The paper proposes a Quantum-Based Parallel Model (QBPM) that uses two quantum circuits running in parallel to classify Alzheimer's disease stages from MRI data. It demonstrates high accuracy on two datasets, remains robust under Gaussian noise, and outperforms five classical transfer learning methods while using fewer circuit parameters. The study highlights QBPM’s potential as a faster, more efficient alternative to classical AI for high-dimensional, noisy medical data.
By Emine Akpinar, Murat Oduncuoglu
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:2609.01537v1 Announce Type: new
Abstract: Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item require...
By Arif Hassan Zidan, Yi Pan, Bowen Guo, Xiang Li, Yu Bao, Yingfeng Wang, Tianming Liu, Wei Zhang
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
arXiv:2608. 19304v1 Announce Type: cross Abstract: Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and management challenges.
By Hamed Javidi, Alex Zajichek, Hakan Doga, Laxmi Parida, Filippo Utro, Peter J. Mazzone
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
The paper evaluates hybrid quantum‑classical machine learning for predicting reduced‑order spatiotemporal brain deformation fields. Using Proper Orthogonal Decomposition to compress high‑dimensional displacement data, the authors compare static temporal‑to‑latent regression and autoregressive latent forecasting models. Classical neural networks outperform all quantum variants, though enhanced quantum circuits improve over minimal ones, indicating that classical architectures still hold a clear advantage in fidelity and stability for this task.
By Tao Liu, Ge He, Dongyu Liang, Wujie Wen
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
arXiv:2609.23476v1 Announce Type: cross
Abstract: A potential path forward is Quantum Machine Learning (QML), which aims to leverage quantum computing in conjunction with classical machine learning t...
By Anand Kumar Mishra, Ramanuj Awasthi
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.
arXiv:2601. 00921v3 Announce Type: replace-cross Abstract: Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important.
By Azadeh Alavi, Hamidreza Khalili, Stanley H. Chan, Fatemeh Kouchmeshki, Muhammad Usman, Ross Vlahos