Easy to integrate API for accessing true random numbers generated with IDQ’s Quantis Appliance

Generating quality random numbers is essential for many computer applications, from preserving data privacy through cryptography and federated learning to artificial intelligence and machine learning. The role of random numbers in such applications is to add the necessary entropy to produce secure keys that protect data, to select data for training and testing machine learning models, or to improve the efficiency of solution space searches in evolutionary algorithms and heuristic methods. The available software methods can only generate pseudo-random numbers, while conventional hardware methods extract randomness from the environment, and these physical processes possess specific self-similar patterns. In contrast, quantum random number generators (QRNGs) produce truly random numbers by exploiting the randomness of quantum state measurement. Software platforms that provide random numbers over the cloud, rendered with network-attached quantum random number generator devices, exist; however, they were not developed as a native C language solution for embedded applications. Therefore, we introduce libqrng, written in C, which offers access to IDQ’s Quantis Appliance high-quality random numbers through an easy-to-integrate API for embedded and distributed computing applications that requires randomness and an interface for using the library from different programming languages (e.g., Python).

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Publication Details

Journal
SoftwareX
Published
2024-08-05
DOI
https://doi.org/10.1016/j.softx.2024.101841
Citations
1
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
0.25
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article

Easy to integrate API for accessing true random numbers generated with IDQ’s Quantis Appliance

Valentin Stângaciu, Sebastian Mihai Ardelean, Mihai Udrescu
1 citations
SoftwareX
Chaos-based Image/Signal Encryption
0.25
article

Easy to integrate API for accessing true random numbers generated with IDQ’s Quantis Appliance

Valentin Stângaciu, Sebastian Mihai Ardelean, Mihai Udrescu
article en
1 citations

Abstract

Generating quality random numbers is essential for many computer applications, from preserving data privacy through cryptography and federated learning to artificial intelligence and machine learning. The role of random numbers in such applications is to add the necessary entropy to produce secure keys that protect data, to select data for training and testing machine learning models, or to improve the efficiency of solution space searches in evolutionary algorithms and heuristic methods. The available software methods can only generate pseudo-random numbers, while conventional hardware methods extract randomness from the environment, and these physical processes possess specific self-similar patterns. In contrast, quantum random number generators (QRNGs) produce truly random numbers by exploiting the randomness of quantum state measurement. Software platforms that provide random numbers over the cloud, rendered with network-attached quantum random number generator devices, exist; however, they were not developed as a native C language solution for embedded applications. Therefore, we introduce libqrng, written in C, which offers access to IDQ’s Quantis Appliance high-quality random numbers through an easy-to-integrate API for embedded and distributed computing applications that requires randomness and an interface for using the library from different programming languages (e.g., Python).

SoftwareXVol. 27
Polytechnic University of Timişoara (RO)
Openalex Percentile: Top 49%
Chaos-based Image/Signal Encryption
0.25
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Easy to integrate API for accessing true random numbers generated with IDQ’s Quantis Appliance — Valentin Stângaciu, Sebastian Mihai Ardelean, et al. · SoftwareX (2024) | TGRS Research Map | TGRS