We propose an efficient method for simultaneously optimizing both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimization perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers. We demonstrate the method for optimizing a variational quantum eigensolver for finding the ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
@article{arxiv.1905.09692,
title = {Structure optimization for parameterized quantum circuits},
author = {Mateusz Ostaszewski and Edward Grant and Marcello Benedetti},
journal= {arXiv preprint arXiv:1905.09692},
year = {2021}
}
Comments
13 pages, 6 figures. Added section "Optimization of circuits with limited expressibility". The previous version was titled "Quantum circuit structure learning"