To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.

Then, each image's intensity values are normalized by scaling them to the range [0, 1] .

In this paper, we propose a novel and powerful method to harness Bayesian optimization for V ariational Quantum Eigensolvers (VQEs)--a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian.