  We initiate the study of neural-network quantum state algorithms for
analyzing continuous-variable lattice quantum systems in first quantization. A
simple family of continuous-variable trial wavefunctons is introduced which
naturally generalizes the restricted Boltzmann machine (RBM) wavefunction
introduced for analyzing quantum spin systems. By virtue of its simplicity, the
same variational Monte Carlo training algorithms that have been developed for
ground state determination and time evolution of spin systems have natural
analogues in the continuum. We offer a proof of principle demonstration in the
context of ground state determination of a stoquastic quantum rotor
Hamiltonian. Results are compared against those obtained from partial
differential equation (PDE) based scalable eigensolvers. This study serves as a
benchmark against which future investigation of continuous-variable neural
quantum states can be compared, and points to the need to consider deep network
architectures and more sophisticated training algorithms.
