This week a KAIST team showed that bound entanglement — real, provable quantum correlation that can't be distilled into clean entangled pairs — can't deliver the exponential learning speedup the quantum machine learning field has long attributed to entanglement generically. This Sunday think piece walks the full fault line: the KAIST result itself, the Huang-Kueng-Preskill and ancilla-qubit camps defending real exponential advantages, the 2018 dequantization tradition that keeps eating QML's boldest claims, and the Schuld/Killoran and Aaronson objections to the whole 'quantum beats classical' framing.

• Linked sources: On the Fundamental Resource for Exponential Advantage in Quantum Channel Learning — Nature Communications
• Entanglement-Enabled Advantage for Learning a Bosonic Random Displacement Channel — KAIST
• Quantum Entanglement Alone Isn't Enough For Quantum Machine Learning — Quantum Zeitgeist
• Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning — arXiv
• Is Quantum Advantage the Right Goal for Quantum Machine Learning? — PRX Quantum / arXiv
• Oh right, quantum computing — Scott Aaronson's blog
• Entanglement-induced provable and robust quantum learning advantages — arXiv

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