Claire Jones Prize

Claire Jones was a PhD student at the University of Edinburgh from 1985-89, where after she went on to work as a Research Fellow in the University's Department of Computer Science.

In honour of Claire Jones, a previous member of the Adelard team and a pioneer in the industrial application of formal methods, we actively promote the advancement of great female minds in the area of Computer Science.

Adelard sponsors the prize for the Best Performing Female MSc Student at the University of Edinburgh. This prize recognises the talent, knowledge and tremendous efforts of those who will no doubt go onto make significant contributions to the world after the completion of their degree.

Claire Jones prize 2022 winner

On the 24th November 2022, Miriam Beddig was awarded the Claire Jones prize as the Best Performing Female MSc Student at The University of Edinburgh's School of Informatics.

Miriam completed an MSc in Computer Science. Her thesis "Variational Quantum Algorithms for the Gaussian Mixture Model on Coresets" investigates how well the coreset technique works for the Gaussian Mixture model.

Even though classical computers are nowadays very powerful, some compute-intensive tasks, such as Machine Learning tasks, can still be infeasible due to the large input size of the data sets. To reduce the complexity, one possible solution is the usage of quantum computers. Quantum computing gained a lot of interest, since there exist quantum algorithms that have significantly lower time complexities for certain problems than their classical counterpart. However, current quantum devices are error-prone and very limited in their size.

To overcome the current limitations of quantum devices, one approach is to use so-called hybrid algorithms where the idea is to let a quantum computer and a classical computer solve a problem working together. In practice, this usually means that most of the computations are still performed on a classical computer, but small subroutines are pushed to a quantum computer. To gain an advantage over completely classical algorithms, these subroutines contain computations that can be computed efficiently on a quantum device but not on a classical device.

In her dissertation, Miriam used variational quantum algorithms - a certain class of hybrid algorithms. These are restricted in the size of the input due to the current limitations of quantum devices. To overcome this issue, Miriam considered the coreset technique which reduces a large data set to a representative smaller data set. The coreset then served as an input to variational quantum algorithms. This technique has been applied to estimate the parameters of the Gaussian Mixture Model. This model is a probabilistic model that is often used to find clusters of a data set.