Noisy Quantum Channel Characterization Using Quantum Neural Networks

Abstract

Channel noise is considered to be the main obstacle in long-distance quantum communication and distributed quantum networks. Here, employing a quantum neural network, we present an efficient method to study the model and detect the noise of quantum channels. Based on various types of noisy quantum channel models, we construct the architecture of the quantum neural network and the model training process. Finally, we perform experiments to verify the training effectiveness of the scheme, and the results show that the cost function of the quantum neural network could approach above 90% of the channel model.