Developing a quantum computing model for sequence annotation of interferon protein

Area of Science:

• Bioinformatics
• Quantum Computing
• Genomics

Background:

• Interferon research is crucial for understanding biological immune systems.
• DNA sequencing has identified interferons across many species, but genome annotation remains incomplete.
• Conventional BLAST algorithm optimization for sequence comparison faces time complexity challenges.

Purpose of the Study:

• To develop a quantum computing model for efficient sequence annotation after DNA sequencing.
• To optimize the time complexity of gene sequence alignment.
• To reduce quantum resource requirements for quantum algorithms.

Main Methods:

• Proposed a quantum gene sequence alignment algorithm combining Grover's search and quantum 01 string alignment.
• Developed a truth-table-based quantum circuit optimization method.
• Created a visualization and analysis website for online circuit generation and experiment running.

Main Results:

• The quantum gene sequence alignment algorithm reduces BLAST's second step time complexity from linear to semilinear.
• Quantum circuit optimization successfully decreased the number of required quantum resources.
• Experimental results validated the proposed methods on both virtual and real quantum computers.

Conclusions:

• The developed quantum computing model offers a viable solution for accelerating sequence annotation.
• The proposed algorithms and optimization techniques are practical and effective for real-world applications.
• The integrated website facilitates accessibility and usability of the quantum algorithms for researchers.