arXiv Machine Learning By Jun Zhuang, Mohammad Al Hasan, Yiyu Shi, Chaowen Guan

SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition

Read the original on arXiv Machine Learning →

arXiv:2607. 16293v1 Announce Type: cross Abstract: Noisy-label learning in small-scale medical image classification is challenging and hinders the superiority of deep neural networks.

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arXiv Machine Learning
Jul 27

Do emulated quantum circuits change what CNNs look at? Performance and explainability comparison in medical image classification

arXiv:2607. 21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.

By Guillermo Rubi\~nos Rodr\'iguez, Mart\'in Ottavianelli, Mateo Alonso, Gonzalo Bl\'azquez Gil, Boris-Stephan Rauchmann, Pablo D\'iez-Valle, Sergio Altares-L\'opez
arXiv Machine Learning
1d ago

Medical Imaging Classification with Cold-Atom Reservoir Computing using Auto-Encoders and Surrogate-Driven Training

The paper presents a hybrid quantum‑classical pipeline that uses neutral‑atom reservoir computing for medical image classification, specifically binary polyp detection. It incorporates a guided auto‑encoder to reduce dimensionality and a differentiable surrogate model to bypass the gradient barrier of quantum measurements, enabling end‑to‑end backpropagation. The resulting compact latent representations are encoded as pulse detuning parameters in a Rydberg Hamiltonian, producing quantum embeddings that feed a linear classifier, and simulations show improved performance over traditional PCA or unguided auto‑encoder methods.

By Nuno Batista, Ana Morgado, Oscar Ferraz, Sagar Silva Pratapsi, Jorge Lobo, Gabriel Falcao