Quantum bioinformatics: a systematic review of methods, trends, and challenges
Mehdi Khalaj1, Steven Rayan2,3, Lingling Jin1
1Department of Computer Science, University of Saskatchewan, 110 Science Pl., Saskatoon, SK, S7N 5C9, Canada.
Briefings in Bioinformatics
|July 17, 2026
Summary
Quantum computing offers new solutions for complex bioinformatics challenges, addressing limitations of classical methods. This review synthesizes current research in quantum bioinformatics, highlighting trends and future directions.
Area of Science:
- Bioinformatics
- Quantum Computing
- Computational Biology
Background:
- Classical computational approaches are limited by the complexity and scale of biological data.
- Quantum computing presents a promising paradigm for tackling computationally hard problems in bioinformatics.
- Existing reviews need supplementation with an updated synthesis of quantum bioinformatics research.
Purpose of the Study:
- To provide a structured overview of the emerging field of quantum bioinformatics.
- To synthesize current research, identify trends, and analyze methodological patterns.
- To examine quantum and hybrid approaches, problem formulations, and encoding strategies.
Main Methods:
- Systematic collection and organization of studies across 10 bioinformatics domains.
- Analysis of quantum and hybrid quantum-classical approaches.
- Evaluation of problem formulations and encoding strategies for quantum computation.
Main Results:
- Identification of research trends, dominant themes, and recurring methodological patterns in quantum bioinformatics.
- Examination of approaches tailored to noisy intermediate-scale quantum (NISQ) devices.
- Synthesis of limitations, open challenges, and prospective research directions.
Conclusions:
- Quantum bioinformatics is an emerging field with significant potential to overcome classical computational limitations.
- Current research focuses on adapting quantum algorithms and addressing NISQ device constraints.
- Further research is needed to fully realize the potential of quantum computing in biological data analysis.
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