This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο ( mN ) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m , however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in $O((m/C)\sqrt{N})$ and $O((m/C)\sqrt{t})$ time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical–experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.
This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο ( mN ) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m , however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in O((m/C)N) and O((m/C)t) time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical–experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.
In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in time, whereas quantum algorithm design is based on Grover's method, which completes the search in time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are , and the exceptional worst case is bounded by . Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.
This article influences the quantum based solutions for traditional pattern matching problem, as the quantum machines can accelerate the computation speed by inherently supporting the parallel executions. The problem identifies solution by suggesting effective algorithms, and as per the classical machines several algorithms are qualified and become criterion for other, importantly noted as Knuth Morris Pratt and Boyer Moore which solves such problem in O(M + N) time. A quantum identical pattern matching algorithms are based upon the principle of high speed Grover's quantum search method which searches an element over unstructured database of "N" elements in O(√N) time. The standard quantum algorithm was proposed by Ramesh and Vinay that could obtain computational speed and provides solution in O(√M + √N) time. We review two already existing quantum based exact and approximate pattern matching algorithms and then by combining the logic of both, a new exact pattern matching algorithm is being proposed which overcomes the algorithmic constraints and substantially proves to be equivalently better than the existing classical and quantum benchmark algorithms. So, this article includes existing and proposed quantum pattern matching algorithms, their flowcharts and examples, mathematical justification over complexity analysis. At last we discuss suitable application domains and further possible variations over the proposed work.
The pattern matching problem is imperative in diversified field of computer science, and it finds occurrences of the pattern string(s) of any arbitrary length “M” in the large text string of length “N”. Since past decades the solutions to a problem have been suggesting through efficient algorithms. Classical benchmark algorithms such as Knuth Morris Pratt and Boyer Moore locates such solution in O(N) time, equivalent quantum algorithms can utilize quantum computations which are inherently parallel and obtains computational speedup by providing the solution in O(√N) time. The quantum pattern matching algorithm uses Grover's search logic that finds an element existence in the large unstructured text data of “N” items in O(√N) time, instead classically it takes O(N) time. The article comprises of introductory pattern matching tactics, quantum basics, Grover's search method, quantum based pattern matching algorithms, their illustration followed by complexity analysis, and finally concludes with the possible algorithmic variations and relevant applications.
Motivation: The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of multiple k-mer patterns across large sequencing data and massive databases remains a significant computational challenge. Method: We implement two quantum algorithms for DNA multi pattern string matching for k-mer detection based on Grover's amplitude amplification with quantum random access memory (QRAM). The first algorithm uses an enumerate-m oracle that sequentially checks a loaded text substring against all m patterns achieving O(√S) query complexity for S text positions but requiring O(m·L) work per oracle call. The second algorithm employs nested Grover search with an outer loop over text positions and an inner loop over pattern space, reducing oracle complexity to O(L) while performing O(√S · √m) in total. Results: We present two quantum implementations of multi-pattern string matching tailored for k-mer detection. Leveraging quantum parallelism and Grover-inspired search primitives, our methods accelerate dictionary-based pattern matching, particularly in contexts involving large sequences, such as genomic data, and extensive pattern sets. Conclusions: While implementation challenges such as QRAM overhead remain, this study demonstrates both the promise and current limitations of quantum-enhanced string matching, establishing a foundational step toward quantum readiness in bioinformatics.
Motivation: The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of multiple k-mer patterns across large sequencing data and massive databases remains a significant computational challenge. Method: We implement two quantum algorithms for DNA multi-pattern string matching for k-mer detection, leveraging Grover's amplitude amplification under the idealized quantum random access memory (QRAM) framework. The first algorithm uses an enumerate-m oracle that sequentially checks a loaded text substring against all m patterns achieving O (√S) query complexity for S text positions but requiring O (m · L) work per oracle call. The second algorithm employs nested Grover search with an outer loop over text positions and an inner loop over pattern space, reducing oracle complexity to O(L) while performing O (√S · √m) in total. These asymptotic gains highlight the potential advantages that could be unlocked by future large-scale, low-noise QRAM architectures, positioning our results as a promising proof-of-concept foundation. Results: This work introduces two quantum implementations of multi-pattern string matching tailored for k-mer detection. Leveraging quantum parallelism and Grover-inspired search primitives, our methods accelerate dictionary-based pattern matching, particularly in contexts involving large sequences, such as genomic data, and extensive pattern sets. Conclusion: While implementation challenges such as QRAM overhead remain, this study demonstrates both the promise and current limitations of quantum-enhanced string matching, establishing a foundational step toward quantum readiness in bioinformatics. Availability and Implementation: To maximize accessibility and practical use, we provide our methodology at: https://github.com/Georgakopoulos-Soares-lab/quantum-multi-motif-finder.
Quantum-enhanced pattern recognition holds considerable promise for revolutionizing biological data analysis, particularly in the complex realm of genomic sequences and other biological data. This paper explores the potential of leveraging quantum phenomena – such as superposition and entanglement – to enhance feature extraction and pattern matching algorithms, addressing the limitations of conventional methods. We propose a novel Q-Bio framework that integrates quantum-inspired techniques with established machine learning paradigms to achieve superior accuracy and robustness in identifying subtle correlations and insights within biological datasets. The core mechanism centers on utilizing quantum-enhanced feature representation and adaptive pattern matching, aiming to surpass current analytical capabilities. This research delves into the theoretical underpinnings, experimental validation, and practical implementation of this approach, presenting a pathway for significant advancements in biological data analysis.