Learnable antinoise-receiver algorithm based on a quantum feedforward neural network in optical quantum communication

Abstract

The quantum communication process usually consists of three stages: 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 antinoise ability and accuracy of quantum communication, this paper proposes a universal optimization strategy of quantum receivers called learnable antinoise receiver (LAN receiver). In this strategy, a parametrized quantum circuit is constructed as a quantum feedforward neural network as the displacement operator to improve the antinoise 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 LAN receiver can resist different kinds of strong quantum noise. The average error rate of the proposed algorithm LAN 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.