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Continuous-variable optimization with neural network quantum states
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DOI：10.1007/s42484-022-00067-z.png)
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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 (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.
Keyword:
Neural quantum states
Quantum information
Graph theory
Quantum rotors
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4.4
论文数:
441
被引数:
796
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