Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits

TL;DR

This paper introduces compact hybrid classical-quantum transfer learning models for image classification, demonstrating their competitive accuracy and efficiency benefits in noisy quantum environments and on real hardware.

Contribution

It proposes new quantum transfer learning architectures combining classical backbones with quantum classifiers, evaluated systematically on real and simulated noisy quantum hardware.

Findings

Quantum models achieve comparable or better accuracy than classical baselines.

Hybrid models reduce training time and energy consumption.

PennyLane implementations offer favorable accuracy-efficiency trade-offs.

Abstract

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 noisy emulation using noise models calibrated from IBM quantum hardware specifications, as well as on real IBM quantum hardware. Experimental results show that the proposed quantum transfer learning architectures achieve competitive and, in several cases, superior accuracy while consistently reducing training time and energy consumption relative to the classical baseline. Among the evaluated approaches, PennyLane-based implementations provide the most favorable trade-off between accuracy and computational efficiency, suggesting that hybrid quantum transfer learning can offer practical benefits in realistic NISQ era settings when feature extraction remains classical.