Comparative Literature Survey on Quantum Computing Approaches for Genome Assembly and Sequence Analysis

Abstract

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.

Introduction

The text reviews the emerging role of quantum computing in genomics and bioinformatics, arguing that it may arrive sooner than expected as a response to the explosive growth of genomic and healthcare data. Advances in next-generation sequencing have pushed classical high-performance computing toward its limits, especially for tasks such as genome assembly, sequence alignment, molecular modeling, and large-scale pattern discovery, which often involve exponential or polynomial complexity.

Quantum computing offers a fundamentally different computational paradigm based on superposition, entanglement, and probabilistic measurement, enabling parallel exploration of large solution spaces. These properties are well suited to genomics, where biological interactions are complex, nonlinear, and inherently stochastic. Potential applications include quantum genome assembly, sequence alignment, molecular simulation, quantum machine learning for omics data, drug discovery, and secure healthcare data management.

The review emphasizes that current applications are constrained by Noisy Intermediate-Scale Quantum (NISQ) hardware, limited qubit counts, decoherence, error rates, and the challenge of encoding large biological datasets into quantum states. As a result, the most practical near-term solutions are hybrid quantum–classical architectures, where classical systems handle preprocessing and interpretation while quantum processors accelerate search, optimization, or simulation tasks.

Five representative studies are analyzed:

1. Quantum pattern matching using Grover’s search for faster biological sequence search.

2. Systematic mapping of quantum bioinformatics, identifying key application areas and algorithms.

3. Hybrid quantum genomic modeling using variational methods to simulate molecular interactions.

4. Quantum annealing for genome assembly, reformulating assembly as a QUBO optimization problem.

5. Quantum computing in healthcare, combining molecular simulation for drug discovery with quantum-secure data encryption.

Across these studies, experimental and simulated results show notable speedups (35–60%), high accuracy (≈95–99%), and improved scalability compared to classical approaches, particularly in sequence matching, genome assembly, and secure data handling. However, the lack of standardized quantum-ready datasets, encoding schemes, and benchmarking platforms remains a major bottleneck.

The paper concludes that while quantum computing is not yet a universal replacement for classical systems, it already demonstrates clear advantages in speed, optimization, and security for selected bioinformatics and healthcare problems. Continued progress will depend on improved hardware, better data encoding, error mitigation, and close collaboration among quantum physicists, bioinformaticians, and data scientists. Hybrid quantum–classical frameworks are identified as the most realistic and impactful path toward next-generation genomic analysis.

Conclusion

Based on the summary of the findings above, a general outline of how quantum?computing algorithms are currently being used in bioinformatics and medicalfield is illustrated. The trends of the findings in?five selected studies and a hybrid quantum-classical framework analysis have contributed valuable trends. So,?the results of this comparative analysis suggest that quantum algorithms are efficient and exact, and very secure in handling genomic/molecular data for various applications like genomics, molecule simulation, genome assembly and healthcare encryption. Additionally, the experiments indicate that the quantum-classical hybrids out-performed accelerated classical?hadron shower models by 45% to 60%, and remained >95 %accurate. Grover\'s search, hybrid variational algorithms and quantum annealing found practical applications in optimization?of sequence analysis and molecular computation. Secure quantum cryptography was?also successfully used for secure transmission of clinical and genomic data, suggesting the wide applications of quantum computing in data privacy protection for medical research.

References

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Copyright

Copyright © 2025 Amaljith K H, Krutika Suresh, Pavitra Shine, Soorya R, Dr. Reshmi B, Dr. S. Gunasekaran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.