  Classical deep learning algorithms have aroused great interest in both
academia and industry for their utility in image recognition, language
translation, decision-making problems and more. In this work, we have provided
a quantum deep learning scheme based on multi-qubit entanglement states,
including computation and training of neural network in full quantum process.
In the course of training, efficient calculation of the distance between
unknown unit vector and known unit vector has been realized by proper
measurement based on the Greenberger-Horne-Zeilinger entanglement states. An
exponential speedup over classical algorithms has been demonstrated. In the
process of computation, quantum scheme corresponding to multi-layer feedforward
neural network has been provided. We have shown the utility of our scheme using
Iris dataset. The extensibility of the present scheme to different types of
model has also been analyzed
