Gauge invariant input to neural network for path optimization method
この論文をさがす
説明
We investigate the efficiency of a gauge invariant input to a neural network for the path optimization method. While the path optimization with a completely gauge-fixed link-variable input has successfully tamed the sign problem in a simple gauge theory, the optimization does not work well when the gauge degrees of freedom remain. We propose to employ a gauge invariant input, such as a plaquette, to overcome this problem. The efficiency of the gauge invariant input to the neural network is evaluated for the two-dimensional U(1) gauge theory with a complex coupling. The average phase factor is significantly enhanced by the path optimization with the plaquette input, indicating good control of the sign problem. It opens a possibility that the path optimization is available to complicated gauge theories, including quantum chromodynamics, in a realistic setup.
収録刊行物
-
- Physical Review D
-
Physical Review D 105 (3), 2022-02
American Physical Society (APS)
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1050574181259334912
-
- ISSN
- 24700029
- 24700010
-
- HANDLE
- 2433/274758
-
- 本文言語コード
- en
-
- 資料種別
- journal article
-
- データソース種別
-
- IRDB
- Crossref
- KAKEN
- OpenAIRE