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 show that the barren plateau problem and local optima can be avoided. Additionally, we propose using energy-standard-deviation measurements at runtime to certify eigenstate accuracy and verify convergence to the global optimum.
@article{arxiv.2602.17612,
title = {Scalable, self-verifying variational quantum eigensolver using adiabatic warm starts},
author = {Bojan Žunkovič and Marco Ballarin and Lewis Wright and Michael Lubasch},
journal= {arXiv preprint arXiv:2602.17612},
year = {2026}
}