Variational quantum algorithm with information sharing

ORAL

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 the 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.

*This work is supported by the Samsung GRC grant, the UK Hub in Quantum Computing and Simulation, part of the UK National Quantum Technologies Programme with funding from UKRI EPSRC grant EP/T001062/1 and the QuantERA ERA-NET Cofund in Quantum Technologies implemented within the European Union's Horizon 2020 Programme. The project TheoryBlind Quantum Control TheBlinQC has received funding from the QuantERA ERA-NET Cofund in Quantum Technologies implemented within the European Unions Horizon 2020 Programme and from EPSRC under the grant EP/R044082/1. F.S. is supported by a studentship in the Quantum Systems Engineering Skills and Training Hub at Imperial College London funded by EPSRC (EP/P510257/1). We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team. Numerical simulations in "Testing the effectiveness of information sharing" were carried out on Imper

–

Publication: Self, C.N., Khosla, K.E., Smith, A.W.R. et al. Variational quantum algorithm with information sharing. npj Quantum Inf 7, 116 (2021). https://doi.org/10.1038/s41534-021-00452-9

Presenters

  • Chris N Self

    • Imperial College London

Authors

  • Chris N Self

    • Imperial College London

  • Kiran E Khosla

    • Imperial College London

  • Alistair W Smith

    • Imperial College London

  • Frédéric Sauvage

    • Imperial College London

  • Peter D Haynes

    • Imperial College London

  • Johannes Knolle

    • Univ of Cambridge
    • Technical University of Munich

  • Florian Mintert

    • Imperial College London

  • Myungshik Kim

    • Imperial College London