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Simulating Models of 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 Models of Challenging Correlated Molecules and Materials on the Sycamore Quantum Processor. PRX Quantum, 3 (4). Art. No. 040318. ISSN 2691-3399. doi:10.1103/prxquantum.3.040318.

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Simulating complex molecules and materials is an anticipated application of quantum devices. With the emergence of hardware designed to target strong quantum advantage in artificial tasks, we examine how the same hardware behaves in 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 classical recompilation and simulated data, we achieve quantitatively meaningful results deploying about one fifth of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over half 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:Article
Related URLs:
URLURL TypeDescription InPhysics : Focus
Tazhigulov, Ruslan N.0000-0002-0679-3078
Sun, Shi-Ning0000-0002-5984-780X
Haghshenas, Reza0000-0002-5593-8915
Zhai, Huanchen0000-0003-0086-0388
Tan, Adrian T. K.0000-0002-6660-0397
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 U.S. Department of Energy, Office of Basic Energy Sciences, under Award No. DE-SC0019374. S.-N.S., A.T.K.T., and A.J.M. were supported by the U.S. National Science Foundation (NSF) under Award No. 1839204. Additional support for R.N.T. was provided by the Dreyfus Foundation. The quantum hardware used in this work was developed by the Google Quantum AI team. Data were collected via cloud access through Google’s Quantum Computing Service. 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., while 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. G.K.-L.C. is a part owner of QSimulate, Inc.
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019374
Camille and Henry Dreyfus FoundationUNSPECIFIED
Issue or Number:4
Record Number:CaltechAUTHORS:20221202-898320400.1
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118206
Deposited By: Research Services Depository
Deposited On:04 Jan 2023 17:14
Last Modified:04 Jan 2023 17:14

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