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 labels and unvalidated sampling.
Learning to Detect Entanglement
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abstract
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.
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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 labels and unvalidated sampling.