Variational circuits for machine learning with near-term devices

Variational circuits are parameter-dependent quantum algorithms that can be optimized for a certain task. One approach in quantum machine learning is to interpret these circuits as machine learning models that can be trained to generalise from data. Such models are often refered to as variational quantum classifiers. This talk will focus on various issues around this approach, for example how to think about the power of variational quantum classifiers, how we can train them and what they might be good for.