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 Machine Learning
Sep 22
Inductive Graph Representation Learning with Quantum Graph Neural Networks
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 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
Aug 28
Classical and Hybrid Quantum Machine Learning for Trigger-Like Event Selection on CMS Open Data: An Eight-Qubit, PCA-Constrained Benchmark
The paper compares classical and hybrid quantum machine learning models for a trigger-like binary classification task using CMS open data. Eight classical models (SVM, ANN, CNN, LSTM) and eight quantum counterparts are evaluated under identical preprocessing, data splits, and decision thresholds, with performance measured by accuracy, ROC‑AUC, F1‑score, precision, and recall. The best classical model is an artificial neural network (93.53 % accuracy, 0.9819 ROC‑AUC), while the best quantum model is a quantum convolutional network (90.89 % accuracy, 0.9731 ROC‑AUC), indicating that within an eight‑qubit budget the quantum models do not surpass the classical ones.
arXiv Machine Learning
Aug 18
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 AI
Sep 17
QiT: Quantum-Inspired Transformer for Visual Recognition Task
QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
arXiv AI
4d ago
Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
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.