A Review of Barren Plateaus in Variational Quantum Computing
<|reference_start|>A Review of Barren Plateaus in Variational Quantum Computing: 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.<|reference_end|>
@article{larocca2024a,
    title={A Review of Barren Plateaus in Variational Quantum Computing},
    author={Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob
  Biamonte, Patrick J. Coles, Lukasz Cincio, Jarrod R. McClean, Zo"e Holmes,
  M. Cerezo},
    journal={arXiv preprint arXiv:2405.00781},
    year={2024},
    number={LA-UR-24-23934},
    archivePrefix={arXiv},
    eprint={2405.00781},
    primaryClass={quant-ph cs.LG stat.ML}
}