arXiv Machine Learning
Jul 8
Benign Overfitting with Quantum Kernels
arXiv:2503. 17020v2 Announce Type: replace-cross Abstract: Kernel methods compare inputs through feature maps.
Enhancing Quantum Machine Learning with Anyons
arXiv:2606. 16090v1 Announce Type: cross Abstract: The power of quantum computing and quantum machine learning relies on harnessing uniquely quantum phenomena as computational resources.
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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.
arXiv Machine Learning
Jul 14
Learning Topological Quantum Phases from Limited Subsystems
arXiv:2607. 10656v1 Announce Type: cross Abstract: Characterizing quantum topological phases requires measuring non-local string order parameters, demanding access to the full system, which is often experimentally unfeasible.
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.
arXiv Machine Learning
Jun 3
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
arXiv:2606. 02785v1 Announce Type: new Abstract: Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example.
arXiv Machine Learning
Sep 10
Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning
arXiv:2609.06016v1 Announce Type: new Abstract: Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this...
arXiv Machine Learning
Aug 26
When Similarity Is Interaction-Driven: Quantum Kernels for Regime-Sensitive Learning
arXiv:2608.24631v1 Announce Type: cross Abstract: Similarity in many decision systems is governed not by distance alone but by interactions among variables. In fraud and anomaly detection, small loca...
arXiv Machine Learning
Aug 18
Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR
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.
arXiv AI
Sep 1
Representation Learning with Quantum Signal Processing
arXiv:2608.28828v1 Announce Type: cross Abstract: Representation learning begins when training changes the features that define similarity between data. A frozen-kernel model only reweights a fixed g...
arXiv Machine Learning
Jul 8
Classification of Financial Data Using Quantum Support Vector Machine
arXiv:2412. 10860v2 Announce Type: replace-cross Abstract: Quantum Support Vector Machine is a kernel-based approach to classification problems.
arXiv Machine Learning
Jun 9
Quantum feature-map learning with reduced resource overhead
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
arXiv Machine Learning
Sep 14
Classical and quantum kernel fusion for two-sample testing
The paper introduces MMD-FUSE, a two-sample test that remains effective on small datasets by fusing classical and quantum kernels. By combining the inductive biases of classical kernels with the expressive power of quantum kernels, the hybrid approach achieves higher test power, especially for small, high‑dimensional data. Experiments on synthetic and real clinical datasets confirm that the method consistently outperforms purely classical counterparts and adapts robustly to varied data characteristics.