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.
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.
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. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
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.
The explosive growth of genomic information calls for computational methods to handle complex sequence analysis and genome assembly assays. Based on the principles of superposition and entanglement, quantum computing represents an another way of computation to achieve potentially faster and more efficient operations than classical systems. This article offers a comparative overview of 4 major studies dedicated to the use of QC in genomics, spanning theoretical algorithmical conceptualization, systematic literature mapping, analyses on practical challenges in QC-aided genomics and hybrid Q-C designs. The review indicates that there is potential for significant speed-up based on theoretical formulation, while current applications are limited by hardware resource constraint, data-loading latency and scalability. Hybrid models, in particular those that hybridize quantum annealing with classical optimization, seem to offer the most practicable route to near-term genomic computing. The analysis also shines a light on the increasingly pivotal positions that quantum-inspired approaches and hybrid orchestration systems are playing as crucial stepping stones towards full-quantum advantage. The results suggest that quantum computing is in its infancy as far as genomic analysis is concerned but the potential seems to be promising for future bioinformatics research and applications. Such opportunities are only accessible with further advancements in quantum hardware, data encoding and benchmark development.
Quantum computing is emerging as a transformative technology with significant potential to revolutionize healthcare through accelerated data analysis, medical imaging, drug discovery, and genomic research. Despite these opportunities, practical deployment in clinical environments remains challenging due to hardware limitations of noisy intermediate-scale quantum (NISQ) devices, data encoding complexities, error rates, and integration difficulties with existing electronic health record systems. Moreover, the absence of standardized datasets and reproducible evaluation benchmarks limits the ability to compare quantum models with classical baselines in healthcare applications. This paper identifies specific research gaps and proposing a hybrid quantum-classical framework tailored to medical imaging and genomic workloads. Proof-of-concept experiments are conducted using publicly available datasets to evaluate performance trade-offs in accuracy, runtime, and computational resource requirements. The results demonstrate both the promise and current limitations of quantum approaches, highlighting that while immediate advantages are modest, carefully designed hybrid architectures and error-mitigation strategies can provide practical pathways toward clinical adoption. This paper proposes a structured roadmap with methodological guidelines to accelerate the safe and effective integration of quantum computing into healthcare workflows.
Quantum computing has emerged as a promising paradigm for addressing computational challenges in physics that remain intractable for classical methods. This study presents a quantitative benchmarking analysis of prominent quantum algorithms applied to representative physics problem classes, with the objective of evaluating their computational performance relative to state-of-the-art classical algorithms. Using standardized performance metrics—including execution time, algorithmic complexity, resource utilization, accuracy, scalability, and quantum advantage ratios—the study systematically compares quantum and classical approaches across multiple experimental configurations. The results demonstrate that quantum algorithms exhibit measurable performance gains for specific problem types, particularly in terms of runtime efficiency and scaling behavior, while also highlighting practical constraints related to qubit requirements and circuit depth. Visual and tabulated analyses further reveal that quantum advantage is highly problem-dependent and influenced by algorithmic structure and system size. Overall, the findings provide empirical evidence that quantum algorithms can offer substantial computational benefits for selected physics applications, while underscoring the importance of quantitative benchmarking in distinguishing theoretical promise from practical performance. This work contributes to ongoing efforts to objectively assess quantum advantage and informs future research directions in quantum algorithm development and physics-based applications.
Quantum science underpins many of the most significant advances in modern physics, chemistry, and information technology. Quantum physics provides the fundamental laws governing matter and energy at microscopic scales, quantum chemistry applies these principles to explain molecular structure, bonding, and reactivity, and quantum computing seeks to exploit quantum phenomena such as superposition and entanglement to enable new computational paradigms. Despite their long-standing theoretical foundations and experimental successes, all three domains remain surrounded by persistent misconceptions, often amplified by abstract formalisms, popular science narratives, and rapid technological developments. This contemporary review critically examines the established facts and common myths associated with quantum physics, quantum chemistry, and quantum computing. Emphasis is placed on experimentally verified principles, computational feasibility, and realistic assessments of current capabilities. Recent advances reported between 2020 and 2025 are reviewed to highlight genuine progress in areas such as quantum simulations, molecular modeling, and noisy intermediate-scale quantum (NISQ) devices, while also addressing their practical limitations. By distinguishing validated achievements from speculative claims, this review provides a balanced, evidence-based perspective on modern quantum science. The article aims to clarify enduring misconceptions, promote conceptual clarity across disciplines, and outline realistic future directions for research and applications in quantum technologies.