Security of Practical Quantum Random Number Generators

Jing Yan Haw^1*

¹Centre for Quantum Technologies, National University of Singapore, Singapore, Singapore

* Presenter:Jing Yan Haw, email:jy.haw@nus.edu.sg

Quantum random number generators (QRNGs) promise entropy rooted in fundamental quantum indeterminacy, but practical deployment hinges on rigorous certification under realistic device assumptions [1]. This talk surveys a progression of QRNG paradigms I have been working on for the past decade, spanning device-dependent, source and measurement-device-independent, and machine learning cryptanalysis for a deployable, certifiable, and accessible quantum randomness.

We begin with device-dependent QRNG, where tight modeling and calibration enable maximization of extractable entropy from continuous-variable measurements and real-time operation with more than 3.5 Gbps [2]. Relaxing trust in the source, we demonstrate source-device-independent (SDI) QRNGs that leverage nonclassical states to enable high-rate, real-time generation with security against untrusted input states [3]. Moving further towards practicality, we present a fully passive protocol that generates certifiable randomness with untrusted light sources while simultaneously relaxing critical detection setup requirement [4]. Complementarily, we address the converse trust regime by certifying randomness with uncharacterised homodyne detection, i.e., measurement-device-independent, via provably secure randomness expansion secure against quantum side information, closing critical certification gaps for practical homodyne-based platforms [5]. To bridge theory and practice, we investigated machine-learning-based cryptanalysis and benchmarking, revealing side-channel–relevant statistical structures and providing tools to validate implementations security [6]. Finally, we highlight our recent open-source efforts, including the RaspiQRNG project, which facilitates prototyping, education, and testing of QRNG setups using accessible hardware platforms [7]. Together, these results chart a pathway from laboratory-grade QRNGs to certifiable, field-ready, and transparent systems.

References
[1] J. Y. Haw et al., Secure Random Number Generation in Continuous Variable Systems, in Quantum Random Number Generation: Theory and Practice, Springer, Cham (2020), pp. 85–112.
[2] J. Y. Haw et al., Maximization of Extractable Randomness in a Quantum Random-Number Generator, Phys. Rev. Applied 3, 054004 (2015).
[3] T. Michel, J. Y. Haw et al., Real-time source independent quantum random number generator with squeezed states, Phys. Rev. Applied 12, 034017 (2019).
[4] C. Wang et al., Provably-secure quantum randomness expansion with uncharacterised homodyne detection, Nature Communications 14, 316 (2023).
[5] K. Qiu, Y. Cai, N. Ng, J. Y. Haw, Fully passive quantum random number generation with untrusted light, APL Quantum 2, 046105 (2025).
[6] N. D. Truong, J. Y. Haw et al., Machine Learning Cryptanalysis of a Quantum Random Number Generator, IEEE Trans. Inf. Forensics and Security 14, 403 (2019).
[7] J. Y. Haw et al., RaspiQRNG: A do-it-yourself Quantum Random Number Generation on a Raspberry Pi, Institute of Physics Singapore Meeting (2021).

Keywords: Quantum Random Number Generator, Quantum Photonics, Quantum Information