Surrogate optimization of variational quantum circuits

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for variational quantum eigensolver or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical central processing unit/graphical processing unit) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, which is passed as input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2025-09-02
DOI
https://doi.org/10.1073/pnas.2408530122
Citations
2
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
4.96

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article

Surrogate optimization of variational quantum circuits

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2 citations
Proceedings of the National Academy of Sciences
Quantum Computing Algorithms and Architecture
4.96
article

Surrogate optimization of variational quantum circuits

Farshud Sorourifar, David E. Bernal, Filip B. Maciejewski, Andy C. Y. Li, Erik Gustafson, J. Wayne Mullinax, Diana Chamaki, Juha Tiihonen, Nicolas P. D. Sawaya, Jaron T. Krogel, Norm M. Tubman
article en
2 citations

Abstract

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for variational quantum eigensolver or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical central processing unit/graphical processing unit) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, which is passed as input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

Proceedings of the National Academy of SciencesVol. 122(36)
Ames Research Center (US), Oak Ridge National Laboratory (US), Fermi National Accelerator Laboratory (US), Universities Space Research Association (US), Davidson College (US), Research Institute for Advanced Computer Science (US), KBR (United States) (US), Purdue University West Lafayette (US), Catholic University of the Maule (CL), Center for NanoScience (DE), The Ohio State University (US), Columbia University (US), University of Jyväskylä (FI)
U.S. Department of Energy, Ames Research Center
Openalex Percentile: Top 5%
Quantum Computing Algorithms and Architecture
4.96
DOIOpen Access PDFOpenAlex
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