Domain adaptation method inspired by quantum convolutional neural network

Quantum machine learning is an important application of quantum computing in the era of noisy intermediate-scale quantum devices. Domain adaptation (DA) is an effective method for addressing the distribution discrepancy problem between the training data and the real data when the neural network model is deployed. In this paper, we propose a variational quantum domain adaptation method inspired by the quantum convolutional neural network, named variational quantum domain adaptation (VQDA). The data are first uploaded by a ‘quantum coding module’, then the feature information is extracted by several ‘quantum convolution layers’ and ‘quantum pooling layers’, which is named ‘Feature Extractor’. Subsequently, the labels and the domains of the samples are obtained by the ‘quantum fully connected layer’. With a gradient reversal module, the trained ‘Feature Extractor’ can extract the features that cannot be distinguished from the source and target domains. The simulations on the local computer and IBM Quantum Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed method. The results show that VQDA (with 8 quantum bits) has 91.46% average classification accuracy for DA task between MNIST→USPS (USPS→ MNIST), achieves 91.16% average classification accuracy for gray-scale and color images (with 10 quantum bits), and has 69.25% average classification accuracy on the DA task for color images (also with 10 quantum bits). VQDA achieves a 9.14% improvement in average classification accuracy compared to its corresponding classical domain adaptation method with the same parameter scale for different DA tasks. Simultaneously, the parameters scale is reduced to 43% by using VQDA when both quantum and classical DA methods have similar classification accuracies.