Beyond the hype: an empirical assessment of quantum computing in bioinformatics

Quantum computing offers great potential for solving intractable computational challenges in bioinformatics, yet the existing literature remains dominated by speculative proposals rather than empirical evidence. To separate theoretical promise from practical reality, this review systematically assesses the empirical state of quantum computing applications across biological domains. We analyzed fifty-three experimental studies spanning sequence analysis, structural biology, phylogenetics, and biomedical machine learning, evaluating them based on their underlying computational paradigms, dataset scales, comparisons against classical baselines, and adherence to open-science reproducibility standards. Our investigation reveals that, while all comparative studies report performance that matches or exceeds that of classical methods, the vast majority rely on simulated environments or quantum-inspired classical heuristics rather than on physical quantum hardware. Moreover, nearly 77% of the evaluated implementations are restricted to heavily reduced toy datasets due to current technological constraints. Severe reproducibility issues plague the field, with just 30% of the studies providing both publicly accessible source code and experimental data. Although quantum bioinformatics has successfully produced viable proof-of-concept prototypes, the field is fundamentally limited by hardware scaling bottlenecks and a lack of standardized benchmarking. To move from conceptual demonstrations to practical scientific impact, we recommend that the community should prioritize rigorous reproducibility practices, establish transparent evaluation metrics, and clearly distinguish genuine quantum execution from classical simulation.