We examine a particular realization of derivative-free method as implemented on tensor train based optimization to the variational quantum eigensolver. As an example, we consider parametrized quantum circuits composed of a low-depth hardware-efficient ansatz and Hamiltonian variational ansatz for addressing the ground state of the transverse field Ising model. We further make a comparison with gradient-based optimization techniques and discuss on the advantage of using tensor train based optimization, especially in the presence of noise.
Cite
@article{arxiv.2306.02024,
title = {Tensor train optimization of parametrized quantum circuits},
author = {Georgii Paradezhenko and Anastasiia Pervishko and Dmitry Yudin},
journal= {arXiv preprint arXiv:2306.02024},
year = {2023}
}