In this paper 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, or biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the hydrophobic-polar model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing toward an experimental realization.
