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.

The problem(1) with µy > 0 is called a weakly convex-strongly concave(WCSC) saddle-point problem, whereas forµy =0,itiscalledaweakly convex-merely concave(WCMC) saddle-point problem.

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.