New framework aids discovery of quantum-ready battery datasets

Researchers have introduced IonSense-QKG, a new metadata framework designed to help discover and evaluate lithium-ion battery datasets for use in quantum machine learning workflows. This framework assigns a Quantum Readiness Score to datasets, assessing their suitability for near-term hybrid quantum-classical applications. The goal is to streamline the selection of appropriate datasets for tasks like state-of-health estimation and anomaly detection in the context of quantum computing. AI

IMPACT Facilitates the use of quantum-classical machine learning for battery analytics by improving dataset discovery and selection.

RANK_REASON The item describes a research paper introducing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• EV-Battery-IonSense
• Hugging Face
• IonSense-QKG
• lithium-ion battery
• noisy intermediate-scale quantum era
• Quantum Readiness Score
• Sakthi Prabhu Gunasekar

• paper
• infra

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