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.
In an RF, feature importance is typically determined by measuring the impact of each feature on the model’s predictive accuracy, e.g
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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.