Encoding lattice structures in Quantum Computational Basis States

TL;DR

This paper introduces a generic method to encode lattice structures into quantum computational basis states, with a specific application demonstrated in protein structure prediction, without proposing new quantum algorithms.

Contribution

The paper presents a novel encoding methodology for lattice structures in quantum basis states, applicable to various physical and biological models.

Findings

Demonstrated encoding of lattice models in quantum states.

Applied encoding to protein structure prediction case.

No new quantum algorithms are proposed.

Abstract

Lattice models or structures are geometrical objects with mathematical forms, that are used to represent physical systems. They have been used widely in diverse fields, namely, in condensed matter physics, to study degrees of freedom of molecules in chemistry and in studying polymer dynamics and protein structures to name a few. In this article we discuss an encoding methodology of lattice structures in computational basis states of qubits (as used in quantum computing algorithms). We demonstrate a specific use case of lattice models in protein structure prediction. We do not propose any quantum algorithm to solve the protein structure prediction problem, instead, we propose a generic encoding methodology of lattice structures.