Machine learning and certification with quantum identities

量子身份（q - id）是兰开斯特大学（Lancaster University）于2017年发明的硬件安全设备，已通过衍生公司Quantum Base Limited （QBL）实现商业化。它们将纳米级的缺陷与它们所附着物体的身份联系起来，提供了一种高度安全和实用的方法来证明这些物体的来源。到目前为止，这项技术已经取得了许多成功，但它的应用受到其实现的限制。该项目将重点发展q - id发展的几个科学分支——包括量子安全设备的新兴学术领域和它们的商业应用。这对学校、学生和我们的合作伙伴公司都是一个巨大的好处。在她与QBL合作的博士项目中，Ella将探索q - id科学发展的三个领域：多路材料- q - id目前指纹单色荧光纳米材料锁定在透明涂层中，但它们是使用智能手机中的彩色相机读取的。Ella将结合多种颜色的发射器，以创建更高密度的指纹。这将需要材料开发和分析指纹在不同颜色通道中的独立性。读取多个q - id并交叉关联它们的id进行认证。q - id的一个很有前途的未来应用是在认证中，在认证中，一个对象和它的物理证书都被标记。Ella将开发新的算法来安全地将这些关联起来。使用机器学习优化阅读处理。目前用于评估q - id（以及一般物理上不可克隆的函数）的算法相对简单，可以从优化中显著受益；由于Ella（被选中的学生，详情如下）在ML方面有经验，这是她渴望在博士学位中探索的一个方面。Ella将获得QBL的实验设备和设施，并得到QBL雇用的项目科学家的支持。她将每周与QBL人员联系。

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.