Classification of four-qubit entangled states via machine learning

Vintskevich, S. V.; Bao, N.; Nomerotski, A.; Stankus, P.; Grigoriev, D. A.

doi:10.1103/physreva.107.032421

Classification of four-qubit entangled states via machine learning

Journal Article · Thu Mar 23 00:00:00 EDT 2023 · Physical Review A

DOI:https://doi.org/10.1103/physreva.107.032421· OSTI ID:2425532

^[1]; Bao, N. ^[2]; Nomerotski, A. ^[2]; Stankus, P. ^[2]; Grigoriev, D. A. ^[3]

Technology Innovation Institute, Masdar City (United Arab Emitates)
Brookhaven National Laboratory (BNL), Upton, NY (United States)
LLP Eqvium, Almaty (Kazakhstan)

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. Here, we also discuss and demonstrate the efficiency of nonlinear kernel SVM methods as applied to four-qubit entangled state classification.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0012704

OSTI ID:: 2425532

Alternate ID(s):: OSTI ID: 1963116

Journal Information:: Physical Review A, Journal Name: Physical Review A Journal Issue: 3 Vol. 107; ISSN 2469-9926

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Classification of four-qubit entangled states via machine learning

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