NIST Post-Quantum Cryptography Standard Algorithms Based on Quantum Random Number Generators

TL;DR

This paper introduces quantum random number generator-based post-quantum cryptography algorithms to enhance security against quantum attacks, detailing their architecture, design, and performance validation.

Contribution

It proposes QRNG-based PQC algorithms, including their architecture, six generator designs, and comprehensive evaluation, addressing security gaps in existing PQC methods.

Findings

QRNGs successfully generate high-quality random numbers for cryptographic use

QRNG-based algorithms meet NIST validation standards for entropy and IID properties

Performance assessments show feasible computation times for practical deployment

Abstract

In recent years, the advancement of quantum computing technology has posed potential security threats to RSA cryptography and elliptic curve cryptography. In response, the National Institute of Standards and Technology (NIST) published several Federal Information Processing Standards (FIPS) of post-quantum cryptography (PQC) in August 2024, including the Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM), Module-Lattice-Based Digital Signature Algorithm (ML-DSA), and Stateless Hash-Based Digital Signature Algorithm (SLH-DSA). Although these PQC algorithms are designed to resist quantum computing attacks, they may not provide adequate security in certain specialized application scenarios. To address this issue, this study proposes quantum random number generator (QRNG)-based PQC algorithms. These algorithms leverage quantum computing to generate random numbers, which serve as the foundation for key pair generation, key encapsulation, and digital signature generation. A generalized architecture of QRNG is proposed, along with the design of six QRNGs. Each generator is evaluated according to the statistical validation procedures outlined in NIST SP 800-90B, including tests for verification of entropy sources and independent and identically distributed (IID) outputs. Experimental results assess the computation time of the six QRNGs, as well as the performance of QRNG-based ML-KEM, QRNG-based ML-DSA, and QRNG-based SLH-DSA. These findings provide valuable reference data for future deployment of PQC systems.