In QELMs using XX evolution on image data, classification accuracy saturates comparably to Haar-random unitaries once moderate entanglement develops, remaining classically simulable due to limited entanglement spread.
Predicting fermionic densities using a projected-quantum-kernel method,
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Entanglement and Classical Simulability in Quantum Extreme Learning Machines
In QELMs using XX evolution on image data, classification accuracy saturates comparably to Haar-random unitaries once moderate entanglement develops, remaining classically simulable due to limited entanglement spread.