IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery

-->

[Submitted on 1 Jul 2026]

Title:IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery

View a PDF of the paper titled IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery, by Sakthi Prabhu Gunasekar and Prasanna Kumar Rangarajan

View PDF HTML (experimental)

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-readiness metadata framework for lithium-ion battery dataset discovery. Starting from the EV-Battery-IonSense index, the proposed framework enriches public battery dataset records with quantum-relevant metadata, including task type, sensing modality, chemistry, label availability, sequence type, preprocessing requirements, candidate quantum encodings, estimated qubit range, and NISQ feasibility. A transparent Quantum Readiness Score is introduced to rank datasets as candidate resources for future hybrid quantum-classical battery benchmarks. The score is intended as a dataset-selection heuristic, not as evidence of quantum advantage.

The framework demonstrates query-based discovery over enriched metadata to identify datasets suitable for compact quantum feature maps, quantum time-series workflows, limited-label anomaly detection, and future battery-health benchmarking. The released artifact includes metadata tables, scoring scripts, robustness checks, link-checking utilities, and SQL-style query examples. IonSense-QKG positions dataset selection as a data-management problem and provides a reproducible foundation for data-centric quantum battery analytics.

Comments: 7 pages, 1 figure, 4 tables. Code and metadata artifact available at GitHub

Subjects:

Machine Learning (cs.LG); Databases (cs.DB)

Cite as: arXiv:2607.01286 [cs.LG]

(or arXiv:2607.01286v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.01286

arXiv-issued DOI via DataCite

Submission history

From: Sakthi Prabhu Gunasekar [view email] [v1] Wed, 1 Jul 2026 09:46:04 UTC (13 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery, by Sakthi Prabhu Gunasekar and Prasanna Kumar Rangarajan

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.LG

new | recent | 2026-07

Change to browse by:

cs cs.DB

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

IArxiv recommender toggle

IArxiv Recommender (What is IArxiv?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)