We examine the usefulness of applying neural networks as a variational state ansatz for many-body quantum systems in the context of quantum information-processing 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 information-processing 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) Neural network states can be used to represent absolutely maximally entangled states, a special type of quantum error-correcting codes. In all three cases, the neural network state ansatz provides an efficient and versatile means as a variational parametrization of these highly entangled states.
Structure of this paperThis paper is structured as follows. In section 2 we introduce the quantum capacity of a channel and state the corresponding coding theorem which expresses the quantum capacity as a regularized formula in terms of an entropic quantity called the coherent information. We then discuss how lower bounds on the quantum capacity can be obtained by solving an entropic optimization problem. In section 3 we review neural network states based on RBMs and feed-forward nets. We then present our main results about representing quantum codes with neural network states. In section 4 we discuss the GADC and the dephrasure channel. We show that the neural network state ansatz finds new quantum codes providing the strongest lower bounds to date on the quantum capacities of these channels. Moreover, we demonstrate that these new codes are not found using a 'direct' parametrization of quantum states. We then show in section 5 for the depolarizing channel how tensor products of repetition codes-i.e. the known optimal codes for k 9 uses of this channel-can be efficiently represented New J. Phys. 22 (2020) 023005 J Bausch and F Leditzky New J. Phys. 22 (2020) 023005