Randomness Quantification for Quantum Random Number Generation Based on Detection of Amplified Spontaneous Emission Noise
Jie Yang, Fan Fan, Jinlu Liu, Qi Su, Yang Li, Wei Huang, and Bingjie, Xu

TL;DR
This paper develops a comprehensive physical model and a quantification method for quantum randomness derived from amplified spontaneous emission noise, enhancing security analysis for quantum random number generators.
Contribution
It introduces a systematic physical model and a verifiable randomness quantification approach for ASE-based QRNGs, filling gaps in physical understanding and security assessment.
Findings
Model accurately fits experimental data
Quantification method isolates quantum-origin randomness
Enhances security analysis for ASE-based QRNGs
Abstract
The amplified spontaneous emission (ASE) noise has been extensively studied and employed to build quantum random number generators (QRNGs). While the previous relative works mainly focus on the realization and verification of the QRNG system, the comprehensive physical model and randomness quantification for the general detection of the ASE noise are still incomplete, which is essential for the quantitative security analysis. In this paper, a systematical physical model for the emission, detection and acquisition of the ASE noise with added electronic noise is developed and verified, based on which the numerical simulations are performed under various setups and the simulation results all significantly fit well with the corresponding experimental data. Then, a randomness quantification method and the corresponding experimentally verifiable approach are proposed and validated, which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsChaos-based Image/Signal Encryption · Quantum Computing Algorithms and Architecture · Digital Media Forensic Detection
