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

Chinese Science Bulletin

Physical true random number generator

An Dang

Published Jan 1, 2009• 0 citations
OpenAlex

Abstract

The requirement of physical true random number generator and the difference between the true random number and pseudo-random number are presented. The recent experiements of true ran-dom number generator,expetically,the various schemes of true random number generator based on lasers,are summarized. Also,we report the progress of the thoretical and experimental research on physical true random number generator in our group.

Related papers

Physical true random number generator

Yu LIU, Hong Mei Guo, 韦 韦 +1 more

The requirement of physical true random number generator and the difference between the true random number and pseudo-random number are presented. The recent experiements of true ran-dom number generator, expetically, the various schemes of true random number generator based on lasers, are summarized. Also, we report the progress of the thoretical and experimental research on physical true random number generator in our group.

Chinese Science Bulletin (Chinese Version)•Dec 1, 2009•27 citations

Exploring quantum systems for pseudo-random number generation

Luis José Mantilla Santa Cruz, Luis Fernando Faina, João Henrique de Souza Pereira

Abstract Quantum realizations have opened a new ways for computer science. The new characteristics and phenomenons allow us to create new algorithms for general proposes. An interesting topic is random number generation. There are two perspectives for this proposes. True random numbers generator (TRN) and pseudo random numbers generator (PRNG). True Random Number Generators has four significant characteristics: randomness, long Period, insensitivity to seeds, repeatability. Additionally, Pseudo Random Number Generators is a fascinating topic that leverages the principles of computer science to craft deterministic algorithms, enabling the creation of a sequence of pseudo-random numbers. A crucial element in this process is the 'seed', which serves as the initial value to generate the entire sequence. This document proposes a algorithm based on a quantum system and the quasi-probability of its possible states to generate a long set of numbers which has pseudo random properties. The quantum system is compound by n qubits, its initial conditions and a small set of rotation gates with a fixed angle. The tests were realized using the battery test called NIST SP800-22, and alpha=0.01 as a threshold to ensures 99\% of precision. The results demonstrate that it is possible generate a long set of pseudo numbers with random characteristics using a quantum simple operation. However, the experiments reveal that a real world implementation require a quantum system with at least 10-9 precision order.

Quantum Studies Mathematics and Foundations•Oct 4, 2024•2 citations

Novel Quantum Random Number Generator for Cryptographical Applications

Maksim Iavich, Tamari Kuchukhidze, Tetyana Okhrimenko +1 more

Random numbers are a fundamental resource in science and engineering, and are also widely used in many fields, such as simulation, cryptography, lotteries, fundamental science. There are algorithmically generated numbers that look like random numbers but are not truly random. These pseudo random numbers are generated from a computer, using deterministic algorithms. Because, we cannot use pseudo random generators in situations, where true randomness is necessary, we use true random number generators. In this case, we use unpredictable random events as a random source. Quantum random number generators (QRNG) output true random numbers based on inherent randomness in quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential. We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It is rather efficient, as it uses the simple version of the detectors with rather few requirements. The novel QRNG produces more than one random bit per each photon detection.

International Scientific-Practical Conference Problems of Infocommunications Science and Technology•Oct 6, 2020•4 citations

A quantum pseudo-random number generation scheme

Yinghua Jiang, Biao Liu, Chenfei Guo +1 more

Abstract Quantum random key distribution based on physical properties of quantum mechanics has high security and true randomness, but it has the disadvantages of low key generation efficiency and high cost. The classic pseudo-random number generator has the advantages of simple algorithm and high key generation efficiency, but it has extremely strict requirements on the security and randomness of the "seed". Based on the analysis of two unrelated technologies, this paper proposes a pseudo-random number generation scheme based on quantum key distribution. This scheme makes full use of quantum key distribution to share a short quantum random number-seed between the two communicating parties, then inputs the seed into a pseudo-random number generator. Finally, the two communicating parties share a long random number to encrypt the communication content. Research shows that compared with classic quantum key distribution and classic pseudo-random number generator, this scheme has stronger security, better randomness, and higher key generation efficiency.

Journal of Physics Conference Series•Aug 1, 2021•3 citations

Random number generator for cryptography

Ram Soorat, K. Madhuri, Ashok Vudayagiri

One key requirement for many cryptograhic schemes is the generation of random numbers.Sequences of random numbers are used at several stages of a standard cryptographic protocol.One simple example is a Vernam cipher, where a string of random numbers is added to message string to generate encrypted code.C = M ⊕ K.It has been mathematically shown that this simple scheme is unbreakable if key K is as long as M and is used only once.The security of a cryptosystem shall not be based on keeping the algorithm secret but solely on keeping the key secret.The security of a random number generator (RNG) is related to the difficulty of predicting its future sequence values from past values.The quality and unpredictability of secret data is critical to securing communication by modern cryptographic techniques.The generation of such data for cryptographic purposes typically requires an unpredictable physical source of random data.We studied a chaotic circuit which consisted of an inductor, capacitance, diode and thus used for the BB84 protocol.We have studied both pseudo random and true random number generators and evaluated them through various tests like frequency, correlation, NIST etc.

Nanosystems Physics Chemistry Mathematics•Oct 27, 2017•4 citations

A Comparative Study and Analysis of Quantum Random Number Generator with True Random Number Generator

Shashi Kant Pandey, R. Jenef

Random numbers play a central role in cryptography, cyber security, and gaming applications. Extracting bits from some random natural activity is one of the primitive ways to generate random numbers. True random number generator(TRNG) and quantum random number generator(QRNG) are examples of this random number sequence. The development of quantum technology made it possible to extract random sequences from quantum entropy sources. This sequence is called quantum random numbers and the whole setup is named a quantum random number generator. A suitable sampling and a physical device for the entropy source are the building blocks for a TRNG. Here we present a comparative analysis of two random number generators. Apart from the conventional statistical test for randomness, here we introduce a straightforward coefficient based on the concept of non-linearity of the Boolean function to determine the availability of any linearity in a chosen random binary sequence. It reveals the cryptographic suitability of the generated sequence w.r.t any correlation attack. A comparison of the nonlinearity of two Boolean sequences generated from two different random sources is presented.

International Conference on Communication Systems & Networks (COMSNETS)•Jan 3, 2024•12 citations
The requirement of physical true random number generator and the difference between the true random number and pseudo-random number are presented. The recent experiements of true ran-dom number generator,expetically,the various schemes of true random number generator based on lasers,are summarized. Also,we report the progress of the thoretical and experimental research on physical true random number generator in our group.
Abstract Quantum realizations have opened a new ways for computer science. The new characteristics and phenomenons allow us to create new algorithms for general proposes. An interesting topic is random number generation. There are two perspectives for this proposes. True random numbers generator (TRN) and pseudo random numbers generator (PRNG). True Random Number Generators has four significant characteristics: randomness, long Period, insensitivity to seeds, repeatability. Additionally, Pseudo Random Number Generators is a fascinating topic that leverages the principles of computer science to craft deterministic algorithms, enabling the creation of a sequence of pseudo-random numbers. A crucial element in this process is the 'seed', which serves as the initial value to generate the entire sequence. This document proposes a algorithm based on a quantum system and the quasi-probability of its possible states to generate a long set of numbers which has pseudo random properties. The quantum system is compound by n qubits, its initial conditions and a small set of rotation gates with a fixed angle. The tests were realized using the battery test called NIST SP800-22, and alpha=0.01 as a threshold to ensures 99\% of precision. The results demonstrate that it is possible generate a long set of pseudo numbers with random characteristics using a quantum simple operation. However, the experiments reveal that a real world implementation require a quantum system with at least 10-9 precision order.
Random numbers are a fundamental resource in science and engineering, and are also widely used in many fields, such as simulation, cryptography, lotteries, fundamental science. There are algorithmically generated numbers that look like random numbers but are not truly random. These pseudo random numbers are generated from a computer, using deterministic algorithms. Because, we cannot use pseudo random generators in situations, where true randomness is necessary, we use true random number generators. In this case, we use unpredictable random events as a random source. Quantum random number generators (QRNG) output true random numbers based on inherent randomness in quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential. We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It is rather efficient, as it uses the simple version of the detectors with rather few requirements. The novel QRNG produces more than one random bit per each photon detection.
Abstract Quantum random key distribution based on physical properties of quantum mechanics has high security and true randomness, but it has the disadvantages of low key generation efficiency and high cost. The classic pseudo-random number generator has the advantages of simple algorithm and high key generation efficiency, but it has extremely strict requirements on the security and randomness of the "seed". Based on the analysis of two unrelated technologies, this paper proposes a pseudo-random number generation scheme based on quantum key distribution. This scheme makes full use of quantum key distribution to share a short quantum random number-seed between the two communicating parties, then inputs the seed into a pseudo-random number generator. Finally, the two communicating parties share a long random number to encrypt the communication content. Research shows that compared with classic quantum key distribution and classic pseudo-random number generator, this scheme has stronger security, better randomness, and higher key generation efficiency.
One key requirement for many cryptograhic schemes is the generation of random numbers.Sequences of random numbers are used at several stages of a standard cryptographic protocol.One simple example is a Vernam cipher, where a string of random numbers is added to message string to generate encrypted code.C = M ⊕ K.It has been mathematically shown that this simple scheme is unbreakable if key K is as long as M and is used only once.The security of a cryptosystem shall not be based on keeping the algorithm secret but solely on keeping the key secret.The security of a random number generator (RNG) is related to the difficulty of predicting its future sequence values from past values.The quality and unpredictability of secret data is critical to securing communication by modern cryptographic techniques.The generation of such data for cryptographic purposes typically requires an unpredictable physical source of random data.We studied a chaotic circuit which consisted of an inductor, capacitance, diode and thus used for the BB84 protocol.We have studied both pseudo random and true random number generators and evaluated them through various tests like frequency, correlation, NIST etc.
Random numbers play a central role in cryptography, cyber security, and gaming applications. Extracting bits from some random natural activity is one of the primitive ways to generate random numbers. True random number generator(TRNG) and quantum random number generator(QRNG) are examples of this random number sequence. The development of quantum technology made it possible to extract random sequences from quantum entropy sources. This sequence is called quantum random numbers and the whole setup is named a quantum random number generator. A suitable sampling and a physical device for the entropy source are the building blocks for a TRNG. Here we present a comparative analysis of two random number generators. Apart from the conventional statistical test for randomness, here we introduce a straightforward coefficient based on the concept of non-linearity of the Boolean function to determine the availability of any linearity in a chosen random binary sequence. It reveals the cryptographic suitability of the generated sequence w.r.t any correlation attack. A comparison of the nonlinearity of two Boolean sequences generated from two different random sources is presented.