Voting and violence in KwaZulu-Natal’s no-go areas: Coercive mobilisation and territorial control in post-conflict elections

Post-confl ict elections have become an important tool of international confl ict resolution over the last decades. Theoretical studies usually point out that in warto- democracy transitions, military logics of territorial control are transformed into electoral logics of peaceful political contestation. Empirical reality, however, shows that the election process is often accompanied by various forms of violence. This paper analyses post-confl ict elections in war-to-democracy transitions by comparing support structures for confl ict parties as well as their coercive mobilisation strategies in times of violent confl ict and post-confl ict elections. It does so through a single case study of KwaZulu-Natal. This South African province faced a civil war-scale political confl ict in the 80s and early 90s in which the two fi ghting parties – the African National Congress (ANC) and the Inkatha Freedom Party (IFP) – used large-scale violence to establish and protect no-go areas of territorial control. This study finds that in the first decade after South Africa’s miraculous transition, these spatial structures of violence and control persisted at local levels. Violent forms of mobilisation and territorial control thus seem to be able to survive even a successful transition to democracy by many years. Measures to open up the political landscape, deescalate heated-up party antagonisms and overcome geopolitical borders of support structures seem to be crucial elements for post-conflict elections that introduce a pluralist democracy beyond the voting process.African Journal on Conflict Resolution,Volume 13, Number 1, 201

How quantum computers learn from data.

Doctor of Philosophy in Physics. University of KwaZulu-Natal, Durban 2017. ,Humans are experts at recognising patterns in past experience and applying them to new tasks. For example, after seeing pictures of a face we can usually tell if another image contains the same person or not. Machine learning is a research discipline at the intersection of computer science, statistics and mathematics that investigates how pattern recognition can be performed by machines and for large amounts of data. Since a few years machine learning has come into the focus of quantum computing in which information processing based on the laws of quantum theory is explored. Although large scale quantum computers are still in the first stages of development, their theoretical description is well-understood and can be used to formulate `quantum software' or `quantum algorithms' for pattern recognition. Researchers can therefore analyse the impact quantum computers may have on intelligent data mining. This approach is part of the emerging research discipline of quantum machine learning that harvests synergies between quantum computing and machine learning. The research objective of this thesis is to understand how we can solve a slightly more specific problem called supervised pattern recognition based on the language that has been developed for universal quantum computers. The contribution it makes is twofold: First, it presents a methodology that understands quantum machine learning as the combination of data encoding into quantum systems and quantum optimisation. Second, it proposes several quantum algorithms for supervised pattern recognition. These include algorithms for convex and non-convex optimisation, implementations of distance-based methods through quantum interference, and the preparation of quantum states from which solutions can be derived via sampling. Amongst the machine learning methods considered are least-squares linear regression, gradient descent and Newton's method, k-nearest neighbour, neural networks as well as ensemble methods. Together with the growing body of literature, this thesis demonstrates that quantum computing offers a number of interesting tools for machine learning applications, and has the potential to create new models of how to learn from data