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

Finding low-energy conformations of lattice protein models by quantum annealing