  Gaussian Processes are used in many applications to model spatial phenomena.
Within this context, a key issue is to decide the set of locations where to
take measurements so as to obtain a better approximation of the underlying
function. Current state of the art techniques select such set to minimize the
posterior variance of the Gaussian process. We explore the feasibility of
solving this problem by proposing a novel Quadratic Unconstrained Binary
Optimization (QUBO) model. In recent years this QUBO formulation has gained
increasing attention since it represents the input for the specialized quantum
annealer D-Wave machines. Hence, our contribution takes an important first step
towards the sampling optimization of Gaussian processes in the context of
quantum computation. Results of our empirical evaluation shows that the optimum
of the QUBO objective function we derived represents a good solution for the
above mentioned problem. In fact we are able to obtain comparable and in some
cases better results than the widely used submodular technique.
