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

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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