  A core technology that has emerged from the artificial intelligence
revolution is the recurrent neural network (RNN). Its unique sequence-based
architecture provides a tractable likelihood estimate with stable training
paradigms, a combination that has precipitated many spectacular advances in
natural language processing and neural machine translation. This architecture
also makes a good candidate for a variational wave function, where the RNN
parameters are tuned to learn the approximate ground state of a quantum
Hamiltonian. In this paper, we demonstrate the ability of RNNs to represent
several many-body wave functions, optimizing the variational parameters using a
stochastic approach. Among other attractive features of these variational wave
functions, their autoregressive nature allows for the efficient calculation of
physical estimators by providing independent samples. We demonstrate the
effectiveness of RNN wave functions by calculating ground state energies,
correlation functions, and entanglement entropies for several quantum spin
models of interest to condensed matter physicists in one and two spatial
dimensions.
