  Inspired by proposals for continuous-variable quantum approximate
optimization (CV-QAOA), we investigate the utility of continuous-variable
neural network quantum states (CV-NQS) for performing continuous optimization,
focusing on the ground state optimization of the classical antiferromagnetic
rotor model. Numerical experiments conducted using variational Monte Carlo with
CV-NQS indicate that although the non-local algorithm succeeds in finding
ground states competitive with the local gradient search methods, the proposal
suffers from unfavorable scaling. A number of proposed extensions are put
forward which may help alleviate the scaling difficulty.
