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

TLDR

Lattice protein folding models, such as the Hydrophobic‑Polar and Miyazawa‑Jernigan models, are coarse‑grained tools that capture protein energy landscapes, yet finding low‑energy 3D structures remains computationally intractable, and quantum annealing has shown promise over classical simulated annealing for such optimization problems. The study aims to benchmark quantum annealing for lattice protein folding by implementing six experiments on up to 81 superconducting qubits. The authors map the HP and MJ lattice folding problems onto a quantum annealer, executing the benchmark experiments to evaluate performance on the hardware. This first biophysical application demonstrates that quantum devices can tackle protein‑folding optimization, opening avenues for studying biophysics and statistical mechanics problems with quantum hardware.

Abstract

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 threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. 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 lattice protein folding problems (six different experiments up to 81 superconducting quantum bits). This first implementation of a biophysical problem paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.