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RNA folding using quantum computers.

Dillion M Fox1, Christopher M MacDermaid1, Andrea M A Schreij1

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This study introduces quantum computing for RNA secondary structure prediction. Quantum annealers show competitive performance in rapidly identifying optimal RNA folding patterns, rivaling traditional algorithms.

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Area of Science:

  • Computational Biology
  • Quantum Computing
  • Biochemistry

Background:

  • RNA 3D structure is determined by base pairing, crucial for function.
  • Predicting RNA secondary structure is an NP-complete problem.
  • Accurate structure prediction is vital for biochemical research.

Purpose of the Study:

  • To explore quantum computing for RNA secondary structure prediction.
  • To develop a quantum approach maximizing base pairs and stem length.
  • To compare quantum annealer performance against classical algorithms.

Main Methods:

  • Formulated RNA folding as a Binary Quadratic Model (BQM) Hamiltonian.
  • Utilized a Quantum Annealer (QA) to find low-energy solutions.
  • Compared QA performance with Replica Exchange Monte Carlo (REMC) and other literature algorithms.

Main Results:

  • The QA approach was highly competitive in identifying low-energy RNA structures.
  • The quantum method demonstrated comparable or superior performance on test sets.
  • The QA rapidly identified optimal solutions for RNA folding.

Conclusions:

  • Quantum computing offers a promising avenue for RNA secondary structure prediction.
  • The proposed BQM Hamiltonian and QA method are effective and competitive.
  • This approach advances computational tools for understanding RNA structure and function.