Sampling a Rare Protein Transition Using Quantum Annealing.

Area of Science:

• Computational Biology
• Quantum Computing
• Biophysics

Background:

• Classical molecular dynamics simulations struggle to capture long-timescale events in macromolecules.
• Path sampling methods improve efficiency but face challenges in generating diverse, uncorrelated transition paths.

Purpose of the Study:

• To develop and validate a hybrid quantum-classical approach for simulating large-timescale biomolecular conformational transitions.
• To leverage quantum annealing for enhanced exploration of complex conformational landscapes.

Main Methods:

• A hybrid paradigm combining classical conformational space exploration with quantum annealing (QA) for generating transition paths.
• Postprocessing classical data using path integral methods to create a coarse-grained kinetic network.
• Utilizing quantum superposition in QA to encode and explore multiple transition pathways simultaneously.

Main Results:

• Successfully performed an all-atom simulation of a millisecond-timescale protein conformational transition.
• Achieved results comparable to those obtained using specialized supercomputers (e.g., Anton).
• Demonstrated the potential of quantum annealing to generate uncorrelated trial trajectories for path sampling.

Conclusions:

• The hybrid quantum-classical approach effectively simulates complex, long-timescale biomolecular events.
• Quantum annealing offers a promising avenue for advancing molecular dynamics simulations and quantum technology applications.
• This study validates the utility of quantum computing for realistic biophysical simulations.