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

Akira Ohnishi | Zendy; Yuto Mori | Zendy; Kouji Kashiwa | Zendy

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

Author(s) -

Akira Ohnishi,

Yuto Mori,

Kouji Kashiwa

Publication year - 2019

Publication title -

proceedings of the 8th international conference on quarks and nuclear physics (qnp2018)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.7566/jpscp.26.024011

Subject(s) - sign (mathematics) , artificial neural network , computer science , path (computing) , field (mathematics) , artificial intelligence , mathematics , computer network , mathematical analysis , pure mathematics

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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