End-to-end Encoders Stabilize Quantum Convolutional Neural Networks for Medical Image Classification

Nau M, Maier A, Tang L (2025)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2025

Publisher: Springer Nature

Series: BVM Workshop

City/Town: Wiesbaden

Book Volume: Informatik aktuell

Pages Range: 158–163

Conference Proceedings Title: Bildverarbeitung für die Medizin 2025

Event location: Regensburg DE

ISBN: 978-3-658

URI: https://link.springer.com/chapter/10.1007/978-3-658-47422-5_36

DOI: 10.1007/978-3-658-47422-5_36

Abstract

The advent of quantum machine learning has led to the development of quantum convolutional neural networks (QCNNs) for image classification across various domains. A key limitation of current quantum hardware is the limited number of available qubits and the bandwidth required to load data, necessitating classical dimensionality reduction before inputting image data into quantum circuits. The classification performance varies significantly depending on the quantum circuit and the parameters used. We propose integrating a classical encoder for dimensionality reduction before the QCNN and training it end-to-end. Tested against principal component analysis and autoencoder methods on the Pneumonia MedMNIST dataset, our approach improves performance and stabilizes results across different quantum circuits. While our method falls short compared to the classical baseline in terms of performance, it uses significantly fewer parameters.

Authors with CRIS profile

Merlin Nau Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Nau, M., Maier, A., & Tang, L. (2025). End-to-end Encoders Stabilize Quantum Convolutional Neural Networks for Medical Image Classification. In Springer Nature (Eds.), Bildverarbeitung für die Medizin 2025 (pp. 158–163). Regensburg, DE: Wiesbaden: Springer Nature.

MLA:

Nau, Merlin, Andreas Maier, and Leyi Tang. "End-to-end Encoders Stabilize Quantum Convolutional Neural Networks for Medical Image Classification." Proceedings of the Bildverarbeitung für die Medizin 2025, Regensburg Ed. Springer Nature, Wiesbaden: Springer Nature, 2025. 158–163.

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