RT Journal Article
T1 QFold: quantum walks and deep learning to solve protein folding
A1 Martín-Delgado Alcántara, Miguel Ángel
A1 Campos Ortiz, Roberto
A1 Moreno Casares, Pablo Antonio
AB We develop quantum computational tools to predict the 3D structure of proteins, one of the most important problems in current biochemical research. We explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum algorithm that, in contrast to previous quantum approaches, does not require a lattice model simplification and instead relies on the much more realistic assumption of parameterization in terms of torsion angles of the amino acids. We compare it with its classical analog for different annealing schedules and find a polynomial quantum advantage, and implement a minimal realization of the quantum Metropolis in IBMQ Casablanca quantum system.
PB IOP Science
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/88980
UL https://hdl.handle.net/20.500.14352/88980
LA eng
NO P. A. M. Casares, R. Campos, and M. A. Martin-Delgado, Quantum Sci. Technol. 7, 025013 (2022).
NO Ministerio de Economía, Comercio y Empresa (España)
NO Comunidad de Madrid
NO European Commission
NO U.S. Army Research Office
NO Ministerio de Educación, Formación Profesional y Deportes (España)
DS Docta Complutense
RD 4 abr 2025