Journal
NATURE REVIEWS PHYSICS
Volume 3, Issue 9, Pages 625-644Publisher
NATURE PORTFOLIO
DOI: 10.1038/s42254-021-00348-9
Keywords
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Categories
Funding
- Laboratory Directed Research and Development (LDRD) programme of Los Alamos National Laboratory (LANL) [20180628ECR]
- LDRD programme of LANL [20190065DR]
- US Department of Energy (DOE), Office of Science, Office of High Energy Physics QuantISED programme [DE-AC52-06NA25396, KA2401032]
- EPSRC Hub grants [EP/M013243/1, EP/T001062/1]
- EU H2020-FETFLAG-03-2018 [820495]
- MEXT Quantum Leap Flagship Program (MEXT QLEAP) [JPMXS0120319794, JPMXS0118068682]
- JST ERATO [JPMJER1601]
- Japan Society for the Promotion of Science (JSPS) KAKENHI [16H02211]
- JST CREST [JPMJCR1673]
- JST PRESTO [JPMJPR2019]
- JSPS KAKENHI [20K22330]
- MEXT QLEAP [JPMXS0120319794, JPMXS0118067394]
- Simons Foundation
- LANL ASC Beyond Moore's Law project
- National Quantum Information Science Research Center of the US DOE
- Grants-in-Aid for Scientific Research [20K22330] Funding Source: KAKEN
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Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers, owing to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will probably 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 parameterized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisaged for quantum computers, and they appear to be 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. The advent of commercial quantum devices has ushered in the era of near-term quantum computing. Variational quantum algorithms are promising candidates to make use of these devices for achieving a practical quantum advantage over classical computers.
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