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.
QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation
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.
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arXiv Machine Learning
Jul 23
Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era
arXiv:2602. 16018v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints.
arXiv AI
Aug 10
Investigating Quantum-Embedded Transformers on Classical Datasets for Cross-Modality Classification
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
arXiv Machine Learning
Jul 30
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv AI
Jul 21
Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.
Hugging Face Trending Papers
Jul 14
MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate categories and thus achieve high Top-K accuracy.
arXiv Machine Learning
1d ago
Hybrid quantum recurrent neural network for remaining useful life prediction of turbofan engines
arXiv:2504. 20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems.
arXiv AI
Aug 6
Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Features
arXiv:2608. 04379v1 Announce Type: cross Abstract: We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy.
arXiv Machine Learning
Jun 26
Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
arXiv:2606. 26873v1 Announce Type: cross Abstract: Graphs provide a natural language for relational data in chemistry, biology and optimisation.
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.
Hugging Face Trending Papers
Aug 5
Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Features
We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy. Unlike prior approaches that employ orthogonal decomposition as preprocessing, we intentionally introduce correlated features that are more physically compatible with QNN.
Hugging Face Trending Papers
Jul 15
PQFA: Parallel Quantum Feature Augmentation of Fused Representations for Multimodal Classification
Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.