Quantum-Assisted Refinement of AlphaFold Protein Structures

Recent advances in deep learning have enabled highly accurate protein structure prediction, yet predicted structures frequently exhibit localized geometric pathologies such as steric clashes, distortions, and poorly formed regions that limit downstream use. These errors are typically rare but high-impact, suggesting that refinement is a tail-dominated problem rather than a uniform optimization task. We propose Q-Refine-AF, a hybrid refinement framework that formulates post-prediction structure correction as an operator learning problem acting on local structural patches. Patch selection is driven by a tail-risk objective inspired by Conditional Value-at-Risk (CVaR), ensuring that refinement effort is concentrated on the most problematic regions while leaving well-formed parts of the structure untouched. The refinement operator is implemented as a parameterized quantum circuit embedded within a classical pipeline and trained to produce structured geometric updates rather than scalar quality scores. Through controlled synthetic experiments, we demonstrate that the proposed framework exhibits two distinct operating regimes: a conservative regime characterized by near-identity updates that preserve structural stability, and an aggressive regime capable of resolving severe synthetic steric conflicts through large corrective displacements. While the latter regime substantially reduces clash counts in highly corrupted structures, the resulting updates are not yet physically constrained and should be interpreted as structural repair rather than biophysically realistic refinement. These results validate the core hypothesis that tail-risk–aware operator learning can selectively target dominant local failure modes in structure prediction pipelines. Although no quantum computational advantage is claimed and physical constraints remain future work, QRefine-AF establishes a foundation for integrating learned, selective refinement operators with existing protein structure prediction and relaxation workflows.