scholarly journals Quantum generators of random numbers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcin M. Jacak ◽  
Piotr Jóźwiak ◽  
Jakub Niemczuk ◽  
Janusz E. Jacak
Keyword(s):  
Information Processing ◽  
Random Number ◽  
Random Number Generator ◽  
Random Numbers ◽  
Generation Process ◽  
Pseudorandom Generator ◽  
Random Number Generators ◽  
Random Generator ◽  
Number Sequences

AbstractGeneration 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.

scholarly journals Novel Certification Method for Quantum Random Number Generators

2021 ◽  
Vol 13 (3) ◽  
pp. 28-38
Author(s):  
Maksim Iavich ◽  
◽  
Tamari Kuchukhidze ◽  
Sergiy Gnatyuk ◽  
Andriy Fesenko
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Advantages And Disadvantages ◽  
Simulation Techniques ◽  
Self Testing ◽  
Certification Method

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.

scholarly journals Enhancing the operational efficiency of quantum random number generators

2021 ◽  
Vol 13 (2) ◽  
pp. 10-18
Author(s):  
Botond L. Márton ◽  
Dóra Istenes ◽  
László Bacsárdi
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Practical Implementation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Original Source ◽  
Negative Effect ◽  
Quantum Phenomena ◽  
The Right

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.

A General-Purpose Uniform Random Number Package

2021 ◽  
pp. 164-176
Author(s):  
E. Jack Chen
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
General Purpose ◽  
Random Numbers ◽  
Pseudorandom Generator ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Modeling And Analysis ◽  
Generating Sequences ◽  
Simulation Systems

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

scholarly journals Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1869 ◽  
Author(s):  
Luca Baldanzi ◽  
Luca Crocetti ◽  
Francesco Falaschi ◽  
Matteo Bertolucci ◽  
Jacopo Belli ◽  
...  
Keyword(s):  
High Performance ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Generation Process ◽  
Number Generator ◽  
Ip Core ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.

Pseudorandom Number Generators

2017 ◽  
pp. 1-25
Keyword(s):  
Random Number ◽  
Pseudorandom Number ◽  
Random Numbers ◽  
Pseudorandom Sequences ◽  
Random Number Generators ◽  
Random Sequences ◽  
Pseudorandom Number Generators ◽  
Pseudo Random Number

In this chapter, the author considers existing methods and means of forming pseudo-random sequences of numbers and also are described the main characteristics of random and pseudorandom sequences of numbers. The main theoretical aspects of the construction of pseudo-random number generators are considered. Classification of pseudorandom number generators is presented. The structures and models of the most popular pseudo-random number generators are considered, the main characteristics of generators that affect the quality of the formation of pseudorandom bit sequences are described. The models of the basic mathematical generators of pseudo-random numbers are considered, and also the principles of building hardware generators are presented.

PSEUDO RANDOM NUMBER GENERATOR BASED ON SYNCHRONIZED CHAOTIC MAPS

2010 ◽  
Vol 21 (02) ◽  
pp. 275-290 ◽  
Author(s):  
AFSHIN AKHSHANI ◽  
SOHRAB BEHNIA ◽  
AMIR AKHAVAN ◽  
SIEW-CHOO LIM ◽  
ZAINURIAH HASSAN
Keyword(s):  
Random Number ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Random Numbers ◽  
Random Generator ◽  
Coupled Maps ◽  
The Individual ◽  
Test Suites ◽  
Pseudo Random Number Generator

In this paper, a hierarchy of coupled maps in synchronized state is introduced. Then some discussions about the individual properties of these chaotic maps are presented, from a dynamical systems viewpoint. Also, one of these chaotic map is used as a Nonlinear Pseudo Random Generator (NPRNG). This paper addresses the chaotic features of this map which are useful for generating nonlinear pseudo random numbers. Results of the analysis and extensive tests such as the NIST, DIEHARD and ENT test suites indicate that the NPRNG exhibits good statistical randomness properties.

scholarly journals Distributed random number generation

1992 ◽  
Vol 2 (2) ◽  
pp. 203-212 ◽  
Author(s):  
F. Warren Burton ◽  
Rex L. Page
Keyword(s):  
Random Number ◽  
Single Point ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Lazy Evaluation ◽  
Functional Program ◽  
New Approach ◽  
Number Sequences ◽  
Program Components

AbstractIn a functional program, a simple random number generator may generate a lazy list of random numbers. This is fine when the random numbers are consumed sequentially at a single point in the program. However, things are more complicated in a program where random numbers are used at many locations, such as in a large simulation. The programmer should not need to worry about providing separate generators with a unique seed at each point where random numbers are used. At the same time, the programmer should not need to coordinate the use of a single stream of random numbers in many parts of the program, which can be particularly difficult with lazy evaluation or parallel processing.We discuss several techniques for distributing random numbers to various parts of a program, and some methods of allowing different program components to evaluate random numbers locally. We then propose a new approach in which a random number sequence can be split at a random point to produce a pair of random number sequences that can be used independently at different points in the computation.The approach can also be used in distributed procedural programs, where it is desirable to avoid dealing with a single source of random numbers. The approach has the added advantage of producing repeatable results, as might be needed in debugging, for example.

scholarly journals Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

2020 ◽  
pp. 17-37
Author(s):  
Kentaro Tamura ◽  
Yutaka Shikano
Keyword(s):  
Random Number ◽  
Quantum Computer ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Black Box ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
Simple Indicator

Abstract 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 from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.

scholarly journals Novel Memristor Based True Random Number Generator

2020 ◽  
Author(s):  
Scott Stoller
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Circuit Board ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
Coin Toss ◽  
True Random Number Generator ◽  
Digital World

Random numbers are an important, but often overlooked part of the modern computing environment. They are used everywhere around us for a variety of purposes, from simple decision making in video games such as a coin toss, to securing financial transactions and encrypting confidential communications. They are even useful for gambling and the lottery. Random numbers are generated in many ways. Pseudo random number generators (PRNGs) generate numbers based on a formula. True random number generators (TRNGs) capture entropy from the environment to generate randomness. As our society and our devices become more connected in the digital world, it is important to develop new ways to generate truly random numbers in order to secure communications and connected devices. In this work a novel memristor-based True Random Number Generator is designed and a physical implementation is fabricated and tested using a W-based self-directed channel (SDC) memristor. The circuit was initially designed and prototyped on a breadboard. A custom Printed Circuit Board (PCB) was fabricated for the final circuit design and testing of the novel memristor-based TRNG. The National Institute of Standards and Technology (NIST) Statistical Test Suite (STS) was used to check the output of the TRNG for randomness. The TRNG was demonstrated to pass 13 statistical tests out of the 15 in the STS.

scholarly journals A simple quantum generator of random numbers

2017 ◽  
Vol 1 ◽  
pp. 7
Author(s):  
Hugo Roussille ◽  
Lionel Djadaojee ◽  
Frédéric Chevy
Keyword(s):  
Quantum Mechanics ◽  
Quantum Measurement ◽  
Random Number ◽  
Random Number Generator ◽  
Random Numbers ◽  
Number Generator ◽  
Random Generator ◽  
Simple Quantum ◽  
Quantum Generator ◽  
Random Generators

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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