Quantum gate algorithm for reference-guided DNA sequence alignment
G. D. Varsamis, I. G. Karafyllidis, K. M. Gilkes, U. Arranz, R., Martin-Cuevas, G. Calleja, P. Dimitrakis, P. Kolovos, R. Sandaltzopoulos, H., C. Jessen, J. Wong

TL;DR
This paper introduces a scalable quantum algorithm for reference-guided DNA sequence alignment, aiming to enhance genomic data processing by leveraging quantum computing's potential as a classical system accelerator.
Contribution
It presents a novel, error-limited quantum algorithm for DNA alignment that can be integrated with classical systems and tested on IBM Quantum hardware.
Findings
Algorithm confirmed correct on IBM Quantum simulators
Scalable and compatible with existing classical DNA sequencing systems
Designed to minimize computational errors in quantum processing
Abstract
Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4 B bits or about 760 MB of storage (National Institute of General Medical Sciences). Today most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Algorithms and Data Compression · Computational Physics and Python Applications
MethodsBalanced Selection
