IonSense-QKG Framework Ranks Battery Datasets for Quantum ML

Researchers have introduced IonSense-QKG, a novel metadata framework aimed at facilitating the discovery of public lithium-ion battery datasets suitable for hybrid quantum-classical machine learning applications. The framework addresses the challenge that existing battery datasets vary widely in their characteristics, making it difficult to identify those compatible with quantum computing workflows. IonSense-QKG extends the EV-Battery-IonSense index by adding crucial quantum-relevant metadata to each dataset record. This includes details such as task type, sensing modality, battery chemistry, label availability, sequence structure, preprocessing needs, potential quantum encodings, estimated qubit range, and NISQ (Noisy Intermediate-Scale Quantum) feasibility. A key feature of the framework is the Quantum Readiness Score, a transparent heuristic designed to rank datasets based on their suitability for future hybrid quantum-classical battery benchmarks. This score enables query-based discovery, allowing researchers to efficiently find datasets for specific quantum tasks like compact quantum feature maps or quantum time-series workflows, thereby providing a structured foundation for data-centric quantum battery analytics.