Is Quantum Advantage the Right Goal for Quantum Machine Learning?

Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: the machine-learning algorithms of today 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 that the narrative of "beating" classical machine learning should continue to dominate the literature in the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.