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 27

Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification

arXiv:2604. 26675v2 Announce Type: replace-cross Abstract: We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding while the readout determines how this representation is exploited.

By Ralntion Komini, Aikaterini Mandilara, Georgios Maragkopoulos, Dimitris Syvridis
arXiv Machine Learning
Jul 23

A Multiclass Quantum Aligned Centroid Kernel

arXiv:2607. 19782v1 Announce Type: cross Abstract: Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of fixed, non-trainable kernels; and (3) the absence of an intrinsic formulation for multiclass classification.

By Kilian Tscharke, Pascal Debus
arXiv Machine Learning
4d ago

Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces

The paper studies the expressivity of Instantaneous Quantum Polynomial (IQP) feature‑map quantum kernels by deriving a Fourier representation of their associated Reproducing Kernel Hilbert Space (RKHS). It identifies three bandwidth regimes—localized, multimodal, and periodic—each with distinct expressive properties, and demonstrates these regimes on synthetic datasets for classification and anomaly detection, comparing against a classical Gaussian kernel. The work provides a clear link between the kernel’s accessible frequencies, its bandwidth hyperparameter, and the structure of the data, offering guidance for bandwidth selection when data exhibit known periodic or multimodal patterns.

By Valeria Repetto, Stefano Melchionna, Romina D'Aurizio