Learning to Detect Entanglement

• URL: http://arxiv.org/abs/1709.03617v2
• Date: Wed, 22 May 2024 17:11:42 GMT
• Title: Learning to Detect Entanglement
• Authors: Bingjie Wang,
• Abstract summary: 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.
• Score: 1.4763055441508717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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