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Quantum-effective exact multiple patterns matching algorithms for biological sequences.

Kapil Kumar Soni1, Akhtar Rasool1

  • 1Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.

Peerj. Computer Science
|May 31, 2022
PubMed
Summary
This summary is machine-generated.

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New quantum algorithms offer efficient multiple pattern matching for biological sequences. These quantum multicore processor solutions significantly outperform classical methods, achieving substantial computational speedups for sequence analysis.

Area of Science:

  • Quantum computing
  • Bioinformatics
  • Computational complexity

Background:

  • Classical multiple pattern matching for biological sequences is time-consuming, with complexity O(mN).
  • Existing quantum methods for multiple pattern matching often iterate through patterns sequentially, retaining a multiplicative factor in complexity.
  • There is a need for efficient quantum solutions for processing multiple patterns in parallel.

Purpose of the Study:

  • To propose novel quantum algorithms for exact multiple pattern matching on biological sequences.
  • To enhance existing single pattern matching algorithms for multicore quantum processors.
  • To achieve significant speedups over classical algorithms for biological sequence analysis.

Main Methods:

  • Development of quantum algorithms utilizing a quantum processing unit with C quantum cores and shared quantum memory.
Keywords:
Biological sequencesGrover’s quantum searchQuantum algorithmsQuantum exact multiple pattern matchingQuantum memory

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  • Enhancement of existing quantum single pattern matching algorithms into multicore versions.
  • Hybrid simulation of quantum algorithms to validate their effectiveness.

    • Main Results:

    • The proposed quantum algorithms achieve improved time complexities of O(log N) and O(log t) for finding all t exact occurrences of multiple patterns.
    • Demonstrated significant performance improvements over classical multiple pattern matching solutions.
    • Validated the effectiveness and efficiency of the quantum algorithms through hybrid simulations.

    Conclusions:

    • The developed quantum algorithms provide efficient solutions for exact multiple pattern matching in biological sequences.
    • The multicore quantum approach effectively removes the multiplicative factor associated with pattern count.
    • These findings represent a significant advancement in applying quantum computing to bioinformatics challenges.