Quantum computing in bioinformatics: a systematic review mapping

Abstract

The field of quantum computing (QC) is expanding, with efforts being made to apply it to areas previously covered by classical algorithms and methods. Bioinformatics is one such domain that is developing in terms of QC. This article offers a broad mapping review of methods and algorithms of QC in bioinformatics, marking the first of its kind. It presents an overview of the domain and aids researchers in identifying further research directions in the early stages of this field of knowledge. The work presented here shows the current state-of-the-art solutions, focuses on general future directions, and highlights the limitations of current methods. The gathered data includes a comprehensive list of identified methods along with descriptions, classifications, and elaborations of their advantages and disadvantages. Results are presented not just in a descriptive table but also in an aggregated and visual format.

Keywords: bioinformatics; mapping review; quantum computing.

Figure 1

The word cloud above shows the unified and cleaned Author keywords taken from the Scopus database for the papers in the final set of publications. The processed keywords were used to create the word cloud after being tokenized, lowercased, and stripped of stop words and punctuation. The size of each word corresponds to its frequency.

Figure 2

The bubble plot above illustrates the number of studies for different QC approaches in bioinformatics. Each bubble represents a combination of a bioinformatics problem and a QC approach, with the size of the bubble indicating the number of studies conducted on that particular combination. QA: Quantum Annealing; QAlg: Quantum Algorithms; QML: Quantum Machine Learning; QCC: Quantum Communication and Cryptography; HA: Hybrid Algorithms. As shown, the main research interests consist of Genomics and Transcriptomics, ML and Data Mining or Structural Bioinformatics (in the Bioinformatics domain), and General Quantum Algorithms, Hybrid Algorithms, and QA (in the Quantum domain). The focus on the general type of applications (in contrast to specific and more sophisticated topics) is a common phenomenon in the early phase of new solution domain adoption. Some of the fields (Functional Genomics, Metabolomics) have very small representation and still are not a subject of research interest.

Figure 3

The percentage distribution of papers across different COSIs at ISMB conferences from 2017 to 2023 and COSI assignments in this study’s dataset.

Figure A1

The diagram illustrates the flow of studies through the different stages of the selection process. The numbers represent the number of studies at each stage.