Noisy Bayesian optimization for variational quantum eigensolvers

Understand

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.