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Published April 7, 2023 | Published + Supplemental Material
Journal Article Open

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

Abstract

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.

Additional Information

© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Work by S.L., H.Z., and G.K.C. was funded by the US Department of Energy, Office of Science, via Award DE-SC0019374. Work by P.H. was funded by the Simons Collaboration on the Many-Electron Problem, and support from the Simons Investigator Award to G.K.C. Work by J.G. was funded by US Department of Energy, Office of Science, via Award no. DOE-SC0018140. Work by Z.C. was funded by the US Department of Energy, Office of Science, via Award DE-SC0019390. Work by W.Y.L. was funded by the US National Science Foundation, via Award no. CHE-2102505. Work by J.P. was funded by the US Department of Energy, Office of Science, via Awards DE-NA0003525, DE-SC0020290, and by the National Science Foundation via Award PHY-1733907. Work by Y.T. was funded by the US Department of Energy, Office of Science via Award DE-SC0017867. Work by L.L. was funded by the National Science Foundation via Award OMA-2016245, and by the Simons Investigator Award. Research by A.K. and E.V. was funded by the US Department of Energy, Office of Science, via Award DE-SC0019374, and the associated software development efforts were supported by the US National Science Foundation via Award OAC-1550456. Some of the discussions and collaboration for this project occurred while using facilities at the Kavli Institute for Theoretical Physics, supported in part by the National Science Foundation under Grant No. NSF PHY-1748958. R.B. thanks members of the Google Quantum AI team for helpful feedback on earlier drafts. Contributions. S.L., J.L., H.Z., M.K., E.T.C., and G.K.C. conceived the original study. S.L., J.L., H.Z., A.K., P.H., J.G., Z.H.C., and W.L. carried out numerical calculations to support the study. S.L., Y.T., A.M.D., L.L., and G.K.C. carried out theoretical analysis to support the study. All authors S.L., J.L., H.Z., Y.T., A.M.D., A.K., P.H., J.G., Z.H.C., W.L., M.K., R.B., J.P., D.R.R., E.T.C., E.F.V., L.L., and G.K.C. discussed the results of the manuscript, and all authors contributed to the writing of the manuscript. Data availability. The FCI/DMRG data for state preparation in Fe-S clusters of nitrogenase are available in Supplementary Notes 3.1, 3.2 and 4.1–4.6. The ASP data are available in Supplementary Notes 3.3, 5.1, and 5.2. The CC data are available in Supplementary Notes 6.1 and 6.2 The PEPS DMRG/VMC data are available in Supplementary Notes 7.1–7.3. Code availability. Source codes are available from the authors on request. Competing interests. G.K.C. is a part owner of QSimulate Inc. The remaining authors declare no competing interests.

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Additional details

Created:
August 22, 2023
Modified:
December 21, 2023