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IEEE Globecom Workshops (GC Wkshps)

Quantum Pattern Recognition for Local Sequence Alignment

Konstantinos P. Prousalis, Nikos Konofaos

Published Dec 1, 2017• 2 citations
Publisher

Abstract

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.

DOI
10.1109/glocomw.2017.8269076

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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.
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.
This paper explores the potential of quantum computing to revolutionize bioinformatics, specifically focusing on enhanced gene sequence analysis and protein structure prediction. The core concept centers around utilizing quantum error correction and quantum computation to accelerate these computationally intensive tasks. We propose a novel algorithm architecture leveraging quantum entanglement and superposition to overcome limitations inherent in classical approaches. The paper details the design of a quantum algorithm for sequence alignment and protein folding, emphasizing the key quantum mechanisms that underpin its enhanced performance. We present a preliminary analysis of the algorithm's potential advantages, focusing on reduced computational complexity and improved accuracy compared to existing classical methods. The research highlights the significance of quantum algorithms in addressing critical challenges within the field of bioinformatics.
This paper introduces a novel approach to gene sequence alignment leveraging the principles of quantum entanglement. Traditional gene sequence alignment methods face significant computational challenges, particularly with increasing genome sizes, leading to slow processing times and limitations in accuracy. Our proposed methodology encodes gene sequences into qubits and utilizes quantum entanglement to establish correlations between sequences, dramatically accelerating the alignment process. We present a theoretical framework outlining the core claim, mechanism, and potential advantages of this approach. The theoretical model focuses on exploiting quantum superposition and entanglement to explore the vast solution space of possible alignments concurrently, offering a significant speedup compared to classical algorithms. We detail the mathematical formulation of the entanglement-based alignment process, including the key equations governing the correlation probability and the estimated reduction in computational complexity. The core aim is to demonstrate the feasibility of using quantum entanglement to enhance gene sequence alignment, potentially revolutionizing bioinformatics research and accelerating the discovery of biological insights.
Quantum search algorithms are considered in the context of protein sequence comparison in bioinformatics. Given a sample protein sequence of length m (i.e., m residues), the problem considered is to find an optimal match in a large database containing N residues. Initially, Grover's quantum search algorithm is applied to a simple illustrative case-namely, where the database forms a complete set of states over the 2(m) basis states of a m qubit register, and thus is known to contain the exact sequence of interest. This example demonstrates explicitly the typical O(square root of [N]) speedup on the classical O(N) requirements. An algorithm is then presented for the (more realistic) case where the database may contain repeat sequences, and may not necessarily contain an exact match to the sample sequence. In terms of minimizing the Hamming distance between the sample sequence and the database subsequences the algorithm finds an optimal alignment, in O(square root of [N]) steps, by employing an extension of Grover's algorithm, due to Boyer et al. for the case when the number of matches is not a priori known.