Application of a neural network to the sign problem via the path optimization method

DOI10.1093/ptep/ptx191OpenAlexW3102950870MaRDI QIDQ5155754

Yuto Mori, Kouji Kashiwa, Akira Ohnishi

Publication date: 8 October 2021

Published in: Progress of Theoretical and Experimental Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.03208

Mathematics Subject Classification ID

Quantum theory (81-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (9)

Complex Langevin and other approaches to the sign problem in quantum many-body physics ⋮ Decoding quantum field theory with machine learning ⋮ Investigation of complex \(\varphi^4\) theory at finite density in two dimensions using TRG ⋮ Distance between configurations in Markov chain Monte Carlo simulations ⋮ A simple approach towards the sign problem using path optimisation ⋮ Complex Langevin analysis of 2D U(1) gauge theory on a torus with a \(\theta\) term ⋮ Tensor renormalization group approach to four-dimensional complex \(\varphi^4\) theory at finite density ⋮ Optimisation of complex integration contours at higher order ⋮ Backpropagating hybrid Monte Carlo algorithm for fast Lefschetz thimble calculations

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