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Peptide conformational sampling using the Quantum Approximate Optimization Algorithm
- Source :
- npj Quantum Information, Vol 9, Iss 1, Pp 1-12 (2023)
- Publication Year :
- 2023
- Publisher :
- Nature Portfolio, 2023.
-
Abstract
- Abstract Protein folding has attracted considerable research effort in biochemistry in recent decades. In this work, we explore the potential of quantum computing to solve a simplified version of protein folding. More precisely, we numerically investigate the performance of the Quantum Approximate Optimization Algorithm (QAOA) in sampling low-energy conformations of short peptides. We start by benchmarking the algorithm on an even simpler problem: sampling self-avoiding walks. Motivated by promising results, we then apply the algorithm to a more complete version of protein folding, including a simplified physical potential. In this case, we find less promising results: deep quantum circuits are required to achieve accurate results, and the performance of QAOA can be matched by random sampling up to a small overhead. Overall, these results cast serious doubt on the ability of QAOA to address the protein folding problem in the near term, even in an extremely simplified setting.
- Subjects :
- Physics
QC1-999
Electronic computers. Computer science
QA75.5-76.95
Subjects
Details
- Language :
- English
- ISSN :
- 20566387
- Volume :
- 9
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- npj Quantum Information
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6376f2beaaa440d8ec1c5cc2559a580
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41534-023-00733-5