IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery
arXiv:2607.01286v1 Announce Type: new Abstract: Public lithium-ion battery datasets are increasingly used for state-of-health estimation, remaining-useful-life prediction, anomaly detection, electrochemical diagnostics, second-life analytics, and battery safety research. However, these datasets vary substantially in chemistry, modality, scale, label quality, sequence structure, access status, and preprocessing complexity. These differences directly affect whether a dataset is feasible for near-term hybrid quantum-classical machine-learning workflows. This paper presents IonSense-QKG, a quantum
The proliferation of diverse lithium-ion battery datasets and the nascent but growing field of hybrid quantum-classical machine learning necessitate standardized metadata frameworks for efficient dataset utilization. Early work in this area suggests developing infrastructure now will be crucial as quantum capabilities mature.
This framework addresses a critical bottleneck in leveraging disparate battery datasets for advanced analytics, particularly as quantum computing begins to offer new computational paradigms for materials science and energy systems. It will accelerate research and development in battery technology lifecycle management.
Battery dataset discovery and integration will become more standardized and efficient, enabling researchers to better assess data suitability for complex and novel computational approaches like hybrid quantum machine learning. This shifts the focus from raw data collection to data interoperability and 'quantum-readiness'.
- · Battery researchers
- · Quantum computing developers
- · Material science research
- · Lithium-ion battery manufacturers
- · Fragmented data repositories
- · Legacy battery R&D approaches
Improved efficiency in battery design and lifespan prediction due to better data utilization.
Accelerated development of next-generation battery chemistries through quantum-enhanced simulations and analytics.
Enhanced energy storage infrastructure and grid stability due to more reliable and longer-lasting batteries.
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