Finding low-energy conformations of lattice protein models by quantum annealing
By Alejandro Perdomo-Ortiz, N. Dickson, Marshall Drew-Brook, G. Rose, Alán Aspuru-Guzik. Scientific Reports, 2012
381 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/ce58b84f218565683b92e66c98204103bbbbe0bf
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Citations in the corpus
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