Quantum-effective exact multiple patterns matching algorithms for biological sequences

Author:

Soni Kapil KumarORCID,Rasool Akhtar

Abstract

This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takesΟ(mN) time for matching m patterns overNsized 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 factorm, 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 withCquantum cores working on shared quantum memory. This quantum parallel design would be effective for searching alltexact 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 alltexact 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.

Publisher

PeerJ

Subject

General Computer Science

Reference57 articles.

1. Quantum algorithms for string processing;Ablayev,2020

2. Quantum approximate string matching for large alphabets;Aborot;Theory & Practice of Computation,2017

3. Biological sequences and the exact string matching problem;Basel,2006

4. Representation of Boolean function in terms of quantum computations;Bogdanova,2018

5. Tight bounds on quantum searching;Boyer;Fortschritte der Physik,1998

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3