Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
07 Apr 2023
Historique:
received: 31 01 2023
accepted: 22 03 2023
medline: 8 4 2023
entrez: 7 4 2023
pubmed: 8 4 2023
Statut: epublish

Résumé

Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem.

Identifiants

pubmed: 37029105
doi: 10.1038/s41467-023-37587-6
pii: 10.1038/s41467-023-37587-6
pmc: PMC10082187
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1952

Subventions

Organisme : DOE | Office of Science (SC)
ID : DE-SC0019374
Organisme : DOE | Office of Science (SC)
ID : DOE-SC0018140
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019390
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019374
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019374
Organisme : DOE | Office of Science (SC)
ID : DE-SC0017867
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019374
Organisme : DOE | Office of Science (SC)
ID : DOE-SC0018140
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019390
Organisme : DOE | Office of Science (SC)
ID : DE-NA0003525
Organisme : DOE | Office of Science (SC)
ID : DE-SC0020290
Organisme : DOE | Office of Science (SC)
ID : DE-SC0019374
Organisme : Simons Foundation
ID : Simons Investigator Award
Organisme : Simons Foundation
ID : Simons Investigator Award
Organisme : Simons Foundation
ID : Simons Investigator Award
Organisme : National Science Foundation (NSF)
ID : CHE-2102505
Organisme : National Science Foundation (NSF)
ID : PHY-1748958
Organisme : National Science Foundation (NSF)
ID : PHY-1748958
Organisme : National Science Foundation (NSF)
ID : OAC-1550456
Organisme : National Science Foundation (NSF)
ID : CHE-2102505
Organisme : National Science Foundation (NSF)
ID : PHY-1748958
Organisme : National Science Foundation (NSF)
ID : PHY-1733907
Organisme : National Science Foundation (NSF)
ID : OAC-1550456
Organisme : National Science Foundation (NSF)
ID : OMA-2016245

Informations de copyright

© 2023. The Author(s).

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Auteurs

Seunghoon Lee (S)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Joonho Lee (J)

Department of Chemistry, Columbia University, New York, NY, 10027, USA.

Huanchen Zhai (H)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Yu Tong (Y)

Department of Mathematics, University of California, Berkeley, CA, 94720, USA.

Alexander M Dalzell (AM)

AWS Center for Quantum Computing, Pasadena, CA, 91125, USA.

Ashutosh Kumar (A)

Department of Chemistry, Virginia Tech, Blacksburg, VA, 24061, USA.
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

Phillip Helms (P)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Johnnie Gray (J)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Zhi-Hao Cui (ZH)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Wenyuan Liu (W)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA.

Michael Kastoryano (M)

AWS Center for Quantum Computing, Pasadena, CA, 91125, USA.
Amazon Quantum Solutions Lab, Seattle, WA, 98170, USA.

Ryan Babbush (R)

Google Quantum AI, 340 Main Street, Venice, CA, 90291, USA.

John Preskill (J)

AWS Center for Quantum Computing, Pasadena, CA, 91125, USA.
Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, 91125, USA.

David R Reichman (DR)

Department of Chemistry, Columbia University, New York, NY, 10027, USA.

Earl T Campbell (ET)

Riverlane, Cambridge, UK.

Edward F Valeev (EF)

Department of Chemistry, Virginia Tech, Blacksburg, VA, 24061, USA.

Lin Lin (L)

Department of Mathematics, University of California, Berkeley, CA, 94720, USA.
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.

Garnet Kin-Lic Chan (GK)

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, 91125, USA. gkc1000@gmail.com.

Classifications MeSH