Continuous-variable optimization with neural network quantum states

2021/08/06 by Yabin Zhang, Zhang, Yabin, David Gorsich +5
Mathematics · Physics and Astronomy · #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC) #Quantum Physics (quant-ph) #math.OC #quant-ph

paper · pdf · doi:10.48550/arxiv.2108.03325

arxiv created 2022/01/06 · arxiv updated 2022/01/07

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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