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Tensor renormalization group approach to four-dimensional complex $\phi^4$ theory at finite density

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arxiv 2005.04645 v1 pith:ZEPJ63VS submitted 2020-05-10 hep-lat

classification hep-lat
keywords tensortheoryapproachfour-dimensionalblazecomplexdensityfield
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Tensor network is an attractive approach to field theory with negative sign problem. The complex $\phi^4$ theory at finite density is a test bed for numerical algorithms to verify their effectiveness. The model shows a characteristic feature called the Silver Blaze phenomenon associated with the sign problem in the large volume limit at low temperature. We analyze the four-dimensional model employing the anisotropic tensor renormalization group algorithm. We find a clear signal of the Silver Blaze phenomenon on a large volume of $V=1024^4$, which implies that the tensor network approach is effective even for four-dimensional field theory beyond two dimensions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tensor renormalization group study of cold and dense QCD in the strong coupling limit

    hep-lat 2026-01 conditional novelty 6.0 of 10

    In strong-coupling lattice QCD at Nτ=8, the chiral and nuclear transition endpoints coincide at m_c≈2.06, and a first-order transition persists at m=2.07 on a 1024^4 zero-temperature lattice.

  2. Applying the Triad network representation to four-dimensional ATRG method

    hep-lat 2024-12 conditional novelty 5.0 of 10

    Triad-ATRG applies the triad and MDTRG decomposition to four-dimensional ATRG, reducing the contraction cost to O(r^2 χ^7) while reproducing ATRG free energies and transition temperatures.

  3. Toward tensor renormalization group study of lattice QCD

    hep-lat 2025-01 conditional novelty 3.0 of 10

    Tensor renormalization group methods for multi-flavor and non-Abelian gauge theories are summarized with 2D Z2 and 3D SU(2) and SU(3) proof-of-principle results.

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