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：变分量子计算作为一种灵活的计算范式，在众多领域展现出应用前景。然而，实现其潜力的主要障碍在于贫瘠高原现象。当模型出现贫瘠高原时，其参数优化空间的平坦度和无特征性会随问题规模增大呈指数级增长。关键在于，算法中所有可调节的组成部分——包括拟设选择、初始态、可观测量、损失函数以及硬件噪声——若配置不当均可能导致贫瘠高原现象。鉴于贫瘠高原对模型可训练性的重大影响，研究者们投入大量精力发展理论与启发式方法，以理解并缓解其效应。因此，贫瘠高原研究已成为一个蓬勃发展的领域，并与量子最优控制、张量网络及学习理论等其他学科领域相互渗透、交叉促进。本文对当前贫瘠高原现象的理论认知进行了系统性综述。