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The barren plateaus of quantum neural networks: review, taxonomy and trends

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Abstract

In the noisy intermediate-scale quantum (NISQ) era, the computing power displayed by quantum computing hardware may be more advantageous than classical computers, but the emergence of the barren plateau (BP) has hindered quantum computing power and cannot solve large-scale problems. This summary analyzes the phenomenon of the BP in the quantum neural network that is rapidly developing in the NISQ era. This article will review the research status of the BP problem in the quantum neural network (QNN) in the past five years from the analysis of the source of the BP, the current stage solution, and the future research direction. First of all, the source of the BP was briefly explained and then classified the BP solution from different perspectives, including quantum embedding in QNN, ansatz parameter selection and structural design, and optimization algorithms. Finally, the BP problem in the QNN is summarized, and the research direction for solving problems in the future is made.

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All data, models, and code generated and used during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. School of Computer Science, Shenyang Aerospace University, Shenyang, 110000, People’s Republic of China

    Han Qi, Lei Wang, Hongsheng Zhu & Changqing Gong

  2. Faculty of Computing and Informatics, University of Malaya, Jalan Universiti, Kuala Lumpur, 50603, Malaysia

    Abdullah Gani

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  1. Han Qi
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Qi, H., Wang, L., Zhu, H. et al. The barren plateaus of quantum neural networks: review, taxonomy and trends. Quantum Inf Process 22, 435 (2023). https://doi.org/10.1007/s11128-023-04188-7

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