Quick Navigation
Topics
Trapped Ion Quantum Computing
Quantum Random Number Generator with Internal Consistency Check and Public Verification
arXiv
Authors: Rodrigo Piera, Gianluca De Santis, Agustin Sanchez, Yury Kurochkin, James A. Grieve
Year
2025
Paper ID
17852
Status
Preprint
Abstract Read
~2 min
Abstract Words
142
Citations
N/A
Abstract
Quantum Random Number Generators provide true physical randomness based on quantum processes, essential for cryptographic and scientific applications. However, practical implementations face challenges in robustness and verifiability: ensuring that the entropy source remains secure and stable over time, and enabling independent confirmation of randomness quality without compromising security. We present a system based on a simple looped beam splitter architecture that uses only passive optical components. The device features an intrinsic self-testing mechanism derived from the stability of detection-probability ratios, allowing continuous validation of correct operation. In addition, the same physical process generates two independent random sequences with identical entropy: a private sequence, used for secure applications, and a public one, enabling external statistical verification with zero mutual information between them. This approach demonstrates that robust, self-testing, and publicly verifiable quantum randomness can be achieved with minimal optical complexity without jeopardizing security.
Why This Paper Matters
- This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- Quantum Random Number Generators provide true physical randomness based on quantum processes, essential for cryptographic and scientific applications.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.