arXiv:2606. 27411v1 Announce Type: cross Abstract: We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data.
By Santanu Ganguly, Xing Liang, Dimitrios Makris
arXiv:2607. 13466v1 Announce Type: new Abstract: Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored.
By Mingzhu Wang, Yun Shang
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.
arXiv:2606. 18970v1 Announce Type: cross Abstract: Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains.
By Syed Mujtaba Haider, Silvia Figini
arXiv:2607. 11701v1 Announce Type: cross Abstract: Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential.
By Mariano Caruso, Daniel Ruiz, Alejandro Giraldo, Guido Bellomo
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains. However, such claims are typically based on single training runs, do not match the parameter budgets of the quantum and classical generators, and do not characterize the data regime in which any benefit appears.