Rapid Initial-State Preparation for the Quantum Simulation of Strongly Correlated Molecules
Abstract
Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground-state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground states of the FeMo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000, our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the FeMo cofactor, producing a total resource estimate of 7.3 ×1010 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.
Copyright and License
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Acknowledgement
D.W.B. worked on this project under a sponsored research agreement with Google Quantum AI. D.W.B. is also supported by Australian Research Council Discovery Projects No. DP210101367 and No. DP220101602. Y.T., L.L., and G.K.C. were supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Systems Accelerator. Work by S.L. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2025-00515475). Work by T.I.K. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2024-00415940).
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Additional details
- Google (United States)
- Google Quantum AI -
- Australian Research Council
- DP210101367
- Australian Research Council
- DP220101602
- United States Department of Energy
- National Quantum Information Science Research Centers
- National Research Foundation of Korea
- RS-2025-00515475
- National Research Foundation of Korea
- RS-2024-00415940
- Accepted
-
2025-04-07
- Caltech groups
- Institute for Quantum Information and Matter, Division of Chemistry and Chemical Engineering (CCE), Division of Physics, Mathematics and Astronomy (PMA)
- Publication Status
- Published