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Simulating challenging correlated molecules and materials on the Sycamore quantum processor

Tazhigulov, Ruslan N. and Sun, Shi-Ning and Haghshenas, Reza and Zhai, Huanchen and Tan, Adrian T. K. and Rubin, Nicholas C. and Babbush, Ryan and Minnich, Austin J. and Chan, Garnet Kin-Lic (2022) Simulating challenging correlated molecules and materials on the Sycamore quantum processor. . (Unpublished)

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Simulating complex molecules and materials is an anticipated application of quantum devices. With strong quantum advantage demonstrated in artificial tasks, we examine how such advantage translates into modeling physical problems of correlated electronic structure. We simulate static and dynamical electronic structure on a superconducting quantum processor derived from Google's Sycamore architecture for two representative correlated electron problems: the nitrogenase iron-sulfur molecular clusters, and α-ruthenium trichloride, a proximate spin-liquid material. To do so, we simplify the electronic structure into low-energy spin models that fit on the device. With extensive error mitigation and assistance from classically simulated data, we achieve quantitatively meaningful results deploying about 1/5 of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over 1/2 of the gate resources when choosing a model that suits the hardware. Our work serves to convert artificial measures of quantum advantage into a physically relevant setting.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemWeber device datasheet
Tazhigulov, Ruslan N.0000-0002-0679-3078
Sun, Shi-Ning0000-0002-5984-780X
Haghshenas, Reza0000-0002-5593-8915
Zhai, Huanchen0000-0003-0086-0388
Rubin, Nicholas C.0000-0003-3963-1830
Babbush, Ryan0000-0001-6979-9533
Minnich, Austin J.0000-0002-9671-9540
Chan, Garnet Kin-Lic0000-0001-8009-6038
Additional Information:R.N.T., R.H., and G.K.-L.C. were supported by the US Department of Energy, Office of Basic Energy Sciences, under Award No. DE-SC0019374. S.-N.S., A.T.K.T, A.J.M were supported by the US NSF under Award No. 1839204. The quantum hardware used in this work was developed by the Google Quantum AI team. Data was collected via cloud access through Google’s Quantum Computing Service. The datasheet of the Weber device can be found at Author contributions. R.N.T., R.H., and G.K.-L.C. conceptualized the project. R.N.T. and S.-N.S. designed and implemented the circuits with help from R.H.. R.N.T. executed the simulations and analyzed the results. R.N.T. and G.K.-L.C. wrote the paper. All authors discussed the results and contributed to the development of the manuscript. Data availability. The data that support the findings of this study are available from the corresponding author upon reasonable request. Code availability. The code used to generate the numerical results presented in this paper can be made available upon reasonable request. Competing interests. G.K.-L.C. is a part owner of QSimulate, Inc.
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019374
Record Number:CaltechAUTHORS:20220712-193849080
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115502
Deposited By: George Porter
Deposited On:13 Jul 2022 21:07
Last Modified:13 Jul 2022 21:07

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