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Topics
Quantum Machine Learning
Quantum Simulation
Learning to Detect Entanglement
arXiv
Authors: Bingjie Wang
Year
2017
Paper ID
7550
Status
Preprint
Abstract Read
~2 min
Abstract Words
84
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2017 reference point for readers tracking recent quantum research.
- Classifying states as entangled or separable is a fundamental, but expensive task.
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