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Learning and Predicting the Aging Properties of Quantum Security Identifiers - APRIL Seed Funding Webinar

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About this event

What to expect

Explore cutting-edge research at the intersection of quantum technology and AI in this 30-minute Seed Funding webinar hosted by the APRIL AI Hub. This online session presents groundbreaking work on predicting the aging properties of Quantum Security Identifiers (QSIs), a critical area for the future of quantum-based security systems.

Research Focus

Dr. Nema Abdelazim from the University of Southampton will present their research project that applies machine learning techniques to understand and predict how Quantum Security Identifiers degrade over time. As quantum technologies mature and move toward commercial deployment, understanding the long-term stability and aging characteristics of quantum security systems becomes essential for maintaining cryptographic integrity and system reliability.

Why This Matters

Quantum Security Identifiers represent a next-generation approach to authentication and security, leveraging quantum mechanical properties to create virtually unbreakable identification systems. However, like all physical systems, QSIs are subject to aging effects that can impact their performance and security guarantees. This research combines quantum physics, materials science, and predictive AI to address a fundamental challenge in quantum technology deployment.

Session Details

The webinar will be chaired by Dr. Alexandros Keros and conducted via Microsoft Teams, making it accessible to researchers, security professionals, and quantum technology enthusiasts worldwide. The compact 30-minute format provides a focused overview of the research methodology, findings, and implications for quantum security infrastructure.

Who Should Attend

This session is particularly relevant for:

  • Researchers working in quantum computing and quantum cryptography
  • Security professionals interested in post-quantum security systems
  • Materials scientists studying quantum device stability
  • AI practitioners applying machine learning to physical systems
  • Industry professionals developing quantum technology roadmaps

As part of APRIL's Seed Funding series, this webinar showcases early-stage research projects that demonstrate innovative applications of AI across diverse scientific domains. The session offers insight into how predictive modeling can address practical challenges in emerging quantum technologies.

[Register for this event](https://www.april.ac.uk/events/webinars/seed-funding-webi

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