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Quantum implementation of multi-pattern string matching for k-mer detection.

Christos Papalitsas1, Ioannis Mouratidis1, Michail Patsakis1

  • 1Division of Pharmacology and Toxicology, College of Pharmacy, The University of Texas at Austin, Dell Paediatric Research Institute, Austin, TX, USA.

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Summary
This summary is machine-generated.

This study introduces two quantum algorithms for efficient k-mer detection in large genomic datasets. These quantum-enhanced string matching methods offer faster identification of DNA patterns, advancing bioinformatics capabilities.

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Area of Science:

  • Bioinformatics
  • Quantum Computing
  • Computational Biology

Background:

  • Genomic data is growing exponentially, creating opportunities for sequence-based discovery.
  • Efficiently identifying multiple k-mer patterns in large datasets is a computational challenge.

Purpose of the Study:

  • To develop and present quantum algorithms for DNA multi-pattern string matching for k-mer detection.
  • To accelerate dictionary-based pattern matching in large genomic sequences.

Main Methods:

  • Implementation of two quantum algorithms based on Grover's amplitude amplification and quantum random access memory (QRAM).
  • Algorithm 1: Enumerate-m oracle achieving O(√S) query complexity.
  • Algorithm 2: Nested Grover search achieving O(√S · √m) total complexity.

Main Results:

  • Demonstration of two quantum implementations for multi-pattern string matching tailored for k-mer detection.
  • Acceleration of pattern matching using quantum parallelism and Grover-inspired search primitives.
  • Identification of promise and limitations of quantum-enhanced string matching.

Conclusions:

  • Quantum algorithms show promise for accelerating k-mer detection in bioinformatics.
  • Implementation challenges like QRAM overhead need further research.
  • This work is a foundational step towards quantum readiness in bioinformatics.