Quantum deep transfer learning

Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms.