Classification of four-qubit entangled states via machine learning
Vintskevich S.V. Bao N. Nomerotski A. Stankus P. Grigoriev D.A.
March 2023American Physical Society
Physical Review A
2023#107Issue 3
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 classification and the description of the four-qubit 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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Technology Innovation Institute, Abu Dhabi, Masdar City, 9639, United Arab Emirates
Brookhaven National Laboratory, Upton, 11973, NY, United States
LLP Eqvium, Almaty, 050009, Kazakhstan
Technology Innovation Institute
Brookhaven National Laboratory
LLP Eqvium
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