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Learning to Detect Entanglement
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Classifying states as entangled or separable is a fundamental, but expensive task. This paper presents a method, the forest algorithm, to improve the amount of resources needed to detect entanglement. Starting from 'optimized' methods for using geometric criterion to detect entanglement, specific steps are replaced with machine learning models. Tests using numerical simulations indicate that the model is able to declare a state as entangled in fewer steps compared to existing methods. This improvement is achieved without affecting the correctness of the original algorithm.
Forward citations
Cited by 2 Pith papers
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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 ele...
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Learning Algebraic Models of Quantum Entanglement
Neural networks trained on algebraic-variety membership can classify separability and degeneracy of small pure quantum states, but the advertised border-rank and 5-qubit hyperdeterminant conclusions rest on flawed lab...
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