  In this report, we explore the use of a quantum optimization algorithm for
obtaining low energy conformations of protein models. We discuss mappings
between protein models and optimization variables, which are in turn mapped to
a system of coupled quantum bits. General strategies are given for constructing
Hamiltonians to be used to solve optimization problems of
physical/chemical/biological interest via quantum computation by adiabatic
evolution. As an example, we implement the Hamiltonian corresponding to the
Hydrophobic-Polar (HP) model for protein folding. Furthermore, we present an
approach to reduce the resulting Hamiltonian to two-body terms gearing towards
an experimental realization.
