Quantum Reinforcement Learning for Semantic Ranking of Enzyme Functionality

Accurate enzyme function prediction is critical for drug discovery, metabolic engineering, and synthetic biology. Current machine learning methods typically treat this task as flat classification, overlooking the structured semantics encoded in the Enzyme Commission (EC) hierarchy. We introduce a Quantum Reinforcement Learning (QRL) framework that reformulates enzyme annotation as a hierarchical traversal and semantic ranking problem, rather than a discrete label prediction. Our QRL agent integrates multimodal biochemical descriptors including protein sequence embeddings, quantum molecular properties, molecular graphs, and reaction SMILES and is trained to navigate the EC hierarchy using a variational quantum policy network. Structured rewards guide the agent toward functionally and semantically consistent paths. Simulated quantum experiments show improved semantic alignment, faster convergence, and enhanced interpretability compared to classical deep Q-networks and supervised baselines. This work demonstrates the potential of QRL to provide biologically meaningful, ontology-aware predictions for novel or low-homology enzymes, bridging symbolic domain knowledge and quantum-enhanced learning.