Variational Quantum Algorithms. Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., & Coles, P. J. Nature Reviews Physics, 3(9):625–644, August, 2021. arXiv:2012.09265 [quant-ph, stat]![Variational Quantum Algorithms [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.
@article{cerezo_variational_2021,
title = {Variational {Quantum} {Algorithms}},
volume = {3},
issn = {2522-5820},
url = {http://arxiv.org/abs/2012.09265},
doi = {10.1038/s42254-021-00348-9},
abstract = {Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.},
language = {en},
number = {9},
urldate = {2023-06-27},
journal = {Nature Reviews Physics},
author = {Cerezo, M. and Arrasmith, Andrew and Babbush, Ryan and Benjamin, Simon C. and Endo, Suguru and Fujii, Keisuke and McClean, Jarrod R. and Mitarai, Kosuke and Yuan, Xiao and Cincio, Lukasz and Coles, Patrick J.},
month = aug,
year = {2021},
note = {arXiv:2012.09265 [quant-ph, stat]},
keywords = {Quantum Physics, Computer Science - Machine Learning, Statistics - Machine Learning},
pages = {625--644},
annote = {Comment: Review Article. 33 pages, 7 figures. Updated to published version},
file = {Cerezo et al. - 2021 - Variational Quantum Algorithms.pdf:/Users/georgehuang/Zotero/storage/3TH6YFD8/Cerezo et al. - 2021 - Variational Quantum Algorithms.pdf:application/pdf},
}
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