IonSense-QKG: 量子就绪元数据框架用于锂离子电池数据集发现

Jul 16, 2026

Public lithium-ion battery datasets enable state-of-health estimation, remaining-useful-life prediction, anomaly detection, electrochemical characterisation, second-life analytics, and battery-safety research. Yet reuse for near-term hybrid quantum–classical machine learning is hindered by wide variation in chemistry, modality, scale, label quality, access constraints, sequence structure, and preprocessing difficulty. IonSense-QKG introduces a quantum-readiness metadata framework that enriches EV-Battery-IonSense with quantum-relevant fields, defines a weighted Quantum Readiness Score, and provides discovery queries and openly released artifacts.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

Introduction

Battery analytics and dataset heterogeneity

Metadata gap for quantum computing researchers

Background and Motivation

Battery datasets are heterogeneous

IonSense-QKG: A Quantum-Readiness Metadata Framework for Lithium-Ion Battery Dataset Discovery Sakthi Prabhu Gunasekar Amrita School of Computing, Amrita Vishwa Vidyapeetham Coimbatore, India [sakthiprabhu.g@gmail.com](sakthiprabhu.g@gmail.com) Prasanna Kumar Rangarajan Amrita School of Computing, Amrita Vishwa Vidyapeetham Coimbatore, India [r_prasannakumar@cb.amrita.edu](r_prasannakumar@cb.amrita.edu) arXiv :2607 .0 1286v 1 [ cs .LG] 1 Jul 2026 Abstract Public lithium-ion battery datasets support state-of-health estimation, remaining-useful-life prediction, anomaly detection, electrochemical characterisation, second-life analytics, and batterysafety research. However, these datasets remain difficult to reuse for near-term hybrid quantum–classical machine learning because they vary widely in chemistry, modality, scale, label quality, access status, sequence structure, and preprocessing complexity. This paper presents IonSense-QKG, a quantum-readiness metadata framework for lithium-ion battery dataset discovery. Starting from EV-Battery-IonSense, we enrich public battery dataset metadata with quantum-relevant fields including task, modality, chemistry, label type, sequence type, access status, preprocessing needs, estimated qubit range, and candidate quantum encodings. We define a weighted Quantum Readiness Score (QRS) for ranking datasets by practical NISQ-era workflow feasibility. QRS is not a claim of quantum advantage and does not imply that quantum models were trained on all datasets; it is a transparent prioritisation heuristic for future hybrid quantum benchmarks. We demonstrate SQL-style discovery queries and release the metadata, scoring script, ranked table, robustness outputs, link-checking script, and query workload as an open artifact. Artifact Availability. The source code, metadata schema, enriched annotations, Quantum Readiness Score computation script, ranked metadata table, robustness outputs, link-checking script, and query workload are available at [https://github.com/SakthiGs/EV-Battery](https://github.com/SakthiGs/EV-Battery)IonSense. 1 Introduction Battery analytics has become a central problem in electric mobility, renewable energy storage, battery second life, and safety-critical energy systems. Public datasets now cover state-of-health (SoH) estimation, state-of-charge (SoC) estimation, remaining useful life (RUL) prediction, thermal fault detection, impedance-based diagnostics, degradation mode analysis, field-usage modelling, and battery imaging. In parallel, hybrid quantum–classical machine learning is increasingly explored for scientific learning tasks where nonlinear feature maps, kernel-based similarity, and parameter-efficient models may be useful under constrained data regimes [2–5] . Despite these parallel developments, a practical barrier remains: not every battery dataset is suitable for near-term quantum machine learning. Some public datasets are extremely large, highdimensional, irregularly sampled, image-heavy, weakly labelled, or difficult to access. Others are compact, well-labelled, and naturally suited to low-dimensional feature maps, short time-series encodings, or quantum-kernel benchmarking. Electrochemical impedance spectroscopy (EIS), for example, can often be represented as compact frequency-domain signatures, while fleet-scale EV telemetry may contain millions or billions of rows that require windowing, aggregation, or representation learning before any quantum encoding is feasible. Existing battery dataset repositories are valuable navigational indexes, but they rarely expose metadata needed by quantum computing researchers: estimated qubit requirements, sequence-window feasibility, candidate encoding strategies, circuit-depth implications, or NISQ suitability. As a result, researchers must manually inspect datasets, infer task compatibility, and estimate whether a dataset can support a feasible hybrid quantum workflow. This paper addresses that gap

FAQs

IonSense-QKG解决了什么问题？

IonSense-QKG面向混合量子–经典机器学习的锂离子电池数据集发现，解决现有数据集元数据难以支持量子研究者评估“是否可在NISQ阶段开展可行工作流”的问题。它通过量子相关字段与可查询的元数据框架来降低人工筛查和兼容性推断成本。

IonSense-QKG包含哪些量子就绪元数据字段？

框架在EV-Battery-IonSense基础上扩展了量子相关元数据，包括任务、模态、化学体系、标签类型、序列类型、访问状态、预处理需求、估计的qubit范围以及候选量子编码等字段。

Quantum Readiness Score（QRS）用于什么，是否代表量子优势？

QRS用于对候选数据集进行优先级排序，以估计在NISQ时代开展混合量子–经典实验的实际可行性。QRS不声称量子优势，也不表示量子模型已在所有数据集上完成训练。

论文提供了哪些可复用的发现与发布物？

论文演示了SQL风格的发现查询，并发布元数据、QRS计算脚本、带评分的排名表、鲁棒性输出、链接检查脚本以及查询工作负载等开放工件，便于复现与扩展数据集发现流程。

Jul 16, 2026

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