arXiv Machine Learning By Vivek Sabarad, Vishal Varma, T. S. Mahesh

Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR

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arXiv:2412. 09557v3 Announce Type: replace-cross Abstract: Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in quantum systems.

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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 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
arXiv AI
Aug 26

QML for Quantum Sensing under Measurement-Induced Information Loss

The paper investigates quantum machine learning (QML) for magnetic-field estimation using nitrogen‑vacancy (NV) centers in diamond. By framing sensing as a supervised regression task, the authors compare classical machine learning models trained on measurement‑based data with quantum kernel‑based models trained on pre‑measurement coherent quantum states. They find that QML performance improves markedly when coherent quantum‑state information is available, whereas changes in model complexity or learning paradigm have little effect, highlighting the need for tightly integrated quantum‑sensor and QML pipelines.

By Sounak Bhowmik, Himanshu Thapliyal