Stop Quantum Machine Learning; do AI-for-Quantum instead

This paper argues that the NeurIPS community should prioritize AI-for-Quantum (AI4Q) over Quantum Machine Learning (QML) on classical data, and should review the two directions by different evidentiary standards. We argue that QML on classical data has not earned high priority because its leading advantage claims rely on strong input assumptions, face trainability and noise barriers, and still lacks persuasive wall-clock wins on the standard benchmarks. By contrast, AI4Q has already produced concrete gains in decoding, compilation, neural-state representation, tomography and readout, and error mitigation. We make the evidentiary standard explicit through falsification criteria and concrete review-process recommendations.