Characterizing the Multipartite Entanglement Structure of Non-Gaussian Continuous-Variable States with a Single Evolution Operator
Multipartite entanglement is an essential resource for quantum information tasks, but characterizing entanglement structures in continuous-variable systems remains challenging, especially in multimode non-Gaussian scenarios. In this Letter, we introduce an efficient method for detecting multipartite entanglement structures in continuous-variable states. Based on the quantum Fisher information, we propose a systematic approach to identify an encoding operator that can efficiently capture the quantum correlations in multimode non-Gaussian states. We demonstrate the effectiveness of our method on over 10^{5} randomly generated multimode-entangled quantum states, achieving a very high success rate in entanglement detection. Additionally, the robustness of our method can be considerably enhanced against losses by expanding the set of accessible operators. This Letter provides a general framework for characterizing entanglement structures in diverse continuous-variable systems, enabling a number of experimentally relevant applications.
Authors
- Boxuan Jing (ORCID: https://orcid.org/0000-0002-9303-604X)
- Matteo Fadel (ORCID: https://orcid.org/0000-0003-3653-0030)
- Mingsheng Tian (ORCID: https://orcid.org/0009-0007-2486-5892)
- Manuel Gessner (ORCID: https://orcid.org/0000-0003-4203-0366)
- Qiongyi He (ORCID: https://orcid.org/0000-0002-2408-4320)
- Feng-Xiao Sun (ORCID: https://orcid.org/0000-0002-5454-413X)
- Xiaoting Gao
Institutions
- King University (US)
- Pennsylvania State University (US)
- Universitat de València (ES)
- Peking University (CN)
- ETH Zurich (CH)
- Hefei University (CN)
- Shanxi University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Physical Review Letters
- Published
- 2025-09-29
- DOI
- https://doi.org/10.1103/21t1-dqn6
- Citations
- 2
- Primary Topic
- Quantum Information and Cryptography
- Type
- article
- Field-Weighted Citation Impact
- 4.96