arXiv Machine Learning

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.

arXiv Machine Learning
Jul 13

Is data-efficient learning feasible with quantum models?

arXiv:2508. 19437v2 Announce Type: replace-cross Abstract: The importance of analyzing nontrivial datasets when testing quantum machine learning (QML) models is becoming increasingly prominent in literature, yet a cohesive framework for understanding dataset characteristics remains elusive.

By Alona Sakhnenko, Christian B. Mendl, Jeanette M. Lorenz
arXiv Machine Learning
Jul 21

Bridging battery design and health assessment through virtual sensing and physics-informed learning

arXiv:2607. 16864v1 Announce Type: new Abstract: Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows.

By Wendi Guo, S{\o}ren Byg Vilsen, Daniel Ioan Stroe, Yaqi Li, Yicun Huang, Ashima Verma, Daniel Brandell
arXiv AI
Jul 24

PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation

arXiv:2605. 25572v2 Announce Type: replace-cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hallucinate PennyLane-specific gate names, misplace device configurations, and produce structurally invalid circuits when faced with specialized quantum coding challenges.

By Minghao Shao, Nouhaila Innan, Hariharan Janardhanan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique
arXiv AI
Jul 22

Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Fragmented Charging Profiles

arXiv:2607. 18330v1 Announce Type: cross Abstract: The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity.

By Shuhao Chen, Tianyu Shi, Chengyi Tu
arXiv Machine Learning
Jul 14

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

arXiv:2607. 11701v1 Announce Type: cross Abstract: Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential.

By Mariano Caruso, Daniel Ruiz, Alejandro Giraldo, Guido Bellomo