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arXiv · 2108.03325

Continuous-variable optimization with neural network quantum states

Yabin Zhang·David Gorsich·Paramsothy Jayakumar·Shravan Veerapaneni
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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.

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Yabin Zhang, David Gorsich, Paramsothy Jayakumar, Shravan Veerapaneni. 2021-08-06. Continuous-variable optimization with neural network quantum states. https://arxiv.org/abs/2108.03325

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