Neural networks assess two-qubit entanglement mainly from coherence-sensitive measurements, while random forests rely most on occupation measurements; the discrepancy disappears when both learn from density matrix elements directly.
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Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests
Neural networks assess two-qubit entanglement mainly from coherence-sensitive measurements, while random forests rely most on occupation measurements; the discrepancy disappears when both learn from density matrix elements directly.