Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits
Quantum transfer learning combines pretrained classical deep learning models with quantum circuits to reuse expressive feature representations while limiting the number of trainable parameters. In this work, we introduce a family of compact quantum transfer learning architectures that attach variational quantum classifiers to frozen convolutional backbones for image classification. We instantiate and evaluate several classical-quantum hybrid models implemented in PennyLane and Qiskit, and systematically compare them with a classical transfer-learning baseline across heterogeneous image datasets. To ensure a realistic assessment, we evaluate all approaches under both ideal simulation and n...
| Authors | D. Martín-Pérez, F. Rodríguez-Díaz, D. Gutiérrez-Avilés et al. |
|---|---|
| Primary category | quant-ph |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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