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Trapped Ion Quantum Computing

Continuous-variable optimization with neural network quantum states

arXiv
Authors: Yabin Zhang, David Gorsich, Paramsothy Jayakumar, Shravan Veerapaneni

Year

2021

Paper ID

62532

Status

Preprint

Abstract Read

~2 min

Abstract Words

84

Citations

N/A

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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  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
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  • Inspired by proposals for continuous-variable quantum approximate optimization (CV-QAOA), we investigate the utility of continuous-variable neural network quantum states...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2108.03325 [2] arXiv https://arxiv.org/abs/2108.03325 [3] Publisher https://arxiv.org/abs/2108.03325

Local Citation Graph (Related-Paper Links)

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