Quantum machine learning: Challenges and Opportunities

In this talk I will pose the general framework of learning and then introduce the different topics which jointly define the area of quantum machine learning. Since machine learning is a intrinsically data driven approach, dependencies and assumptions play a major role. I will therefore introduce different input and output assumptions and discuss corresponding data access models before giving a high level explanation of the different techniques which have been proposed. I will finally discuss current and future challenges and opportunities of the field.