Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

• Published in: npj quantum information Vol. 9; no. 1; pp. 70 - 12
• Main Authors: Boulebnane, Sami, Lucas, Xavier, Meyder, Agnes, Adaszewski, Stanislaw, Montanaro, Ashley
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17-07-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/117; 639/766/483/481; Algorithms; Biochemistry; Circuits; Classical and Quantum Gravitation; Energy; Monte Carlo simulation; Optimization algorithms; Peptides; Physics; Physics and Astronomy; Protein folding; Quantum Computing; Quantum Field Theories; Quantum Information Technology; Quantum Physics; Relativity Theory; Spintronics; Statistical sampling; String Theory
• ISSN: 2056-6387; 2056-6387
• DOI: 10.1038/s41534-023-00733-5

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.