Data-driven approach to mixed-state multipartite entanglement characterisation

Fuente: arXiv
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Main Authors: Brunner, Eric, Xie, Aaron, Dufour, Gabriel, Buchleitner, Andreas
Format: Preprint
Published: 2024
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author Brunner, Eric
Xie, Aaron
Dufour, Gabriel
Buchleitner, Andreas
author_facet Brunner, Eric
Xie, Aaron
Dufour, Gabriel
Buchleitner, Andreas
contents We develop a statistical framework, based on a manifold learning embedding, to extract relevant features of multipartite entanglement structures of mixed quantum states from the measurable correlation data of a quantum computer. We show that the statistics of the measured correlators contains sufficient information to characterise the entanglement, and to quantify the mixedness of the state of the computer's register. The transition to the maximally mixed regime, in the embedding space, displays a sharp boundary between entangled and separable states. Away from this boundary, the multipartite entanglement structure is robust to finite noise.
format Preprint
id arxiv_https___arxiv_org_abs_2407_18014
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Data-driven approach to mixed-state multipartite entanglement characterisation
Brunner, Eric
Xie, Aaron
Dufour, Gabriel
Buchleitner, Andreas
Quantum Physics
We develop a statistical framework, based on a manifold learning embedding, to extract relevant features of multipartite entanglement structures of mixed quantum states from the measurable correlation data of a quantum computer. We show that the statistics of the measured correlators contains sufficient information to characterise the entanglement, and to quantify the mixedness of the state of the computer's register. The transition to the maximally mixed regime, in the embedding space, displays a sharp boundary between entangled and separable states. Away from this boundary, the multipartite entanglement structure is robust to finite noise.
title Data-driven approach to mixed-state multipartite entanglement characterisation
topic Quantum Physics
url https://arxiv.org/abs/2407.18014