Variational quantum algorithm with information sharing

TL;DR

This paper presents a novel variational quantum algorithm that leverages information sharing and Bayesian optimisation to significantly improve efficiency, enabling scalable solutions for complex quantum problems.

Contribution

It introduces a parallelised variational quantum algorithm using information sharing and Bayesian optimisation, enhancing scalability and efficiency for multi-dimensional quantum problems.

Findings

Achieved 100-fold efficiency improvement over naive methods

Successfully obtained energy surfaces for small molecules and spin models

Demonstrated suitability for large-scale variational problems

Abstract

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.