Implementation of a quantum sequence alignment algorithm for quantum bioinformatics

TL;DR

This paper demonstrates a quantum sequence alignment algorithm tailored for NISQ-era quantum computers, utilizing genetic algorithms for data encoding, and validates its practicality through simulation.

Contribution

It adapts a classical quantum sequence alignment algorithm for current quantum hardware using genetic algorithms for data encoding, and tests it in simulated environments.

Findings

Successful implementation of QSA in simulated NISQ environments

GASP effectively creates encoding circuits for biological data

Demonstrates potential for quantum bioinformatics applications

Abstract

This paper presents the implementation of a quantum sequence alignment (QSA) algorithm on biological data in environments simulating noisy intermediate-scale quantum (NISQ) computers. The approach to quantum bioinformatics adapts the original QSA algorithm proposed in 2000 to current capabilities and limitations of NISQ-era quantum computers and uses a genetic algorithm for state preparation (GASP) to create encoding circuits to load both database and target sequences into the quantum data registers. The implementation is tested in a simulated quantum computer environment to validate the approach and refine the GASP data-loading circuit designs. The results demonstrate the practicalities of deploying the QSA algorithm and exemplify the potential of GASP for data encoding in the realm of quantum circuit design, particularly for complex algorithms in quantum bioinformatics and other data-rich problems.