Classical-to-quantum convolutional neural network transfer learning

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.

16 pages, 7 figures

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• Very Deep Convolutional Networks for Large-Scale Image Recognition
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• Quantum convolutional neural network for classical data classification
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Cited by in corpus (8)

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• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
• Quantum support vector data description for anomaly detection
• ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
• Expressivity of deterministic quantum computation with one qubit
• Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer