We examine the usefulness of applying neural networks as a variational state ansatz for manybody quantum systems in the context of quantum informationprocessing tasks. In the neural network state ansatz, the complex amplitude function of a quantum state is computed by a neural network. The resulting multipartite entanglement structure captured by this ansatz has proven rich enough to describe the ground states and unitary dynamics of various physical systems of interest. In the present paper, we initiate the study of neural network states in quantum informationprocessing tasks. We demonstrate that neural network states are capable of efficiently representing quantum codes for quantum information transmission and quantum error correction, supplying further evidence for the usefulness of neural network states to describe multipartite entanglement. In particular, we show the following main results: (a) neural network states yield quantum codes with a high coherent information for two important quantum channels, the generalized amplitude damping channel and the dephrasure channel. These codes outperform all other known codes for these channels, and cannot be found using a direct parametrization of the quantum state. (b) For the depolarizing channel, the neural network state ansatz reliably finds the best known codes given by repetition codes. (c)more »
 Award ID(s):
 1839232
 Publication Date:
 NSFPAR ID:
 10297297
 Journal Name:
 ArXivorg
 Page Range or eLocationID:
 2012.15608
 ISSN:
 23318422
 Sponsoring Org:
 National Science Foundation
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