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The variational quantum eigensolver (VQE) is a hybrid quantum-classicalalgorithm used to find the ground state of a Hamiltonian using variationalmethods. In the context of this Lattice symposium, the procedure can be used tostudy lattice gauge theories (LGTs) in the Hamiltonian formulation. Bayesianoptimization (BO) based on Gaussian process regression (GPR) is a powerfulalgorithm for finding the global minimum of a cost function, e.g. the energy,with a very low number of iterations using data affected by statistical noise.This work proposes an implementation of GPR and BO specifically tailored toperform VQE on quantum computers already available today.

Noisy Bayesian optimization for variational quantum eigensolvers