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Communications in computer and information science

A Comparative Study of Hybrid Classical–Quantum Transfer Learning

Daniel Martín-Pérez, Francesc Rodríguez-Díaz, Alicia Troncoso, Francisco Martinez Alvarez

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-19763-4_31

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This paper proposes a quantum transfer learning (QTL) algorithm that combines a transfer learning (TL) algorithm with parameterized quantum circuits (PQC) to investigate the relationship between PQC and model performance in hybrid quantum classical neural networks (HQCNN). The QTL algorithm effectively improves training speed compared to current quantum algorithms, facilitating a more effective exploration of the relationship between PQC and HQCNN. Experimental results show that the expressibility of PQCs is closely related to circuit structure, overlap depth, and the number of qubits. The all-circuits and annular-circuits (APQC) demonstrate better expressibility. Additionally, the expressibility of annular-circuits strongly correlates with the accuracy of the QTL model. The paper also explores the impact of pre-trained models on QTL performance. Ultimately, the pre-trained model ResNet18 and the quantum classifier based on APQC are chosen to form the QTL model. In terms of training time, QTL completes 20 epochs in 4 hours, saving 90% of time compared to general QCHNN.
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.