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This article discusses the problem of exponential concentration in quantum kernel methods, which can hinder the trainability of quantum neural networks. The authors provide a systematic treatment of this issue and caution against using deep encoding schemes in the near-term. Their results apply to both supervised and unsupervised learning tasks, with a focus on supervised learning on classical data.