In this talk, I introduce a hybrid quantum-classical algorithm to solve optimization problems with an adiabatic quantum computer. In the presented scheme, the representation of the given problem into the quantum architecture is not determined a priori but learned within a convergent iterative structure where the quantum resources are applied to implement "memory effects" avoiding a redundant search in the solution space.