A Unified Toolbox for Multipartite Entanglement Certification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916874699145216 |
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| author | Liu, Ye-Chao Halbey, Jannis Pokutta, Sebastian Designolle, Sébastien |
| author_facet | Liu, Ye-Chao Halbey, Jannis Pokutta, Sebastian Designolle, Sébastien |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Unified Toolbox for Multipartite Entanglement Certification Liu, Ye-Chao Halbey, Jannis Pokutta, Sebastian Designolle, Sébastien Quantum Physics Optimization and Control 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. |
| title | A Unified Toolbox for Multipartite Entanglement Certification |
| topic | Quantum Physics Optimization and Control |
| url | https://arxiv.org/abs/2507.17435 |