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Engineering a Phase-Noise-Based Quantum Random Number Generator for Real-Time Secure Applications: Design, Validation, and Scalability

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arXiv:2604.00741v1 Announce Type: cross Abstract: Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are classified into Pseudo-Random Number Generators (PRNGs), True Random Number Generators (TRNGs), and Quantum Random Number Generators (QRNGs). This work presents the end-to-end development of a high-speed, high-efficiency, phase-noise-based QRNG system that taps into the quantum phase noise of a sin

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    Quantum Physics [Submitted on 1 Apr 2026] Engineering a Phase-Noise-Based Quantum Random Number Generator for Real-Time Secure Applications: Design, Validation, and Scalability Anurag K. S. V., Shubham Chouhan, K. Srinivasan, G. Raghavan, Kanaka Raju P Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are classified into Pseudo-Random Number Generators (PRNGs), True Random Number Generators (TRNGs), and Quantum Random Number Generators (QRNGs). This work presents the end-to-end development of a high-speed, high-efficiency, phase-noise-based QRNG system that taps into the quantum phase noise of a single-frequency laser, with randomness originating from spontaneous emission. Using a self-heterodyne measurement with a semiconductor laser (linewidth \approx 5.23 GHz) operated near threshold and a \sim48 cm fiber delay line, a raw data generation rate of 2.0 Gbps is achieved. To ensure uniform randomness in the QRNG output, robust extraction techniques developed in-house, such as the Toeplitz Strong Extractor (TSE), are used. Randomness validation using the NIST and Diehard test suites confirms that all statistical tests pass at standard confidence levels. The developed system achieves a post-processed generation rate of 1.0 Gbps in operation and attains a Technology Readiness Level (TRL) of 7, approaching TRL 8, making it suitable for real-time secure applications such as cryptographic key generation and stochastic modeling. Comments: 14 pages, 8 figures Subjects: Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Optics (physics.optics) Cite as: arXiv:2604.00741 [quant-ph]   (or arXiv:2604.00741v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.00741 Focus to learn more Journal reference: Proc. SPIE 14168, Sixth International Conference on Optical and Wireless Technologies (OWT 2025), 141682I (29 March 2026) Related DOI: https://doi.org/10.1117/12.3108280 Focus to learn more Submission history From: Anurag S. V. Krovvidi [view email] [v1] Wed, 1 Apr 2026 11:12:31 UTC (1,032 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CR physics physics.optics References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation 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 Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Apr 02, 2026
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    Apr 02, 2026
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