Machine learning and certification with quantum identities
Machine learning and certification with quantum identities
批准号:
2893837
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
Quantum identities (Q-IDs) are hardware security devices invented at Lancaster University in 2017, which have been commercialised through a spin-out company Quantum Base Limited (QBL). They link nanoscale imperfections to the identity of the objects to which they are attached, providing a highly secure and practical means to prove the provenance of these objects. This technology has led to many successes to date, but the applications that it can be used for are limited by its implementation. This project will focus on developing several scientific strands of Q-IDs development - both the nascent academic field of quantum security devices and their commercial application. This will be a huge benefit to both the university, the student and our partner company.In her PhD project Ella work with QBL will explore three areas of scientific development for Q-IDs:Multiplexing materials - Q-IDs currently fingerprint single-colour fluorescent nanomaterials lock into transparent coatings, but they are read using colour cameras in smartphones. Ella will combine multiple colours of emitter, to create higher density fingerprints. This will require material development and analysis of the independence of the fingerprints in different colour channels.Reading multiple Q-IDs and cross-associating their IDs for certification. A promising future application for Q-IDs is in certification in which both an object and a physical certificate for it are both tagged. Ella will develop novel algorithms for associating these securely will be developed.Optimising the reading processing using machine learning. The algorithms currently used to assess Q-IDs (and physically unclonable functions in general) are relatively simple and could benefit significantly from optimisation; as Ella (the selected student, details below), has experience in ML this is an aspect that she is keen to explore in her PhD.Ella will be given access to QBL's experimental equipment and facilities, ad have support from project scientists employed by QBL. She will be in contact with QBL personnel on a weekly basis.
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