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arXiv · 2405.00781

Barren Plateaus in Variational Quantum Computing

Martin Larocca·Supanut Thanasilp·Samson Wang·Kunal Sharma·Jacob Biamonte·Patrick J. Coles·Lukasz Cincio·Jarrod R. McClean·Zoë Holmes·M. Cerezo
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Abstract

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.

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Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J. Coles, Lukasz Cincio, Jarrod R. McClean, Zoë Holmes, M. Cerezo. 2024-05-01. Barren Plateaus in Variational Quantum Computing. https://doi.org/10.1038/s42254-025-00813-9

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