Continuous-variable optimization with neural network quantum states
Quantum Physics
2022-01-07 v3 Optimization and Control
Abstract
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.
Keywords
Cite
@article{arxiv.2108.03325,
title = {Continuous-variable optimization with neural network quantum states},
author = {Yabin Zhang and David Gorsich and Paramsothy Jayakumar and Shravan Veerapaneni},
journal= {arXiv preprint arXiv:2108.03325},
year = {2022}
}