Research And Application Of Key Issues In Quantum Communication Based On Quantum Machine Learnin

The optical quantum communication system usually consists of the following parts.1.A sender to prepare encode the carrier;2.The turbulent quantum channels;3.Quantum repeater with quantum memory;4.Quantum receivers.The quantum communication network can be composed of quantum memories and multiple quantum channels.This work focuses on optimization strategies on quantum receivers and the capacities of storing and transmitting quantum information of nodes in quantum communication networks.This work aims at improving the accuracy and robustness of quantum communication under turbulent channels,as well as the storage and transmission capabilities of communication nodes in complex quantum communication networks.The details are as follows:(1)The quantum communication process usually consists of the sender who prepares encoded carriers,the transmission in noisy channels,and the quantum receivers.The transmitted quantum information can be inevitably affected by kinds of quantum noise in the environment.Thus,quantum protocols are extensively studied to improve communication efficiency and accuracy under the influence of quantum noise.The optimization strategies usually occur in these three stages.In this paper,we focus on the optimization strategy of quantum receivers in the third stage.In quantum receiver algorithms,the key to distinguish received non-orthogonal coherent states in free-space optical quantum communication is to construct an optimum displacement operator for transforming the current coherent state into a state that is easier to distinguish than before.To improve the anti-noise ability and accuracy of quantum communication,this paper proposes a universal optimization strategy of quantum receivers called learnable anti-noise receiver.In this strategy,a parametrized quantum circuit is constructed as a quantum feedforward neural network as the displacement operator to improve the anti-noise ability.The parameters used in the quantum circuit are updated by gradient descent continuously to find the best parameter combination of the quantum circuit that minimizes the error rate and the qubits affected by quantum noise are used as training and testing data.The simulation of the proposed algorithm shows that the earnable anti-noise receiver can resist different kinds of strong quantum noise.The average error rate of the proposed algorithm earnable anti-noise receiver under the strong noise channel is 0.18,which has better performance than other type of receivers under the influence of strong quantum noise.(2)In view of the limitation of data storage ability and transmission capacity of communication nodes in quantum communication networks,this work proposes a generalized quantum walk algorithm,which can be used in the dynamic data storage and transmission in grid communication networks.Compared with fully connected grid networks,the proposed algorithm can save the storage by 8.3% to 16.7%.The communication scheme is flexible,and has great potential to apply to quantum Internet of things system in the near come future.An example on simulation platform is also given.By combining with quantum neural network,the communication scheme can be successfully applied to the traffic congestion prediction of intelligent transportation of the Internet of things.The average prediction error rate of the scheme is 0.21,and it can effectively resist environmental turbulence.