arXiv Machine Learning
Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 15
Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $\beta$-VAE and Quantum Kernels
arXiv:2606. 14194v1 Announce Type: cross Abstract: This paper presents a two-stage Hybrid Classical-Quantum (HCQ) pipeline for binary Alzheimer's disease (AD) classification from 3D T1-weighted structural MRI volumes, where the classical and quantum components are designed to complement each other rather than operate independently.
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 27
Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support
arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
arXiv Machine Learning
Jun 26
Tailor Made Embeddings for Quantum Machine Learning
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
arXiv AI
Jun 29
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
arXiv:2606. 27411v1 Announce Type: cross Abstract: We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data.
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.