Unified toolbox for multipartite entanglement certification

We present a unified framework for multipartite entanglement characterization based on the conditional gradient (CG) method, incorporating both fast heuristic detection and rigorous witness construction with numerical error control. Our method enables entanglement certification in quantum systems of up to ten qubits and applies to arbitrary entanglement structures. We demonstrate its power by closing the gap between entanglement and separability bounds in white noise robustness benchmarks for a class of bound entangled states. Furthermore, the framework extends to entanglement robustness under general quantum noise channels, providing accurate thresholds in cases beyond the reach of previous algorithmic methods. These results position CG methods as a powerful tool for practical and scalable entanglement analysis in realistic experimental settings.

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Publication Details

Journal
Physical Review A
Published
2026-10-14
DOI
https://doi.org/10.1103/tfsh-lwdj
Primary Topic
Quantum Information and Cryptography
Type
article
Field-Weighted Citation Impact
0.00

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article

Unified toolbox for multipartite entanglement certification

Sébastien Designolle, Ye‐Chao Liu, Sebastian Pokutta, Jannis Halbey
Physical Review A
Quantum Information and Cryptography
article

Unified toolbox for multipartite entanglement certification

Sébastien Designolle, Ye‐Chao Liu, Sebastian Pokutta, Jannis Halbey
article en

Abstract

We present a unified framework for multipartite entanglement characterization based on the conditional gradient (CG) method, incorporating both fast heuristic detection and rigorous witness construction with numerical error control. Our method enables entanglement certification in quantum systems of up to ten qubits and applies to arbitrary entanglement structures. We demonstrate its power by closing the gap between entanglement and separability bounds in white noise robustness benchmarks for a class of bound entangled states. Furthermore, the framework extends to entanglement robustness under general quantum noise channels, providing accurate thresholds in cases beyond the reach of previous algorithmic methods. These results position CG methods as a powerful tool for practical and scalable entanglement analysis in realistic experimental settings.

Physical Review A
Université Claude Bernard Lyon 1 (FR), École Normale Supérieure de Lyon (FR), Zuse Institute Berlin (DE), Laboratoire de l'Informatique du Parallélisme (FR), QINFO: Traitement optimal de l'information avec des dispositifs quantiques (FR)
European Commission, Deutsche Forschungsgemeinschaft, QuantERA
Openalex Percentile: Top 99%
Quantum Information and Cryptography
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