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arXiv 2108.03325quant-phmath.OC
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基于神经网络量子态的连续变量优化

Continuous-variable optimization with neural network quantum states

Yabin Zhang, David Gorsich, Paramsothy Jayakumar, Shravan Veerapaneni

摘要:

受连续变量量子近似优化(CV-QAOA)建议的启发,我们研究了连续变量神经网络量子态(CV-NQS)用于连续优化的效用,重点研究了经典反铁磁转子模型的基态优化。用CV-NQS变分Monte Carlo方法进行的数值实验表明,虽然非局部算法在寻找基态方面比局部梯度搜索方法有优势,但该方法的尺度效应并不理想.提出了一些建议的扩展,这可能有助于减轻缩放困难。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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