Certifying Unknown Genuine Multipartite Entanglement by Neural Networks
- URL: http://arxiv.org/abs/2210.13837v2
- Date: Tue, 15 Nov 2022 06:30:14 GMT
- Title: Certifying Unknown Genuine Multipartite Entanglement by Neural Networks
- Authors: Zhenyu Chen, Xiaodie Lin and Zhaohui Wei
- Abstract summary: We show that neural networks can provide a nice solution to this problem.
By testing our models on many specific multipartite quantum states, we show that they can certify genuine multipartite entanglement very accurately.
- Score: 1.8689488822130746
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Suppose we have an unknown multipartite quantum state, how can we
experimentally find out whether it is genuine multipartite entangled or not?
Recall that even for a bipartite quantum state whose density matrix is known,
it is already NP-Hard to determine whether it is entangled or not. Therefore,
it is hard to efficiently solve the above problem generally. However, since
genuine multipartite entanglement is such a fundamental concept that plays a
crucial role in many-body physics and quantum information processing tasks,
finding realistic approaches to certify genuine multipartite entanglement is
undoubtedly necessary. In this work, we show that neural networks can provide a
nice solution to this problem, where measurement statistics data produced by
measuring involved quantum states with local measurement devices serve as input
features of neural networks. By testing our models on many specific
multipartite quantum states, we show that they can certify genuine multipartite
entanglement very accurately, which even include some new results unknown
before. We also exhibit a possible way to improve the efficiency of our models
by reducing the size of features. Lastly, we show that our models enjoy
remarkable robustness against flaws in measurement devices, implying that they
are very experiment-friendly.
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