Exploring quantum annealing for coarse-grained protein folding.

Area of Science:

• Computational Biology
• Quantum Computing

Background:

• The protein structure problem is a significant challenge in computational biology.
• Quantum computing offers novel approaches to complex optimization problems like protein folding.

Purpose of the Study:

• To evaluate quantum annealing for ab initio protein folding.
• To compare different quantum folding models and analyze their performance.
• To introduce a new encoding for protein models on a tetrahedral lattice.

Main Methods:

• Comparison of several ab initio protein folding models for quantum computers.
• Analysis of scaling and performance for classical and quantum heuristics.
• Introduction of a novel interleaved grid-based encoding for coordinate models.

Main Results:

• Significant variations in model performance were observed.
• One model produced unphysical configurations within the solution space.
• Current quantum annealing hardware is limited to proof-of-concept due to embedding challenges.

Conclusions:

• Quantum annealing hardware is not yet mature for large-scale protein folding problems.
• A potential scaling advantage over simulated annealing was noted, but only for embedded problems.
• Further development in quantum hardware and algorithms is needed for practical applications.