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Optimization of tripartite quantum steering inequalities via machine learning

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Abstract

We investigate the possibility of optimizing genuine tripartite quantum steering inequalities via machine learning. In particular, we consider two types of hybrid scenarios: one-sided device-independent scenario (where one party is considered to be untrusted) and two-sided device-independent scenario (where two parties are considered to be untrusted). In both scenarios, we apply a method of machine learning, known as artificial neuron networks, to optimize the quantum steering inequalities for the family of noisy Greenberger–Horne–Zeilinger states and noisy W states. The results show that the optimized steering classifiers can verify quantum steering well with only a small number of Pauli measurements, which can be easily realized in experiment. The method can be generalized to other multipartite or high-dimensional quantum systems.

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Acknowledgements

This work is supported by the Natural Science Foundation of Anhui Province under Grant Nos. 2008085MA16 and 2008085MA20, the University Synergy Innovation Program of Anhui Province under Grant Nos. GXXT-2022-039 and GXXT-2021-026, the Excellent Talents Support Program of Anhui Province under Grant No. gxyq2021208, the Natural Science Research Key Project of Education Department of Anhui Province under Grant Nos. 2022AH051681, KJ2020A0760 and KJ2021A0943, the Research Foundation for Advanced Talents of West Anhui University under Grant Nos. WGKQ202001004 and WGKQ2021004.

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Authors and Affiliations

  1. School of Electrical and Photoelectric Engineering, West Anhui university, Lu’an, 237012, China

    Guo-Zhu Pan, Jian Zhou, Jun Zhou, Ming Kong & Gang Zhang

  2. School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China

    Ming Yang

  3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

    Ming Yang

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  1. Guo-Zhu Pan
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  6. Gang Zhang
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Correspondence to Gang Zhang.

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Pan, GZ., Yang, M., Zhou, J. et al. Optimization of tripartite quantum steering inequalities via machine learning. Quantum Inf Process 22, 162 (2023). https://doi.org/10.1007/s11128-023-03873-x

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