Quantum deep transfer learning

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