  We introduce a new 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.
