On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models

TL;DR

This paper develops methods for constructing Hamiltonians in adiabatic quantum computation to find low-energy conformations of lattice protein models, demonstrating a specific implementation for the HP protein folding model.

Contribution

It introduces strategies for mapping protein folding problems onto quantum Hamiltonians and reduces complex Hamiltonians to two-body interactions for experimental feasibility.

Findings

Successfully mapped protein folding to quantum Hamiltonians

Implemented the HP model Hamiltonian for protein folding

Proposed reduction of Hamiltonians to two-body terms

Abstract

In this report, we explore the use of a quantum optimization algorithm for obtaining low energy conformations of protein models. We discuss mappings between protein models and optimization variables, which are in turn mapped to a system of coupled quantum bits. General strategies are given for constructing Hamiltonians to be used to solve optimization problems of physical/chemical/biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the Hydrophobic-Polar (HP) model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing towards an experimental realization.