Machine Learning-Derived Entanglement Witnesses

Zhu, Eric Y.; Wu, Larry; Li, Qian

doi:10.48550/arxiv.2107.02301

Cited by 2 publications

(4 citation statements)

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“…We demonstrated successful applications of the linear SVM algorithm for both of these cases. It is important to mention that our analysis is akin to that proposed in [40], where the authors also emphasized the direct relationship of the entanglement witness functional and linear SVM. In our work here, we focus on the successful applicability of the SVM technique specifically for the coarse-grained classification of entangled states joined in to certain families.…”

Section: Discussionmentioning

confidence: 96%

“…It is worth emphasizing that in recent years, machine learning-based methods have demonstrated remarkable efficiency in application to various areas of quantum physics [34][35][36][37]. For instance, others have used the neural networks [38,39] and also the SVM [40] to find EW operators, which efficiently distinguish between separable and entangled states of a particular type.…”

Section: Introductionmentioning

confidence: 99%

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Classification of four-qubit entangled states via machine learning

et al. 2023

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We apply the support vector machine (SVM) algorithm to derive a set of entanglement witnesses (EW) to identify entanglement patterns in families of four-qubit states. The effectiveness of SVM for practical EW implementations stems from the coarse-grained description of families of equivalent entangled quantum states. The equivalence criteria in our work is based on the stochastic local operations and classical communication (SLOCC) classification and the description of the fourqubit entangled Werner states. We numerically verify that the SVM approach provides an effective tool to address the entanglement witness problem when the coarse-grained description of a given family state is available. We also discuss and demonstrate the efficiency of nonlinear kernel SVM methods as applied to four-qubit entangled state classification.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Classification of four-qubit entangled states via machine learning

et al. 2023

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show abstract

“…We demonstrated successful applications of the linear SVM algorithm for both of these cases. It is important to mention that our analysis is akin to that proposed in [31], where the authors also emphasized the direct relationship of the entanglement witness functional and linear SVM. In our work here, we focus on the successful applicability of the SVM technique specifically for the coarse-grained classification of entangled states joined in some families.…”

Section: Discussionmentioning

confidence: 96%

“…It is worth emphasizing that in recent years, machine learning-based methods have demonstrated remarkable efficiency in application to various areas of quantum physics [25][26][27][28]. For instance, others have used neural networks [29,30] and also the SVM [31] to find EW operators, which efficiently distinguish between separable and entangled states of a particular type.…”

Section: Introductionmentioning

confidence: 99%