Variational quantum algorithms

Cerezo de la Roca, Marco Vinicio Sebastian; Arrasmith, Andrew Thomas; Babbush, Ryan; Benjamin, Simon C.; Endo, Suguru; Fujii, Keisuke; McClean, Jarrod R.; Mitarai, Kosuke; Yuan, Xiao; Cincio, Lukasz; Coles, Patrick Joseph

doi:10.1038/s42254-021-00348-9

Variational quantum algorithms

Journal Article · Thu Aug 12 00:00:00 EDT 2021 · Nature Reviews Physics

DOI:https://doi.org/10.1038/s42254-021-00348-9· OSTI ID:1880487

^[1]; ^[1]; Babbush, Ryan ^[2]; Benjamin, Simon C. ^[3]; Endo, Suguru ^[4]; Fujii, Keisuke ^[5]; ^[2]; Mitarai, Kosuke ^[6]; ^[7]; ^[1]; ^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Quantum Science Center, Oak Ridge, TN (United States)
Google Quantum AI Team, Venice, CA (United States)
Univ. of Oxford (United Kingdom)
NTT Corporation, Tokyo (Japan)
Osaka Univ. (Japan); RIKEN, Saitama (Japan)
Osaka Univ. (Japan); Japan Science and Technology Agency (JST), Saitama (Japan). PRESTO
Peking Univ., Beijing (China); Stanford Univ., CA (United States)

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 finally, we highlight the exciting prospects for using them to obtain quantum advantage.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), High Energy Physics (HEP); Engineering and Physical Sciences Research Council (EPSRC); Ministry of Education, Culture, Sports, Science and Technology (MEXT); Japan Science and Technology Agency (JST); Japan Society for the Promotion of Science (JSPS); Simons Foundation; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: 89233218CNA000001; AC52-06NA25396

OSTI ID:: 1880487

Report Number(s):: LA-UR-20-30142

Journal Information:: Nature Reviews Physics, Journal Name: Nature Reviews Physics Journal Issue: 9 Vol. 3; ISSN 2522-5820

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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Error-Mitigated Digital Quantum Simulation McArdle, Sam; Yuan, Xiao; Benjamin, Simon Physical Review Letters, Vol. 122, Issue 18 https://doi.org/10.1103/PhysRevLett.122.180501	journal	May 2019
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