• Open Access

Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model

Andy C. Y. Li, M. Sohaib Alam, Thomas Iadecola, Ammar Jahin, Joshua Job, Doga Murat Kurkcuoglu, Richard Li, Peter P. Orth, A. Barış Özgüler, Gabriel N. Perdue, and Norm M. Tubman
Phys. Rev. Research 5, 033071 – Published 1 August 2023

Abstract

Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of scientific interest prior to the practical implementation of quantum error correction. The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers. Lattice models are of broad interest for use on near-term quantum hardware due to the sparsity of the number of Hamiltonian terms and the possibility of matching the lattice geometry to the hardware geometry. Here, we consider the Kitaev spin model on a hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational Ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational Ansatz (HVA) and the potential of probing the system's phase diagram using VQE. We further demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.

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  • Received 16 December 2022
  • Accepted 1 April 2023

DOI:https://doi.org/10.1103/PhysRevResearch.5.033071

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.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Andy C. Y. Li1, M. Sohaib Alam2, Thomas Iadecola3,4, Ammar Jahin5, Joshua Job6, Doga Murat Kurkcuoglu1, Richard Li7, Peter P. Orth3,4, A. Barış Özgüler1, Gabriel N. Perdue1, and Norm M. Tubman8

  • 1Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
  • 2Rigetti Computing, Berkeley, California 94701, USA
  • 3Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA
  • 4Ames National Laboratory, Ames, Iowa 50011, USA
  • 5Department of Physics, University of Florida, 2001 Museum Road, Gainesville, Florida 32611, USA
  • 6Lockheed Martin Advanced Technology Center, Sunnyvale, California 94089, USA
  • 7Department of Physics, Yale University, New Haven, Connecticut 06520, USA
  • 8Quantum Artificial Intelligence Laboratory, Exploration Technology Directorate, NASA Ames Research Center, Moffett Field, California 94035, USA

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Issue

Vol. 5, Iss. 3 — August - October 2023

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