Resource-efficient quantum algorithm for protein folding

Abstract Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half...

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Autores principales:	Anton Robert, Panagiotis Kl. Barkoutsos, Stefan Woerner, Ivano Tavernelli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/10c48308b29c41958c920067b06a39e8
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spelling	oai:doaj.org-article:10c48308b29c41958c920067b06a39e82021-12-02T14:04:43ZResource-efficient quantum algorithm for protein folding10.1038/s41534-021-00368-42056-6387https://doaj.org/article/10c48308b29c41958c920067b06a39e82021-02-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00368-4https://doaj.org/toc/2056-6387Abstract Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the sampling of the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even when reduced to the simplest Hydrophobic-Polar model. On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with $${\mathcal{O}}({N}^{4})$$ O ( N 4 ) scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. In addition, we use a robust and versatile optimization scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to simulate the folding of the 10 amino acid Angiotensin on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.Anton RobertPanagiotis Kl. BarkoutsosStefan WoernerIvano TavernelliNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Anton Robert Panagiotis Kl. Barkoutsos Stefan Woerner Ivano Tavernelli Resource-efficient quantum algorithm for protein folding
description	Abstract Predicting the three-dimensional structure of a protein from its primary sequence of amino acids is known as the protein folding problem. Due to the central role of proteins’ structures in chemistry, biology and medicine applications, this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions for the sampling of the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even when reduced to the simplest Hydrophobic-Polar model. On the other hand, while fault-tolerant quantum computers are beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used in noisy state-of-the-art quantum computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with $${\mathcal{O}}({N}^{4})$$ O ( N 4 ) scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with N monomers on a lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. In addition, we use a robust and versatile optimization scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies to simulate the folding of the 10 amino acid Angiotensin on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.
format	article
author	Anton Robert Panagiotis Kl. Barkoutsos Stefan Woerner Ivano Tavernelli
author_facet	Anton Robert Panagiotis Kl. Barkoutsos Stefan Woerner Ivano Tavernelli
author_sort	Anton Robert
title	Resource-efficient quantum algorithm for protein folding
title_short	Resource-efficient quantum algorithm for protein folding
title_full	Resource-efficient quantum algorithm for protein folding
title_fullStr	Resource-efficient quantum algorithm for protein folding
title_full_unstemmed	Resource-efficient quantum algorithm for protein folding
title_sort	resource-efficient quantum algorithm for protein folding
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/10c48308b29c41958c920067b06a39e8
work_keys_str_mv	AT antonrobert resourceefficientquantumalgorithmforproteinfolding AT panagiotisklbarkoutsos resourceefficientquantumalgorithmforproteinfolding AT stefanwoerner resourceefficientquantumalgorithmforproteinfolding AT ivanotavernelli resourceefficientquantumalgorithmforproteinfolding
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Resource-efficient quantum algorithm for protein folding

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