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  • Letter
  • Open Access

Trainable, self-verifying variational quantum eigensolver using adiabatic warm starts

Bojan Žunkovič1,2,*, Marco Ballarin3, Lewis Wright3, and Michael Lubasch3,†

  • *Contact author: bojan.zunkovic@fri.uni-lj.si
  • †Contact author: michael.lubasch@quantinuum.com
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Phys. Rev. Research 8, L042013 – Published 29 October, 2026

DOI: https://doi.org/10.1103/m538-45pk

Abstract

We study an adiabatic variant of the variational quantum eigensolver (VQE) in which VQE is performed iteratively for a sequence of Hamiltonians along an adiabatic path. We derive the conditions under which gradient-based optimization successfully prepares the adiabatic ground states. These conditions define a trainable tracking region. Additionally, when combined with a gap lower bound, energy-standard-deviation measurements certify eigenstate accuracy at run-time and verify convergence to the global optimum.

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Outline

Information

Phys. Rev. Research 8, L042013– Published 29 October, 2026
  • Received 30 April 2026
  • Accepted 22 September 2026
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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.

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