A resource-efficient variational quantum algorithm for mRNA codon optimization

TL;DR

This paper introduces a resource-efficient variational quantum algorithm for mRNA codon optimization that reduces qubit requirements and achieves results comparable to exact solutions, enabling longer sequence processing on current quantum hardware.

Contribution

It presents a denser encoding method for mRNA codon optimization using variational quantum eigensolver algorithms, reducing qubit needs by half compared to prior approaches.

Findings

Reduces qubit requirement by 50%

Achieves results closely matching exact solutions

Enables longer sequence optimization on existing quantum hardware

Abstract

Optimizing the mRNA codon has an essential impact on gene expression for a specific target protein. It is an NP-hard problem; thus, exact solutions to such optimization problems become computationally intractable for realistic problem sizes on both classical and quantum computers. However, approximate solutions via heuristics can substantially impact the application they enable. Quantum approximate optimization is an alternative computation paradigm promising for tackling such problems. Recently, there has been some research in quantum algorithms for bioinformatics, specifically for mRNA codon optimization. This research presents a denser way to encode codons for implementing mRNA codon optimization via the variational quantum eigensolver algorithms on a gate-based quantum computer. This reduces the qubit requirement by half compared to the existing quantum approach, thus allowing longer sequences to be executed on existing quantum processors. The performance of the proposed algorithm is evaluated by comparing its results to exact solutions, showing well-matching results.