Quantum bioinformatics: a systematic review of methods, trends, and challenges
Abstract
Abstract Modern bioinformatics faces escalating challenges stemming from both the inherent computational hardness of many fundamental problems and the rapidly growing scale and complexity of biological data, increasingly limiting the effectiveness of classical computational approaches. Quantum computing has emerged as a promising paradigm for addressing these challenges by enabling alternative problem representations and novel search strategies for exploring complex solution spaces. This systematic review provides a structured overview of the emerging field of quantum bioinformatics and aims to supplement recent reviews on this topic in the journal by providing an updated and structured synthesis of current research. We systematically collect and organize existing studies across 10 bioinformatics domains to identify research trends, dominant themes, and recurring methodological patterns. The review examines quantum and hybrid quantum-classical approaches, problem formulations, and encoding strategies, with particular attention to the constraints of noisy intermediate-scale quantum devices, including noise, limited scalability, and the need for error mitigation. We further synthesize reported limitations, open challenges, and prospective research directions.
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Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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