Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm -- choices of ansatz, initial state, observable, loss function and hardware noise -- can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon.

翻译：变分量子计算提供了一种灵活的计算范式，可应用于多个领域。然而，实现其潜力的一个关键障碍是贫瘠高原（Barren Plateau, BP）现象。当模型出现贫瘠高原时，其参数优化景观会随着问题规模的增大而变得指数级平坦且缺乏特征。重要的是，算法中的所有组成部分——包括拟设选择、初始态、可观测量、损失函数以及硬件噪声——若设计不当，均可能导致贫瘠高原的出现。由于贫瘠高原对可训练性的显著影响，研究人员投入了大量精力发展理论与启发式方法，以理解并缓解其影响。因此，贫瘠高原研究已成为一个蓬勃发展的领域，不仅促进了自身进展，还交叉渗透至量子最优控制、张量网络和学习理论等其他领域。本文对贫瘠高原现象的当前认知进行了全面综述。