Performance Optimization Method for Laser-Phase-Noise based Quantum Random Number Generation
Summarize this article with:
Quantum Physics arXiv:2604.14511 (quant-ph) [Submitted on 16 Apr 2026] Title:Performance Optimization Method for Laser-Phase-Noise based Quantum Random Number Generation Authors:Jinlu Liu, Jie Yang, Yu Gao, Guowei Zhang, Yan Pan, Heng Wang, Yuyang Ding, Yang Li, Wei Huang, Bingjie Xu, Wei Chen View a PDF of the paper titled Performance Optimization Method for Laser-Phase-Noise based Quantum Random Number Generation, by Jinlu Liu and 10 other authors View PDF Abstract:The quantum random number generation based on laser phase noise, which is featured with high generation rate and ease for photonic integration, has been extensively investigated and demonstrated. Despite these advancements, a theoretical model to achieve optimal performance in terms of maximizing the generation rate or entropy is still incomplete. In this work, a comprehensive physical model for this scheme is introduced to accurately predict the power spectrum and probability distribution of raw data, based on which the entropy source bandwidth and quantum min-entropy can be accordingly calculated and thus the system performance can be quantitatively evaluated. The model is sufficiently validated through both simulation and experiment with significant agreement under various setups. Furthermore, our proposal enables the priori configuration of experimental setups to achieve designed power spectrum and probability distribution of the raw data, thereby maximizing the generation rate or the min-entropy for system performance optimization. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.14511 [quant-ph] (or arXiv:2604.14511v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.14511 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Yan Pan [view email] [v1] Thu, 16 Apr 2026 00:57:19 UTC (1,613 KB) Full-text links: Access Paper: View a PDF of the paper titled Performance Optimization Method for Laser-Phase-Noise based Quantum Random Number Generation, by Jinlu Liu and 10 other authorsView PDF view license Current browse context: quant-ph new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)