  Quantum machine learning is an important application of quantum computing in
the era of noisy intermediate-scale quantum devices. Domain adaptation 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, a variational quantum domain adaptation method is proposed by
using a quantum convolutional neural network, together with a gradient reversal
module, and two quantum fully connected layers, named variational quantum
domain adaptation(VQDA). The simulations on the local computer and IBM Quantum
Experience (IBM Q) platform by Qiskit show the effectiveness of the proposed
method. The results demonstrate that, compared to its classical corresponding
domain adaptation method, VQDA achieves an average improvement of 4% on the
accuracy for MNIST to USPS domain transfer under the same parameter scales.
Similarly, for SYNDigits to SVHN domain transfer, VQDA achieves an average
improvement of 2% on the accuracy under the same parameter scales.
