  An adiabatic quantum algorithm is essentially given by three elements: An
initial Hamiltonian with known ground state, a problem Hamiltonian whose ground
state corresponds to the solution of the given problem and an evolution
schedule such that the adiabatic condition is satisfied. A correct choice of
these elements is crucial for an efficient adiabatic quantum computation. In
this paper we propose a hybrid quantum-classical algorithm to solve
optimization problems with an adiabatic machine assuming restrictions on the
class of available problem Hamiltonians. The scheme is based on repeated calls
to the quantum machine into a classical iterative structure. In particular we
present a technique to learn the encoding of a given optimization problem into
a problem Hamiltonian and we prove the convergence of the algorithm. Moreover
the output of the proposed algorithm can be used to learn efficient adiabatic
algorithms from examples.
