Noisy Bayesian optimization for variational quantum eigensolvers

TL;DR

This paper introduces a noise-aware Bayesian optimization method tailored for variational quantum eigensolvers, enhancing the efficiency of finding ground states in quantum systems with current noisy quantum hardware.

Contribution

It develops a specialized Gaussian process regression and Bayesian optimization framework optimized for noisy VQE applications on existing quantum computers.

Findings

Improved convergence in noisy VQE scenarios

Reduced number of quantum evaluations needed

Enhanced robustness to statistical noise

Abstract

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the ground state of a Hamiltonian using variational methods. In the context of this Lattice symposium, the procedure can be used to study lattice gauge theories (LGTs) in the Hamiltonian formulation. Bayesian optimization (BO) based on Gaussian process regression (GPR) is a powerful algorithm 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 to perform VQE on quantum computers already available today.