基于神经网络的场理论符号问题路径优化

Proceedings of the 8th International Conference on Quarks and Nuclear Physics (QNP2018) Pub Date : 2019-11-08 DOI:10.7566/jpscp.26.024011

A. Ohnishi, Y. Mori, K. Kashiwa

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引用次数: 7

摘要

利用前馈神经网络(FNN)，利用路径优化方法研究了场理论中的符号问题。在POM的框架下，利用模糊神经网络编制和优化场理论中指定积分路径(流形)的试函数。利用FNN的POM方法已应用于若干具有符号问题的场论。具体来说，讨论了0+1维QCD。结果表明，该方法显著提高了平均相位因子，减小了观测值的统计误差。

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Path Optimization for the Sign Problem in Field Theories Using Neural Network

We investigate the sign problem in field theories by using the path optimization method (POM) with use of the feedforward neural network (FNN). We utilize FNN to prepare and optimize the trial function specifying the integration path (manifold) in field theories in the framework of POM. POM with use of FNN has been applied to several field theories having the sign problem. Specifically, the 0+1 dimensional QCD is discussed. It is found that the average phase factor is enhanced significantly and we can reduce the statistical errors of observables.

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Proceedings of the 8th International Conference on Quarks and Nuclear Physics (QNP2018)

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