We introduce an optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations. The effectiveness of our approach is shown by obtaining multi-dimensional energy surfaces for small molecules and a spin model. Our method solves related variational problems in parallel by exploiting the global nature of Bayesian optimisation and sharing information between different optimisers. Parallelisation makes our method ideally suited to next generation of variational problems with many physical degrees of freedom. This addresses a key challenge in scaling-up quantum algorithms towards demonstrating quantum advantage for problems of real-world interest.
@article{arxiv.2103.16161,
title = {Variational quantum algorithm with information sharing},
author = {Chris N. Self and Kiran E. Khosla and Alistair W. R. Smith and Frederic Sauvage and Peter D. Haynes and Johannes Knolle and Florian Mintert and M. S. Kim},
journal= {arXiv preprint arXiv:2103.16161},
year = {2021}
}