QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI

Computer Science > Cryptography and Security [Submitted on 26 May 2026] QSignAI: Quantum-Randomness-Seeded Identity Signatures at the Intersection of AI for Science and Science for AI Dongping Liu, Aoyu Zhang, Luyao Zhang The 2024--2025 Nobel and Turing awards recognised artificial intelligence and quantum science in the same breath -- machine learning as a physical science, artificial intelligence solving 50-year scientific problems, superconducting quantum circuits as the hardware foundation of quantum computing, and quantum information principles as computing's highest achievement. Yet no deployed artificial intelligence system has brought these two streams together for the general public: identity systems still rely on pseudo-random tokens, and quantum circuits remain invisible to the billions of people who use bot-enabled social messaging platforms daily. This paper presents QSignAI, a production-deployed open-source platform demonstrating a bidirectional relationship between artificial intelligence and quantum science in a real-time event participation system. We address three research questions: first, can quantum-randomness generation via real quantum circuits be embedded in an artificial-intelligence-driven social platform with acceptable latency and cost; second, can an artificial intelligence bot make quantum phenomena perceptually legible to general audiences with no prior technical knowledge; and third, does a system combining both directions work in practice. A conversational artificial intelligence bot routes each participant's first message through a two-circuit quantum pipeline on a cloud quantum simulator, producing a unique quantum-randomness-seeded identity signature per participant. The first two questions are answered through system design and qualitative deployment evidence; measurable comparisons are identified as priority future work. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.27729 [cs.CR]   (or arXiv:2605.27729v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.27729 Focus to learn more Submission history From: Luyao Zhang [view email] [v1] Tue, 26 May 2026 22:04:47 UTC (92 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations 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?)