Exploring Quantum Annealing for Coarse-Grained Protein Folding

TL;DR

This paper investigates the application of quantum annealing to protein folding, comparing models, introducing a new encoding, and assessing hardware limitations and scaling advantages.

Contribution

It introduces a novel coordinate encoding for quantum annealing in protein folding and evaluates model performance and hardware scalability.

Findings

Significant variation in model performance, with some producing unphysical configurations.

Current quantum hardware is not yet suitable for large-scale protein folding problems.

Observed scaling advantage of quantum annealing over classical simulated annealing on embedded problems.

Abstract

We explore the potential application of quantum annealing to address the protein structure problem. To this end, we compare several proposed ab initio protein folding models for quantum computers and analyze their scaling and performance for classical and quantum heuristics. Furthermore, we introduce a novel encoding of coordinate based models on the tetrahedral lattice, based on interleaved grids. Our findings reveal significant variations in model performance, with one model yielding unphysical configurations within the feasible solution space. Furthermore, we conclude that current quantum annealing hardware is not yet suited for tackling problems beyond a proof-of-concept size, primarily due to challenges in the embedding. Nonetheless, we observe a scaling advantage over our in-house simulated annealing implementation, which, however, is only noticeable when comparing performance on the embedded problems.