The prospects of quantum computing in computational molecular biology

TL;DR

Quantum computing holds transformative potential for computational molecular biology by enabling faster data processing, improved simulations, and advanced optimization, but faces significant technical challenges and hype.

Contribution

This review highlights the potential applications and limitations of quantum algorithms in computational biology, emphasizing future prospects and current challenges.

Findings

Quantum algorithms could revolutionize drug discovery processes.

Potential for quantum computing to enhance protein structure prediction.

Challenges include hardware limitations and algorithm development hurdles.

Abstract

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.