Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice

TL;DR

This paper introduces a quantum algorithm leveraging the face-centered cubic lattice model for protein structure prediction, demonstrating successful implementation on IBM quantum computers and showing hardware improvements enhance prediction accuracy.

Contribution

It is the first to apply a quantum algorithm with FCC lattice modeling for protein structures, using two quantum methods on real quantum hardware.

Findings

Successful deployment of quantum algorithms on IBM quantum computers.

Significant hardware improvements lead to better prediction accuracy.

FCC lattice better models realistic protein secondary structures.

Abstract

In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode the FCC lattice stems from our observation that the FCC lattice is more capable in terms of modeling realistic secondary structures in proteins compared to other lattices, as demonstrated using root mean square deviation (RMSD). We utilize two quantum methods to solve this problem: a polynomial fitting approach (PolyFit) and the Variational Quantum Eigensolver with constraints (VQEC) based on the Lagrangian duality principle. Both methods are successfully deployed on Eagle R3 (ibm_cleveland) and Heron R2 (ibm_kingston) quantum computers, where we are able to recover ground state configurations for the 6-amino acid sequence KLVFFA under noise. A comparative analysis of the outcomes generated by the two QPUs reveals a significant enhancement (reaching nearly a two-fold improvement for PolyFit and a three-fold improvement for VQEC) in the prediction and sampling of the optimal solution (ground state conformations) on the newer Heron R2 architecture, highlighting the impact of quantum hardware advancements for this application.