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:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).
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:2607. 07754v1 Announce Type: new Abstract: Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for the required feature extraction and classification tasks.
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:2512. 09586v2 Announce Type: replace-cross Abstract: Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv:2503.24111v4 Announce Type: replace-cross Abstract: Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classica...
arXiv:2606. 04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance.
The paper investigates the role of quantum computing in network‑security classification through two experiments. First, it evaluates near‑term quantum‑kernel support vector machines on datasets such as KDD Cup 1999, CICIDS2017, and BoT‑IoT, finding that quantum kernels can match or sometimes improve classical baselines, though classical RBF kernels often remain stronger. Second, it explores long‑term memory efficiency using quantum oracle sketching (QOS), showing that quantum methods can achieve comparable accuracy with a smaller effective memory footprint than explicit storage, suggesting a potential advantage in memory‑efficient data access for streaming classification tasks.
arXiv:2608. 12903v1 Announce Type: new Abstract: The $k$-Nearest Neighbor~(KNN) algorithm is widely used across various tasks.