Barren plateaus in variational quantum computing

作者：Martín Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob D. Biamonte, Patrick J. Coles, L. Cincio, J. McClean, Zoe Holmes, M. Cerezo · 发表于：Nature Reviews Physics · 年份：2024 · DOI：10.1038/s42254-025-00813-9 · 被引用次数：580 · 研究领域：Computer Science、Physics、Mathematics

Variational quantum computing offers a flexible computational approach with a broad range of applications. 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 if they are ill-suited. As BPs strongly impact 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 exchanging ideas with other fields such as quantum optimal control, tensor networks and learning theory. This article provides a review of the current understanding of the BP phenomenon. Barren plateaus are widely considered as one of the main limitations for variational quantum algorithms. This Review summarizes the latest understandings of barren plateaus, indicating its causes, architecture that will suffer from this phenomenon, and discusses strategies that can — and cannot — avoid it. Variational quantum algorithms (VQAs) — this hybrid computational approach aims at training a quantum learning model (usually a parametrized quantum circuit) to solve a given task. The parameters in the model are trained by minimizin...