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- Title
Deep learning the hierarchy of steering measurement settings of qubit-pair states.
- Authors
Wang, Hong-Ming; Ku, Huan-Yu; Lin, Jie-Yien; Chen, Hong-Bin
- Abstract
Quantum steering has attracted increasing research attention because of its fundamental importance, as well as its applications in quantum information science. Here we leverage the power of the deep learning model to infer the steerability of quantum states with specific numbers of measurement settings, which form a hierarchical structure. A computational protocol consisting of iterative tests is constructed to overcome the optimization, meanwhile, generating the necessary training data. According to the responses of the well-trained models to the different physics-driven features encoding the states to be recognized, we can numerically conclude that the most compact characterization of the Alice-to-Bob steerability is Alice's regularly aligned steering ellipsoid; whereas Bob's ellipsoid is irrelevant. We have also provided an explanation to this result with the one-way stochastic local operations and classical communication. Additionally, our approach is versatile in revealing further insights into the hierarchical structure of quantum steering and detecting the hidden steerability. To prepare steerable assembles from a bipartite quantum state is a cumbersome task due to the optimization over all possible incompatible measurements. Here the authors leverage the power of the deep learning model to infer the hierarchy of steering measurement settings and reveal the most compact parameters to characterize the Alice-to-Bob steerability.
- Subjects
QUANTUM information science; QUANTUM states; BEAM steering; DEEP learning
- Publication
Communications Physics, 2024, Vol 7, Issue 1, p1
- ISSN
2399-3650
- Publication type
Article
- DOI
10.1038/s42005-024-01563-3