Barren Plateaus in Variational Quantum Computing
This addresses a critical obstacle for researchers and practitioners in quantum computing, but it is an incremental review article rather than presenting new findings.
The paper tackles the Barren Plateau phenomenon in variational quantum computing, which causes exponentially flat optimization landscapes as problem size increases, and reviews current theoretical and heuristic methods to understand and mitigate its effects on trainability.
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.