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Post-conflict elections have become an important tool of international conflict 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-conflict elections in war-to-democracy transitions by comparing support structures for conflict parties as well as their coercive mobilisation strategies in times of violent conflict and post-conflict elections. It does so through a single case study of KwaZulu-Natal. This South African province faced a civil war-scale political conflict in the 80s and early 90s in which the two fighting 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. [ABSTRACT FROM AUTHOR]

Biological systems are dynamical, constantly exchanging energy and matter with the environment in order to maintain the non-equilibrium state synonymous with living. Developments in observational techniques have allowed us to study biological dynamics on increasingly small scales. Such studies have revealed evidence of quantum mechanical effects, which cannot be accounted for by classical physics, in a range of biological processes. Quantum biology is the study of such processes, and here we provide an outline of the current state of the field, as well as insights into future directions., (© 2018 The Author(s).)

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.