  Machine learning is frequently listed among the most promising applications
for quantum computing. This is in fact a curious choice: Today's machine
learning algorithms are notoriously powerful in practice, but remain
theoretically difficult to study. Quantum computing, in contrast, does not
offer practical benchmarks on realistic scales, and theory is the main tool we
have to judge whether it could become relevant for a problem. In this
perspective we explain why it is so difficult to say something about the
practical power of quantum computers for machine learning with the tools we are
currently using. We argue that these challenges call for a critical debate on
whether quantum advantage and the narrative of 'beating' classical machine
learning should continue to dominate the literature the way it does, and
highlight examples for how other perspectives in existing research provide an
important alternative to the focus on advantage.
