RinQ: Predicting central sites in proteins on current quantum computers
Shah Ishmam Mohtashim · ChemRxiv · 2025
We present RinQ, a hybrid quantum-classical approach for identifying functionally critical residues in proteins by leveraging quantum computing techniques for optimization. Proteins, modeled as residue interaction networks, alongside the task of selecting top central residues, is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is then solved using D-Wave’s simulated annealing. Applied across a diverse set of proteins, the presented approach consistently identifies central residues aligned with classically calculated central sites. RinQ thus exemplifies the potential of near-term quantum and quantum-inspired methods to accelerate protein analysis and inform drug discovery. Our findings match classical benchmarks for small peptides and sets the stage for extending QUBO-based centrality detection to larger proteins using real quantum hardware.