Continuous-variable optimization with neural network quantum states

• URL: http://arxiv.org/abs/2108.03325v3
• Date: Thu, 6 Jan 2022 18:53:12 GMT
• Title: Continuous-variable optimization with neural network quantum states
• Authors: Yabin Zhang, David Gorsich, Paramsothy Jayakumar, Shravan Veerapaneni
• Abstract summary: We investigate the utility of continuous-variable neural network quantum states (CV-NQS) for performing continuous optimization. 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.
• Score: 6.791920570692005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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