Barren Plateaus in Variational Quantum Computing
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915278188707840 |
|---|---|
| author | Larocca, Martin Thanasilp, Supanut Wang, Samson Sharma, Kunal Biamonte, Jacob Coles, Patrick J. Cincio, Lukasz McClean, Jarrod R. Holmes, Zoë Cerezo, M. |
| author_facet | Larocca, Martin Thanasilp, Supanut Wang, Samson Sharma, Kunal Biamonte, Jacob Coles, Patrick J. Cincio, Lukasz McClean, Jarrod R. Holmes, Zoë Cerezo, M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00781 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Barren Plateaus in Variational Quantum Computing Larocca, Martin Thanasilp, Supanut Wang, Samson Sharma, Kunal Biamonte, Jacob Coles, Patrick J. Cincio, Lukasz McClean, Jarrod R. Holmes, Zoë Cerezo, M. Quantum Physics Machine Learning 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. |
| title | Barren Plateaus in Variational Quantum Computing |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2405.00781 |