shot noise. Particularly, the impact of the sampling device in the practical system is analyzed. Furthermore, a related post-processing method, which maintains the fine distribution and autocorrelation properties of raw data, is exploited to extend the precision of generated Gaussian distributed random numbers to over 20 bits, making the sequences possible to be utilized by the following system with requiring high precision numbers. Finally, the results of normality and randomness tests prove that the generated sequences satisfy Gaussian distribution and can pass the randomness testing well.
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11

Chen, I.-Te. "Random Numbers Generated from Audio and Video Sources." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/285373.

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Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator.
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12

Wi, Hansaem, Seyoon Lee, and Okyeon Yi. "DIM-Based Random Number Generation Using Quantum Noise Resources." Wireless Communications and Mobile Computing 2022 (November 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/8984789.

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Currently, unmanned aircraft systems (UASs) or drones are in service in various industrial fields, and each UAS operator establishes and operates their own independent drone system. These individual drone systems interact only with their own components without any integrated management. As the number of UASs is increasing due to the expansion of the drone industry, standardized operation is required. Therefore, to integrate and manage existing drone systems, the Federal Aviation Administration and National Aeronautics and Space Administration devised UAS Traffic Management (UTM). The drone identity module (DIM), which is being developed as a drone identification device, securely stores the remote identification (RID) of each drone and performs a cryptographic operation to secure information between the drone and UTM infrastructure. The DIM performs cryptographic authentication protocols to achieve cryptographic identification and authentication with the UTM infrastructure, which requires random numbers. Modern cryptographic systems rely on difficult computations, and an environment capable of generating secure cryptographic random numbers must be configured to provide high computational costs to attackers. In this paper, we explain the need for random numbers in the DIM, analyze random number generators used in related drone-based studies, and analyze the characteristics of noise resource generation devices that can be used in existing drone systems. Subsequently, based on the analysis results, existing methods are used to generate random numbers in the DIM, and limitations are derived. To overcome these limitations, we propose a method of generating random numbers in the DIM using quantum noise resources. For our proposal, we conduct an analysis of the physical specifications of noise resource generation devices, DIM prototypes, and quantum noise resource generators in existing drone systems, and we present the results of NIST 800-90B entropy measurement using data collected from quantum random number generators.
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13

Chernov, P. S., V. S. Volkov, and D. A. Surovtsev. "Towards Self-testing Quantum Random Number Generators in Integrated Design." IOP Conference Series: Materials Science and Engineering 454 (December 12, 2018): 012087. http://dx.doi.org/10.1088/1757-899x/454/1/012087.

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14

Acerbi, Fabio, Nicola Massari, Leonardo Gasparini, Alessandro Tomasi, Nicola Zorzi, Giorgio Fontana, Lorenzo Pavesi, and Alberto Gola. "Structures and Methods for Fully-Integrated Quantum Random Number Generators." IEEE Journal of Selected Topics in Quantum Electronics 26, no. 3 (May 2020): 1–8. http://dx.doi.org/10.1109/jstqe.2020.2990216.

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15

Stefanov, André, Nicolas Gisin, Olivier Guinnard, Laurent Guinnard, and Hugo Zbinden. "Optical quantum random number generator." Journal of Modern Optics 47, no. 4 (March 2000): 595–98. http://dx.doi.org/10.1080/09500340008233380.

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16

Saini, Anish, Athanasios Tsokanos, and Raimund Kirner. "Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs." Information 13, no. 8 (July 27, 2022): 358. http://dx.doi.org/10.3390/info13080358.

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Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.
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17

Nariezhnii, O. P., V. V. Semenets, and T. O. Grinenko. "Method for measuring quantum phase noise and working transition line width of radio-optical system of random number generator." Radiotekhnika, no. 193 (May 15, 2018): 118–32. http://dx.doi.org/10.30837/rt.2018.2.193.12.

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Results of comprehensive theoretical and experimental studies on creation of a Quantum Random Number Generator (QRNG) prototype based on the implementation of double radio optical resonance in the vapor of the rubidium isotope are presented. A method is developed for the analytical and numerical solution of the coupled-mode QRNG equation, which describes the mode of interaction of quantum generators in the process of measuring their parameters. A feature of the proposed method is the possibility of studying quantum phase noise and the width of the quantum discriminators line on alkali metal vapors in the presence of an interaction error.
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18

Akashi, Nozomi, Kohei Nakajima, Mitsuru Shibayama, and Yasuo Kuniyoshi. "A mechanical true random number generator." New Journal of Physics 24, no. 1 (January 1, 2022): 013019. http://dx.doi.org/10.1088/1367-2630/ac45ca.

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Abstract Random number generation has become an indispensable part of information processing: it is essential for many numerical algorithms, security applications, and in securing fairness in everyday life. Random number generators (RNGs) find application in many devices, ranging from dice and roulette wheels, via computer algorithms, lasers to quantum systems, which inevitably capitalize on their physical dynamics at respective spatio-temporal scales. Herein, to the best of our knowledge, we propose the first mathematically proven true RNG (TRNG) based on a mechanical system, particularly the triple linkage of Thurston and Weeks. By using certain parameters, its free motion has been proven to be an Anosov flow, from which we can show that it has an exponential mixing property and structural stability. We contend that this mechanical Anosov flow can be used as a TRNG, which requires that the random number should be unpredictable, irreproducible, robust against the inevitable noise seen in physical implementations, and the resulting distribution’s controllability (an important consideration in practice). We investigate the proposed system’s properties both theoretically and numerically based on the above four perspectives. Further, we confirm that the random bits numerically generated pass the standard statistical tests for random bits.
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19

Foreman, Cameron, Sherilyn Wright, Alec Edgington, Mario Berta, and Florian J. Curchod. "Practical randomness amplification and privatisation with implementations on quantum computers." Quantum 7 (March 30, 2023): 969. http://dx.doi.org/10.22331/q-2023-03-30-969.

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We present an end-to-end and practical randomness amplification and privatisation protocol based on Bell tests. This allows the building of device-independent random number generators which output (near-)perfectly unbiased and private numbers, even if using an uncharacterised quantum device potentially built by an adversary. Our generation rates are linear in the repetition rate of the quantum device and the classical randomness post-processing has quasi-linear complexity – making it efficient on a standard personal laptop. The statistical analysis is also tailored for real-world quantum devices. Our protocol is then showcased on several different quantum computers. Although not purposely built for the task, we show that quantum computers can run faithful Bell tests by adding minimal assumptions. In this semi-device-independent manner, our protocol generates (near-)perfectly unbiased and private random numbers on today's quantum computers.
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20

Stefanov, Andre, Nicolas Gisin, Olivier Guinnard, Laurent Guinnard, and Hugo Zbinden. "Letter Optical quantum random number generator." Journal of Modern Optics 47, no. 4 (March 20, 2000): 595–98. http://dx.doi.org/10.1080/095003400147908.

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21

Huang, Min, Ziyang Chen, Yichen Zhang, and Hong Guo. "A Phase Fluctuation Based Practical Quantum Random Number Generator Scheme with Delay-Free Structure." Applied Sciences 10, no. 7 (April 2, 2020): 2431. http://dx.doi.org/10.3390/app10072431.

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Quantum random number generators are widely used in many applications, ranging from sampling and simulation, fundamental science to cryptography, such as a quantum key distribution system. Among all the previous works, quantum noise from phase fluctuation of laser diodes is one of the most commonly used random source in the quantum random number generation, and many practical schemes based on phase noise with compact systems have been proposed so far. Here, we proposed a new structure of phase noise scheme, utilizing the phase fluctuation from two laser diodes with a slight difference of center wavelength. By analyzing the frequency components and adopting an appropriate band-pass filter, we prove that our scheme extracts quantum noise and filtered other classical noises substantially. Results of a randomness test shows that the extracted random sequences are of good performance. Due to lack of delay-line and the low requirement on other devices in this system, our scheme is promising in future scenarios for miniaturized quantum random number generation systems.
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Molotkov, S. N. "Homodyne detection in quantum optics: deterministic extractors and quantum random number generators on ‘vacuum fluctuations’." Laser Physics 32, no. 5 (April 7, 2022): 055202. http://dx.doi.org/10.1088/1555-6611/ac5ccc.

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Abstract Quantum random number generators with a continuous variable are considered based on a primary randomness of the outcomes of homodyne measurements of a coherent state. A deterministic method of extraction of truly random 0 and 1 from the primary sequence of measurements of the quadrature of the field in homodyne detection is considered. The method, in the case of independence of successive measurement outcomes, in the asymptotic limit of long sequences, allows us to extract with a polynomial complexity all the true randomness contained in the primary sequence. The method does not require knowledge of the probability distribution function of the primary random sequence, and also does not require additional randomness in the extraction of random 0 and 1. The approach with deterministic randomness extractors, unlike other methods, contains fewer assumptions and conditions that need to be satisfied in the experimental implementation of such generators, and is significantly more effective and simple in experimental implementation. The fundamental limitations dictated by nature for achieving statistical independence of successive measurement outcomes are also considered. The statistical independence of the measurement outcomes is the equivalent of true randomness, in the sense that is possible in the case of the independence of the measurement outcomes, provably, with deterministic extractor, to extract a ‘truly random sequence of 0 and 1’. It is shown that in the asymptotic limit it is possible to extract all the true randomness contained in the outcomes of physical measurements.
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23

Bird, Jordan J., Anikó Ekárt, and Diego R. Faria. "On the effects of pseudorandom and quantum-random number generators in soft computing." Soft Computing 24, no. 12 (October 28, 2019): 9243–56. http://dx.doi.org/10.1007/s00500-019-04450-0.

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Abstract In this work, we argue that the implications of pseudorandom 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 implications are yet to be explored in soft computing until this work. We use a CPU and a QPU to generate random numbers for multiple machine learning techniques. Random numbers are employed in the random initial weight distributions of dense and convolutional neural networks, in which results show a profound difference in learning patterns for the two. In 50 dense neural networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at + 0.1%, and QRNG exceeded PRNG for mental state EEG classification by + 2.82%. In 50 convolutional neural networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, and in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by + 0.92%. The n-random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification data sets, a QRT seemed inferior to a RT as it performed on average worse by − 0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by − 0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF). Ten to 100 ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF (100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional behaviour.
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Haylock, Ben, Daniel Peace, Francesco Lenzini, Christian Weedbrook, and Mirko Lobino. "Multiplexed Quantum Random Number Generation." Quantum 3 (May 13, 2019): 141. http://dx.doi.org/10.22331/q-2019-05-13-141.

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Fast secure random number generation is essential for high-speed encrypted communication, and is the backbone of information security. Generation of truly random numbers depends on the intrinsic randomness of the process used and is usually limited by electronic bandwidth and signal processing data rates. Here we use a multiplexing scheme to create a fast quantum random number generator structurally tailored to encryption for distributed computing, and high bit-rate data transfer. We use vacuum fluctuations measured by seven homodyne detectors as quantum randomness sources, multiplexed using a single integrated optical device. We obtain a real-time random number generation rate of 3.08 Gbit/s, from only 27.5 MHz of sampled detector bandwidth. Furthermore, we take advantage of the multiplexed nature of our system to demonstrate an unseeded strong extractor with a generation rate of 26 Mbit/s.
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Ghersi, Dario, Abhishek Parakh, and Mihaly Mezei. "Comparison of a quantum random number generator with pseudorandom number generators for their use in molecular Monte Carlo simulations." Journal of Computational Chemistry 38, no. 31 (September 18, 2017): 2713–20. http://dx.doi.org/10.1002/jcc.25065.

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Kwon, Osung, Young-Wook Cho, and Yoon-Ho Kim. "Quantum random number generator using photon-number path entanglement." Applied Optics 48, no. 9 (March 19, 2009): 1774. http://dx.doi.org/10.1364/ao.48.001774.

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YU Heng-wei, 余恒炜, 孙晓娟 SUN Xiao-juan, 王星辰 WANG Xing-chen, 蒋. 科. JIANG Ke, 吴. 忧. WU-You, 程东碧 CHENG Dong-bi, 石芝铭 SHI Zhi-ming, 贾玉萍 JIA Yu-ping, and 黎大兵 LI Da-bing. "Quantum random number Gaussian noise signal generator." Optics and Precision Engineering 27, no. 7 (2019): 1492–99. http://dx.doi.org/10.3788/ope.20192707.1492.

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Pervushin, B. E., M. A. Fadeev, A. V. Zinovev, R. K. Goncharov, A. A. Santev, A. E. Ivanova, and E. O. Samsonov. "Quantum random number generator using vacuum fluctuations." Nanosystems: Physics, Chemistry, Mathematics 12, no. 2 (April 29, 2021): 156–60. http://dx.doi.org/10.17586/2220-8054-2021-12-2-156-160.

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Feng MingMing, Qin XiaoLin, Zhou ChunYuan, Xiong Li, and Ding LiangEn. "Quantum random number generator based on polarization." Acta Physica Sinica 52, no. 1 (2003): 72. http://dx.doi.org/10.7498/aps.52.72.

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Ma, Hai-Qiang, Wu Zhu, Ke-Jin Wei, Rui-Xue Li, and Hong-Wei Liu. "A hybrid-type quantum random number generator." Chinese Physics B 25, no. 5 (May 2016): 050304. http://dx.doi.org/10.1088/1674-1056/25/5/050304.

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Wang, P. X., G. L. Long, and Y. S. Li. "Scheme for a quantum random number generator." Journal of Applied Physics 100, no. 5 (September 2006): 056107. http://dx.doi.org/10.1063/1.2338830.

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32

Guo, Xiaomin, Chen Cheng, Mingchuan Wu, Qinzhong Gao, Pu Li, and Yanqiang Guo. "Parallel real-time quantum random number generator." Optics Letters 44, no. 22 (November 11, 2019): 5566. http://dx.doi.org/10.1364/ol.44.005566.

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Wei Shihai, 魏世海, 樊矾 Fan Fan, 杨杰 Yang Jie, 黄伟 Huang Wei, 何远杭 He Yuanhang, 李大双 Li Dashuang, and 徐兵杰 Xu Bingjie. "Ultrafast Compact Optical Quantum Random Number Generator." Chinese Journal of Lasers 45, no. 5 (2018): 0512001. http://dx.doi.org/10.3788/cjl201845.0512001.

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Nguyen, Lac, Patrick Rehain, Yong Meng Sua, and Yu-Ping Huang. "Programmable quantum random number generator without postprocessing." Optics Letters 43, no. 4 (February 6, 2018): 631. http://dx.doi.org/10.1364/ol.43.000631.

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35

Ferreira, Maurício J., Nuno A. Silva, Armando N. Pinto, and Nelson J. Muga. "Characterization of a Quantum Random Number Generator Based on Vacuum Fluctuations." Applied Sciences 11, no. 16 (August 12, 2021): 7413. http://dx.doi.org/10.3390/app11167413.

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Quantum random number generators (QRNGs) are currently in high demand across a large number of cryptographic applications as secure sources of true randomness. In this work, we characterize the conditions from which randomness can be extracted in a QRNG based on homodyne measurements of vacuum fluctuations by assessing the impact of experimental limitations, such as the digitizer resolution or the presence of excess local oscillator (LO) noise due to an unbalanced detection. Moreover, we propose an estimation method to quantify the excess entropy contribution introduced by an unbalanced detection and analyze the implementation of the post-processing algorithm. Finally, we submitted the generated numbers to a set of statistical tests to assess the quality of its output randomness and verified that it passes the standard libraries.
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36

Xavier, G. B., T. Ferreira da Silva, G. Vilela de Faria, G. P. Temporao, and J. P. von der Weid. "Practical random number generation protocol for entanglement-based quantum key distribution." Quantum Information and Computation 9, no. 7&8 (July 2009): 683–92. http://dx.doi.org/10.26421/qic9.7-8-10.

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A simple protocol which takes advantage of the inherent random times of detections in single photon counting modules is presented for random active basis choices when using entanglement-based protocols for Quantum Key Distribution (QKD). It may also be applicable to the BB84 protocol in certain cases. The scheme presented uses the single photon detectors already present on a QKD setup, working on the same rate as the system is capable of detecting, and is, therefore, not limited by the output rates of quantum random number generators. This protocol only requires small hardware modifications making it an attractive solution. We perform a proof-of-principle experiment employing a spontaneous parametric down-conversion process in a $\chi^{(2)}$ non-linear crystal to demonstrate the feasibility of our scheme, and show that the generated sequence passes randomness tests.
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37

Choi, Youngrak, Yongjin Yeom, and Ju-Sung Kang. "Practical Entropy Accumulation for Random Number Generators with Image Sensor-Based Quantum Noise Sources." Entropy 25, no. 7 (July 13, 2023): 1056. http://dx.doi.org/10.3390/e25071056.

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The efficient generation of high-quality random numbers is essential in the operation of cryptographic modules. The quality of a random number generator is evaluated by the min-entropy of its entropy source. The typical method used to achieve high min-entropy of the output sequence is an entropy accumulation based on a hash function. This is grounded in the famous Leftover Hash Lemma, which guarantees a lower bound on the min-entropy of the output sequence. However, the hash function-based entropy accumulation has slow speed in general. For a practical perspective, we need a new efficient entropy accumulation with the theoretical background for the min-entropy of the output sequence. In this work, we obtain the theoretical bound for the min-entropy of the output random sequence through the very efficient entropy accumulation using only bitwise XOR operations, where the input sequences from the entropy source are independent. Moreover, we examine our theoretical results by applying them to the quantum random number generator that uses dark shot noise arising from image sensor pixels as its entropy source.
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38

Kish, Laszlo B., Claes G. Granqvist, Tamas Horvath, Andreas Klappenecker, He Wen, and Sergey M. Bezrukov. "Bird's-eye view on noise-based logic." International Journal of Modern Physics: Conference Series 33 (January 2014): 1460363. http://dx.doi.org/10.1142/s2010194514603639.

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Noise-based logic is a practically deterministic logic scheme inspired by the randomness of neural spikes and uses a system of uncorrelated stochastic processes and their superposition to represent the logic state. We briefly discuss various questions such as (i) What does practical determinism mean? (ii) Is noise-based logic a Turing machine? (iii) Is there hope to beat (the dreams of) quantum computation by a classical physical noise-based processor, and what are the minimum hardware requirements for that? Finally, (iv) we address the problem of random number generators and show that the common belief that quantum number generators are superior to classical (thermal) noise-based generators is nothing but a myth.
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39

Guo, Xiaomin, Ripeng Liu, Pu Li, Chen Cheng, Mingchuan Wu, and Yanqiang Guo. "Enhancing Extractable Quantum Entropy in Vacuum-Based Quantum Random Number Generator." Entropy 20, no. 11 (October 24, 2018): 819. http://dx.doi.org/10.3390/e20110819.

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Information-theoretically provable unique true random numbers, which cannot be correlated or controlled by an attacker, can be generated based on quantum measurement of vacuum state and universal-hashing randomness extraction. Quantum entropy in the measurements decides the quality and security of the random number generator (RNG). At the same time, it directly determines the extraction ratio of true randomness from the raw data, in other words, it obviously affects quantum random bits generating rate. In this work, we commit to enhancing quantum entropy content in the vacuum noise based quantum RNG. We have taken into account main factors in this proposal to establish the theoretical model of quantum entropy content, including the effects of classical noise, the optimum dynamical analog-digital convertor (ADC) range, the local gain and the electronic gain of the homodyne system. We demonstrate that by amplifying the vacuum quantum noise, abundant quantum entropy is extractable in the step of post-processing even classical noise excursion, which may be deliberately induced by an eavesdropper, is large. Based on the discussion and the fact that the bandwidth of quantum vacuum noise is infinite, we propose large dynamical range and moderate TIA gain to pursue higher local oscillator (LO) amplification of vacuum quadrature and broader detection bandwidth in homodyne system. High true randomness extraction ratio together with high sampling rate is attainable. Experimentally, an extraction ratio of true randomness of 85.3% is achieved by finite enhancement of the laser power of the LO when classical noise excursions of the raw data is obvious.
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40

Marosits, Ádám, Ágoston Schranz, and Eszter Udvary. "Amplified spontaneous emission based quantum random number generator." Infocommunications journal 12, no. 2 (2020): 12–17. http://dx.doi.org/10.36244/icj.2020.2.2.

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41

Zhang, Qiang, Xiaowei Deng, Caixing Tian, and Xiaolong Su. "Quantum random number generator based on twin beams." Optics Letters 42, no. 5 (February 17, 2017): 895. http://dx.doi.org/10.1364/ol.42.000895.

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42

Jennewein, Thomas, Ulrich Achleitner, Gregor Weihs, Harald Weinfurter, and Anton Zeilinger. "A fast and compact quantum random number generator." Review of Scientific Instruments 71, no. 4 (April 2000): 1675–80. http://dx.doi.org/10.1063/1.1150518.

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43

Song, Xiao-Tian, Hong-Wei Li, Zhen-Qiang Yin, Wen-Ye Liang, Chun-Mei Zhang, Yun-Guang Han, Wei Chen, and Zheng-Fu Han. "Phase-Coding Self-Testing Quantum Random Number Generator." Chinese Physics Letters 32, no. 8 (August 2015): 080302. http://dx.doi.org/10.1088/0256-307x/32/8/080302.

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44

Zheng, Ziyong, Yichen Zhang, Min Huang, Ziyang Chen, Song Yu, and Hong Guo. "Bias-free source-independent quantum random number generator." Optics Express 28, no. 15 (July 14, 2020): 22388. http://dx.doi.org/10.1364/oe.396461.

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45

Pandit, Anupama Arjun, Atul Kumar, and Arun Mishra. "LWR-based Quantum-Safe Pseudo-Random Number Generator." Journal of Information Security and Applications 73 (March 2023): 103431. http://dx.doi.org/10.1016/j.jisa.2023.103431.

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46

Ivanova, A. E., S. A. Chivilikhin, and A. V. Gleim. "Using of optical splitters in quantum random number generators, based on fluctuations of vacuum." Journal of Physics: Conference Series 735 (August 2016): 012077. http://dx.doi.org/10.1088/1742-6596/735/1/012077.

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47

Molotkov, S. N. "On the limiting characteristics of quantum random number generators at various clusterings of photocounts." JETP Letters 105, no. 6 (March 2017): 395–401. http://dx.doi.org/10.1134/s0021364017060091.

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48

Ivanova, A. E., S. A. Chivilikhin, and A. V. Gleim. "Quantum random number generator based on quantum nature of vacuum fluctuations." Journal of Physics: Conference Series 917 (November 2017): 062008. http://dx.doi.org/10.1088/1742-6596/917/6/062008.

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49

HONGO, Kenta, and Ryo MAEZONO. "Quantum Monte Carlo Simulations with RANLUX Random Number Generator." Progress in Nuclear Science and Technology 2 (October 1, 2011): 51–55. http://dx.doi.org/10.15669/pnst.2.51.

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50

Shakhovoy, R., E. Maksimova, V. Sharoglazova, M. Puplauskis, and Y. Kurochkin. "Fast and compact VCSEL-based quantum random number generator." Journal of Physics: Conference Series 1984, no. 1 (July 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/1984/1/012005.

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