  Lattice protein folding models are a cornerstone of computational biophysics.
Although these models are a coarse grained representation, they provide useful
insight into the energy landscape of natural proteins. Finding low-energy
three-dimensional structures is an intractable problem even in the simplest
model, the Hydrophobic-Polar (HP) model. Exhaustive search of all possible
global minima is limited to sequences in the tens of amino acids. Description
of protein-like properties are more accurately described by generalized models,
such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take
into account the unique interactions among all 20 amino acids. There is
theoretical and experimental evidence of the advantage of solving classical
optimization problems using quantum annealing over its classical analogue
(simulated annealing). In this report, we present a benchmark implementation of
quantum annealing for a biophysical problem (six different experiments up to 81
superconducting quantum bits). Although the cases presented here can be solved
in a classical computer, we present the first implementation of lattice protein
folding on a quantum device under the Miyazawa-Jernigan model. This paves the
way towards studying optimization problems in biophysics and statistical
mechanics using quantum devices.
