Work

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

Year: 2026

Type: article

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.

Source: Physical Review Research

DOI: 10.1103/m538-45pk

Authors: Bojan Žunkovič, Marco Ballarin, Lewis Wright, Michael Lubasch

Institutions: University of Ljubljana, Rudolfovo – Science and Technology Centre Novo mesto, Quantinuum (United Kingdom), Quantinuum (United States)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funder: Javna Agencija za Raziskovalno Dejavnost RS