Classical-to-quantum convolutional neural network transfer learning

TL;DR

This paper introduces a transfer learning approach from classical CNNs to quantum CNNs, enabling complex image classification with small quantum circuits and demonstrating improved accuracy over classical models.

Contribution

It proposes a novel classical-to-quantum transfer learning framework that leverages pre-trained classical CNNs to enhance quantum CNN performance on image classification tasks.

Findings

Quantum transfer learning outperforms classical transfer learning in accuracy.

Pre-trained classical CNNs effectively initialize quantum models for complex tasks.

Numerical simulations validate the approach on MNIST and Fashion-MNIST datasets.

Abstract

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.