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.