Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations
Abstract
Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machinelearning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite entanglement classes based on states sampled from their local unitary (LU) orbits. Compared with traditional techniques, our method reduces experimental measurement-resource requirements and computational overhead, showing high adaptability to large-scale systems. The classification accuracy of our method reaches 99.5% in 20-qubit systems. The numerical validation is performed on states generated by random LU transformations, which preserve the SLOCC class. Within this setting, the proposed method offers strong effectiveness, scalability, and robustness for multipartite entanglement classification.
Related
- Sharp State-Independent Uncertainty Relations for Multipartite systems
- Observation of multipartite spin entanglement in a cuprate chain
- Multipartite GHZ diagonal states and their entanglement properties
- Eigenvalues of multipartite entanglement witnesses
- Can Multipartite Entanglement Probe Wormhole Moduli Invisible to Bipartite Entanglement?
BibTeX
@misc{li2026neural,
title = {{Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations}},
author = {Qiyi Li and Xiao Zheng and Guofeng Zhang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22768},
eprint = {2609.22768},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22768},
note = {CC0},
}
RIS
TY - UNPB
TI - Neural network-based multipartite entanglement classification with prior guidance from quantum uncertainty relations
AU - Qiyi Li
AU - Xiao Zheng
AU - Guofeng Zhang
PY - 2026
DA - 2026/09/19
PB - arXiv
DO - 10.48550/arxiv.2609.22768
UR - https://arxiv.org/abs/2609.22768
AB - Quantum entanglement is a crucial resource in quantum information processing, yet its efficient, scalable and robust classification in multipartite systems remains theoretically challenging. Although supervised machinelearning has been applied to this task, most existing methods still suffer from high measurement costs, computational consumption, and weak noise robustness. In this work, by incorporating multipartite uncertainty relations as prior guidance, we propose a neural network approach to classify distinct Stochastic Local Operations and Classical Communication (SLOCC) multipartite entanglement classes based on states sampled from their local unitary (LU) orbits. Compared with traditional techniques, our method reduces experimental measurement-resource requirements and computational overhead, showing high adaptability to large-scale systems. The classification accuracy of our method reaches 99.5% in 20-qubit systems. The numerical validation is performed on states generated by random LU transformations, which preserve the SLOCC class. Within this setting, the proposed method offers strong effectiveness, scalability, and robustness for multipartite entanglement classification.
ER -