Quantum annealing has been proposed as an alternative to classical computing algorithms for solving combinatorial Quadratic Unconstrained Binary Optimization (QUBO) problems, which are employed for describing many real-world problems, as the analysis of new drugs and integrated circuits routing. QUBO formulation involves unipolar binary variables and cost functions show constraints on at most couples of variables, equivalently to the Ising model, describing the energy of a spins system depending on their orientations. In this case, the problem cost function is encoded onto the system energy profile, so the optimal solution corresponds to the lowest-energy configuration. The main difference between quantum annealing and classical approaches is that it uses quantum fluctuation instead of thermal one, as in simulated annealing, to overcome the energy barriers of the profile, thus permitting an easier exploration of problems showing high and narrow peaks.