Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

Sami Boulebnane

doi:10.1038/s41534-023-00733-5

Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

2023-07-17

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DOI：10.1038/s41534-023-00733-5

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Abstract

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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.

Keywords:

FORCE-FIELDS

PROTEIN

MECHANICS

BACKBONE

SIMULATIONS

DYNAMICS

MODEL

Peptide conformational sampling using the Quantum Approximate Optimization Algorithm

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