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Scientific Reports

Α Quantum Pattern Recognition Method for Improving Pairwise Sequence Alignment

Konstantinos P. Prousalis, Nikos Konofaos

Published May 10, 2019• 33 citations
Publisher

Abstract

Quantum pattern recognition techniques have recently raised attention as potential candidates in analyzing vast amount of data. The necessity to obtain faster ways to process data is imperative where data generation is rapid. The ever-growing size of sequence databases caused by the development of high throughput sequencing is unprecedented. Current alignment methods have blossomed overnight but there is still the need for more efficient methods that preserve accuracy in high levels. In this work, a complex method is proposed to treat the alignment problem better than its classical counterparts by means of quantum computation. The basic principal of the standard dot-plot method is combined with a quantum algorithm, giving insight into the effect of quantum pattern recognition on pairwise alignment. The central feature of quantum algorithmic -quantum parallelism- and the diffraction patterns of x-rays are synthesized to provide a clever array indexing structure on the growing sequence databases. A completely different approach is considered in contrast to contemporary conventional aligners and a variety of competitive classical counterparts are classified and organized in order to compare with the quantum setting. The proposed method seems to exhibit high alignment quality and prevail among the others in terms of time and space complexity.

DOI
10.1038/s41598-019-43697-3

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Quantum pattern recognition techniques have recently raised attention as potential candidates in analyzing vast amount of data. The necessity to obtain faster ways to process data is imperative where data generation is rapid. The ever-growing size of sequence databases caused by the development of high throughput sequencing is unprecedented. Current alignment methods have blossomed overnight but there is still the need for more efficient methods that preserve accuracy in high levels. In this work, a complex method is proposed to treat the alignment problem better than its classical counterparts by means of quantum computation. The basic principal of the standard dot-plot method is combined with a quantum algorithm, giving insight into the effect of quantum pattern recognition on pairwise alignment. The central feature of quantum algorithmic -quantum parallelism- and the diffraction patterns of x-rays are synthesized to provide a clever array indexing structure on the growing sequence databases. A completely different approach is considered in contrast to contemporary conventional aligners and a variety of competitive classical counterparts are classified and organized in order to compare with the quantum setting. The proposed method seems to exhibit high alignment quality and prevail among the others in terms of time and space complexity.
A dot matrix approach for sequence alignment is combined with a known quantum multi-pattern recognition method in order to improve the problem of sequence alignment. This dot matrix technique allows the application of some quantum computing principles on the pattern recognition problems like those used in the Grover's algorithm. When the recognized patterns exceed the limit of the 1/3 of the overall patterns, the multi-pattern recognition is accomplished simultaneously with the probability of 100%. The contribution concerns the way that the adopted quantum pattern recognition algorithm is applied speeding up the sequence alignment process. An example application demonstrates the effectiveness of the proposed method. String alignments isn't an essential task only in bioinformatics, but also in calculating the edit distance cost between strings in a natural language or in financial data.
Over the last two decades, there have been some challenging proposals on the field of pattern recognition by means of quantum technology. The application of them is considered for a popular branch of bioinformatics which analyzes massive amounts of sequence data for genes and proteins. More specific, the Smith-Waterman algorithm is studied under the more general term of local sequence alignment. The steps of this algorithm are totally reformed and powered by the quantum mechanics computing theory. The proposed method is based on R. Schützhold's pattern recognition quantum algorithm. A binary and unstructured data set is formed after the comparison of the sequences under alignment which is used by R. Schützhold's algorithm to identify and locate potential patterns. It is achieved with the aid of a spatial light modulator. The adopted quantum algorithm exhibits an exponential speed-up in comparison with its classical counterparts.
A novel quantum algorithm for biological sequence alignment is presented and analyzed. The large volumes of data generated through genome sequencing, de novo assembly, resequencing, and transcriptome sequencing at the DNA and RNA levels foreshadow the growing demand for higher computational power and more sophisticated alignment methodologies. The rapid advancement of modern sequencing technologies in genomics has motivated the reconsideration of existing approaches for the design and implementation of alignment protocols. Emerging quantum computing accelerators may provide transformative solutions in this domain as quantum hardware progressively reaches higher levels of gate-operation maturity. This work proposes a computer-vision-based approach that exploits the unique properties of quantum entanglement within a dot-matrix representation to address the increasing demand for efficient processing of biological data. A quantum-accelerated protocol is developed and evaluated using the Qiskit software framework of IBM. Runtime experiments support the potential of the proposed methodology to provide advantageous sequence-alignment performance in terms of accuracy, completeness, and computational complexity. The system is evaluated under multiple operational conditions and demonstrates promising performance advantages.
Abstract A novel quantum algorithm for use in Biological Sequence Alignment is presented and analyzed. The large amounts of data extracted from genome sequencing, de novo assembly sequencing, resequencing, and transcriptome sequencing at the DNA or RNA level, foreshadow the need for higher computing power as well as more sophisticated alignment methods. Modern and faster sequencing techniques in genomics have led to the reconsideration of current methods of designing or implementing alignment protocols. Novel quantum computing accelerators may provide drastic solutions in this field reaching the desired levels of gate operation maturity. This paper proposes a computer vision-based approach, using the unprecedented power of entanglement in a dot-matrix, to address the high demand for fast harnessing of biological data. A quantum accelerated protocol is demonstrated and tested using the Qiskit software framework of IBM. Runtime tests verify the expectations to obtain a potentially advantageous sequence alignment algorithmic process in terms of accuracy, completeness and computing complexity. The performance has been tested under various conditions and promises a clear and viable advantage.
Quantum-enhanced pattern recognition aims to accelerate and improve the identification of subtle patterns and correlations in complex datasets, offering a potential paradigm shift compared to classical methods. This paper explores the application of quantum machine learning techniques to address challenges posed by high-dimensional data. We propose a novel quantum algorithm designed to efficiently explore the data space and uncover hidden correlations, ultimately surpassing classical approaches in specific tasks. The core mechanism centers around leveraging quantum superposition and entanglement to enhance data representation and pattern recognition capabilities. The paper will detail the proposed quantum algorithm, its potential advantages, and the challenges associated with its implementation.
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.